Identification of residue pairing in interacting β-strands from a predicted residue contact map.

Science.gov (United States)

Mao, Wenzhi; Wang, Tong; Zhang, Wenxuan; Gong, Haipeng

2018-04-19

Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we propose a novel ridge-detection-based β-β contact predictor to identify residue pairing in β strands from any predicted residue contact map. Our algorithm RDb 2 C adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb 2 C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~ 62% and ~ 76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb 2 C achieves impressively higher performance, with F1-scores reaching ~ 76% and ~ 86% at the residue level and strand level, respectively. In a test of structural modeling using the top 1 L predicted contacts as constraints, for 61 mainly β proteins, the average TM-score achieves 0.442 when using the raw RaptorX-Contact prediction, but increases to 0.506 when using the improved prediction by RDb 2 C. Our method can significantly improve the prediction of β-β contacts from any predicted residue contact maps. Prediction results of our algorithm could be directly applied to effectively facilitate the practical structure prediction of mainly β proteins. All source data and codes are available at http://166.111.152.91/Downloads.html or the GitHub address of https://github.com/wzmao/RDb2C .

An observation on the variance of a predicted response in ...

African Journals Online (AJOL)

... these properties and computational simplicity. To avoid over fitting, along with the obvious advantage of having a simpler equation, it is shown that the addition of a variable to a regression equation does not reduce the variance of a predicted response. Key words: Linear regression; Partitioned matrix; Predicted response ...

Computational prediction of protein hot spot residues.

Science.gov (United States)

Morrow, John Kenneth; Zhang, Shuxing

2012-01-01

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

African Journals Online (AJOL)

FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL STRESSES IN ... the transverse residual stress in the x-direction (σx) had a maximum value of 375MPa ... the finite element method are in fair agreement with the experimental results.

Seismic attenuation relationship with homogeneous and heterogeneous prediction-error variance models

Science.gov (United States)

Mu, He-Qing; Xu, Rong-Rong; Yuen, Ka-Veng

2014-03-01

Peak ground acceleration (PGA) estimation is an important task in earthquake engineering practice. One of the most well-known models is the Boore-Joyner-Fumal formula, which estimates the PGA using the moment magnitude, the site-to-fault distance and the site foundation properties. In the present study, the complexity for this formula and the homogeneity assumption for the prediction-error variance are investigated and an efficiency-robustness balanced formula is proposed. For this purpose, a reduced-order Monte Carlo simulation algorithm for Bayesian model class selection is presented to obtain the most suitable predictive formula and prediction-error model for the seismic attenuation relationship. In this approach, each model class (a predictive formula with a prediction-error model) is evaluated according to its plausibility given the data. The one with the highest plausibility is robust since it possesses the optimal balance between the data fitting capability and the sensitivity to noise. A database of strong ground motion records in the Tangshan region of China is obtained from the China Earthquake Data Center for the analysis. The optimal predictive formula is proposed based on this database. It is shown that the proposed formula with heterogeneous prediction-error variance is much simpler than the attenuation model suggested by Boore, Joyner and Fumal (1993).

Diagnostic checking in linear processes with infinit variance

OpenAIRE

Krämer, Walter; Runde, Ralf

1998-01-01

We consider empirical autocorrelations of residuals from infinite variance autoregressive processes. Unlike the finite-variance case, it emerges that the limiting distribution, after suitable normalization, is not always more concentrated around zero when residuals rather than true innovations are employed.

Prediction of interface residue based on the features of residue interaction network.

Science.gov (United States)

Jiao, Xiong; Ranganathan, Shoba

2017-11-07

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits

DEFF Research Database (Denmark)

Gebreyesus, Grum; Lund, Mogens Sandø; Buitenhuis, Albert Johannes

2017-01-01

Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci...... of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we...... developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls...

Genetic Variance Partitioning and Genome-Wide Prediction with Allele Dosage Information in Autotetraploid Potato.

Science.gov (United States)

Endelman, Jeffrey B; Carley, Cari A Schmitz; Bethke, Paul C; Coombs, Joseph J; Clough, Mark E; da Silva, Washington L; De Jong, Walter S; Douches, David S; Frederick, Curtis M; Haynes, Kathleen G; Holm, David G; Miller, J Creighton; Muñoz, Patricio R; Navarro, Felix M; Novy, Richard G; Palta, Jiwan P; Porter, Gregory A; Rak, Kyle T; Sathuvalli, Vidyasagar R; Thompson, Asunta L; Yencho, G Craig

2018-05-01

As one of the world's most important food crops, the potato ( Solanum tuberosum L.) has spurred innovation in autotetraploid genetics, including in the use of SNP arrays to determine allele dosage at thousands of markers. By combining genotype and pedigree information with phenotype data for economically important traits, the objectives of this study were to (1) partition the genetic variance into additive vs. nonadditive components, and (2) determine the accuracy of genome-wide prediction. Between 2012 and 2017, a training population of 571 clones was evaluated for total yield, specific gravity, and chip fry color. Genomic covariance matrices for additive ( G ), digenic dominant ( D ), and additive × additive epistatic ( G # G ) effects were calculated using 3895 markers, and the numerator relationship matrix ( A ) was calculated from a 13-generation pedigree. Based on model fit and prediction accuracy, mixed model analysis with G was superior to A for yield and fry color but not specific gravity. The amount of additive genetic variance captured by markers was 20% of the total genetic variance for specific gravity, compared to 45% for yield and fry color. Within the training population, including nonadditive effects improved accuracy and/or bias for all three traits when predicting total genotypic value. When six F 1 populations were used for validation, prediction accuracy ranged from 0.06 to 0.63 and was consistently lower (0.13 on average) without allele dosage information. We conclude that genome-wide prediction is feasible in potato and that it will improve selection for breeding value given the substantial amount of nonadditive genetic variance in elite germplasm. Copyright © 2018 by the Genetics Society of America.

Modelos de regressão aleatória com diferentes estruturas de variância residual para descrever o tamanho da leitegada Random regression models with different residual variance structures for describing litter size in swine

Directory of Open Access Journals (Sweden)

Aderbal Cavalcante-Neto

2011-12-01

Full Text Available Objetivou-se comparar modelos de regressão aleatória com diferentes estruturas de variância residual, a fim de se buscar a melhor modelagem para a característica tamanho da leitegada ao nascer (TLN. Utilizaram-se 1.701 registros de TLN, que foram analisados por meio de modelo animal, unicaracterística, de regressão aleatória. As regressões fixa e aleatórias foram representadas por funções contínuas sobre a ordem de parto, ajustadas por polinômios ortogonais de Legendre de ordem 3. Para averiguar a melhor modelagem para a variância residual, considerou-se a heterogeneidade de variância por meio de 1 a 7 classes de variância residual. O modelo geral de análise incluiu grupo de contemporâneo como efeito fixo; os coeficientes de regressão fixa para modelar a trajetória média da população; os coeficientes de regressão aleatória do efeito genético aditivo-direto, do comum-de-leitegada e do de ambiente permanente de animal; e o efeito aleatório residual. O teste da razão de verossimilhança, o critério de informação de Akaike e o critério de informação bayesiano de Schwarz apontaram o modelo que considerou homogeneidade de variância como o que proporcionou melhor ajuste aos dados utilizados. As herdabilidades obtidas foram próximas a zero (0,002 a 0,006. O efeito de ambiente permanente foi crescente da 1ª (0,06 à 5ª (0,28 ordem, mas decrescente desse ponto até a 7ª ordem (0,18. O comum-de-leitegada apresentou valores baixos (0,01 a 0,02. A utilização de homogeneidade de variância residual foi mais adequada para modelar as variâncias associadas à característica tamanho da leitegada ao nascer nesse conjunto de dado.The objective of this work was to compare random regression models with different residual variance structures, so as to obtain the best modeling for the trait litter size at birth (LSB in swine. One thousand, seven hundred and one records of LSB were analyzed. LSB was analyzed by means of a

Predicting the residual life of plant equipment - Why worry

International Nuclear Information System (INIS)

Jaske, C.E.

1985-01-01

Predicting the residual life of plant equipment that has been in service for 20 to 30 years or more is a major concern of many industries. This paper reviews the reasons for increased concern for residual-life assessment and the general procedures used in performing such assessments. Some examples and case histories illustrating procedures for assessing remaining service life are discussed. Areas where developments are needed to improve the technology for remaining-life estimation are pointed out. Then, some of the critical issues involved in residual-life assessment are identified. Finally, the future role of residual-life prediction is addressed

Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix.

Science.gov (United States)

Zhang, Haicang; Gao, Yujuan; Deng, Minghua; Wang, Chao; Zhu, Jianwei; Li, Shuai Cheng; Zheng, Wei-Mou; Bu, Dongbo

2016-03-25

Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix. We trained our LRS-based method on the PSICOV dataset, and tested it on both GREMLIN and CASP11 datasets. Our experimental results suggested that LRS significantly improves the contact prediction precision. For example, when equipped with the LRS technique, the prediction precision of MI and mfDCA increased from 0.25 to 0.67 and from 0.58 to 0.70, respectively (Top L/10 predicted contacts, sequence separation: 5 AA, dataset: GREMLIN). In addition, our LRS technique also consistently outperforms the popular denoising technique APC (average product correction), on both local (MI_LRS: 0.67 vs MI_APC: 0.34) and global measures (mfDCA_LRS: 0.70 vs mfDCA_APC: 0.67). Interestingly, we found out that when equipped with our LRS technique, local inference strategies performed in a comparable manner to that of global inference strategies, implying that the application of LRS technique narrowed down the performance gap between local and global inference strategies. Overall, our LRS technique greatly facilitates

Genetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle.

Science.gov (United States)

Vandenplas, J; Bastin, C; Gengler, N; Mulder, H A

2013-09-01

Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual variance between animals. However, residual variance between animals is usually assumed to be homogeneous in traditional genetic evaluations. The aim of this study was to investigate genetic heterogeneity of residual variance by estimating variance components in residual variance for milk yield, somatic cell score, contents in milk (g/dL) of 2 groups of milk fatty acids (i.e., saturated and unsaturated fatty acids), and the content in milk of one individual fatty acid (i.e., oleic acid, C18:1 cis-9), for first-parity Holstein cows in the Walloon Region of Belgium. A total of 146,027 test-day records from 26,887 cows in 747 herds were available. All cows had at least 3 records and a known sire. These sires had at least 10 cows with records and each herd × test-day had at least 5 cows. The 5 traits were analyzed separately based on fixed lactation curve and random regression test-day models for the mean. Estimation of variance components was performed by running iteratively expectation maximization-REML algorithm by the implementation of double hierarchical generalized linear models. Based on fixed lactation curve test-day mean models, heritability for residual variances ranged between 1.01×10(-3) and 4.17×10(-3) for all traits. The genetic standard deviation in residual variance (i.e., approximately the genetic coefficient of variation of residual variance) ranged between 0.12 and 0.17. Therefore, some genetic variance in micro-environmental sensitivity existed in the Walloon Holstein dairy cattle for the 5 studied traits. The standard deviations due to herd × test-day and permanent environment in residual variance ranged between 0.36 and 0.45 for herd × test-day effect and between 0.55 and 0.97 for permanent environmental effect. Therefore, nongenetic effects also

Downside Variance Risk Premium

OpenAIRE

Feunou, Bruno; Jahan-Parvar, Mohammad; Okou, Cedric

2015-01-01

We propose a new decomposition of the variance risk premium in terms of upside and downside variance risk premia. The difference between upside and downside variance risk premia is a measure of skewness risk premium. We establish that the downside variance risk premium is the main component of the variance risk premium, and that the skewness risk premium is a priced factor with significant prediction power for aggregate excess returns. Our empirical investigation highlights the positive and s...

Genetic heterogeneity of within-family variance of body weight in Atlantic salmon (Salmo salar).

Science.gov (United States)

Sonesson, Anna K; Odegård, Jørgen; Rönnegård, Lars

2013-10-17

Canalization is defined as the stability of a genotype against minor variations in both environment and genetics. Genetic variation in degree of canalization causes heterogeneity of within-family variance. The aims of this study are twofold: (1) quantify genetic heterogeneity of (within-family) residual variance in Atlantic salmon and (2) test whether the observed heterogeneity of (within-family) residual variance can be explained by simple scaling effects. Analysis of body weight in Atlantic salmon using a double hierarchical generalized linear model (DHGLM) revealed substantial heterogeneity of within-family variance. The 95% prediction interval for within-family variance ranged from ~0.4 to 1.2 kg2, implying that the within-family variance of the most extreme high families is expected to be approximately three times larger than the extreme low families. For cross-sectional data, DHGLM with an animal mean sub-model resulted in severe bias, while a corresponding sire-dam model was appropriate. Heterogeneity of variance was not sensitive to Box-Cox transformations of phenotypes, which implies that heterogeneity of variance exists beyond what would be expected from simple scaling effects. Substantial heterogeneity of within-family variance was found for body weight in Atlantic salmon. A tendency towards higher variance with higher means (scaling effects) was observed, but heterogeneity of within-family variance existed beyond what could be explained by simple scaling effects. For cross-sectional data, using the animal mean sub-model in the DHGLM resulted in biased estimates of variance components, which differed substantially both from a standard linear mean animal model and a sire-dam DHGLM model. Although genetic differences in canalization were observed, selection for increased canalization is difficult, because there is limited individual information for the variance sub-model, especially when based on cross-sectional data. Furthermore, potential macro

Allometric scaling of population variance with mean body size is predicted from Taylor's law and density-mass allometry.

Science.gov (United States)

Cohen, Joel E; Xu, Meng; Schuster, William S F

2012-09-25

Two widely tested empirical patterns in ecology are combined here to predict how the variation of population density relates to the average body size of organisms. Taylor's law (TL) asserts that the variance of the population density of a set of populations is a power-law function of the mean population density. Density-mass allometry (DMA) asserts that the mean population density of a set of populations is a power-law function of the mean individual body mass. Combined, DMA and TL predict that the variance of the population density is a power-law function of mean individual body mass. We call this relationship "variance-mass allometry" (VMA). We confirmed the theoretically predicted power-law form and the theoretically predicted parameters of VMA, using detailed data on individual oak trees (Quercus spp.) of Black Rock Forest, Cornwall, New York. These results connect the variability of population density to the mean body mass of individuals.

Combining specificity determining and conserved residues improves functional site prediction

Directory of Open Access Journals (Sweden)

Gelfand Mikhail S

2009-06-01

Full Text Available Abstract Background Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core. Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities. Results Here we present a novel method for functional site prediction based on identification of conserved positions, as well as those responsible for determining ligand specificity. We define Specificity-Determining Positions (SDPs, as those occupied by conserved residues within sub-groups of proteins in a family having a common specificity, but differ between groups, and are thus likely to account for specific recognition events. We benchmark the approach on enzyme families of known 3D structure with bound substrates, and find that in nearly all families residues predicted by SDPsite are in contact with the bound substrate, and that the addition of SDPs significantly improves functional site prediction accuracy. We apply SDPsite to various families of proteins containing known three-dimensional structures, but lacking clear functional annotations, and discusse several illustrative examples. Conclusion The results suggest a better means to predict functional details for the thousands of protein structures determined prior to a clear understanding of molecular function.

finite element model for predicting residual stresses in shielded

African Journals Online (AJOL)

eobe

This paper investigates the prediction of residual stresses developed ... steel plates through Finite Element Model simulation and experiments. ... The experimental values as measured by the X-Ray diffractometer were of ... Based on this, it can be concluded that Finite Element .... Comparison of Residual Stresses from X.

RSARF: Prediction of residue solvent accessibility from protein sequence using random forest method

KAUST Repository

Ganesan, Pugalenthi; Kandaswamy, Krishna Kumar Umar; Chou -, Kuochen; Vivekanandan, Saravanan; Kolatkar, Prasanna R.

2012-01-01

Prediction of protein structure from its amino acid sequence is still a challenging problem. The complete physicochemical understanding of protein folding is essential for the accurate structure prediction. Knowledge of residue solvent accessibility gives useful insights into protein structure prediction and function prediction. In this work, we propose a random forest method, RSARF, to predict residue accessible surface area from protein sequence information. The training and testing was performed using 120 proteins containing 22006 residues. For each residue, buried and exposed state was computed using five thresholds (0%, 5%, 10%, 25%, and 50%). The prediction accuracy for 0%, 5%, 10%, 25%, and 50% thresholds are 72.9%, 78.25%, 78.12%, 77.57% and 72.07% respectively. Further, comparison of RSARF with other methods using a benchmark dataset containing 20 proteins shows that our approach is useful for prediction of residue solvent accessibility from protein sequence without using structural information. The RSARF program, datasets and supplementary data are available at http://caps.ncbs.res.in/download/pugal/RSARF/. - See more at: http://www.eurekaselect.com/89216/article#sthash.pwVGFUjq.dpuf

Correction for Measurement Error from Genotyping-by-Sequencing in Genomic Variance and Genomic Prediction Models

DEFF Research Database (Denmark)

Ashraf, Bilal; Janss, Luc; Jensen, Just

sample). The GBSeq data can be used directly in genomic models in the form of individual SNP allele-frequency estimates (e.g., reference reads/total reads per polymorphic site per individual), but is subject to measurement error due to the low sequencing depth per individual. Due to technical reasons....... In the current work we show how the correction for measurement error in GBSeq can also be applied in whole genome genomic variance and genomic prediction models. Bayesian whole-genome random regression models are proposed to allow implementation of large-scale SNP-based models with a per-SNP correction...... for measurement error. We show correct retrieval of genomic explained variance, and improved genomic prediction when accounting for the measurement error in GBSeq data...

A Decomposition Algorithm for Mean-Variance Economic Model Predictive Control of Stochastic Linear Systems

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Dammann, Bernd; Madsen, Henrik

2014-01-01

This paper presents a decomposition algorithm for solving the optimal control problem (OCP) that arises in Mean-Variance Economic Model Predictive Control of stochastic linear systems. The algorithm applies the alternating direction method of multipliers to a reformulation of the OCP...

A proposed residual stress model for oblique turning

International Nuclear Information System (INIS)

Elkhabeery, M. M.

2001-01-01

A proposed mathematical model is presented for predicting the residual stresses caused by turning. Effects of change in tool free length, cutting speed, feed rate, and the tensile strength of work piece material on the maximum residual stress are investigated. The residual stress distribution in the surface region due to turning under unlubricated condition is determined using a deflection etching technique. To reduce the number of experiments required and build the mathematical model for these variables, Response Surface Methodology (RSM) is used. In addition, variance analysis and an experimental check are conducted to determine the prominent parameters and the adequacy of the model. The results show that the tensile stress of the work piece material, cutting speed, and feed rate have significant effects on the maximum residual stresses. The proposed model, that offering good correlation between the experimental and predicted results, is useful in selecting suitable cutting parameters for the machining of different materials. (author)

Prediction of the residual strength of clay using functional networks

Directory of Open Access Journals (Sweden)

S.Z. Khan

2016-01-01

Full Text Available Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks (FN using data available in the literature. The performance of FN was compared with support vector machine (SVM and artificial neural network (ANN based on statistical parameters like correlation coefficient (R, Nash--Sutcliff coefficient of efficiency (E, absolute average error (AAE, maximum average error (MAE and root mean square error (RMSE. Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output.

Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

Science.gov (United States)

Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

2016-11-01

Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.

PINGU: PredIction of eNzyme catalytic residues usinG seqUence information.

Directory of Open Access Journals (Sweden)

Priyadarshini P Pai

Full Text Available Identification of catalytic residues can help unveil interesting attributes of enzyme function for various therapeutic and industrial applications. Based on their biochemical roles, the number of catalytic residues and sequence lengths of enzymes vary. This article describes a prediction approach (PINGU for such a scenario. It uses models trained using physicochemical properties and evolutionary information of 650 non-redundant enzymes (2136 catalytic residues in a support vector machines architecture. Independent testing on 200 non-redundant enzymes (683 catalytic residues in predefined prediction settings, i.e., with non-catalytic per catalytic residue ranging from 1 to 30, suggested that the prediction approach was highly sensitive and specific, i.e., 80% or above, over the incremental challenges. To learn more about the discriminatory power of PINGU in real scenarios, where the prediction challenge is variable and susceptible to high false positives, the best model from independent testing was used on 60 diverse enzymes. Results suggested that PINGU was able to identify most catalytic residues and non-catalytic residues properly with 80% or above accuracy, sensitivity and specificity. The effect of false positives on precision was addressed in this study by application of predicted ligand-binding residue information as a post-processing filter. An overall improvement of 20% in F-measure and 0.138 in Correlation Coefficient with 16% enhanced precision could be achieved. On account of its encouraging performance, PINGU is hoped to have eventual applications in boosting enzyme engineering and novel drug discovery.

Predicting evolutionary responses when genetic variance and selection covary with the environment: a large-scale Open Access Data approach

NARCIS (Netherlands)

Ramakers, J.J.C.; Culina, A.; Visser, M.E.; Gienapp, P.

2017-01-01

Additive genetic variance and selection are the key ingredients for evolution. In wild populations, however, predicting evolutionary trajectories is difficult, potentially by an unrecognised underlying environment dependency of both (additive) genetic variance and selection (i.e. G×E and S×E).

Uncertainty Quantification and Comparison of Weld Residual Stress Measurements and Predictions.

Energy Technology Data Exchange (ETDEWEB)

Lewis, John R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brooks, Dusty Marie [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2016-10-01

In pressurized water reactors, the prevention, detection, and repair of cracks within dissimilar metal welds is essential to ensure proper plant functionality and safety. Weld residual stresses, which are difficult to model and cannot be directly measured, contribute to the formation and growth of cracks due to primary water stress corrosion cracking. Additionally, the uncertainty in weld residual stress measurements and modeling predictions is not well understood, further complicating the prediction of crack evolution. The purpose of this document is to develop methodology to quantify the uncertainty associated with weld residual stress that can be applied to modeling predictions and experimental measurements. Ultimately, the results can be used to assess the current state of uncertainty and to build confidence in both modeling and experimental procedures. The methodology consists of statistically modeling the variation in the weld residual stress profiles using functional data analysis techniques. Uncertainty is quantified using statistical bounds (e.g. confidence and tolerance bounds) constructed with a semi-parametric bootstrap procedure. Such bounds describe the range in which quantities of interest, such as means, are expected to lie as evidenced by the data. The methodology is extended to provide direct comparisons between experimental measurements and modeling predictions by constructing statistical confidence bounds for the average difference between the two quantities. The statistical bounds on the average difference can be used to assess the level of agreement between measurements and predictions. The methodology is applied to experimental measurements of residual stress obtained using two strain relief measurement methods and predictions from seven finite element models developed by different organizations during a round robin study.

Prediction of residual stress using explicit finite element method

Directory of Open Access Journals (Sweden)

W.A. Siswanto

2015-12-01

Full Text Available This paper presents the residual stress behaviour under various values of friction coefficients and scratching displacement amplitudes. The investigation is based on numerical solution using explicit finite element method in quasi-static condition. Two different aeroengine materials, i.e. Super CMV (Cr-Mo-V and Titanium alloys (Ti-6Al-4V, are examined. The usage of FEM analysis in plate under normal contact is validated with Hertzian theoretical solution in terms of contact pressure distributions. The residual stress distributions along with normal and shear stresses on elastic and plastic regimes of the materials are studied for a simple cylinder-on-flat contact configuration model subjected to normal loading, scratching and followed by unloading. The investigated friction coefficients are 0.3, 0.6 and 0.9, while scratching displacement amplitudes are 0.05 mm, 0.10 mm and 0.20 mm respectively. It is found that friction coefficient of 0.6 results in higher residual stress for both materials. Meanwhile, the predicted residual stress is proportional to the scratching displacement amplitude, higher displacement amplitude, resulting in higher residual stress. It is found that less residual stress is predicted on Super CMV material compared to Ti-6Al-4V material because of its high yield stress and ultimate strength. Super CMV material with friction coefficient of 0.3 and scratching displacement amplitude of 0.10 mm is recommended to be used in contact engineering applications due to its minimum possibility of fatigue.

The Ising model for prediction of disordered residues from protein sequence alone

International Nuclear Information System (INIS)

Lobanov, Michail Yu; Galzitskaya, Oxana V

2011-01-01

Intrinsically disordered regions serve as molecular recognition elements, which play an important role in the control of many cellular processes and signaling pathways. It is useful to be able to predict positions of disordered residues and disordered regions in protein chains using protein sequence alone. A new method (IsUnstruct) based on the Ising model for prediction of disordered residues from protein sequence alone has been developed. According to this model, each residue can be in one of two states: ordered or disordered. The model is an approximation of the Ising model in which the interaction term between neighbors has been replaced by a penalty for changing between states (the energy of border). The IsUnstruct has been compared with other available methods and found to perform well. The method correctly finds 77% of disordered residues as well as 87% of ordered residues in the CASP8 database, and 72% of disordered residues as well as 85% of ordered residues in the DisProt database

Genetic variance components for residual feed intake and feed ...

African Journals Online (AJOL)

Feeding costs of animals is a major determinant of profitability in livestock production enterprises. Genetic selection to improve feed efficiency aims to reduce feeding cost in beef cattle and thereby improve profitability. This study estimated genetic (co)variances between weaning weight and other production, reproduction ...

Protein structure based prediction of catalytic residues.

Science.gov (United States)

Fajardo, J Eduardo; Fiser, Andras

2013-02-22

Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

Structural changes and out-of-sample prediction of realized range-based variance in the stock market

Science.gov (United States)

Gong, Xu; Lin, Boqiang

2018-03-01

This paper aims to examine the effects of structural changes on forecasting the realized range-based variance in the stock market. Considering structural changes in variance in the stock market, we develop the HAR-RRV-SC model on the basis of the HAR-RRV model. Subsequently, the HAR-RRV and HAR-RRV-SC models are used to forecast the realized range-based variance of S&P 500 Index. We find that there are many structural changes in variance in the U.S. stock market, and the period after the financial crisis contains more structural change points than the period before the financial crisis. The out-of-sample results show that the HAR-RRV-SC model significantly outperforms the HAR-BV model when they are employed to forecast the 1-day, 1-week, and 1-month realized range-based variances, which means that structural changes can improve out-of-sample prediction of realized range-based variance. The out-of-sample results remain robust across the alternative rolling fixed-window, the alternative threshold value in ICSS algorithm, and the alternative benchmark models. More importantly, we believe that considering structural changes can help improve the out-of-sample performances of most of other existing HAR-RRV-type models in addition to the models used in this paper.

An Empirical Temperature Variance Source Model in Heated Jets

Science.gov (United States)

Khavaran, Abbas; Bridges, James

2012-01-01

An acoustic analogy approach is implemented that models the sources of jet noise in heated jets. The equivalent sources of turbulent mixing noise are recognized as the differences between the fluctuating and Favre-averaged Reynolds stresses and enthalpy fluxes. While in a conventional acoustic analogy only Reynolds stress components are scrutinized for their noise generation properties, it is now accepted that a comprehensive source model should include the additional entropy source term. Following Goldstein s generalized acoustic analogy, the set of Euler equations are divided into two sets of equations that govern a non-radiating base flow plus its residual components. When the base flow is considered as a locally parallel mean flow, the residual equations may be rearranged to form an inhomogeneous third-order wave equation. A general solution is written subsequently using a Green s function method while all non-linear terms are treated as the equivalent sources of aerodynamic sound and are modeled accordingly. In a previous study, a specialized Reynolds-averaged Navier-Stokes (RANS) solver was implemented to compute the variance of thermal fluctuations that determine the enthalpy flux source strength. The main objective here is to present an empirical model capable of providing a reasonable estimate of the stagnation temperature variance in a jet. Such a model is parameterized as a function of the mean stagnation temperature gradient in the jet, and is evaluated using commonly available RANS solvers. The ensuing thermal source distribution is compared with measurements as well as computational result from a dedicated RANS solver that employs an enthalpy variance and dissipation rate model. Turbulent mixing noise predictions are presented for a wide range of jet temperature ratios from 1.0 to 3.20.

A COSMIC VARIANCE COOKBOOK

International Nuclear Information System (INIS)

Moster, Benjamin P.; Rix, Hans-Walter; Somerville, Rachel S.; Newman, Jeffrey A.

2011-01-01

Deep pencil beam surveys ( 2 ) are of fundamental importance for studying the high-redshift universe. However, inferences about galaxy population properties (e.g., the abundance of objects) are in practice limited by 'cosmic variance'. This is the uncertainty in observational estimates of the number density of galaxies arising from the underlying large-scale density fluctuations. This source of uncertainty can be significant, especially for surveys which cover only small areas and for massive high-redshift galaxies. Cosmic variance for a given galaxy population can be determined using predictions from cold dark matter theory and the galaxy bias. In this paper, we provide tools for experiment design and interpretation. For a given survey geometry, we present the cosmic variance of dark matter as a function of mean redshift z-bar and redshift bin size Δz. Using a halo occupation model to predict galaxy clustering, we derive the galaxy bias as a function of mean redshift for galaxy samples of a given stellar mass range. In the linear regime, the cosmic variance of these galaxy samples is the product of the galaxy bias and the dark matter cosmic variance. We present a simple recipe using a fitting function to compute cosmic variance as a function of the angular dimensions of the field, z-bar , Δz, and stellar mass m * . We also provide tabulated values and a software tool. The accuracy of the resulting cosmic variance estimates (δσ v /σ v ) is shown to be better than 20%. We find that for GOODS at z-bar =2 and with Δz = 0.5, the relative cosmic variance of galaxies with m * >10 11 M sun is ∼38%, while it is ∼27% for GEMS and ∼12% for COSMOS. For galaxies of m * ∼ 10 10 M sun , the relative cosmic variance is ∼19% for GOODS, ∼13% for GEMS, and ∼6% for COSMOS. This implies that cosmic variance is a significant source of uncertainty at z-bar =2 for small fields and massive galaxies, while for larger fields and intermediate mass galaxies, cosmic

Quantitative milk genomics: estimation of variance components and prediction of fatty acids in bovine milk

DEFF Research Database (Denmark)

Krag, Kristian

The composition of bovine milk fat, used for human consumption, is far from the recommendations for human fat nutrition. The aim of this PhD was to describe the variance components and prediction probabilities of individual fatty acids (FA) in bovine milk, and to evaluate the possibilities...

Development of residual stress prediction model in pipe weldment

Energy Technology Data Exchange (ETDEWEB)

Eom, Yun Yong; Lim, Se Young; Choi, Kang Hyeuk; Cho, Young Sam; Lim, Jae Hyuk [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)

2002-03-15

When Leak Before Break(LBB) concepts is applied to high energy piping of nuclear power plants, residual weld stresses is a important variable. The main purpose of his research is to develop the numerical model which can predict residual weld stresses. Firstly, basic theories were described which need to numerical analysis of welding parts. Before the analysis of pipe, welding of a flat plate was analyzed and compared. Appling the data of used pipes, thermal/mechanical analysis were accomplished and computed temperature gradient and residual stress distribution. For thermal analysis, proper heat flux was regarded as the heat source and convection/radiation heat transfer were considered at surfaces. The residual stresses were counted from the computed temperature gradient and they were compared and verified with a result of another research.

Method for calculating the variance and prediction intervals for biomass estimates obtained from allometric equations

CSIR Research Space (South Africa)

Kirton, A

2010-08-01

Full Text Available for calculating the variance and prediction intervals for biomass estimates obtained from allometric equations A KIRTON B SCHOLES S ARCHIBALD CSIR Ecosystem Processes and Dynamics, Natural Resources and the Environment P.O. BOX 395, Pretoria, 0001, South... intervals (confidence intervals for predicted values) for allometric estimates can be obtained using an example of estimating tree biomass from stem diameter. It explains how to deal with relationships which are in the power function form - a common form...

Predictions and measurements of residual stress in repair welds in plates

Energy Technology Data Exchange (ETDEWEB)

Brown, T.B. [Mitsui Babcock Energy Limited, Technology and Engineering, Porterfield Road, Renfrew, PA4 8DJ, Scotland (United Kingdom)]. E-mail: bbrown@mitsuibabcock.com; Dauda, T.A. [Mitsui Babcock Energy Limited, Technology and Engineering, Porterfield Road, Renfrew, PA4 8DJ, Scotland (United Kingdom); Truman, C.E. [Department of Mechanical Engineering, University of Bristol, Bristol BS8 1TR, England (United Kingdom); Smith, D.J. [Department of Mechanical Engineering, University of Bristol, Bristol BS8 1TR (United Kingdom); Memhard, D. [Fraunhofer-Institut fuer Werkstoffmechanik, Freiburg (Germany); Pfeiffer, W. [Fraunhofer-Institut fuer Werkstoffmechanik, Freiburg (Germany)

2006-11-15

This paper presents the work, from the European Union FP-5 project ELIXIR, on a series of rectangular repair welds in P275 and S690 steels to validate the numerical modelling techniques used in the determination of the residual stresses generated during the repair process. The plates were 1,000 mm by 800 mm with thicknesses of 50 and 100 mm. The repair welds were 50%, 75% and 100% through the plate thickness. The repair welds were modelled using the finite element method to make predictions of the as-welded residual stress distributions. These predictions were compared with surface-strain measurements made on the parent plates during welding and found to be in good agreement. Through-thickness residual stress measurements were obtained from the test plates through, and local to, the weld repairs using the deep hole drilling technique. Comparisons between the measurements and the finite element predictions generally showed good agreement, thus providing confidence in the method.

Predictions and measurements of residual stress in repair welds in plates

International Nuclear Information System (INIS)

Brown, T.B.; Dauda, T.A.; Truman, C.E.; Smith, D.J.; Memhard, D.; Pfeiffer, W.

2006-01-01

This paper presents the work, from the European Union FP-5 project ELIXIR, on a series of rectangular repair welds in P275 and S690 steels to validate the numerical modelling techniques used in the determination of the residual stresses generated during the repair process. The plates were 1,000 mm by 800 mm with thicknesses of 50 and 100 mm. The repair welds were 50%, 75% and 100% through the plate thickness. The repair welds were modelled using the finite element method to make predictions of the as-welded residual stress distributions. These predictions were compared with surface-strain measurements made on the parent plates during welding and found to be in good agreement. Through-thickness residual stress measurements were obtained from the test plates through, and local to, the weld repairs using the deep hole drilling technique. Comparisons between the measurements and the finite element predictions generally showed good agreement, thus providing confidence in the method

Dynamics of Variance Risk Premia, Investors' Sentiment and Return Predictability

DEFF Research Database (Denmark)

Rombouts, Jerome V.K.; Stentoft, Lars; Violante, Francesco

We develop a joint framework linking the physical variance and its risk neutral expectation implying variance risk premia that are persistent, appropriately reacting to changes in level and variability of the variance and naturally satisfying the sign constraint. Using option market data and real...... events and only marginally by the premium associated with normal price fluctuations....

Ensemble Architecture for Prediction of Enzyme-ligand Binding Residues Using Evolutionary Information.

Science.gov (United States)

Pai, Priyadarshini P; Dattatreya, Rohit Kadam; Mondal, Sukanta

2017-11-01

Enzyme interactions with ligands are crucial for various biochemical reactions governing life. Over many years attempts to identify these residues for biotechnological manipulations have been made using experimental and computational techniques. The computational approaches have gathered impetus with the accruing availability of sequence and structure information, broadly classified into template-based and de novo methods. One of the predominant de novo methods using sequence information involves application of biological properties for supervised machine learning. Here, we propose a support vector machines-based ensemble for prediction of protein-ligand interacting residues using one of the most important discriminative contributing properties in the interacting residue neighbourhood, i. e., evolutionary information in the form of position-specific- scoring matrix (PSSM). The study has been performed on a non-redundant dataset comprising of 9269 interacting and 91773 non-interacting residues for prediction model generation and further evaluation. Of the various PSSM-based models explored, the proposed method named ROBBY (pRediction Of Biologically relevant small molecule Binding residues on enzYmes) shows an accuracy of 84.0 %, Matthews Correlation Coefficient of 0.343 and F-measure of 39.0 % on 78 test enzymes. Further, scope of adding domain knowledge such as pocket information has also been investigated; results showed significant enhancement in method precision. Findings are hoped to boost the reliability of small-molecule ligand interaction prediction for enzyme applications and drug design. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Genetic Gain Increases by Applying the Usefulness Criterion with Improved Variance Prediction in Selection of Crosses.

Science.gov (United States)

Lehermeier, Christina; Teyssèdre, Simon; Schön, Chris-Carolin

2017-12-01

A crucial step in plant breeding is the selection and combination of parents to form new crosses. Genome-based prediction guides the selection of high-performing parental lines in many crop breeding programs which ensures a high mean performance of progeny. To warrant maximum selection progress, a new cross should also provide a large progeny variance. The usefulness concept as measure of the gain that can be obtained from a specific cross accounts for variation in progeny variance. Here, it is shown that genetic gain can be considerably increased when crosses are selected based on their genomic usefulness criterion compared to selection based on mean genomic estimated breeding values. An efficient and improved method to predict the genetic variance of a cross based on Markov chain Monte Carlo samples of marker effects from a whole-genome regression model is suggested. In simulations representing selection procedures in crop breeding programs, the performance of this novel approach is compared with existing methods, like selection based on mean genomic estimated breeding values and optimal haploid values. In all cases, higher genetic gain was obtained compared with previously suggested methods. When 1% of progenies per cross were selected, the genetic gain based on the estimated usefulness criterion increased by 0.14 genetic standard deviation compared to a selection based on mean genomic estimated breeding values. Analytical derivations of the progeny genotypic variance-covariance matrix based on parental genotypes and genetic map information make simulations of progeny dispensable, and allow fast implementation in large-scale breeding programs. Copyright © 2017 by the Genetics Society of America.

Prediction method of seismic residual deformation of caisson quay wall in liquefied foundation

Science.gov (United States)

Wang, Li-Yan; Liu, Han-Long; Jiang, Peng-Ming; Chen, Xiang-Xiang

2011-03-01

The multi-spring shear mechanism plastic model in this paper is defined in strain space to simulate pore pressure generation and development in sands under cyclic loading and undrained conditions, and the rotation of principal stresses can also be simulated by the model with cyclic behavior of anisotropic consolidated sands. Seismic residual deformations of typical caisson quay walls under different engineering situations are analyzed in detail by the plastic model, and then an index of liquefaction extent is applied to describe the regularity of seismic residual deformation of caisson quay wall top under different engineering situations. Some correlated prediction formulas are derived from the results of regression analysis between seismic residual deformation of quay wall top and extent of liquefaction in the relative safety backfill sand site. Finally, the rationality and the reliability of the prediction methods are validated by test results of a 120 g-centrifuge shaking table, and the comparisons show that some reliable seismic residual deformation of caisson quay can be predicted by appropriate prediction formulas and appropriate index of liquefaction extent.

Genomic Prediction Within and Across Biparental Families: Means and Variances of Prediction Accuracy and Usefulness of Deterministic Equations

Directory of Open Access Journals (Sweden)

Pascal Schopp

2017-11-01

Full Text Available A major application of genomic prediction (GP in plant breeding is the identification of superior inbred lines within families derived from biparental crosses. When models for various traits were trained within related or unrelated biparental families (BPFs, experimental studies found substantial variation in prediction accuracy (PA, but little is known about the underlying factors. We used SNP marker genotypes of inbred lines from either elite germplasm or landraces of maize (Zea mays L. as parents to generate in silico 300 BPFs of doubled-haploid lines. We analyzed PA within each BPF for 50 simulated polygenic traits, using genomic best linear unbiased prediction (GBLUP models trained with individuals from either full-sib (FSF, half-sib (HSF, or unrelated families (URF for various sizes (Ntrain of the training set and different heritabilities (h2 . In addition, we modified two deterministic equations for forecasting PA to account for inbreeding and genetic variance unexplained by the training set. Averaged across traits, PA was high within FSF (0.41–0.97 with large variation only for Ntrain < 50 and h2 < 0.6. For HSF and URF, PA was on average ∼40–60% lower and varied substantially among different combinations of BPFs used for model training and prediction as well as different traits. As exemplified by HSF results, PA of across-family GP can be very low if causal variants not segregating in the training set account for a sizeable proportion of the genetic variance among predicted individuals. Deterministic equations accurately forecast the PA expected over many traits, yet cannot capture trait-specific deviations. We conclude that model training within BPFs generally yields stable PA, whereas a high level of uncertainty is encountered in across-family GP. Our study shows the extent of variation in PA that must be at least reckoned with in practice and offers a starting point for the design of training sets composed of multiple BPFs.

Non-"g" Residuals of the SAT and ACT Predict Specific Abilities

Science.gov (United States)

Coyle, Thomas R.; Purcell, Jason M.; Snyder, Anissa C.; Kochunov, Peter

2013-01-01

This research examined whether non-"g" residuals of the SAT and ACT subtests, obtained after removing g, predicted specific abilities. Non-"g" residuals of the verbal and math subtests of the SAT and ACT were correlated with academic (verbal and math) and non-academic abilities (speed and shop), both based on the Armed Services…

InterMap3D: predicting and visualizing co-evolving protein residues

DEFF Research Database (Denmark)

Oliveira, Rodrigo Gouveia; Roque, francisco jose sousa simôes almeida; Wernersson, Rasmus

2009-01-01

InterMap3D predicts co-evolving protein residues and plots them on the 3D protein structure. Starting with a single protein sequence, InterMap3D automatically finds a set of homologous sequences, generates an alignment and fetches the most similar 3D structure from the Protein Data Bank (PDB......). It can also accept a user-generated alignment. Based on the alignment, co-evolving residues are then predicted using three different methods: Row and Column Weighing of Mutual Information, Mutual Information/Entropy and Dependency. Finally, InterMap3D generates high-quality images of the protein...

Prediction of residues in discontinuous B-cell epitopes using protein 3D structures

DEFF Research Database (Denmark)

Andersen, P.H.; Nielsen, Morten; Lund, Ole

2006-01-01

. We show that the new structure-based method has a better performance for predicting residues of discontinuous epitopes than methods based solely on sequence information, and that it can successfully predict epitope residues that have been identified by different techniques. DiscoTope detects 15...... experimental epitope mapping in both rational vaccine design and development of diagnostic tools, and may lead to more efficient epitope identification....

Prediction of Detailed Enzyme Functions and Identification of Specificity Determining Residues by Random Forests

Science.gov (United States)

Nagao, Chioko; Nagano, Nozomi; Mizuguchi, Kenji

2014-01-01

Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily. PMID:24416252

Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.

Science.gov (United States)

Zhu, Jianwei; Zhang, Haicang; Li, Shuai Cheng; Wang, Chao; Kong, Lupeng; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo

2017-12-01

Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5% and 78.8% at family, superfamily and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed consistent

Prediction of three-dimensional residual stresses at localised indentations in pipes

International Nuclear Information System (INIS)

Hyde, T.H.; Luo, R.; Becker, A.A.

2012-01-01

Residual stresses are investigated using Finite Element (FE) analyses at localised indentations in pipes with and without internal pressures due to reverse plasticity caused by springback of the surrounding material after removal of the indenter. The indentation loading is applied via rigid 3D short indenters. The effects of the residual indentation depth, internal pressure, indenter size and different material properties on the residual stresses for different pipes have been investigated by carrying out parametric sensitivity studies. In order to predict the residual stresses, empirical formulations have been developed, which show a good correlation with the FE for residual stresses for pipes with diameter to thickness ratios of 35–72. - Highlights: ► A comprehensive elastic–plastic FE analysis of residual stresses caused by localised pipe indentations is presented. ► The effects of residual indentation depth, internal pressure, indenter size and material properties have been studied. ► Empirical formulations have been developed, which show a good correlation with the FE for residual stresses for pipes with diameter to thickness ratios of 35–72.

Residual Structures in Latent Growth Curve Modeling

Science.gov (United States)

Grimm, Kevin J.; Widaman, Keith F.

2010-01-01

Several alternatives are available for specifying the residual structure in latent growth curve modeling. Two specifications involve uncorrelated residuals and represent the most commonly used residual structures. The first, building on repeated measures analysis of variance and common specifications in multilevel models, forces residual variances…

Different finite element techniques to predict welding residual stresses in aluminum alloy plates

International Nuclear Information System (INIS)

Moein, Hadi; Sattari-Far, Iradj

2014-01-01

This study is a 3D thermomechanical finite element (FE) analysis of a single-pass and butt-welded work-hardened aluminum (Al) 5456 plates. It aims to validate the use of FE welding simulations to predict residual stress states in assessing the integrity of welded components. The predicted final residual stresses in the plate from the FE simulations are verified through comparison with experimental measurements. Three techniques are used to simulate the welding process. In the first two approaches, welding deposition is applied by using element birth and interaction techniques. In the third approach, the entire weld zone is simultaneously deposited. Results show a value at approximately the yield strength for longitudinal residual stresses of the welded center of the butt-welded Al alloy plates with a thickness of 2 mm. Considering the application of a comprehensive heat source, along with heat loss modeling and the temperature dependent properties of the material, the approach without deposition predicts a reasonable distribution of residual stresses. However, the element birth and interaction techniques, compared with the no-deposit technique, provide more accurate results in calculating residual stresses. Furthermore, the element interaction technique, compared with the element birth technique, exhibits higher efficiency and flexibility in modeling the deposition of welded metals as well as less modeling cost.

Prediction of Active Site and Distal Residues in E. coli DNA Polymerase III alpha Polymerase Activity.

Science.gov (United States)

Parasuram, Ramya; Coulther, Timothy A; Hollander, Judith M; Keston-Smith, Elise; Ondrechen, Mary Jo; Beuning, Penny J

2018-02-20

The process of DNA replication is carried out with high efficiency and accuracy by DNA polymerases. The replicative polymerase in E. coli is DNA Pol III, which is a complex of 10 different subunits that coordinates simultaneous replication on the leading and lagging strands. The 1160-residue Pol III alpha subunit is responsible for the polymerase activity and copies DNA accurately, making one error per 10 5 nucleotide incorporations. The goal of this research is to determine the residues that contribute to the activity of the polymerase subunit. Homology modeling and the computational methods of THEMATICS and POOL were used to predict functionally important amino acid residues through their computed chemical properties. Site-directed mutagenesis and biochemical assays were used to validate these predictions. Primer extension, steady-state single-nucleotide incorporation kinetics, and thermal denaturation assays were performed to understand the contribution of these residues to the function of the polymerase. This work shows that the top 15 residues predicted by POOL, a set that includes the three previously known catalytic aspartate residues, seven remote residues, plus five previously unexplored first-layer residues, are important for function. Six previously unidentified residues, R362, D405, K553, Y686, E688, and H760, are each essential to Pol III activity; three additional residues, Y340, R390, and K758, play important roles in activity.

RANDOM FUNCTIONS AND INTERVAL METHOD FOR PREDICTING THE RESIDUAL RESOURCE OF BUILDING STRUCTURES

Directory of Open Access Journals (Sweden)

Shmelev Gennadiy Dmitrievich

2017-11-01

Full Text Available Subject: possibility of using random functions and interval prediction method for estimating the residual life of building structures in the currently used buildings. Research objectives: coordination of ranges of values to develop predictions and random functions that characterize the processes being predicted. Materials and methods: when performing this research, the method of random functions and the method of interval prediction were used. Results: in the course of this work, the basic properties of random functions, including the properties of families of random functions, are studied. The coordination of time-varying impacts and loads on building structures is considered from the viewpoint of their influence on structures and representation of the structures’ behavior in the form of random functions. Several models of random functions are proposed for predicting individual parameters of structures. For each of the proposed models, its scope of application is defined. The article notes that the considered approach of forecasting has been used many times at various sites. In addition, the available results allowed the authors to develop a methodology for assessing the technical condition and residual life of building structures for the currently used facilities. Conclusions: we studied the possibility of using random functions and processes for the purposes of forecasting the residual service lives of structures in buildings and engineering constructions. We considered the possibility of using an interval forecasting approach to estimate changes in defining parameters of building structures and their technical condition. A comprehensive technique for forecasting the residual life of building structures using the interval approach is proposed.

Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach

International Nuclear Information System (INIS)

Cheng, Yujie; Lu, Chen; Li, Tieying; Tao, Laifa

2015-01-01

Existing methods for predicting lithium-ion (Li-ion) battery residual lifetime mostly depend on a priori knowledge on aging mechanism, the use of chemical or physical formulation and analytical battery models. This dependence is usually difficult to determine in practice, which restricts the application of these methods. In this study, we propose a new prediction method for Li-ion battery residual lifetime evaluation based on FPCA (functional principal component analysis) and Bayesian approach. The proposed method utilizes FPCA to construct a nonparametric degradation model for Li-ion battery, based on which the residual lifetime and the corresponding confidence interval can be evaluated. Furthermore, an empirical Bayes approach is utilized to achieve real-time updating of the degradation model and concurrently determine residual lifetime distribution. Based on Bayesian updating, a more accurate prediction result and a more precise confidence interval are obtained. Experiments are implemented based on data provided by the NASA Ames Prognostics Center of Excellence. Results confirm that the proposed prediction method performs well in real-time battery residual lifetime prediction. - Highlights: • Capacity is considered functional and FPCA is utilized to extract more information. • No features required which avoids drawbacks induced by feature extraction. • A good combination of both population and individual information. • Avoiding complex aging mechanism and accurate analytical models of batteries. • Easily applicable to different batteries for life prediction and RLD calculation.

The influence of mean climate trends and climate variance on beaver survival and recruitment dynamics.

Science.gov (United States)

Campbell, Ruairidh D; Nouvellet, Pierre; Newman, Chris; Macdonald, David W; Rosell, Frank

2012-09-01

Ecologists are increasingly aware of the importance of environmental variability in natural systems. Climate change is affecting both the mean and the variability in weather and, in particular, the effect of changes in variability is poorly understood. Organisms are subject to selection imposed by both the mean and the range of environmental variation experienced by their ancestors. Changes in the variability in a critical environmental factor may therefore have consequences for vital rates and population dynamics. Here, we examine ≥90-year trends in different components of climate (precipitation mean and coefficient of variation (CV); temperature mean, seasonal amplitude and residual variance) and consider the effects of these components on survival and recruitment in a population of Eurasian beavers (n = 242) over 13 recent years. Within climatic data, no trends in precipitation were detected, but trends in all components of temperature were observed, with mean and residual variance increasing and seasonal amplitude decreasing over time. A higher survival rate was linked (in order of influence based on Akaike weights) to lower precipitation CV (kits, juveniles and dominant adults), lower residual variance of temperature (dominant adults) and lower mean precipitation (kits and juveniles). No significant effects were found on the survival of nondominant adults, although the sample size for this category was low. Greater recruitment was linked (in order of influence) to higher seasonal amplitude of temperature, lower mean precipitation, lower residual variance in temperature and higher precipitation CV. Both climate means and variance, thus proved significant to population dynamics; although, overall, components describing variance were more influential than those describing mean values. That environmental variation proves significant to a generalist, wide-ranging species, at the slow end of the slow-fast continuum of life histories, has broad implications for

Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study

Science.gov (United States)

Mathew, J.; Moat, R. J.; Paddea, S.; Francis, J. A.; Fitzpatrick, M. E.; Bouchard, P. J.

2017-12-01

Economic and safe management of nuclear plant components relies on accurate prediction of welding-induced residual stresses. In this study, the distribution of residual stress through the thickness of austenitic stainless steel welds has been measured using neutron diffraction and the contour method. The measured data are used to validate residual stress profiles predicted by an artificial neural network approach (ANN) as a function of welding heat input and geometry. Maximum tensile stresses with magnitude close to the yield strength of the material were observed near the weld cap in both axial and hoop direction of the welds. Significant scatter of more than 200 MPa was found within the residual stress measurements at the weld center line and are associated with the geometry and welding conditions of individual weld passes. The ANN prediction is developed in an attempt to effectively quantify this phenomenon of `innate scatter' and to learn the non-linear patterns in the weld residual stress profiles. Furthermore, the efficacy of the ANN method for defining through-thickness residual stress profiles in welds for application in structural integrity assessments is evaluated.

Practical guidance on representing the heteroscedasticity of residual errors of hydrological predictions

Science.gov (United States)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Kuczera, George

2016-04-01

Appropriate representation of residual errors in hydrological modelling is essential for accurate and reliable probabilistic streamflow predictions. In particular, residual errors of hydrological predictions are often heteroscedastic, with large errors associated with high runoff events. Although multiple approaches exist for representing this heteroscedasticity, few if any studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating a range of approaches for representing heteroscedasticity in residual errors. These approaches include the 'direct' weighted least squares approach and 'transformational' approaches, such as logarithmic, Box-Cox (with and without fitting the transformation parameter), logsinh and the inverse transformation. The study reports (1) theoretical comparison of heteroscedasticity approaches, (2) empirical evaluation of heteroscedasticity approaches using a range of multiple catchments / hydrological models / performance metrics and (3) interpretation of empirical results using theory to provide practical guidance on the selection of heteroscedasticity approaches. Importantly, for hydrological practitioners, the results will simplify the choice of approaches to represent heteroscedasticity. This will enhance their ability to provide hydrological probabilistic predictions with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality).

Variance components for body weight in Japanese quails (Coturnix japonica

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RO Resende

2005-03-01

Full Text Available The objective of this study was to estimate the variance components for body weight in Japanese quails by Bayesian procedures. The body weight at hatch (BWH and at 7 (BW07, 14 (BW14, 21 (BW21 and 28 days of age (BW28 of 3,520 quails was recorded from August 2001 to June 2002. A multiple-trait animal model with additive genetic, maternal environment and residual effects was implemented by Gibbs sampling methodology. A single Gibbs sampling with 80,000 rounds was generated by the program MTGSAM (Multiple Trait Gibbs Sampling in Animal Model. Normal and inverted Wishart distributions were used as prior distributions for the random effects and the variance components, respectively. Variance components were estimated based on the 500 samples that were left after elimination of 30,000 rounds in the burn-in period and 100 rounds of each thinning interval. The posterior means of additive genetic variance components were 0.15; 4.18; 14.62; 27.18 and 32.68; the posterior means of maternal environment variance components were 0.23; 1.29; 2.76; 4.12 and 5.16; and the posterior means of residual variance components were 0.084; 6.43; 22.66; 31.21 and 30.85, at hatch, 7, 14, 21 and 28 days old, respectively. The posterior means of heritability were 0.33; 0.35; 0.36; 0.43 and 0.47 at hatch, 7, 14, 21 and 28 days old, respectively. These results indicate that heritability increased with age. On the other hand, after hatch there was a marked reduction in the maternal environment variance proportion of the phenotypic variance, whose estimates were 0.50; 0.11; 0.07; 0.07 and 0.08 for BWH, BW07, BW14, BW21 and BW28, respectively. The genetic correlation between weights at different ages was high, except for those estimates between BWH and weight at other ages. Changes in body weight of quails can be efficiently achieved by selection.

The prediction of the residual life of electromechanical equipment based on the artificial neural network

Science.gov (United States)

Zhukovskiy, Yu L.; Korolev, N. A.; Babanova, I. S.; Boikov, A. V.

2017-10-01

This article is devoted to the prediction of the residual life based on an estimate of the technical state of the induction motor. The proposed system allows to increase the accuracy and completeness of diagnostics by using an artificial neural network (ANN), and also identify and predict faulty states of an electrical equipment in dynamics. The results of the proposed system for estimation the technical condition are probability technical state diagrams and a quantitative evaluation of the residual life, taking into account electrical, vibrational, indirect parameters and detected defects. Based on the evaluation of the technical condition and the prediction of the residual life, a decision is made to change the control of the operating and maintenance modes of the electric motors.

A New Approach for Predicting the Variance of Random Decrement Functions

DEFF Research Database (Denmark)

Asmussen, J. C.; Brincker, Rune

mean Gaussian distributed processes the RD functions are proportional to the correlation functions of the processes. If a linear structur is loaded by Gaussian white noise the modal parameters can be extracted from the correlation funtions of the response, only. One of the weaknesses of the RD...... technique is that no consistent approach to estimate the variance of the RD functions is known. Only approximate relations are available, which can only be used under special conditions. The variance of teh RD functions contains valuable information about accuracy of the estimates. Furthermore, the variance...... can be used as basis for a decision about how many time lags from the RD funtions should be used in the modal parameter extraction procedure. This paper suggests a new method for estimating the variance of the RD functions. The method is consistent in the sense that the accuracy of the approach...

A New Approach for Predicting the Variance of Random Decrement Functions

DEFF Research Database (Denmark)

Asmussen, J. C.; Brincker, Rune

1998-01-01

mean Gaussian distributed processes the RD functions are proportional to the correlation functions of the processes. If a linear structur is loaded by Gaussian white noise the modal parameters can be extracted from the correlation funtions of the response, only. One of the weaknesses of the RD...... technique is that no consistent approach to estimate the variance of the RD functions is known. Only approximate relations are available, which can only be used under special conditions. The variance of teh RD functions contains valuable information about accuracy of the estimates. Furthermore, the variance...... can be used as basis for a decision about how many time lags from the RD funtions should be used in the modal parameter extraction procedure. This paper suggests a new method for estimating the variance of the RD functions. The method is consistent in the sense that the accuracy of the approach...

firestar--advances in the prediction of functionally important residues.

Science.gov (United States)

Lopez, Gonzalo; Maietta, Paolo; Rodriguez, Jose Manuel; Valencia, Alfonso; Tress, Michael L

2011-07-01

firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php.

Impact of Damping Uncertainty on SEA Model Response Variance

Science.gov (United States)

Schiller, Noah; Cabell, Randolph; Grosveld, Ferdinand

2010-01-01

Statistical Energy Analysis (SEA) is commonly used to predict high-frequency vibroacoustic levels. This statistical approach provides the mean response over an ensemble of random subsystems that share the same gross system properties such as density, size, and damping. Recently, techniques have been developed to predict the ensemble variance as well as the mean response. However these techniques do not account for uncertainties in the system properties. In the present paper uncertainty in the damping loss factor is propagated through SEA to obtain more realistic prediction bounds that account for both ensemble and damping variance. The analysis is performed on a floor-equipped cylindrical test article that resembles an aircraft fuselage. Realistic bounds on the damping loss factor are determined from measurements acquired on the sidewall of the test article. The analysis demonstrates that uncertainties in damping have the potential to significantly impact the mean and variance of the predicted response.

Prediction of Weld Residual Stress of Narrow Gap Welds

International Nuclear Information System (INIS)

Yang, Jun Seog; Huh, Nam Su

2010-01-01

The conventional welding technique such as shield metal arc welding has been mostly applied to the piping system of the nuclear power plants. It is well known that this welding technique causes the overheating and welding defects due to the large groove angle of weld. On the other hand, the narrow gap welding(NGW) technique has many merits, for instance, the reduction of welding time, the shrinkage of weld and the small deformation of the weld due to the small groove angle and welding bead width comparing with the conventional welds. These characteristics of NGW affect the deformation behavior and the distribution of welding residual stress of NGW, thus it is believed that the residual stress results obtained from conventional welding procedure may not be applied to structural integrity evaluation of NGW. In this paper, the welding residual stress of NGW was predicted using the nonlinear finite element analysis to simulate the thermal and mechanical effects of the NGW. The present results can be used as the important information to perform the flaw evaluation and to improve the weld procedure of NGW

Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information

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Panwar Bharat

2013-02-01

Full Text Available Abstract Background The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. Results In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL. It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i vitamin interacting residues (VIRs, (ii vitamin-A interacting residues (VAIRs, (iii vitamin-B interacting residues (VBIRs and (iv pyridoxal-5-phosphate (vitamin B6 interacting residues (PLPIRs have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM features of protein sequences. Finally, we selected best performing SVM modules and

Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information.

Science.gov (United States)

Panwar, Bharat; Gupta, Sudheer; Raghava, Gajendra P S

2013-02-07

The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0

Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers.

Directory of Open Access Journals (Sweden)

Guosheng Su

Full Text Available Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1 a simple additive genetic model (MA, 2 a model including both additive and additive by additive epistatic genetic effects (MAE, 3 a model including both additive and dominance genetic effects (MAD, and 4 a full model including all three genetic components (MAED. Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.

Validation of consistency of Mendelian sampling variance.

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Tyrisevä, A-M; Fikse, W F; Mäntysaari, E A; Jakobsen, J; Aamand, G P; Dürr, J; Lidauer, M H

2018-03-01

Experiences from international sire evaluation indicate that the multiple-trait across-country evaluation method is sensitive to changes in genetic variance over time. Top bulls from birth year classes with inflated genetic variance will benefit, hampering reliable ranking of bulls. However, none of the methods available today enable countries to validate their national evaluation models for heterogeneity of genetic variance. We describe a new validation method to fill this gap comprising the following steps: estimating within-year genetic variances using Mendelian sampling and its prediction error variance, fitting a weighted linear regression between the estimates and the years under study, identifying possible outliers, and defining a 95% empirical confidence interval for a possible trend in the estimates. We tested the specificity and sensitivity of the proposed validation method with simulated data using a real data structure. Moderate (M) and small (S) size populations were simulated under 3 scenarios: a control with homogeneous variance and 2 scenarios with yearly increases in phenotypic variance of 2 and 10%, respectively. Results showed that the new method was able to estimate genetic variance accurately enough to detect bias in genetic variance. Under the control scenario, the trend in genetic variance was practically zero in setting M. Testing cows with an average birth year class size of more than 43,000 in setting M showed that tolerance values are needed for both the trend and the outlier tests to detect only cases with a practical effect in larger data sets. Regardless of the magnitude (yearly increases in phenotypic variance of 2 or 10%) of the generated trend, it deviated statistically significantly from zero in all data replicates for both cows and bulls in setting M. In setting S with a mean of 27 bulls in a year class, the sampling error and thus the probability of a false-positive result clearly increased. Still, overall estimated genetic

A Mean-Variance Criterion for Economic Model Predictive Control of Stochastic Linear Systems

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Dammann, Bernd; Madsen, Henrik

2014-01-01

, the tractability of the resulting optimal control problem is addressed. We use a power management case study to compare different variations of the mean-variance strategy with EMPC based on the certainty equivalence principle. The certainty equivalence strategy is much more computationally efficient than the mean......-variance strategies, but it does not account for the variance of the uncertain parameters. Openloop simulations suggest that a single-stage mean-variance approach yields a significantly lower operating cost than the certainty equivalence strategy. In closed-loop, the single-stage formulation is overly conservative...... be modified to perform almost as well as the two-stage mean-variance formulation. Nevertheless, we argue that the mean-variance approach can be used both as a strategy for evaluating less computational demanding methods such as the certainty equivalence method, and as an individual control strategy when...

Variance in exposed perturbations impairs retention of visuomotor adaptation.

Science.gov (United States)

Canaveral, Cesar Augusto; Danion, Frédéric; Berrigan, Félix; Bernier, Pierre-Michel

2017-11-01

Sensorimotor control requires an accurate estimate of the state of the body. The brain optimizes state estimation by combining sensory signals with predictions of the sensory consequences of motor commands using a forward model. Given that both sensory signals and predictions are uncertain (i.e., noisy), the brain optimally weights the relative reliance on each source of information during adaptation. In support, it is known that uncertainty in the sensory predictions influences the rate and generalization of visuomotor adaptation. We investigated whether uncertainty in the sensory predictions affects the retention of a new visuomotor relationship. This was done by exposing three separate groups to a visuomotor rotation whose mean was common at 15° counterclockwise but whose variance around the mean differed (i.e., SD of 0°, 3.2°, or 4.5°). Retention was assessed by measuring the persistence of the adapted behavior in a no-vision phase. Results revealed that mean reach direction late in adaptation was similar across groups, suggesting it depended mainly on the mean of exposed rotations and was robust to differences in variance. However, retention differed across groups, with higher levels of variance being associated with a more rapid reversion toward nonadapted behavior. A control experiment ruled out the possibility that differences in retention were accounted for by differences in success rates. Exposure to variable rotations may have increased the uncertainty in sensory predictions, making the adapted forward model more labile and susceptible to change or decay. NEW & NOTEWORTHY The brain predicts the sensory consequences of motor commands through a forward model. These predictions are subject to uncertainty. We use visuomotor adaptation and modulate uncertainty in the sensory predictions by manipulating the variance in exposed rotations. Results reveal that variance does not influence the final extent of adaptation but selectively impairs the retention of

Efficient Scores, Variance Decompositions and Monte Carlo Swindles.

Science.gov (United States)

1984-08-28

to ;r Then a version .of Pythagoras ’ theorem gives the variance decomposition (6.1) varT var S var o(T-S) P P0 0 0 One way to see this is to note...complete sufficient statistics for (B, a) , and that the standard- ized residuals a(y - XB) 6 are ancillary. Basu’s sufficiency- ancillarity theorem

Predicting logging residues: an interim equation for Appalachian oak sawtimber

Science.gov (United States)

A. Jeff Martin

1975-01-01

An equation, using dbh, dbh², bole length, and sawlog height to predict the cubic-foot volume of logging residue per tree, was developed from data collected on 36 mixed oaks in southwestern Virginia. The equation produced reliable results for small sawtimber trees, but additional research is needed for other species, sites, and utilization practices.

Emotion regulation and Residual Depression Predict Psychosocial Functioning in Bipolar Disorder: Preliminary Study

OpenAIRE

Becerra, Rodrigo; Cruise, Kate; Harms, Craig; Allan, Alfred; Bassett, Darryl; Hood, Sean; Murray, Greg

2015-01-01

This study explores the predictive value of various clinical, neuropsychological, functional, and emotion regulation processes for recovery in Bipolar Disorder. Clinical and demographic information was collected for 27 euthymic or residually depressed BD participants. Seventy one percent of the sample reported some degree of impairment in psychosocial functioning. Both residual depression and problems with emotion regulation were identified as significant predictors of poor psychosocial funct...

Reliability residual-life prediction method for thermal aging based on performance degradation

International Nuclear Information System (INIS)

Ren Shuhong; Xue Fei; Yu Weiwei; Ti Wenxin; Liu Xiaotian

2013-01-01

The paper makes the study of the nuclear power plant main pipeline. The residual-life of the main pipeline that failed due to thermal aging has been studied by the use of performance degradation theory and Bayesian updating methods. Firstly, the thermal aging impact property degradation process of the main pipeline austenitic stainless steel has been analyzed by the accelerated thermal aging test data. Then, the thermal aging residual-life prediction model based on the impact property degradation data is built by Bayesian updating methods. Finally, these models are applied in practical situations. It is shown that the proposed methods are feasible and the prediction accuracy meets the needs of the project. Also, it provides a foundation for the scientific management of aging management of the main pipeline. (authors)

Measurement and prediction of residual stress in a bead-on-plate weld benchmark specimen

International Nuclear Information System (INIS)

Ficquet, X.; Smith, D.J.; Truman, C.E.; Kingston, E.J.; Dennis, R.J.

2009-01-01

This paper presents measurements and predictions of the residual stresses generated by laying a single weld bead on a flat, austenitic stainless steel plate. The residual stress field that is created is strongly three-dimensional and is considered representative of that found in a repair weld. Through-thickness measurements are made using the deep hole drilling technique, and near-surface measurements are made using incremental centre hole drilling. Measurements are compared to predictions at the same locations made using finite element analysis incorporating an advanced, non-linear kinematic hardening model. The work was conducted as part of an European round robin exercise, coordinated as part of the NeT network. Overall, there was broad agreement between measurements and predictions, but there were notable differences

A Multifeatures Fusion and Discrete Firefly Optimization Method for Prediction of Protein Tyrosine Sulfation Residues.

Science.gov (United States)

Guo, Song; Liu, Chunhua; Zhou, Peng; Li, Yanling

2016-01-01

Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields.

Investigation of Diesel’s Residual Noise on Predictive Vehicles Noise Cancelling using LMS Adaptive Algorithm

Science.gov (United States)

Arttini Dwi Prasetyowati, Sri; Susanto, Adhi; Widihastuti, Ida

2017-04-01

Every noise problems require different solution. In this research, the noise that must be cancelled comes from roadway. Least Mean Square (LMS) adaptive is one of the algorithm that can be used to cancel that noise. Residual noise always appears and could not be erased completely. This research aims to know the characteristic of residual noise from vehicle’s noise and analysis so that it is no longer appearing as a problem. LMS algorithm was used to predict the vehicle’s noise and minimize the error. The distribution of the residual noise could be observed to determine the specificity of the residual noise. The statistic of the residual noise close to normal distribution with = 0,0435, = 1,13 and the autocorrelation of the residual noise forming impulse. As a conclusion the residual noise is insignificant.

Greenhouse crop residues: Energy potential and models for the prediction of their higher heating value

Energy Technology Data Exchange (ETDEWEB)

Callejon-Ferre, A.J.; Lopez-Martinez, J.A.; Manzano-Agugliaro, F. [Departamento de Ingenieria Rural, Universidad de Almeria, Ctra. Sacramento s/n, La Canada de San Urbano, 04120 Almeria (Spain); Velazquez-Marti, B. [Departamento de Ingenieria Rural y Agroalimentaria, Universidad Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia (Spain)

2011-02-15

Almeria, in southeastern Spain, generates some 1,086,261 t year{sup -1} (fresh weight) of greenhouse crop (Cucurbita pepo L., Cucumis sativus L., Solanum melongena L., Solanum lycopersicum L., Phaseoulus vulgaris L., Capsicum annuum L., Citrillus vulgaris Schrad. and Cucumis melo L.) residues. The energy potential of this biomass is unclear. The aim of the present work was to accurately quantify this variable, differentiating between crop species while taking into consideration the area they each occupy. This, however, required the direct analysis of the higher heating value (HHV) of these residues, involving very expensive and therefore not commonly available equipment. Thus, a further aim was to develop models for predicting the HHV of these residues, taking into account variables measured by elemental and/or proximate analysis, thus providing an economically attractive alternative to direct analysis. All the analyses in this work involved the use of worldwide-recognised standards and methods. The total energy potential for these plant residues, as determined by direct analysis, was 1,003,497.49 MW h year{sup -1}. Twenty univariate and multivariate equations were developed to predict the HHV. The R{sup 2} and adjusted R{sup 2} values obtained for the univariate and multivariate models were 0.909 and 0.946 or above respectively. In all cases, the mean absolute percentage error varied between 0.344 and 2.533. These results show that any of these 20 equations could be used to accurately predict the HHV of crop residues. The residues produced by the Almeria greenhouse industry would appear to be an interesting source of renewable energy. (author)

HemeBIND: a novel method for heme binding residue prediction by combining structural and sequence information

Directory of Open Access Journals (Sweden)

Hu Jianjun

2011-05-01

Full Text Available Abstract Background Accurate prediction of binding residues involved in the interactions between proteins and small ligands is one of the major challenges in structural bioinformatics. Heme is an essential and commonly used ligand that plays critical roles in electron transfer, catalysis, signal transduction and gene expression. Although much effort has been devoted to the development of various generic algorithms for ligand binding site prediction over the last decade, no algorithm has been specifically designed to complement experimental techniques for identification of heme binding residues. Consequently, an urgent need is to develop a computational method for recognizing these important residues. Results Here we introduced an efficient algorithm HemeBIND for predicting heme binding residues by integrating structural and sequence information. We systematically investigated the characteristics of binding interfaces based on a non-redundant dataset of heme-protein complexes. It was found that several sequence and structural attributes such as evolutionary conservation, solvent accessibility, depth and protrusion clearly illustrate the differences between heme binding and non-binding residues. These features can then be separately used or combined to build the structure-based classifiers using support vector machine (SVM. The results showed that the information contained in these features is largely complementary and their combination achieved the best performance. To further improve the performance, an attempt has been made to develop a post-processing procedure to reduce the number of false positives. In addition, we built a sequence-based classifier based on SVM and sequence profile as an alternative when only sequence information can be used. Finally, we employed a voting method to combine the outputs of structure-based and sequence-based classifiers, which demonstrated remarkably better performance than the individual classifier alone

Variance computations for functional of absolute risk estimates.

Science.gov (United States)

Pfeiffer, R M; Petracci, E

2011-07-01

We present a simple influence function based approach to compute the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates.

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

Science.gov (United States)

Korfiatis, Panagiotis; Kline, Timothy L; Lachance, Daniel H; Parney, Ian F; Buckner, Jan C; Erickson, Bradley J

2017-10-01

Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/- 3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/- 13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/- 20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p deep neural architectures can be used to predict molecular biomarkers from routine medical images.

Computational models for residual creep life prediction of power plant components

International Nuclear Information System (INIS)

Grewal, G.S.; Singh, A.K.; Ramamoortry, M.

2006-01-01

All high temperature - high pressure power plant components are prone to irreversible visco-plastic deformation by the phenomenon of creep. The steady state creep response as well as the total creep life of a material is related to the operational component temperature through, respectively, the exponential and inverse exponential relationships. Minor increases in the component temperature can thus have serious consequences as far as the creep life and dimensional stability of a plant component are concerned. In high temperature steam tubing in power plants, one mechanism by which a significant temperature rise can occur is by the growth of a thermally insulating oxide film on its steam side surface. In the present paper, an elegantly simple and computationally efficient technique is presented for predicting the residual creep life of steel components subjected to continual steam side oxide film growth. Similarly, fabrication of high temperature power plant components involves extensive use of welding as the fabrication process of choice. Naturally, issues related to the creep life of weldments have to be seriously addressed for safe and continual operation of the welded plant component. Unfortunately, a typical weldment in an engineering structure is a zone of complex microstructural gradation comprising of a number of distinct sub-zones with distinct meso-scale and micro-scale morphology of the phases and (even) chemistry and its creep life prediction presents considerable challenges. The present paper presents a stochastic algorithm, which can be' used for developing experimental creep-cavitation intensity versus residual life correlations for welded structures. Apart from estimates of the residual life in a mean field sense, the model can be used for predicting the reliability of the plant component in a rigorous probabilistic setting. (author)

Analysis of conditional genetic effects and variance components in developmental genetics.

Science.gov (United States)

Zhu, J

1995-12-01

A genetic model with additive-dominance effects and genotype x environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t-1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects.

The prediction of reliability and residual life of reactor pressure components

International Nuclear Information System (INIS)

Nemec, J.; Antalovsky, S.

1978-01-01

The paper deals with the problem of PWR pressure components reliability and residual life evaluation and prediction. A physical model of damage cumulation which serves as a theoretical basis for all considerations presents two major aspects. The first one describes the dependence of the degree of damage in the crack leading-edge in pressure components on the reactor system load-time history, i.e. on the number of transient loads. Both stages, fatigue crack initiation and growth through the wall until the critical length is reached, are investigated. The crack is supposed to initiate at the flaws in a strength weld joint or in the bimetallic weld of the base ferritic steel and the austenitic stainless overlay cladding. The growth rates of developed cracks are analysed in respect to different load-time histories. Important cyclic properties of some steels are derived from the low-cycle fatigue theory. The second aspect is the load-time history-dependent process of precipitation, deformation and radiation aging, characterized entirely by the critical crack-length value mentioned above. The fracture point, defined by the equation ''crack-length=critical value'' and hence the residual life, can be evaluated using this model and verified by in-service inspection. The physical model described is randomized by considering all the parameters of the model as random. Monte Carlo methods are applied and fatigue crack initiation and growth is simulated. This permits evaluation of the reliability and residual life of the component. The distributions of material and load-time history parameters are needed for such simulation. Both the deterministic and computer-simulated probabilistic predictions of reliability and residual life are verified by prior-to-failure sequential testing of data coming from in-service NDT periodical inspections. (author)

Prediction of residual stresses in electron beam welded Ti-6Al-4V plates

Energy Technology Data Exchange (ETDEWEB)

Xu, Lianyong; Ge, Keke; Jing, Hongyang; Zhao, Lei; Lv, Xiaoqing [Tianjin Univ. (China); Han, Yongdian [Tianjin Univ. (China). Key Lab. of Advanced Joining Technology

2017-05-01

A thermo-metallurgical procedure based on the SYSWELD code was developed to predict welding temperature field, microstructure and residual stress in butt-welded Ti-6Al-4V plate taking into account phase transformation. The formation of martensite was confirmed by the CCT diagram and microstructure in the weld joint, which significantly affects the magnitude of residual stress. The hole drilling procedure was utilized to measure the values of residual stress at the top surface of the specimen, which are in well agreement with the numerical results. Both simulated and test results show that the magnitude and distribution of residual stress on the surface of the plate present a large gradient feature from the weld joint to the base metal. Moreover, the distribution law of residual stresses in the plate thickness was further analyzed for better understanding of its generation and evolution.

Prediction of microstructure, residual stress, and deformation in laser powder bed fusion process

Science.gov (United States)

Yang, Y. P.; Jamshidinia, M.; Boulware, P.; Kelly, S. M.

2017-12-01

Laser powder bed fusion (L-PBF) process has been investigated significantly to build production parts with a complex shape. Modeling tools, which can be used in a part level, are essential to allow engineers to fine tune the shape design and process parameters for additive manufacturing. This study focuses on developing modeling methods to predict microstructure, hardness, residual stress, and deformation in large L-PBF built parts. A transient sequentially coupled thermal and metallurgical analysis method was developed to predict microstructure and hardness on L-PBF built high-strength, low-alloy steel parts. A moving heat-source model was used in this analysis to accurately predict the temperature history. A kinetics based model which was developed to predict microstructure in the heat-affected zone of a welded joint was extended to predict the microstructure and hardness in an L-PBF build by inputting the predicted temperature history. The tempering effect resulting from the following built layers on the current-layer microstructural phases were modeled, which is the key to predict the final hardness correctly. It was also found that the top layers of a build part have higher hardness because of the lack of the tempering effect. A sequentially coupled thermal and mechanical analysis method was developed to predict residual stress and deformation for an L-PBF build part. It was found that a line-heating model is not suitable for analyzing a large L-PBF built part. The layer heating method is a potential method for analyzing a large L-PBF built part. The experiment was conducted to validate the model predictions.

Prediction of microstructure, residual stress, and deformation in laser powder bed fusion process

Science.gov (United States)

Yang, Y. P.; Jamshidinia, M.; Boulware, P.; Kelly, S. M.

2018-05-01

Laser powder bed fusion (L-PBF) process has been investigated significantly to build production parts with a complex shape. Modeling tools, which can be used in a part level, are essential to allow engineers to fine tune the shape design and process parameters for additive manufacturing. This study focuses on developing modeling methods to predict microstructure, hardness, residual stress, and deformation in large L-PBF built parts. A transient sequentially coupled thermal and metallurgical analysis method was developed to predict microstructure and hardness on L-PBF built high-strength, low-alloy steel parts. A moving heat-source model was used in this analysis to accurately predict the temperature history. A kinetics based model which was developed to predict microstructure in the heat-affected zone of a welded joint was extended to predict the microstructure and hardness in an L-PBF build by inputting the predicted temperature history. The tempering effect resulting from the following built layers on the current-layer microstructural phases were modeled, which is the key to predict the final hardness correctly. It was also found that the top layers of a build part have higher hardness because of the lack of the tempering effect. A sequentially coupled thermal and mechanical analysis method was developed to predict residual stress and deformation for an L-PBF build part. It was found that a line-heating model is not suitable for analyzing a large L-PBF built part. The layer heating method is a potential method for analyzing a large L-PBF built part. The experiment was conducted to validate the model predictions.

Variance swap payoffs, risk premia and extreme market conditions

DEFF Research Database (Denmark)

Rombouts, Jeroen V.K.; Stentoft, Lars; Violante, Francesco

This paper estimates the Variance Risk Premium (VRP) directly from synthetic variance swap payoffs. Since variance swap payoffs are highly volatile, we extract the VRP by using signal extraction techniques based on a state-space representation of our model in combination with a simple economic....... The latter variables and the VRP generate different return predictability on the major US indices. A factor model is proposed to extract a market VRP which turns out to be priced when considering Fama and French portfolios....

Technical note: Equivalent genomic models with a residual polygenic effect.

Science.gov (United States)

Liu, Z; Goddard, M E; Hayes, B J; Reinhardt, F; Reents, R

2016-03-01

Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multi-step genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A simulation methodology of spacer grid residual spring deflection for predictive and interpretative purposes

International Nuclear Information System (INIS)

Kim, K. T.; Kim, H. K.; Yoon, K. H.

1994-01-01

The in-reactor fuel rod support conditions against the fretting wear-induced damage can be evaluated by spacer grid residual spring deflection. In order to predict the spacer grid residual spring deflection as a function of burnup for various spring designs, a simulation methodology of spacer grid residual spring deflection has been developed and implemented in the GRIDFORCE program. The simulation methodology takes into account cladding creep rate, initial spring deflection, initial spring force, and spring force relaxation rate as the key parameters affecting the residual spring deflection. The simulation methodology developed in this study can be utilized as an effective tool in evaluating the capability of a newly designed spacer grid spring to prevent the fretting wear-induced damage

A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein.

Science.gov (United States)

Agrawal, Neeraj J; Helk, Bernhard; Trout, Bernhardt L

2014-01-21

Identifying hot-spot residues - residues that are critical to protein-protein binding - can help to elucidate a protein's function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein-protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36-57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein-protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors

Science.gov (United States)

David, McInerney; Mark, Thyer; Dmitri, Kavetski; George, Kuczera

2017-04-01

This study provides guidance to hydrological researchers which enables them to provide probabilistic predictions of daily streamflow with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality). Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. It is commonly known that hydrological model residual errors are heteroscedastic, i.e. there is a pattern of larger errors in higher streamflow predictions. Although multiple approaches exist for representing this heteroscedasticity, few studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating 8 common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter, lambda) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and USA, and two lumped hydrological models. We find the choice of heteroscedastic error modelling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with lambda of 0.2 and 0.5, and the log scheme (lambda=0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks

DEFF Research Database (Denmark)

Helles, Glennie; Fonseca, Rasmus

2009-01-01

residue in the input-window. The trained neural network shows a significant improvement (4-68%) in predicting the most probable bin (covering a 30°×30° area of the dihedral angle space) for all amino acids in the data set compared to first order statistics. An accuracy comparable to that of secondary...... seem to have a significant influence on the dihedral angles adopted by the individual amino acids in coil segments. In this work we attempt to predict a probability distribution of these dihedral angles based on the flanking residues. While attempts to predict dihedral angles of coil segments have been...... done previously, none have, to our knowledge, presented comparable results for the probability distribution of dihedral angles. Results: In this paper we develop an artificial neural network that uses an input-window of amino acids to predict a dihedral angle probability distribution for the middle...

Prediction of residual stress distribution in multi-stacked thin film by curvature measurement and iterative FEA

International Nuclear Information System (INIS)

Choi, Hyeon Chang; Park, Jun Hyub

2005-01-01

In this study, residual stress distribution in multi-stacked film by MEMS (Micro-Electro Mechanical System) process is predicted using Finite Element Method (FEM). We develop a finite element program for REsidual Stress Analysis (RESA) in multi-stacked film. The RESA predicts the distribution of residual stress field in multi-stacked film. Curvatures of multi-stacked film and single layers which consist of the multi-stacked film are used as the input to the RESA. To measure those curvatures is easier than to measure a distribution of residual stress. To verify the RESA, mean stresses and stress gradients of single and multilayers are measured. The mean stresses are calculated from curvatures of deposited wafer by using Stoney's equation. The stress gradients are calculated from the vertical deflection at the end of cantilever beam. To measure the mean stress of each layer in multi-stacked film, we measure the curvature of wafer with the film after etching layer by layer in multi-stacked film

SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

Directory of Open Access Journals (Sweden)

Xiaoxia Yang

Full Text Available Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

Science.gov (United States)

Yang, Xiaoxia; Wang, Jia; Sun, Jun; Liu, Rong

2015-01-01

Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

Validity of a Residualized Dependent Variable after Pretest Covariance Adjustments: Still the Same Variable?

Science.gov (United States)

Nimon, Kim; Henson, Robin K.

2015-01-01

The authors empirically examined whether the validity of a residualized dependent variable after covariance adjustment is comparable to that of the original variable of interest. When variance of a dependent variable is removed as a result of one or more covariates, the residual variance may not reflect the same meaning. Using the pretest-posttest…

Prediction of breeding values and selection responses with genetic heterogeneity of environmental variance

NARCIS (Netherlands)

Mulder, H.A.; Bijma, P.; Hill, W.G.

2007-01-01

There is empirical evidence that genotypes differ not only in mean, but also in environmental variance of the traits they affect. Genetic heterogeneity of environmental variance may indicate genetic differences in environmental sensitivity. The aim of this study was to develop a general framework

Large-scale evaluation of dynamically important residues in proteins predicted by the perturbation analysis of a coarse-grained elastic model

Directory of Open Access Journals (Sweden)

Tekpinar Mustafa

2009-07-01

Full Text Available Abstract Backgrounds It is increasingly recognized that protein functions often require intricate conformational dynamics, which involves a network of key amino acid residues that couple spatially separated functional sites. Tremendous efforts have been made to identify these key residues by experimental and computational means. Results We have performed a large-scale evaluation of the predictions of dynamically important residues by a variety of computational protocols including three based on the perturbation and correlation analysis of a coarse-grained elastic model. This study is performed for two lists of test cases with >500 pairs of protein structures. The dynamically important residues predicted by the perturbation and correlation analysis are found to be strongly or moderately conserved in >67% of test cases. They form a sparse network of residues which are clustered both in 3D space and along protein sequence. Their overall conservation is attributed to their dynamic role rather than ligand binding or high network connectivity. Conclusion By modeling how the protein structural fluctuations respond to residue-position-specific perturbations, our highly efficient perturbation and correlation analysis can be used to dissect the functional conformational changes in various proteins with a residue level of detail. The predictions of dynamically important residues serve as promising targets for mutational and functional studies.

An elementary components of variance analysis for multi-center quality control

International Nuclear Information System (INIS)

Munson, P.J.; Rodbard, D.

1977-01-01

The serious variability of RIA results from different laboratories indicates the need for multi-laboratory collaborative quality control (QC) studies. Statistical analysis methods for such studies using an 'analysis of variance with components of variance estimation' are discussed. This technique allocates the total variance into components corresponding to between-laboratory, between-assay, and residual or within-assay variability. Components of variance analysis also provides an intelligent way to combine the results of several QC samples run at different evels, from which we may decide if any component varies systematically with dose level; if not, pooling of estimates becomes possible. We consider several possible relationships of standard deviation to the laboratory mean. Each relationship corresponds to an underlying statistical model, and an appropriate analysis technique. Tests for homogeneity of variance may be used to determine if an appropriate model has been chosen, although the exact functional relationship of standard deviation to lab mean may be difficult to establish. Appropriate graphical display of the data aids in visual understanding of the data. A plot of the ranked standard deviation vs. ranked laboratory mean is a convenient way to summarize a QC study. This plot also allows determination of the rank correlation, which indicates a net relationship of variance to laboratory mean. (orig.) [de

A log-sinh transformation for data normalization and variance stabilization

Science.gov (United States)

Wang, Q. J.; Shrestha, D. L.; Robertson, D. E.; Pokhrel, P.

2012-05-01

When quantifying model prediction uncertainty, it is statistically convenient to represent model errors that are normally distributed with a constant variance. The Box-Cox transformation is the most widely used technique to normalize data and stabilize variance, but it is not without limitations. In this paper, a log-sinh transformation is derived based on a pattern of errors commonly seen in hydrological model predictions. It is suited to applications where prediction variables are positively skewed and the spread of errors is seen to first increase rapidly, then slowly, and eventually approach a constant as the prediction variable becomes greater. The log-sinh transformation is applied in two case studies, and the results are compared with one- and two-parameter Box-Cox transformations.

Selection for uniformity in livestock by exploiting genetic heterogeneity of environmental variance

NARCIS (Netherlands)

Mulder, H.A.; Bijma, P.; Hill, W.G.

2008-01-01

In some situations, it is worthwhile to change not only the mean, but also the variability of traits by selection. Genetic variation in residual variance may be utilised to improve uniformity in livestock populations by selection. The objective was to investigate the effects of genetic parameters,

A two-stage approach for improved prediction of residue contact maps

Directory of Open Access Journals (Sweden)

Pollastri Gianluca

2006-03-01

Full Text Available Abstract Background Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced nature of the problem (with far fewer examples of contacts than non-contacts, the formidable challenge of capturing long-range interactions in the maps, the intrinsic difficulty of mapping one-dimensional input sequences into two-dimensional output maps. In order to alleviate these problems and achieve improved contact map predictions, in this paper we split the task into two stages: the prediction of a map's principal eigenvector (PE from the primary sequence; the reconstruction of the contact map from the PE and primary sequence. Predicting the PE from the primary sequence consists in mapping a vector into a vector. This task is less complex than mapping vectors directly into two-dimensional matrices since the size of the problem is drastically reduced and so is the scale length of interactions that need to be learned. Results We develop architectures composed of ensembles of two-layered bidirectional recurrent neural networks to classify the components of the PE in 2, 3 and 4 classes from protein primary sequence, predicted secondary structure, and hydrophobicity interaction scales. Our predictor, tested on a non redundant set of 2171 proteins, achieves classification performances of up to 72.6%, 16% above a base-line statistical predictor. We design a system for the prediction of contact maps from the predicted PE. Our results show that predicting maps through the PE yields sizeable gains especially for long-range contacts which are particularly critical for accurate protein 3D reconstruction. The final predictor's accuracy on a non-redundant set of 327 targets is 35

An analytical model to predict and minimize the residual stress of laser cladding process

Science.gov (United States)

Tamanna, N.; Crouch, R.; Kabir, I. R.; Naher, S.

2018-02-01

Laser cladding is one of the advanced thermal techniques used to repair or modify the surface properties of high-value components such as tools, military and aerospace parts. Unfortunately, tensile residual stresses generate in the thermally treated area of this process. This work focuses on to investigate the key factors for the formation of tensile residual stress and how to minimize it in the clad when using dissimilar substrate and clad materials. To predict the tensile residual stress, a one-dimensional analytical model has been adopted. Four cladding materials (Al2O3, TiC, TiO2, ZrO2) on the H13 tool steel substrate and a range of preheating temperatures of the substrate, from 300 to 1200 K, have been investigated. Thermal strain and Young's modulus are found to be the key factors of formation of tensile residual stresses. Additionally, it is found that using a preheating temperature of the substrate immediately before laser cladding showed the reduction of residual stress.

Predictive hydrogeochemical modelling of bauxite residue sand in field conditions.

Science.gov (United States)

Wissmeier, Laurin; Barry, David A; Phillips, Ian R

2011-07-15

The suitability of residue sand (the coarse fraction remaining from Bayer's process of bauxite refining) for constructing the surface cover of closed bauxite residue storage areas was investigated. Specifically, its properties as a medium for plant growth are of interest to ensure residue sand can support a sustainable ecosystem following site closure. The geochemical evolution of the residue sand under field conditions, its plant nutrient status and soil moisture retention were studied by integrated modelling of geochemical and hydrological processes. For the parameterization of mineral reactions, amounts and reaction kinetics of the mineral phases natron, calcite, tricalcium aluminate, sodalite, muscovite and analcime were derived from measured acid neutralization curves. The effective exchange capacity for ion adsorption was measured using three independent exchange methods. The geochemical model, which accounts for mineral reactions, cation exchange and activity corrected solution speciation, was formulated in the geochemical modelling framework PHREEQC, and partially validated in a saturated-flow column experiment. For the integration of variably saturated flow with multi-component solute transport in heterogeneous 2D domains, a coupling of PHREEQC with the multi-purpose finite-element solver COMSOL was established. The integrated hydrogeochemical model was applied to predict water availability and quality in a vertical flow lysimeter and a cover design for a storage facility using measured time series of rainfall and evaporation from southwest Western Australia. In both scenarios the sand was fertigated and gypsum-amended. Results show poor long-term retention of fertilizer ions and buffering of the pH around 10 for more than 5 y of leaching. It was concluded that fertigation, gypsum amendment and rainfall leaching alone were insufficient to render the geochemical conditions of residue sand suitable for optimal plant growth within the given timeframe. The

A longitudinal study on dual-tasking effects on gait: cognitive change predicts gait variance in the elderly.

Directory of Open Access Journals (Sweden)

Rebecca K MacAulay

Full Text Available Neuropsychological abilities have found to explain a large proportion of variance in objective measures of walking gait that predict both dementia and falling within the elderly. However, to this date there has been little research on the interplay between changes in these neuropsychological processes and walking gait overtime. To our knowledge, the present study is the first to investigate intra-individual changes in neurocognitive test performance and gait step time at two-time points across a one-year span. Neuropsychological test scores from 440 elderly individuals deemed cognitively normal at Year One were analyzed via repeated measures t-tests to assess for decline in cognitive performance at Year Two. 34 of these 440 individuals neuropsychological test performance significantly declined at Year Two; whereas the "non-decliners" displayed improved memory, working memory, attention/processing speed test performance. Neuropsychological test scores were also submitted to factor analysis at both time points for data reduction purposes and to assess the factor stability overtime. Results at Year One yielded a three-factor solution: Language/Memory, Executive Attention/Processing Speed, and Working Memory. Year Two's test scores also generated a three-factor solution (Working Memory, Language/Executive Attention/Processing Speed, and Memory. Notably, language measures loaded on Executive Attention/Processing Speed rather than on the Memory factor at Year Two. Hierarchal multiple regression revealed that both Executive Attention/Processing Speed and sex significantly predicted variance in dual task step time at both time points. Remarkably, in the "decliners", the magnitude of the contribution of the neuropsychological characteristics to gait variance significantly increased at Year Two. In summary, this study provides longitudinal evidence of the dynamic relationship between intra-individual cognitive change and its influence on dual task gait

The development of techniques for determining the residual life time prediction on NPP equipment

International Nuclear Information System (INIS)

Antonov, Alexander V.; Dagaev, Alexander V.; Volnikov, Ivan S.

1999-01-01

The problem of determining the residual life prediction of NPP equipment is presently highly pressing. NPP residual life resources are 30 years, but for particular equipment it is much less. Thus, residual life resource for equipment of control and protection system of NPP unit is 5-10 years. The NPP equipment is expensive and its replacing requires much expense. Hence an urgent problem is to study residual life resources of equipment on the basis of statistic information obtained during operation. Deterministic approach of determining residual life resources for particular equipment is widely known in the literature. Physical and statistical models are also being developed for determining the residual life, e.g. the model (loading-bearing capability). The present work offers the techniques of the residual life determination reasoning from statistic information of functioning objects in the process of operation. To put the techniques into effect it is necessary to have information about the time of operation of a group of objects of the same type, the number of failures; it is desirable to know failure operating time, order of the object replacement and the reason which caused the replacement (failure or planned preventive maintenance). Metrics is based on studying the parameters for the series of failures resulted from real statistic data. Then we can proceed to distribution density of the failure working time. For this purpose the Voltarra's equation of the second order is solved f(t) = ω(t) + ∫ 0 t f(t - τ)ω(τ)dτ. Since statistics of data sampling related to failure is small due to difficulties in solution of Voltaire's equation, the authors offer moderate method of solution for the above equation. After distribution density of the failure working time is determined the calculation of equipment residual life is made by the following formula: T τ (t) 1/P(τ)∫ 0 ∞ P(t)dt. The proposed techniques are realised as the software. In the course of working

Finite Element Simulation of Shot Peening: Prediction of Residual Stresses and Surface Roughness

Science.gov (United States)

Gariépy, Alexandre; Perron, Claude; Bocher, Philippe; Lévesque, Martin

Shot peening is a surface treatment that consists of bombarding a ductile surface with numerous small and hard particles. Each impact creates localized plastic strains that permanently stretch the surface. Since the underlying material constrains this stretching, compressive residual stresses are generated near the surface. This process is commonly used in the automotive and aerospace industries to improve fatigue life. Finite element analyses can be used to predict residual stress profiles and surface roughness created by shot peening. This study investigates further the parameters and capabilities of a random impact model by evaluating the representative volume element and the calculated stress distribution. Using an isotropic-kinematic hardening constitutive law to describe the behaviour of AA2024-T351 aluminium alloy, promising results were achieved in terms of residual stresses.

Estimating the spatial scale of herbicide and soil interactions by nested sampling, hierarchical analysis of variance and residual maximum likelihood

Energy Technology Data Exchange (ETDEWEB)

Price, Oliver R., E-mail: oliver.price@unilever.co [Warwick-HRI, University of Warwick, Wellesbourne, Warwick, CV32 6EF (United Kingdom); University of Reading, Soil Science Department, Whiteknights, Reading, RG6 6UR (United Kingdom); Oliver, Margaret A. [University of Reading, Soil Science Department, Whiteknights, Reading, RG6 6UR (United Kingdom); Walker, Allan [Warwick-HRI, University of Warwick, Wellesbourne, Warwick, CV32 6EF (United Kingdom); Wood, Martin [University of Reading, Soil Science Department, Whiteknights, Reading, RG6 6UR (United Kingdom)

2009-05-15

An unbalanced nested sampling design was used to investigate the spatial scale of soil and herbicide interactions at the field scale. A hierarchical analysis of variance based on residual maximum likelihood (REML) was used to analyse the data and provide a first estimate of the variogram. Soil samples were taken at 108 locations at a range of separating distances in a 9 ha field to explore small and medium scale spatial variation. Soil organic matter content, pH, particle size distribution, microbial biomass and the degradation and sorption of the herbicide, isoproturon, were determined for each soil sample. A large proportion of the spatial variation in isoproturon degradation and sorption occurred at sampling intervals less than 60 m, however, the sampling design did not resolve the variation present at scales greater than this. A sampling interval of 20-25 m should ensure that the main spatial structures are identified for isoproturon degradation rate and sorption without too great a loss of information in this field. - Estimating the spatial scale of herbicide and soil interactions by nested sampling.

Estimating the spatial scale of herbicide and soil interactions by nested sampling, hierarchical analysis of variance and residual maximum likelihood

International Nuclear Information System (INIS)

Price, Oliver R.; Oliver, Margaret A.; Walker, Allan; Wood, Martin

2009-01-01

An unbalanced nested sampling design was used to investigate the spatial scale of soil and herbicide interactions at the field scale. A hierarchical analysis of variance based on residual maximum likelihood (REML) was used to analyse the data and provide a first estimate of the variogram. Soil samples were taken at 108 locations at a range of separating distances in a 9 ha field to explore small and medium scale spatial variation. Soil organic matter content, pH, particle size distribution, microbial biomass and the degradation and sorption of the herbicide, isoproturon, were determined for each soil sample. A large proportion of the spatial variation in isoproturon degradation and sorption occurred at sampling intervals less than 60 m, however, the sampling design did not resolve the variation present at scales greater than this. A sampling interval of 20-25 m should ensure that the main spatial structures are identified for isoproturon degradation rate and sorption without too great a loss of information in this field. - Estimating the spatial scale of herbicide and soil interactions by nested sampling.

Study on predicting residual life of elevator links by fracture mechanics approach

Energy Technology Data Exchange (ETDEWEB)

Li Helin; Zhang Yi; Deng Zengjie [China National Petroleum Corp., Xi`an, Shaanxi (China). Tubular Goods Research Center; Jin Dazeng [Xi`an Jiaotong Univ., Xi`an, Shaanxi (China)

1995-12-31

On the basis of investigation, failure and fracture analysis of elevator links, residual life prediction of links using fracture mechanics approach is studied, and mechanical properties, fracture toughness value K{sub IC} and fatigue crack propagation rage da/dN of the steel for elevator links are determined. Using the relation between stress intensity factor K{sub I} and the strain-energy release rate, the two-dimensional conversion thickness finite element method has been used to calculate the stress intensity factors K{sub I} for dangerous sections in the ring part of links. Furthermore, the reliability of calculations of the finite element stress intensity factors K{sub I} for dangerous sections of elevator links and the residual life computation for links are verified by fatigue tests of actual links. Finally, the experimental verification of computed results by 150T link fractured at site indicates that the computed critical crack lengths and residual life tally well with those measured and meet the needs of oil drilling.

A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors

NARCIS (Netherlands)

Schoups, G.; Vrugt, J.A.

2010-01-01

Estimation of parameter and predictive uncertainty of hydrologic models has traditionally relied on several simplifying assumptions. Residual errors are often assumed to be independent and to be adequately described by a Gaussian probability distribution with a mean of zero and a constant variance.

The Achilles Heel of Normal Determinations via Minimum Variance Techniques: Worldline Dependencies

Science.gov (United States)

Ma, Z.; Scudder, J. D.; Omidi, N.

2002-12-01

Time series of data collected across current layers are usually organized by divining coordinate transformations (as from minimum variance) that permits a geometrical interpretation for the data collected. Almost without exception the current layer geometry is inferred by supposing that the current carrying layer is locally planar. Only after this geometry is ``determined'' can the various quantities predicted by theory calculated. The precision of reconnection rated ``measured'' and the quantitative support for or against component reconnection be evaluated. This paper defines worldline traversals across fully resolved Hall two fluid models of reconnecting current sheets (with varying sizes of guide fields) and across a 2-D hybrid solution of a super critical shock layer. Along each worldline various variance techniques are used to infer current sheet normals based on the data observed along this worldline alone. We then contrast these inferred normals with those known from the overview of the fully resolved spatial pictures of the layer. Absolute errors of 20 degrees in the normal are quite commonplace, but errors of 40-90 deg are also implied, especially for worldlines that make more and more oblique angles to the true current sheet normal. These mistaken ``inferences'' are traceable to the degree that the data collected sample 2-D variations within these layers or not. While it is not surprising that these variance techniques give incorrect errors in the presence of layers that possess 2-D variations, it is illuminating that such large errors need not be signalled by the traditional error formulae for the error cones on normals that have been previously used to estimate the errors of normal choices. Frequently the absolute errors that depend on worldline path can be 10 times the random error that formulae would predict based on eigenvalues of the covariance matrix. A given time series cannot be associated in any a priori way with a specific worldline

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

Science.gov (United States)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Lerat, Julien; Kuczera, George

2017-03-01

Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. This study focuses on approaches for representing error heteroscedasticity with respect to simulated streamflow, i.e., the pattern of larger errors in higher streamflow predictions. We evaluate eight common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter λ) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and the United States, and two lumped hydrological models. Performance is quantified using predictive reliability, precision, and volumetric bias metrics. We find the choice of heteroscedastic error modeling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with λ of 0.2 and 0.5, and the log scheme (λ = 0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Paradoxically, calibration of λ is often counterproductive: in perennial catchments, it tends to overfit low flows at the expense of abysmal precision in high flows. The log-sinh transformation is dominated by the simpler Pareto optimal schemes listed above. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Initial contents of residue quality parameters predict effects of larger soil fauna on decomposition of contrasting quality residues

Directory of Open Access Journals (Sweden)

Ratikorn Sanghaw

2017-10-01

Full Text Available A 52-week decomposition study employing the soil larger fauna exclusion technique through litter bags of two mesh sizes (20 and 0.135 mm was conducted in a long-term (18 yr field experiment. Organic residues of contrasting quality of N, lignin (L, polyphenols (PP and cellulose (CL all in grams per kilogram: rice straw (RS: 4.5N, 22.2L, 3.9PP, 449CL, groundnut stover (GN: 21.2N, 71.4L, 8.1PP, 361CL, dipterocarp leaf litter (DP: 5.1N, 303L, 68.9PP, 271CL and tamarind leaf litter (TM: 11.6N, 190L, 27.7PP, 212CL were applied to soil annually to assess and predict soil larger fauna effects (LFE on decomposition based on the initial contents of the residue chemical constituents. Mass losses in all residues were not different under soil fauna inclusion and exclusion treatments during the early stage (up to week 4 after residue incorporation but became significantly higher under the inclusion than the exclusion treatments during the later stage (week 8 onwards. LFE were highest (2–51% under the resistant DP at most decomposition stages. During the early stage (weeks 1–4, both the initial contents of labile (N and CL and recalcitrant C, and recalcitrant C interaction with labile constituents of residues showed significant correlations (r = 0.64–0.90 with LFE. In the middle stage (week 16, LFE under resistant DP and TM had significant positive correlations with L, L + PP and L/CL. They were also affected by these quality parameters as shown by the multiple regression analysis. In the later stages (weeks 26–52, the L/CL ratio was the most prominent quality parameter affecting LFE. Keywords: Mesofauna and macrofauna, Microorganisms, Recalcitrant and labile compounds, Residue chemical composition, Tropical sandy soil

A Minimum Variance Algorithm for Overdetermined TOA Equations with an Altitude Constraint.

Energy Technology Data Exchange (ETDEWEB)

Romero, Louis A; Mason, John J.

2018-04-01

We present a direct (non-iterative) method for solving for the location of a radio frequency (RF) emitter, or an RF navigation receiver, using four or more time of arrival (TOA) measurements and an assumed altitude above an ellipsoidal earth. Both the emitter tracking problem and the navigation application are governed by the same equations, but with slightly different interpreta- tions of several variables. We treat the assumed altitude as a soft constraint, with a specified noise level, just as the TOA measurements are handled, with their respective noise levels. With 4 or more TOA measurements and the assumed altitude, the problem is overdetermined and is solved in the weighted least squares sense for the 4 unknowns, the 3-dimensional position and time. We call the new technique the TAQMV (TOA Altitude Quartic Minimum Variance) algorithm, and it achieves the minimum possible error variance for given levels of TOA and altitude estimate noise. The method algebraically produces four solutions, the least-squares solution, and potentially three other low residual solutions, if they exist. In the lightly overdermined cases where multiple local minima in the residual error surface are more likely to occur, this algebraic approach can produce all of the minima even when an iterative approach fails to converge. Algorithm performance in terms of solution error variance and divergence rate for bas eline (iterative) and proposed approach are given in tables.

Disentangling evolutionary signals: conservation, specificity determining positions and coevolution. Implication for catalytic residue prediction

DEFF Research Database (Denmark)

Teppa, Elin; Wilkins, Angela D.; Nielsen, Morten

2012-01-01

Background: A large panel of methods exists that aim to identify residues with critical impact on protein function based on evolutionary signals, sequence and structure information. However, it is not clear to what extent these different methods overlap, and if any of the methods have higher...... predictive potential compared to others when it comes to, in particular, the identification of catalytic residues (CR) in proteins. Using a large set of enzymatic protein families and measures based on different evolutionary signals, we sought to break up the different components of the information content......-value Evolutionary Trace (rvET) methods and conservation, another containing mutual information (MI) methods, and the last containing methods designed explicitly for the identification of specificity determining positions (SDPs): integer-value Evolutionary Trace (ivET), SDPfox, and XDET. In terms of prediction of CR...

Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.

Directory of Open Access Journals (Sweden)

Huiying Zhao

Full Text Available As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions. A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC of 0.77 with high precision (94% and high sensitivity (65%. We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA] is available as an on-line server at http://sparks-lab.org.

The pricing of long and short run variance and correlation risk in stock returns

NARCIS (Netherlands)

Cosemans, M.

2011-01-01

This paper studies the pricing of long and short run variance and correlation risk. The predictive power of the market variance risk premium for returns is driven by the correlation risk premium and the systematic part of individual variance premia. Furthermore, I find that aggregate volatility risk

Calculating the variance and prediction intervals for estimates obtained from allometric relationships

CSIR Research Space (South Africa)

Nickless, A

2010-09-01

Full Text Available that across the range of x values, the variability in the error does not change (i.e. no heteroscedasticity). Often the power function in allometry is used: y = axbε This can be converted to: ln(yi) = β0 + β1 ln(xi) + εi The above assumptions now apply... to the regression relationship with the logged variables. Therefore ln(yi) is assumed to be normally distributed with mean µ=β0+β1 ln(xi) and variance σ2*. From regression theory it is known that the expected value (e) and variance (Var) of ln(yi) is given by...

Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

Science.gov (United States)

Ge, Jing; Zhang, Guoping

2015-01-01

Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

An elementary components of variance analysis for multi-centre quality control

International Nuclear Information System (INIS)

Munson, P.J.; Rodbard, D.

1978-01-01

The serious variability of RIA results from different laboratories indicates the need for multi-laboratory collaborative quality-control (QC) studies. Simple graphical display of data in the form of histograms is useful but insufficient. The paper discusses statistical analysis methods for such studies using an ''analysis of variance with components of variance estimation''. This technique allocates the total variance into components corresponding to between-laboratory, between-assay, and residual or within-assay variability. Problems with RIA data, e.g. severe non-uniformity of variance and/or departure from a normal distribution violate some of the usual assumptions underlying analysis of variance. In order to correct these problems, it is often necessary to transform the data before analysis by using a logarithmic, square-root, percentile, ranking, RIDIT, ''Studentizing'' or other transformation. Ametric transformations such as ranks or percentiles protect against the undue influence of outlying observations, but discard much intrinsic information. Several possible relationships of standard deviation to the laboratory mean are considered. Each relationship corresponds to an underlying statistical model and an appropriate analysis technique. Tests for homogeneity of variance may be used to determine whether an appropriate model has been chosen, although the exact functional relationship of standard deviation to laboratory mean may be difficult to establish. Appropriate graphical display aids visual understanding of the data. A plot of the ranked standard deviation versus ranked laboratory mean is a convenient way to summarize a QC study. This plot also allows determination of the rank correlation, which indicates a net relationship of variance to laboratory mean

Measurement properties and usability of non-contact scanners for measuring transtibial residual limb volume.

Science.gov (United States)

Kofman, Rianne; Beekman, Anna M; Emmelot, Cornelis H; Geertzen, Jan H B; Dijkstra, Pieter U

2018-06-01

Non-contact scanners may have potential for measurement of residual limb volume. Different non-contact scanners have been introduced during the last decades. Reliability and usability (practicality and user friendliness) should be assessed before introducing these systems in clinical practice. The aim of this study was to analyze the measurement properties and usability of four non-contact scanners (TT Design, Omega Scanner, BioSculptor Bioscanner, and Rodin4D Scanner). Quasi experimental. Nine (geometric and residual limb) models were measured on two occasions, each consisting of two sessions, thus in total 4 sessions. In each session, four observers used the four systems for volume measurement. Mean for each model, repeatability coefficients for each system, variance components, and their two-way interactions of measurement conditions were calculated. User satisfaction was evaluated with the Post-Study System Usability Questionnaire. Systematic differences between the systems were found in volume measurements. Most of the variances were explained by the model (97%), while error variance was 3%. Measurement system and the interaction between system and model explained 44% of the error variance. Repeatability coefficient of the systems ranged from 0.101 (Omega Scanner) to 0.131 L (Rodin4D). Differences in Post-Study System Usability Questionnaire scores between the systems were small and not significant. The systems were reliable in determining residual limb volume. Measurement systems and the interaction between system and residual limb model explained most of the error variances. The differences in repeatability coefficient and usability between the four CAD/CAM systems were small. Clinical relevance If accurate measurements of residual limb volume are required (in case of research), modern non-contact scanners should be taken in consideration nowadays.

Gravity interpretation of dipping faults using the variance analysis method

International Nuclear Information System (INIS)

Essa, Khalid S

2013-01-01

A new algorithm is developed to estimate simultaneously the depth and the dip angle of a buried fault from the normalized gravity gradient data. This algorithm utilizes numerical first horizontal derivatives computed from the observed gravity anomaly, using filters of successive window lengths to estimate the depth and the dip angle of a buried dipping fault structure. For a fixed window length, the depth is estimated using a least-squares sense for each dip angle. The method is based on computing the variance of the depths determined from all horizontal gradient anomaly profiles using the least-squares method for each dip angle. The minimum variance is used as a criterion for determining the correct dip angle and depth of the buried structure. When the correct dip angle is used, the variance of the depths is always less than the variances computed using wrong dip angles. The technique can be applied not only to the true residuals, but also to the measured Bouguer gravity data. The method is applied to synthetic data with and without random errors and two field examples from Egypt and Scotland. In all cases examined, the estimated depths and other model parameters are found to be in good agreement with the actual values. (paper)

Effect of heteroscedasticity treatment in residual error models on model calibration and prediction uncertainty estimation

Science.gov (United States)

Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli

2017-11-01

The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.

Analysis of covariance with pre-treatment measurements in randomized trials under the cases that covariances and post-treatment variances differ between groups.

Science.gov (United States)

Funatogawa, Takashi; Funatogawa, Ikuko; Shyr, Yu

2011-05-01

When primary endpoints of randomized trials are continuous variables, the analysis of covariance (ANCOVA) with pre-treatment measurements as a covariate is often used to compare two treatment groups. In the ANCOVA, equal slopes (coefficients of pre-treatment measurements) and equal residual variances are commonly assumed. However, random allocation guarantees only equal variances of pre-treatment measurements. Unequal covariances and variances of post-treatment measurements indicate unequal slopes and, usually, unequal residual variances. For non-normal data with unequal covariances and variances of post-treatment measurements, it is known that the ANCOVA with equal slopes and equal variances using an ordinary least-squares method provides an asymptotically normal estimator for the treatment effect. However, the asymptotic variance of the estimator differs from the variance estimated from a standard formula, and its property is unclear. Furthermore, the asymptotic properties of the ANCOVA with equal slopes and unequal variances using a generalized least-squares method are unclear. In this paper, we consider non-normal data with unequal covariances and variances of post-treatment measurements, and examine the asymptotic properties of the ANCOVA with equal slopes using the variance estimated from a standard formula. Analytically, we show that the actual type I error rate, thus the coverage, of the ANCOVA with equal variances is asymptotically at a nominal level under equal sample sizes. That of the ANCOVA with unequal variances using a generalized least-squares method is asymptotically at a nominal level, even under unequal sample sizes. In conclusion, the ANCOVA with equal slopes can be asymptotically justified under random allocation. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Life prediction of steam generator tubing due to stress corrosion crack using Monte Carlo Simulation

International Nuclear Information System (INIS)

Hu Jun; Liu Fei; Cheng Guangxu; Zhang Zaoxiao

2011-01-01

Highlights: → A life prediction model for SG tubing was proposed. → The initial crack length for SCC was determined. → Two failure modes called rupture mode and leak mode were considered. → A probabilistic life prediction code based on Monte Carlo method was developed. - Abstract: The failure of steam generator tubing is one of the main accidents that seriously affects the availability and safety of a nuclear power plant. In order to estimate the probability of the failure, a probabilistic model was established to predict the whole life-span and residual life of steam generator (SG) tubing. The failure investigated was stress corrosion cracking (SCC) after the generation of one through-wall axial crack. Two failure modes called rupture mode and leak mode based on probabilistic fracture mechanics were considered in this proposed model. It took into account the variance in tube geometry and material properties, and the variance in residual stresses and operating conditions, all of which govern the propagations of cracks. The proposed model was numerically calculated by using Monte Carlo Simulation (MCS). The plugging criteria were first verified and then the whole life-span and residual life of the SG tubing were obtained. Finally, important sensitivity analysis was also carried out to identify the most important parameters affecting the life of SG tubing. The results will be useful in developing optimum strategies for life-cycle management of the feedwater system in nuclear power plants.

Leptonic Dirac CP violation predictions from residual discrete symmetries

Directory of Open Access Journals (Sweden)

I. Girardi

2016-01-01

Full Text Available Assuming that the observed pattern of 3-neutrino mixing is related to the existence of a (lepton flavour symmetry, corresponding to a non-Abelian discrete symmetry group Gf, and that Gf is broken to specific residual symmetries Ge and Gν of the charged lepton and neutrino mass terms, we derive sum rules for the cosine of the Dirac phase δ of the neutrino mixing matrix U. The residual symmetries considered are: i Ge=Z2 and Gν=Zn, n>2 or Zn×Zm, n,m≥2; ii Ge=Zn, n>2 or Zn×Zm, n,m≥2 and Gν=Z2; iii Ge=Z2 and Gν=Z2; iv Ge is fully broken and Gν=Zn, n>2 or Zn×Zm, n,m≥2; and v Ge=Zn, n>2 or Zn×Zm, n,m≥2 and Gν is fully broken. For given Ge and Gν, the sum rules for cos⁡δ thus derived are exact, within the approach employed, and are valid, in particular, for any Gf containing Ge and Gν as subgroups. We identify the cases when the value of cos⁡δ cannot be determined, or cannot be uniquely determined, without making additional assumptions on unconstrained parameters. In a large class of cases considered the value of cos⁡δ can be unambiguously predicted once the flavour symmetry Gf is fixed. We present predictions for cos⁡δ in these cases for the flavour symmetry groups Gf=S4, A4, T′ and A5, requiring that the measured values of the 3-neutrino mixing parameters sin2⁡θ12, sin2⁡θ13 and sin2⁡θ23, taking into account their respective 3σ uncertainties, are successfully reproduced.

Prediction of welding residual distortions of large structures using a local/global approach

International Nuclear Information System (INIS)

Duan, Y. G.; Bergheau, J. M.; Vincent, Y.; Boitour, F.; Leblond, J. B.

2007-01-01

Prediction of welding residual distortions is more difficult than that of the microstructure and residual stresses. On the one hand, a fine mesh (often 3D) has to be used in the heat affected zone for the sake of the sharp variations of thermal, metallurgical and mechanical fields in this region. On the other hand, the whole structure is required to be meshed for the calculation of residual distortions. But for large structures, a 3D mesh is inconceivable caused by the costs of the calculation. Numerous methods have been developed to reduce the size of models. A local/global approach has been proposed to determine the welding residual distortions of large structures. The plastic strains and the microstructure due to welding are supposed can be determined from a local 3D model which concerns only the weld and its vicinity. They are projected as initial strains into a global 3D model which consists of the whole structure and obviously much less fine in the welded zone than the local model. The residual distortions are then calculated using a simple elastic analysis, which makes this method particularly effective in an industrial context. The aim of this article is to present the principle of the local/global approach then show the capacity of this method in an industrial context and finally study the definition of the local model

The finite element analysis for prediction of residual stresses induced by shot peening

International Nuclear Information System (INIS)

Kim, Cheol; Yang, Won Ho; Sung, Ki Deug; Cho, Myoung Rae; Ko, Myung Hoon

2000-01-01

The shot peening is largely used for a surface treatment in which small spherical parts called shots are blasted on a surface of a metallic components with velocities up to 100m/s. This treatment leads to an improvement of fatigue behavior due to the developed compressive residual stresses, and so it has gained widespread acceptance in the automobile and aerospace industries. The residual stress profile on surface layer depends on the parameters of shot peening, which are, shot velocity, shot diameter, coverage, impact angle, material properties etc. and the method to confirm this profile is only measurement by X-ray diffractometer. Despite its importance to automobile and aerospace industries, little attention has been devoted to the accurate modeling of the process. In this paper, the simulation technique is applied to predict the magnitude and distribution of the residual stress and plastic deformation caused by shot peening with the help of the finite element analysis

A residual life prediction model based on the generalized σ -N curved surface

Directory of Open Access Journals (Sweden)

Zongwen AN

2016-06-01

Full Text Available In order to investigate change rule of the residual life of structure under random repeated load, firstly, starting from the statistic meaning of random repeated load, the joint probability density function of maximum stress and minimum stress is derived based on the characteristics of order statistic (maximum order statistic and minimum order statistic; then, based on the equation of generalized σ -N curved surface, considering the influence of load cycles number on fatigue life, a relationship among minimum stress, maximum stress and residual life, that is the σmin(n- σmax(n-Nr(n curved surface model, is established; finally, the validity of the proposed model is demonstrated by a practical case. The result shows that the proposed model can reflect the influence of maximum stress and minimum stress on residual life of structure under random repeated load, which can provide a theoretical basis for life prediction and reliability assessment of structure.

Finite element analysis for prediction of the residual stresses induced by shot peening II

International Nuclear Information System (INIS)

Kim, Cheol; Seok, Chang Sung; Yang, Won Ho; Ryu, Myung Hai

2002-01-01

Shot peening is a surface impact treatment widely used to improve the performance of metal parts and welded details subjected to fatigue loading, contact fatigue, stress corrosion and other damage mechanisms. The better performance of the peened parts is mainly due to the residual stresses resulting from the plastic deformation of the surface layers of the material caused by the impact of the shot. In this paper the simulation technique is applied to predict the magnitude and distribution of the residual stress and plastic deformation caused by shot peening with the help of finite element analysis

Accounting for Genotype-by-Environment Interactions and Residual Genetic Variation in Genomic Selection for Water-Soluble Carbohydrate Concentration in Wheat.

Science.gov (United States)

Ovenden, Ben; Milgate, Andrew; Wade, Len J; Rebetzke, Greg J; Holland, James B

2018-05-31

Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat under field conditions. A panel of 358 varieties and breeding lines constrained for maturity was evaluated under rainfed and irrigated treatments across two locations and two years. Whole-genome marker profiles and factor analytic mixed models were used to generate genomic estimated breeding values (GEBVs) for specific environments and environment groups. Additive genetic variance was smaller than residual genetic variance for WSCC, such that genotypic values were dominated by residual genetic effects rather than additive breeding values. As a result, GEBVs were not accurate predictors of genotypic values of the extant lines, but GEBVs should be reliable selection criteria to choose parents for intermating to produce new populations. The accuracy of GEBVs for untested lines was sufficient to increase predicted genetic gain from genomic selection per unit time compared to phenotypic selection if the breeding cycle is reduced by half by the use of GEBVs in off-season generations. Further, genomic prediction accuracy depended on having phenotypic data from environments with strong correlations with target production environments to build prediction models. By combining high-density marker genotypes, stress-managed field evaluations, and mixed models that model simultaneously covariances among genotypes and covariances of complex trait performance between pairs of environments, we were able to train models with good accuracy to facilitate genetic gain from genomic selection. Copyright © 2018 Ovenden et al.

CMB-S4 and the hemispherical variance anomaly

Science.gov (United States)

O'Dwyer, Márcio; Copi, Craig J.; Knox, Lloyd; Starkman, Glenn D.

2017-09-01

Cosmic microwave background (CMB) full-sky temperature data show a hemispherical asymmetry in power nearly aligned with the Ecliptic. In real space, this anomaly can be quantified by the temperature variance in the Northern and Southern Ecliptic hemispheres, with the Northern hemisphere displaying an anomalously low variance while the Southern hemisphere appears unremarkable [consistent with expectations from the best-fitting theory, Lambda Cold Dark Matter (ΛCDM)]. While this is a well-established result in temperature, the low signal-to-noise ratio in current polarization data prevents a similar comparison. This will change with a proposed ground-based CMB experiment, CMB-S4. With that in mind, we generate realizations of polarization maps constrained by the temperature data and predict the distribution of the hemispherical variance in polarization considering two different sky coverage scenarios possible in CMB-S4: full Ecliptic north coverage and just the portion of the North that can be observed from a ground-based telescope at the high Chilean Atacama plateau. We find that even in the set of realizations constrained by the temperature data, the low Northern hemisphere variance observed in temperature is not expected in polarization. Therefore, observing an anomalously low variance in polarization would make the hypothesis that the temperature anomaly is simply a statistical fluke more unlikely and thus increase the motivation for physical explanations. We show, within ΛCDM, how variance measurements in both sky coverage scenarios are related. We find that the variance makes for a good statistic in cases where the sky coverage is limited, however, full northern coverage is still preferable.

Cutoff Value of Pharyngeal Residue in Prognosis Prediction After Neuromuscular Electrical Stimulation Therapy for Dysphagia in Subacute Stroke Patients

OpenAIRE

Park, Jeong Mee; Yong, Sang Yeol; Kim, Ji Hyun; Jung, Hong Sun; Chang, Sei Jin; Kim, Ki Young; Kim, Hee

2014-01-01

Objective To determine the cutoff value of the pharyngeal residue for predicting reduction of aspiration, by measuring the residue of valleculae and pyriformis sinuses through videofluoroscopic swallowing studies (VFSS) after treatment with neuromuscular electrical stimulator (VitalStim) in stroke patients with dysphagia. Methods VFSS was conducted on first-time stroke patients before and after the VitalStim therapy. The results were analyzed for comparison of the pharyngeal residue in the im...

Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting

Directory of Open Access Journals (Sweden)

Stanislav Anatolyev

2015-08-01

Full Text Available Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the return distribution affect the quality of estimation of the volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions are more precisely estimated when no variance targeting is employed. Bias properties are exacerbated for a heavier-tailed distribution of standardized returns, while the distributional asymmetry has little or moderate impact, these phenomena tending to be more pronounced under variance targeting. Some effects further intensify if one uses ML based on a leptokurtic distribution in place of normal QML. The sample size has also a more favorable effect on estimation precision when no variance targeting is used. Thus, if computational costs are not prohibitive, variance targeting should probably be avoided.

Prediction of residual life of low-cycle fatigue in austenitic stainless steel based on indentation test

International Nuclear Information System (INIS)

Yonezu, Akio; Touda, Yuya; Kim, HakGui; Yoneda, Keishi; Sakihara, Masayuki; Minoshima; Kohji

2011-01-01

In this study, a method to predict residual life of low-cycle fatigue in austenitic stainless steel (SUS316NG) was proposed based on indentation test. Low-cycle fatigue tests for SUS316NG were first conducted based on uniaxial tensile-compressive loading under the control of true strain range. Applied strain ranges were varied from about 3 to 12%. Their hysteresis loops of stress and strain were monitored during the fatigue tests. Plastic deformation range in hysteresis loop at each cycle could be roughly expressed by bi-linear hardening rule, whose plastic properties involve yield stress and work-hardening coefficient. The cyclic plastic properties were found to be dependent on the number of cycles and applied strain range, due to work-hardening. We experimentally investigated the empirical relationship between the plastic properties and number of cycles for each applied strain range. It is found that the relationship quantitatively predicts the applied strain range and number of cycles, when the plastic properties, or yield stress and work-hardening coefficient were known. Indentation tests were applied to the samples subjected to low cycle fatigue test, in order to quantitatively determine the plastic properties. The estimated properties were assigned to the proposed relationship, yielding the applied strain range and the cycle numbers. The proposed method was applied to the several stainless steel samples subjected to low cycle fatigue tests, suggesting that their residual lives could be reasonably predicted. Our method is thus useful for predicting the residual life of low-cycle fatigue in austenitic stainless steel. (author)

Predicting the concentration of residual methanol in industrial formalin using machine learning

OpenAIRE

Heidkamp, William

2016-01-01

In this thesis, a machine learning approach was used to develop a predictive model for residual methanol concentration in industrial formalin produced at the Akzo Nobel factory in Kristinehamn, Sweden. The MATLABTM computational environment supplemented with the Statistics and Machine LearningTM toolbox from the MathWorks were used to test various machine learning algorithms on the formalin production data from Akzo Nobel. As a result, the Gaussian Process Regression algorithm was found to pr...

FreeContact: fast and free software for protein contact prediction from residue co-evolution.

Science.gov (United States)

Kaján, László; Hopf, Thomas A; Kalaš, Matúš; Marks, Debora S; Rost, Burkhard

2014-03-26

20 years of improved technology and growing sequences now renders residue-residue contact constraints in large protein families through correlated mutations accurate enough to drive de novo predictions of protein three-dimensional structure. The method EVfold broke new ground using mean-field Direct Coupling Analysis (EVfold-mfDCA); the method PSICOV applied a related concept by estimating a sparse inverse covariance matrix. Both methods (EVfold-mfDCA and PSICOV) are publicly available, but both require too much CPU time for interactive applications. On top, EVfold-mfDCA depends on proprietary software. Here, we present FreeContact, a fast, open source implementation of EVfold-mfDCA and PSICOV. On a test set of 140 proteins, FreeContact was almost eight times faster than PSICOV without decreasing prediction performance. The EVfold-mfDCA implementation of FreeContact was over 220 times faster than PSICOV with negligible performance decrease. EVfold-mfDCA was unavailable for testing due to its dependency on proprietary software. FreeContact is implemented as the free C++ library "libfreecontact", complete with command line tool "freecontact", as well as Perl and Python modules. All components are available as Debian packages. FreeContact supports the BioXSD format for interoperability. FreeContact provides the opportunity to compute reliable contact predictions in any environment (desktop or cloud).

Beyond the Mean: Sensitivities of the Variance of Population Growth.

Science.gov (United States)

Trotter, Meredith V; Krishna-Kumar, Siddharth; Tuljapurkar, Shripad

2013-03-01

Populations in variable environments are described by both a mean growth rate and a variance of stochastic population growth. Increasing variance will increase the width of confidence bounds around estimates of population size, growth, probability of and time to quasi-extinction. However, traditional sensitivity analyses of stochastic matrix models only consider the sensitivity of the mean growth rate. We derive an exact method for calculating the sensitivity of the variance in population growth to changes in demographic parameters. Sensitivities of the variance also allow a new sensitivity calculation for the cumulative probability of quasi-extinction. We apply this new analysis tool to an empirical dataset on at-risk polar bears to demonstrate its utility in conservation biology We find that in many cases a change in life history parameters will increase both the mean and variance of population growth of polar bears. This counterintuitive behaviour of the variance complicates predictions about overall population impacts of management interventions. Sensitivity calculations for cumulative extinction risk factor in changes to both mean and variance, providing a highly useful quantitative tool for conservation management. The mean stochastic growth rate and its sensitivities do not fully describe the dynamics of population growth. The use of variance sensitivities gives a more complete understanding of population dynamics and facilitates the calculation of new sensitivities for extinction processes.

Prediction of residual metabolic activity after treatment in NSCLC patients

International Nuclear Information System (INIS)

Rios Velazquez, Emmanuel; Aerts, Hugo J.W.L.; Oberije, Cary; Ruysscher, Dirk De; Lambin, Philippe

2010-01-01

Purpose. Metabolic response assessment is often used as a surrogate of local failure and survival. Early identification of patients with residual metabolic activity is essential as this enables selection of patients who could potentially benefit from additional therapy. We report on the development of a pre-treatment prediction model for metabolic response using patient, tumor and treatment factors. Methods. One hundred and one patients with inoperable NSCLC (stage I-IV), treated with 3D conformal radical (chemo)-radiotherapy were retrospectively included in this study. All patients received a pre and post-radiotherapy fluorodeoxyglucose positron emission tomography-computed tomography FDG-PET-CT scan. The electronic medical record system and the medical patient charts were reviewed to obtain demographic, clinical, tumor and treatment data. Primary outcome measure was examined using a metabolic response assessment on a post-radiotherapy FDG-PET-CT scan. Radiotherapy was delivered in fractions of 1.8 Gy, twice a day, with a median prescribed dose of 60 Gy. Results. Overall survival was worse in patients with residual metabolic active areas compared with the patients with a complete metabolic response (p=0.0001). In univariate analysis, three variables were significantly associated with residual disease: larger primary gross tumor volume (GTVprimary, p=0.002), higher pre-treatment maximum standardized uptake value (SUV max , p=0.0005) in the primary tumor and shorter overall treatment time (OTT, p=0.046). A multivariate model including GTVprimary, SUV max , equivalent radiation dose at 2 Gy corrected for time (EQD2, T) and OTT yielded an area under the curve assessed by the leave-one-out cross validation of 0.71 (95% CI, 0.65-0.76). Conclusion. Our results confirmed the validity of metabolic response assessment as a surrogate of survival. We developed a multivariate model that is able to identify patients at risk of residual disease. These patients may benefit from

Temporal variance reverses the impact of high mean intensity of stress in climate change experiments.

Science.gov (United States)

Benedetti-Cecchi, Lisandro; Bertocci, Iacopo; Vaselli, Stefano; Maggi, Elena

2006-10-01

Extreme climate events produce simultaneous changes to the mean and to the variance of climatic variables over ecological time scales. While several studies have investigated how ecological systems respond to changes in mean values of climate variables, the combined effects of mean and variance are poorly understood. We examined the response of low-shore assemblages of algae and invertebrates of rocky seashores in the northwest Mediterranean to factorial manipulations of mean intensity and temporal variance of aerial exposure, a type of disturbance whose intensity and temporal patterning of occurrence are predicted to change with changing climate conditions. Effects of variance were often in the opposite direction of those elicited by changes in the mean. Increasing aerial exposure at regular intervals had negative effects both on diversity of assemblages and on percent cover of filamentous and coarsely branched algae, but greater temporal variance drastically reduced these effects. The opposite was observed for the abundance of barnacles and encrusting coralline algae, where high temporal variance of aerial exposure either reversed a positive effect of mean intensity (barnacles) or caused a negative effect that did not occur under low temporal variance (encrusting algae). These results provide the first experimental evidence that changes in mean intensity and temporal variance of climatic variables affect natural assemblages of species interactively, suggesting that high temporal variance may mitigate the ecological impacts of ongoing and predicted climate changes.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials

Science.gov (United States)

Cuevas, Jaime; Granato, Italo; Fritsche-Neto, Roberto; Montesinos-Lopez, Osval A.; Burgueño, Juan; Bandeira e Sousa, Massaine; Crossa, José

2018-01-01

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy. PMID:29476023

The VIX, the Variance Premium, and Expected Returns

DEFF Research Database (Denmark)

Osterrieder, Daniela Maria; Ventosa-Santaulària, Daniel; Vera-Valdés, Eduardo

2018-01-01

. These problems are eliminated if risk is captured by the variance premium (VP) instead; it is unobservable, however. We propose a 2SLS estimator that produces consistent estimates without observing the VP. Using this method, we find a positive risk–return trade-off and long-run return predictability. Our...

Variance-based sensitivity indices for models with dependent inputs

International Nuclear Information System (INIS)

Mara, Thierry A.; Tarantola, Stefano

2012-01-01

Computational models are intensively used in engineering for risk analysis or prediction of future outcomes. Uncertainty and sensitivity analyses are of great help in these purposes. Although several methods exist to perform variance-based sensitivity analysis of model output with independent inputs only a few are proposed in the literature in the case of dependent inputs. This is explained by the fact that the theoretical framework for the independent case is set and a univocal set of variance-based sensitivity indices is defined. In the present work, we propose a set of variance-based sensitivity indices to perform sensitivity analysis of models with dependent inputs. These measures allow us to distinguish between the mutual dependent contribution and the independent contribution of an input to the model response variance. Their definition relies on a specific orthogonalisation of the inputs and ANOVA-representations of the model output. In the applications, we show the interest of the new sensitivity indices for model simplification setting. - Highlights: ► Uncertainty and sensitivity analyses are of great help in engineering. ► Several methods exist to perform variance-based sensitivity analysis of model output with independent inputs. ► We define a set of variance-based sensitivity indices for models with dependent inputs. ► Inputs mutual contributions are distinguished from their independent contributions. ► Analytical and computational tests are performed and discussed.

A more realistic estimate of the variances and systematic errors in spherical harmonic geomagnetic field models

DEFF Research Database (Denmark)

Lowes, F.J.; Olsen, Nils

2004-01-01

Most modern spherical harmonic geomagnetic models based on satellite data include estimates of the variances of the spherical harmonic coefficients of the model; these estimates are based on the geometry of the data and the fitting functions, and on the magnitude of the residuals. However...

Effect of sequence variants on variance in glucose levels predicts type 2 diabetes risk and accounts for heritability.

Science.gov (United States)

Ivarsdottir, Erna V; Steinthorsdottir, Valgerdur; Daneshpour, Maryam S; Thorleifsson, Gudmar; Sulem, Patrick; Holm, Hilma; Sigurdsson, Snaevar; Hreidarsson, Astradur B; Sigurdsson, Gunnar; Bjarnason, Ragnar; Thorsson, Arni V; Benediktsson, Rafn; Eyjolfsson, Gudmundur; Sigurdardottir, Olof; Olafsson, Isleifur; Zeinali, Sirous; Azizi, Fereidoun; Thorsteinsdottir, Unnur; Gudbjartsson, Daniel F; Stefansson, Kari

2017-09-01

Sequence variants that affect mean fasting glucose levels do not necessarily affect risk for type 2 diabetes (T2D). We assessed the effects of 36 reported glucose-associated sequence variants on between- and within-subject variance in fasting glucose levels in 69,142 Icelanders. The variant in TCF7L2 that increases fasting glucose levels increases between-subject variance (5.7% per allele, P = 4.2 × 10 -10 ), whereas variants in GCK and G6PC2 that increase fasting glucose levels decrease between-subject variance (7.5% per allele, P = 4.9 × 10 -11 and 7.3% per allele, P = 7.5 × 10 -18 , respectively). Variants that increase mean and between-subject variance in fasting glucose levels tend to increase T2D risk, whereas those that increase the mean but reduce variance do not (r 2 = 0.61). The variants that increase between-subject variance increase fasting glucose heritability estimates. Intuitively, our results show that increasing the mean and variance of glucose levels is more likely to cause pathologically high glucose levels than increase in the mean offset by a decrease in variance.

Cesium residue leachate migration in the tailings management area of a mine site : predicted vs. actual

Energy Technology Data Exchange (ETDEWEB)

Solylo, P.; Ramsey, D. [Wardrop Engineering, Winnipeg, MB (Canada). Mining and Minerals Section

2009-07-01

This paper reported on a study at a cesium products facility (CPF) that manufactures a non-toxic cesium-formate drilling fluid. The facility operates adjacent to a pollucite/tantalum/spodumene mine. The CPF was developed as a closed system, with the residue tailings slurry from the CPF process discharged to doublelined containment cells. Groundwater monitoring has shown that leachate has affected near-surface porewater quality within the tailings management area (TMA). Elevated concentrations of calcium, sulphate, strontium, cesium, and rubidium were used to identify the leachate. Porewater at the base of the tailings and in the overburden beneath the tailings has not been affected. A geochemical investigation was initiated to determine how the leachate behaves in the groundwater/tailings porewater system. Over the past 7 years of residue placement in the TMA, the footprint of the residue placement area has changed, making the comparison of predicted versus actual rate of leachate migration very subjective and difficult to quantify. Based solely on the analytical data, the source of the leachate is unknown, either from the original residue pile or the 2007 residue placement area. For purposes of long term residue management, an investigation of the geochemical behaviour of residue leachate in the groundwater/tailings system of the TMA is currently underway. 5 refs., 1 tab., 2 figs.

Effects of Sex on Intra-Individual Variance in Urinary Solutes in Stone-Formers Collected from a Single Clinical Laboratory.

Directory of Open Access Journals (Sweden)

Guy M L Perry

Full Text Available Our work in a rodent model of urinary calcium suggests genetic and gender effects on increased residual variability in urine chemistries. Based on these findings, we hypothesized that sex would similarly be associated with residual variation in human urine solutes. Sex-related effects on residuals might affect the establishment of physiological baselines and error in medical assays.We tested the effects of sex on residual variation in urine chemistry by estimating coefficients of variation (CV for urinary solutes in paired sequential 24-h urines (≤72 hour interval in 6,758 females and 9,024 males aged 16-80 submitted to a clinical laboratory.Females had higher CVs than males for urinary phosphorus overall at the False Discovery Rate (P0.3. Males had higher CVs for citrate (P<0.01 from ages 16-45 and females higher CVs for citrate (P<0.01 from ages 56-80, suggesting effects of an extant oestral cycle on residual variance.Our findings indicate the effects of sex on residual variance of the excretion of urinary solutes including phosphorus and citrate; differences in CV by sex might reflect dietary lability, differences in the fidelity of reporting or genetic differentiation in renal solute consistency. Such an effect could complicate medical analysis by the addition of random error to phenotypic assays. Renal analysis might require explicit incorporation of heterogeneity among factorial effects, and for sex in particular.

R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization.

Science.gov (United States)

Dazard, Jean-Eudes; Xu, Hua; Rao, J Sunil

2011-01-01

We present an implementation in the R language for statistical computing of our recent non-parametric joint adaptive mean-variance regularization and variance stabilization procedure. The method is specifically suited for handling difficult problems posed by high-dimensional multivariate datasets ( p ≫ n paradigm), such as in 'omics'-type data, among which are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. The implementation offers a complete set of features including: (i) normalization and/or variance stabilization function, (ii) computation of mean-variance-regularized t and F statistics, (iii) generation of diverse diagnostic plots, (iv) synthetic and real 'omics' test datasets, (v) computationally efficient implementation, using C interfacing, and an option for parallel computing, (vi) manual and documentation on how to setup a cluster. To make each feature as user-friendly as possible, only one subroutine per functionality is to be handled by the end-user. It is available as an R package, called MVR ('Mean-Variance Regularization'), downloadable from the CRAN.

Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models.

Science.gov (United States)

Felleki, M; Lee, D; Lee, Y; Gilmour, A R; Rönnegård, L

2012-12-01

The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of the algorithm is that it can fit a correlation between the random effects for mean and variance. An h-likelihood estimator is implemented in the R software and an iterative reweighted least square (IRWLS) approximation of the h-likelihood is implemented using ASReml. The difference in variance component estimates between the two implementations is investigated, as well as the potential bias of the methods, using simulations. IRWLS gives the same results as h-likelihood in simple cases with no severe indication of bias. For more complex cases, only IRWLS could be used, and bias did appear. The IRWLS is applied on the pig litter size data previously analysed by Sorensen & Waagepetersen (2003) using Bayesian methodology. The estimates we obtained by using IRWLS are similar to theirs, with the estimated correlation between the random genetic effects being -0·52 for IRWLS and -0·62 in Sorensen & Waagepetersen (2003).

Predicting protein folding rate change upon point mutation using residue-level coevolutionary information.

Science.gov (United States)

Mallik, Saurav; Das, Smita; Kundu, Sudip

2016-01-01

Change in folding kinetics of globular proteins upon point mutation is crucial to a wide spectrum of biological research, such as protein misfolding, toxicity, and aggregations. Here we seek to address whether residue-level coevolutionary information of globular proteins can be informative to folding rate changes upon point mutations. Generating residue-level coevolutionary networks of globular proteins, we analyze three parameters: relative coevolution order (rCEO), network density (ND), and characteristic path length (CPL). A point mutation is considered to be equivalent to a node deletion of this network and respective percentage changes in rCEO, ND, CPL are found linearly correlated (0.84, 0.73, and -0.61, respectively) with experimental folding rate changes. The three parameters predict the folding rate change upon a point mutation with 0.031, 0.045, and 0.059 standard errors, respectively. © 2015 Wiley Periodicals, Inc.

Prediction of residual stress for dissimilar metals welding at nuclear power plants using fuzzy neural network models

International Nuclear Information System (INIS)

Na, Man Gyun; Kim, Jin Weon; Lim, Dong Hyuk

2007-01-01

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones

Estimation of measurement variances

International Nuclear Information System (INIS)

Anon.

1981-01-01

In the previous two sessions, it was assumed that the measurement error variances were known quantities when the variances of the safeguards indices were calculated. These known quantities are actually estimates based on historical data and on data generated by the measurement program. Session 34 discusses how measurement error parameters are estimated for different situations. The various error types are considered. The purpose of the session is to enable participants to: (1) estimate systematic error variances from standard data; (2) estimate random error variances from data as replicate measurement data; (3) perform a simple analysis of variances to characterize the measurement error structure when biases vary over time

Simulation study on heterogeneous variance adjustment for observations with different measurement error variance

DEFF Research Database (Denmark)

Pitkänen, Timo; Mäntysaari, Esa A; Nielsen, Ulrik Sander

2013-01-01

of variance correction is developed for the same observations. As automated milking systems are becoming more popular the current evaluation model needs to be enhanced to account for the different measurement error variances of observations from automated milking systems. In this simulation study different...... models and different approaches to account for heterogeneous variance when observations have different measurement error variances were investigated. Based on the results we propose to upgrade the currently applied models and to calibrate the heterogeneous variance adjustment method to yield same genetic......The Nordic Holstein yield evaluation model describes all available milk, protein and fat test-day yields from Denmark, Finland and Sweden. In its current form all variance components are estimated from observations recorded under conventional milking systems. Also the model for heterogeneity...

Allowing variance may enlarge the safe operating space for exploited ecosystems.

Science.gov (United States)

Carpenter, Stephen R; Brock, William A; Folke, Carl; van Nes, Egbert H; Scheffer, Marten

2015-11-17

Variable flows of food, water, or other ecosystem services complicate planning. Management strategies that decrease variability and increase predictability may therefore be preferred. However, actions to decrease variance over short timescales (2-4 y), when applied continuously, may lead to long-term ecosystem changes with adverse consequences. We investigated the effects of managing short-term variance in three well-understood models of ecosystem services: lake eutrophication, harvest of a wild population, and yield of domestic herbivores on a rangeland. In all cases, actions to decrease variance can increase the risk of crossing critical ecosystem thresholds, resulting in less desirable ecosystem states. Managing to decrease short-term variance creates ecosystem fragility by changing the boundaries of safe operating spaces, suppressing information needed for adaptive management, cancelling signals of declining resilience, and removing pressures that may build tolerance of stress. Thus, the management of variance interacts strongly and inseparably with the management of resilience. By allowing for variation, learning, and flexibility while observing change, managers can detect opportunities and problems as they develop while sustaining the capacity to deal with them.

Use of genomic models to study genetic control of environmental variance

DEFF Research Database (Denmark)

Yang, Ye; Christensen, Ole Fredslund; Sorensen, Daniel

2011-01-01

. The genomic model commonly found in the literature, with marker effects affecting mean only, is extended to investigate putative effects at the level of the environmental variance. Two classes of models are proposed and their behaviour, studied using simulated data, indicates that they are capable...... of detecting genetic variation at the level of mean and variance. Implementation is via Markov chain Monte Carlo (McMC) algorithms. The models are compared in terms of a measure of global fit, in their ability to detect QTL effects and in terms of their predictive power. The models are subsequently fitted...... to back fat thickness data in pigs. The analysis of back fat thickness shows that the data support genomic models with effects on the mean but not on the variance. The relative sizes of experiment necessary to detect effects on mean and variance is discussed and an extension of the McMC algorithm...

A versatile omnibus test for detecting mean and variance heterogeneity.

Science.gov (United States)

Cao, Ying; Wei, Peng; Bailey, Matthew; Kauwe, John S K; Maxwell, Taylor J

2014-01-01

Recent research has revealed loci that display variance heterogeneity through various means such as biological disruption, linkage disequilibrium (LD), gene-by-gene (G × G), or gene-by-environment interaction. We propose a versatile likelihood ratio test that allows joint testing for mean and variance heterogeneity (LRT(MV)) or either effect alone (LRT(M) or LRT(V)) in the presence of covariates. Using extensive simulations for our method and others, we found that all parametric tests were sensitive to nonnormality regardless of any trait transformations. Coupling our test with the parametric bootstrap solves this issue. Using simulations and empirical data from a known mean-only functional variant, we demonstrate how LD can produce variance-heterogeneity loci (vQTL) in a predictable fashion based on differential allele frequencies, high D', and relatively low r² values. We propose that a joint test for mean and variance heterogeneity is more powerful than a variance-only test for detecting vQTL. This takes advantage of loci that also have mean effects without sacrificing much power to detect variance only effects. We discuss using vQTL as an approach to detect G × G interactions and also how vQTL are related to relationship loci, and how both can create prior hypothesis for each other and reveal the relationships between traits and possibly between components of a composite trait.

Predictive models of forest logging residues of Triplochiton ...

African Journals Online (AJOL)

The model developed indicated that logarithmic functions performed better than other form of equation. The findings of this study revealed that there is significant logging residues left to waste in the forest after timber harvest and quantifying this logging residue in terms of biomass model can serve as management tools in ...

Effects of feather wear and temperature on prediction of food intake and residual food consumption.

Science.gov (United States)

Herremans, M; Decuypere, E; Siau, O

1989-03-01

Heat production, which accounts for 0.6 of gross energy intake, is insufficiently represented in predictions of food intake. Especially when heat production is elevated (for example by lower temperature or poor feathering) the classical predictions based on body weight, body-weight change and egg mass are inadequate. Heat production was reliably estimated as [35.5-environmental temperature (degree C)] x [Defeathering (=%IBPW) + 21]. Including this term (PHP: predicted heat production) in equations predicting food intake significantly increased accuracy of prediction, especially under suboptimal conditions. Within the range of body weights tested (from 1.6 kg in brown layers to 2.8 kg in dwarf broiler breeders), body weight as an independent variable contributed little to the prediction of food intake; especially within strains its effect was better included in the intercept. Significantly reduced absolute values of residual food consumption were obtained over a wide range of conditions by using predictions of food intake based on body-weight change, egg mass, predicted heat production (PHP) and an intercept, instead of body weight, body-weight change, egg mass and an intercept.

Correcting Spatial Variance of RCM for GEO SAR Imaging Based on Time-Frequency Scaling

Science.gov (United States)

Yu, Ze; Lin, Peng; Xiao, Peng; Kang, Lihong; Li, Chunsheng

2016-01-01

Compared with low-Earth orbit synthetic aperture radar (SAR), a geosynchronous (GEO) SAR can have a shorter revisit period and vaster coverage. However, relative motion between this SAR and targets is more complicated, which makes range cell migration (RCM) spatially variant along both range and azimuth. As a result, efficient and precise imaging becomes difficult. This paper analyzes and models spatial variance for GEO SAR in the time and frequency domains. A novel algorithm for GEO SAR imaging with a resolution of 2 m in both the ground cross-range and range directions is proposed, which is composed of five steps. The first is to eliminate linear azimuth variance through the first azimuth time scaling. The second is to achieve RCM correction and range compression. The third is to correct residual azimuth variance by the second azimuth time-frequency scaling. The fourth and final steps are to accomplish azimuth focusing and correct geometric distortion. The most important innovation of this algorithm is implementation of the time-frequency scaling to correct high-order azimuth variance. As demonstrated by simulation results, this algorithm can accomplish GEO SAR imaging with good and uniform imaging quality over the entire swath. PMID:27428974

Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences

KAUST Repository

Chen, Peng; Li, Jinyan; Limsoon, Wong; Kuwahara, Hiroyuki; Huang, Jianhua Z.; Gao, Xin

2013-01-01

Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013 Wiley Periodicals, Inc.

Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences

KAUST Repository

Chen, Peng

2013-07-23

Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013 Wiley Periodicals, Inc.

Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences.

Science.gov (United States)

Chen, Peng; Li, Jinyan; Wong, Limsoon; Kuwahara, Hiroyuki; Huang, Jianhua Z; Gao, Xin

2013-08-01

Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. Copyright © 2013 Wiley Periodicals, Inc.

Relative variance of the mean-squared pressure in multimode media: rehabilitating former approaches.

Science.gov (United States)

Monsef, Florian; Cozza, Andrea; Rodrigues, Dominique; Cellard, Patrick; Durocher, Jean-Noel

2014-11-01

The commonly accepted model for the relative variance of transmission functions in room acoustics, derived by Weaver, aims at including the effects of correlation between eigenfrequencies. This model is based on an analytical expression of the relative variance derived by means of an approximated correlation function. The relevance of the approximation used for modeling such correlation is questioned here. Weaver's model was motivated by the fact that earlier models derived by Davy and Lyon assumed independent eigenfrequencies and led to an overestimation with respect to relative variances found in practice. It is shown here that this overestimation is due to an inadequate truncation of the modal expansion, and to an improper choice of the frequency range over which ensemble averages of the eigenfrequencies is defined. An alternative definition is proposed, settling the inconsistency; predicted relative variances are found to be in good agreement with experimental data. These results rehabilitate former approaches that were based on independence assumptions between eigenfrequencies. Some former studies showed that simpler correlation models could be used to predict the statistics of some field-related physical quantity at low modal overlap. The present work confirms that this is also the case when dealing with transmission functions.

Predictions of the residue cross-sections for the elements Z=113 and Z=114

OpenAIRE

Bouriquet, Bertrand; Abe, Yasuhisa; Kosenko, Grigori

2003-01-01

An extremely good reproduction of experimental excitation function of the 1n reactions producing Z=110,Z=111 and Z=112 is obtained by the two-step model and the statistical decay code KEWPIE. Thus, an extension of the recipe permits us to predict reliable values of the residue cross-sections of the elements Z=113 and Z=114 which will be a useful guide for planning of experiments.

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

Science.gov (United States)

Ma, Xin; Guo, Jing; Sun, Xiao

2016-01-01

DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

MCNP variance reduction overview

International Nuclear Information System (INIS)

Hendricks, J.S.; Booth, T.E.

1985-01-01

The MCNP code is rich in variance reduction features. Standard variance reduction methods found in most Monte Carlo codes are available as well as a number of methods unique to MCNP. We discuss the variance reduction features presently in MCNP as well as new ones under study for possible inclusion in future versions of the code

Spectral Ambiguity of Allan Variance

Science.gov (United States)

Greenhall, C. A.

1996-01-01

We study the extent to which knowledge of Allan variance and other finite-difference variances determines the spectrum of a random process. The variance of first differences is known to determine the spectrum. We show that, in general, the Allan variance does not. A complete description of the ambiguity is given.

How does variance in fertility change over the demographic transition?

Science.gov (United States)

Hruschka, Daniel J; Burger, Oskar

2016-04-19

Most work on the human fertility transition has focused on declines in mean fertility. However, understanding changes in the variance of reproductive outcomes can be equally important for evolutionary questions about the heritability of fertility, individual determinants of fertility and changing patterns of reproductive skew. Here, we document how variance in completed fertility among women (45-49 years) differs across 200 surveys in 72 low- to middle-income countries where fertility transitions are currently in progress at various stages. Nearly all (91%) of samples exhibit variance consistent with a Poisson process of fertility, which places systematic, and often severe, theoretical upper bounds on the proportion of variance that can be attributed to individual differences. In contrast to the pattern of total variance, these upper bounds increase from high- to mid-fertility samples, then decline again as samples move from mid to low fertility. Notably, the lowest fertility samples often deviate from a Poisson process. This suggests that as populations move to low fertility their reproduction shifts from a rate-based process to a focus on an ideal number of children. We discuss the implications of these findings for predicting completed fertility from individual-level variables. © 2016 The Author(s).

Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds

Directory of Open Access Journals (Sweden)

Felix Simkovic

2016-07-01

Full Text Available For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based structure prediction. Such models can be used in structure solution by molecular replacement (MR where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions (`decoys', is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue–residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.

Variance Risk Premia on Stocks and Bonds

DEFF Research Database (Denmark)

Mueller, Philippe; Sabtchevsky, Petar; Vedolin, Andrea

We study equity (EVRP) and Treasury variance risk premia (TVRP) jointly and document a number of findings: First, relative to their volatility, TVRP are comparable in magnitude to EVRP. Second, while there is mild positive co-movement between EVRP and TVRP unconditionally, time series estimates...... equity returns for horizons up to 6-months, long maturity TVRP contain robust information for long run equity returns. Finally, exploiting the dynamics of real and nominal Treasuries we document that short maturity break-even rates are a power determinant of the joint dynamics of EVRP, TVRP and their co-movement...... of correlation display distinct spikes in both directions and have been notably volatile since the financial crisis. Third $(i)$ short maturity TVRP predict excess returns on short maturity bonds; $(ii)$ long maturity TVRP and EVRP predict excess returns on long maturity bonds; and $(iii)$ while EVRP predict...

A simulation model for the prediction of tissue:plasma partition coefficients for drug residues in natural casings.

NARCIS (Netherlands)

Haritova, A.M.; Fink-Gremmels, J.

2010-01-01

Tissue residues arise from the exposure of animals to undesirable substances in animal feed materials and drinking water and to the therapeutic or zootechnical use of veterinary medicinal products. In the framework of this study, an advanced toxicokinetic model was developed to predict the

Prediction of beta-turns from amino acid sequences using the residue-coupled model.

Science.gov (United States)

Guruprasad, K; Shukla, S

2003-04-01

We evaluated the prediction of beta-turns from amino acid sequences using the residue-coupled model with an enlarged representative protein data set selected from the Protein Data Bank. Our results show that the probability values derived from a data set comprising 425 protein chains yielded an overall beta-turn prediction accuracy 68.74%, compared with 94.7% reported earlier on a data set of 30 proteins using the same method. However, we noted that the overall beta-turn prediction accuracy using probability values derived from the 30-protein data set reduces to 40.74% when tested on the data set comprising 425 protein chains. In contrast, using probability values derived from the 425 data set used in this analysis, the overall beta-turn prediction accuracy yielded consistent results when tested on either the 30-protein data set (64.62%) used earlier or a more recent representative data set comprising 619 protein chains (64.66%) or on a jackknife data set comprising 476 representative protein chains (63.38%). We therefore recommend the use of probability values derived from the 425 representative protein chains data set reported here, which gives more realistic and consistent predictions of beta-turns from amino acid sequences.

The efficiency of the crude oil markets: Evidence from variance ratio tests

Energy Technology Data Exchange (ETDEWEB)

Charles, Amelie, E-mail: acharles@audencia.co [Audencia Nantes, School of Management, 8 route de la Joneliere, 44312 Nantes (France); Darne, Olivier, E-mail: olivier.darne@univ-nantes.f [LEMNA, University of Nantes, IEMN-IAE, Chemin de la Censive du Tertre, 44322 Nantes (France)

2009-11-15

This study examines the random walk hypothesis for the crude oil markets, using daily data over the period 1982-2008. The weak-form efficient market hypothesis for two crude oil markets (UK Brent and US West Texas Intermediate) is tested with non-parametric variance ratio tests developed by [Wright J.H., 2000. Alternative variance-ratio tests using ranks and signs. Journal of Business and Economic Statistics, 18, 1-9] and [Belaire-Franch J. and Contreras D., 2004. Ranks and signs-based multiple variance ratio tests. Working paper, Department of Economic Analysis, University of Valencia] as well as the wild-bootstrap variance ratio tests suggested by [Kim, J.H., 2006. Wild bootstrapping variance ratio tests. Economics Letters, 92, 38-43]. We find that the Brent crude oil market is weak-form efficiency while the WTI crude oil market seems to be inefficiency on the 1994-2008 sub-period, suggesting that the deregulation have not improved the efficiency on the WTI crude oil market in the sense of making returns less predictable.

The efficiency of the crude oil markets. Evidence from variance ratio tests

International Nuclear Information System (INIS)

Charles, Amelie; Darne, Olivier

2009-01-01

This study examines the random walk hypothesis for the crude oil markets, using daily data over the period 1982-2008. The weak-form efficient market hypothesis for two crude oil markets (UK Brent and US West Texas Intermediate) is tested with non-parametric variance ratio tests developed by [Wright J.H., 2000. Alternative variance-ratio tests using ranks and signs. Journal of Business and Economic Statistics, 18, 1-9] and [Belaire-Franch J. and Contreras D., 2004. Ranks and signs-based multiple variance ratio tests. Working paper, Department of Economic Analysis, University of Valencia] as well as the wild-bootstrap variance ratio tests suggested by [Kim, J.H., 2006. Wild bootstrapping variance ratio tests. Economics Letters, 92, 38-43]. We find that the Brent crude oil market is weak-form efficiency while the WTI crude oil market seems to be inefficiency on the 1994-2008 sub-period, suggesting that the deregulation have not improved the efficiency on the WTI crude oil market in the sense of making returns less predictable. (author)

The efficiency of the crude oil markets. Evidence from variance ratio tests

Energy Technology Data Exchange (ETDEWEB)

Charles, Amelie [Audencia Nantes, School of Management, 8 route de la Joneliere, 44312 Nantes (France); Darne, Olivier [LEMNA, University of Nantes, IEMN-IAE, Chemin de la Censive du Tertre, 44322 Nantes (France)

2009-11-15

This study examines the random walk hypothesis for the crude oil markets, using daily data over the period 1982-2008. The weak-form efficient market hypothesis for two crude oil markets (UK Brent and US West Texas Intermediate) is tested with non-parametric variance ratio tests developed by [Wright J.H., 2000. Alternative variance-ratio tests using ranks and signs. Journal of Business and Economic Statistics, 18, 1-9] and [Belaire-Franch J. and Contreras D., 2004. Ranks and signs-based multiple variance ratio tests. Working paper, Department of Economic Analysis, University of Valencia] as well as the wild-bootstrap variance ratio tests suggested by [Kim, J.H., 2006. Wild bootstrapping variance ratio tests. Economics Letters, 92, 38-43]. We find that the Brent crude oil market is weak-form efficiency while the WTI crude oil market seems to be inefficiency on the 1994-2008 sub-period, suggesting that the deregulation have not improved the efficiency on the WTI crude oil market in the sense of making returns less predictable. (author)

Resilient modulus prediction of soft low-plasticity Piedmont residual soil using dynamic cone penetrometer

Directory of Open Access Journals (Sweden)

S. Hamed Mousavi

2018-04-01

Full Text Available Dynamic cone penetrometer (DCP has been used for decades to estimate the shear strength and stiffness properties of the subgrade soils. There are several empirical correlations in the literature to predict the resilient modulus values at only a specific stress state from DCP data, corresponding to the predefined thicknesses of pavement layers (a 50 mm asphalt wearing course, a 100 mm asphalt binder course and a 200 mm aggregate base course. In this study, field-measured DCP data were utilized to estimate the resilient modulus of low-plasticity subgrade Piedmont residual soil. Piedmont residual soils are in-place weathered soils from igneous and metamorphic rocks, as opposed to transported or compacted soils. Hence the existing empirical correlations might not be applicable for these soils. An experimental program was conducted incorporating field DCP and laboratory resilient modulus tests on “undisturbed” soil specimens. The DCP tests were carried out at various locations in four test sections to evaluate subgrade stiffness variation laterally and with depth. Laboratory resilient modulus test results were analyzed in the context of the mechanistic-empirical pavement design guide (MEPDG recommended universal constitutive model. A new approach for predicting the resilient modulus from DCP by estimating MEPDG constitutive model coefficients (k1, k2 and k3 was developed through statistical analyses. The new model is capable of not only taking into account the in situ soil condition on the basis of field measurements, but also representing the resilient modulus at any stress state which addresses a limitation with existing empirical DCP models and its applicability for a specific case. Validation of the model is demonstrated by using data that were not used for model development, as well as data reported in the literature. Keywords: Dynamic cone penetrometer (DCP, Resilient modulus, Mechanistic-empirical pavement design guide (MEPDG, Residual

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.

Science.gov (United States)

Walia, Rasna R; Caragea, Cornelia; Lewis, Benjamin A; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2012-05-10

RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM

Improved crop residue cover estimates by coupling spectral indices for residue and moisture

Science.gov (United States)

Remote sensing assessment of soil residue cover (fR) and tillage intensity will improve our predictions of the impact of agricultural practices and promote sustainable management. Spectral indices for estimating fR are sensitive to soil and residue water content, therefore, the uncertainty of estima...

Genetic selection for increased mean and reduced variance of twinning rate in Belclare ewes.

Science.gov (United States)

Cottle, D J; Gilmour, A R; Pabiou, T; Amer, P R; Fahey, A G

2016-04-01

It is sometimes possible to breed for more uniform individuals by selecting animals with a greater tendency to be less variable, that is, those with a smaller environmental variance. This approach has been applied to reproduction traits in various animal species. We have evaluated fecundity in the Irish Belclare sheep breed by analyses of flocks with differing average litter size (number of lambs per ewe per year, NLB) and have estimated the genetic variance in environmental variance of lambing traits using double hierarchical generalized linear models (DHGLM). The data set comprised of 9470 litter size records from 4407 ewes collected in 56 flocks. The percentage of pedigreed lambing ewes with singles, twins and triplets was 30, 54 and 14%, respectively, in 2013 and has been relatively constant for the last 15 years. The variance of NLB increases with the mean in this data; the correlation of mean and standard deviation across sires is 0.50. The breeding goal is to increase the mean NLB without unduly increasing the incidence of triplets and higher litter sizes. The heritability estimates for lambing traits were NLB, 0.09; triplet occurrence (TRI) 0.07; and twin occurrence (TWN), 0.02. The highest and lowest twinning flocks differed by 23% (75% versus 52%) in the proportion of ewes lambing twins. Fitting bivariate sire models to NLB and the residual from the NLB model using a double hierarchical generalized linear model (DHGLM) model found a strong genetic correlation (0.88 ± 0.07) between the sire effect for the magnitude of the residual (VE ) and sire effects for NLB, confirming the general observation that increased average litter size is associated with increased variability in litter size. We propose a threshold model that may help breeders with low litter size increase the percentage of twin bearers without unduly increasing the percentage of ewes bearing triplets in Belclare sheep. © 2015 Blackwell Verlag GmbH.

Development of computer program ENMASK for prediction of residual environmental masking-noise spectra, from any three independent environmental parameters

Energy Technology Data Exchange (ETDEWEB)

Chang, Y.-S.; Liebich, R. E.; Chun, K. C.

2000-03-31

Residual environmental sound can mask intrusive4 (unwanted) sound. It is a factor that can affect noise impacts and must be considered both in noise-impact studies and in noise-mitigation designs. Models for quantitative prediction of sensation level (audibility) and psychological effects of intrusive noise require an input with 1/3 octave-band spectral resolution of environmental masking noise. However, the majority of published residual environmental masking-noise data are given with either octave-band frequency resolution or only single A-weighted decibel values. A model has been developed that enables estimation of 1/3 octave-band residual environmental masking-noise spectra and relates certain environmental parameters to A-weighted sound level. This model provides a correlation among three environmental conditions: measured residual A-weighted sound-pressure level, proximity to a major roadway, and population density. Cited field-study data were used to compute the most probable 1/3 octave-band sound-pressure spectrum corresponding to any selected one of these three inputs. In turn, such spectra can be used as an input to models for prediction of noise impacts. This paper discusses specific algorithms included in the newly developed computer program ENMASK. In addition, the relative audibility of the environmental masking-noise spectra at different A-weighted sound levels is discussed, which is determined by using the methodology of program ENAUDIBL.

Variance analysis of forecasted streamflow maxima in a wet temperate climate

Science.gov (United States)

Al Aamery, Nabil; Fox, James F.; Snyder, Mark; Chandramouli, Chandra V.

2018-05-01

Coupling global climate models, hydrologic models and extreme value analysis provides a method to forecast streamflow maxima, however the elusive variance structure of the results hinders confidence in application. Directly correcting the bias of forecasts using the relative change between forecast and control simulations has been shown to marginalize hydrologic uncertainty, reduce model bias, and remove systematic variance when predicting mean monthly and mean annual streamflow, prompting our investigation for maxima streamflow. We assess the variance structure of streamflow maxima using realizations of emission scenario, global climate model type and project phase, downscaling methods, bias correction, extreme value methods, and hydrologic model inputs and parameterization. Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima versus peak over threshold method applied, albeit we stress that researchers strictly adhere to rules from extreme value theory when applying the peak over threshold method. Regardless of which method is applied, extreme value model fitting does add variance to the projection, and the variance is an increasing function of the return period. Unlike the relative change of mean streamflow, results show that the variance of the maxima's relative change was dependent on all climate model factors tested as well as hydrologic model inputs and calibration. Ensemble projections forecast an increase of streamflow maxima for 2050 with pronounced forecast standard error, including an increase of +30(±21), +38(±34) and +51(±85)% for 2, 20 and 100 year streamflow events for the wet temperate region studied. The variance of maxima projections was dominated by climate model factors and extreme value analyses.

The mean and variance of environmental temperature interact to determine physiological tolerance and fitness.

Science.gov (United States)

Bozinovic, Francisco; Bastías, Daniel A; Boher, Francisca; Clavijo-Baquet, Sabrina; Estay, Sergio A; Angilletta, Michael J

2011-01-01

Global climate change poses one of the greatest threats to biodiversity. Most analyses of the potential biological impacts have focused on changes in mean temperature, but changes in thermal variance will also impact organisms and populations. We assessed the combined effects of the mean and variance of temperature on thermal tolerances, organismal survival, and population growth in Drosophila melanogaster. Because the performance of ectotherms relates nonlinearly to temperature, we predicted that responses to thermal variation (±0° or ±5°C) would depend on the mean temperature (17° or 24°C). Consistent with our prediction, thermal variation enhanced the rate of population growth (r(max)) at a low mean temperature but depressed this rate at a high mean temperature. The interactive effect on fitness occurred despite the fact that flies improved their heat and cold tolerances through acclimation to thermal conditions. Flies exposed to a high mean and a high variance of temperature recovered from heat coma faster and survived heat exposure better than did flies that developed at other conditions. Relatively high survival following heat exposure was associated with low survival following cold exposure. Recovery from chill coma was affected primarily by the mean temperature; flies acclimated to a low mean temperature recovered much faster than did flies acclimated to a high mean temperature. To develop more realistic predictions about the biological impacts of climate change, one must consider the interactions between the mean environmental temperature and the variance of environmental temperature.

An analytical method to assess the damage and predict the residual strength of a ship in a shoal grounding accident scenario

Directory of Open Access Journals (Sweden)

Sun Bin

2016-04-01

Full Text Available In this paper, a simplified analytical method used to predict the residual ultimate strength of a ship hull after a shoal grounding accident is proposed. Shoal grounding accidents always lead to severe denting, though not tearing, of the ship bottom structure, which may threaten the global hull girder resistance and result in even worse consequences, such as hull collapse. Here, the degree of damage of the bottom structure is predicted by a series of analytical methods based on the plastic-elastic deformation mechanism. The energy dissipation of a ship bottom structure is obtained from individual components to determine the sliding distance of the seabed obstruction. Then, a new approach to assess the residual strength of the damaged ship subjected to shoal grounding is proposed based on the improved Smith's method. This analytical method is verified by comparing the results of the proposed method and those generated by numerical simulation using the software ABAQUS. The proposed analytical method can be used to assess the safety of a ship with a double bottom during its design phase and predict the residual ultimate strength of a ship after a shoal grounding accident occurs.

Residual Stress Estimation and Fatigue Life Prediction of an Autofrettaged Pressure Vessel

Energy Technology Data Exchange (ETDEWEB)

Song, Kyung Jin; Kim, Eun Kyum; Koh, Seung Kee [Kunsan Nat’l Univ., Kunsan (Korea, Republic of)

2017-09-15

Fatigue failure of an autofrettaged pressure vessel with a groove at the outside surface occurs owing to the fatigue crack initiation and propagation at the groove root. In order to predict the fatigue life of the autofrettaged pressure vessel, residual stresses in the autofrettaged pressure vessel were evaluated using the finite element method, and the fatigue properties of the pressure vessel steel were obtained from the fatigue tests. Fatigue life of a pressure vessel obtained through summation of the crack initiation and propagation lives was calculated to be 2,598 cycles for an 80% autofrettaged pressure vessel subjected to a pulsating internal pressure of 424 MPa.

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.

Science.gov (United States)

Zhang, Huiling; Huang, Qingsheng; Bei, Zhendong; Wei, Yanjie; Floudas, Christodoulos A

2016-03-01

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/. © 2016 Wiley Periodicals, Inc.

Variances as order parameter and complexity measure for random Boolean networks

International Nuclear Information System (INIS)

Luque, Bartolo; Ballesteros, Fernando J; Fernandez, Manuel

2005-01-01

Several order parameters have been considered to predict and characterize the transition between ordered and disordered phases in random Boolean networks, such as the Hamming distance between replicas or the stable core, which have been successfully used. In this work, we propose a natural and clear new order parameter: the temporal variance. We compute its value analytically and compare it with the results of numerical experiments. Finally, we propose a complexity measure based on the compromise between temporal and spatial variances. This new order parameter and its related complexity measure can be easily applied to other complex systems

Variances as order parameter and complexity measure for random Boolean networks

Energy Technology Data Exchange (ETDEWEB)

Luque, Bartolo [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain); Ballesteros, Fernando J [Observatori Astronomic, Universitat de Valencia, Ed. Instituts d' Investigacio, Pol. La Coma s/n, E-46980 Paterna, Valencia (Spain); Fernandez, Manuel [Departamento de Matematica Aplicada y EstadIstica, Escuela Superior de Ingenieros Aeronauticos, Universidad Politecnica de Madrid, Plaza Cardenal Cisneros 3, Madrid 28040 (Spain)

2005-02-04

Several order parameters have been considered to predict and characterize the transition between ordered and disordered phases in random Boolean networks, such as the Hamming distance between replicas or the stable core, which have been successfully used. In this work, we propose a natural and clear new order parameter: the temporal variance. We compute its value analytically and compare it with the results of numerical experiments. Finally, we propose a complexity measure based on the compromise between temporal and spatial variances. This new order parameter and its related complexity measure can be easily applied to other complex systems.

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

Science.gov (United States)

Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

WALS Prediction

NARCIS (Netherlands)

Magnus, J.R.; Wang, W.; Zhang, Xinyu

2012-01-01

Abstract: Prediction under model uncertainty is an important and difficult issue. Traditional prediction methods (such as pretesting) are based on model selection followed by prediction in the selected model, but the reported prediction and the reported prediction variance ignore the uncertainty

Variance in predicted cup size by 2-dimensional vs 3-dimensional computerized tomography-based templating in primary total hip arthroplasty.

Science.gov (United States)

Osmani, Feroz A; Thakkar, Savyasachi; Ramme, Austin; Elbuluk, Ameer; Wojack, Paul; Vigdorchik, Jonathan M

2017-12-01

Preoperative total hip arthroplasty templating can be performed with radiographs using acetate prints, digital viewing software, or with computed tomography (CT) images. Our hypothesis is that 3D templating is more precise and accurate with cup size prediction as compared to 2D templating with acetate prints and digital templating software. Data collected from 45 patients undergoing robotic-assisted total hip arthroplasty compared cup sizes templated on acetate prints and OrthoView software to MAKOplasty software that uses CT scan. Kappa analysis determined strength of agreement between each templating modality and the final size used. t tests compared mean cup-size variance from the final size for each templating technique. Interclass correlation coefficient (ICC) determined reliability of digital and acetate planning by comparing predictions of the operating surgeon and a blinded adult reconstructive fellow. The Kappa values for CT-guided, digital, and acetate templating with the final size was 0.974, 0.233, and 0.262, respectively. Both digital and acetate templating significantly overpredicted cup size, compared to CT-guided methods ( P cup size when compared to the significant overpredictions of digital and acetate templating. CT-guided templating may also lead to better outcomes due to bone stock preservation from a smaller and more accurate cup size predicted than that of digital and acetate predictions.

Origin and consequences of the relationship between protein mean and variance.

Science.gov (United States)

Vallania, Francesco Luigi Massimo; Sherman, Marc; Goodwin, Zane; Mogno, Ilaria; Cohen, Barak Alon; Mitra, Robi David

2014-01-01

Cell-to-cell variance in protein levels (noise) is a ubiquitous phenomenon that can increase fitness by generating phenotypic differences within clonal populations of cells. An important challenge is to identify the specific molecular events that control noise. This task is complicated by the strong dependence of a protein's cell-to-cell variance on its mean expression level through a power-law like relationship (σ2∝μ1.69). Here, we dissect the nature of this relationship using a stochastic model parameterized with experimentally measured values. This framework naturally recapitulates the power-law like relationship (σ2∝μ1.6) and accurately predicts protein variance across the yeast proteome (r2 = 0.935). Using this model we identified two distinct mechanisms by which protein variance can be increased. Variables that affect promoter activation, such as nucleosome positioning, increase protein variance by changing the exponent of the power-law relationship. In contrast, variables that affect processes downstream of promoter activation, such as mRNA and protein synthesis, increase protein variance in a mean-dependent manner following the power-law. We verified our findings experimentally using an inducible gene expression system in yeast. We conclude that the power-law-like relationship between noise and protein mean is due to the kinetics of promoter activation. Our results provide a framework for understanding how molecular processes shape stochastic variation across the genome.

Body Composition Explains Greater Variance in Weight-for-Length Z-scores than Mid-Upper Arm Circumference during Infancy - A Secondary Data Analysis

International Nuclear Information System (INIS)

Grijalva-Eternod, Carlos; Andersen, Gregers Stig; Girma, Tsinuel; Admassu, Bitiya; Kæstel, Pernille; Michaelsen, Kim F; Friis, Henrik; Wells, Jonathan CK

2014-01-01

with length at all ages (correlation values range 0.42 to 0.61) compared to WLZ which correlated negatively with length only between birth and 2.5 months (range -12 to -15). Both MUAC and WLZ were strongly and positively correlated with LM and FM standardised residuals with correlation values being systematically greater for WLZ (range 0.53 – 0.82 and 0.54 – 0.77, for LM and FM respectively) than for MUAC (range 0.28 – 0.42 and 0.45 – 0.63, respectively). Together LM and FM standardised residuals (controlled for sex) explained over 93% of WLZ variance at all ages (see table 1). In contrast, LM and FM residuals explained between 37 – 52% MUAC variance. Conclusions: LM and FM values have stronger associations with WLZ and together they explain almost all the variance of this anthropometric indicator compared to MUAC in children aged 0-6 months. Given these findings, it is unlikely that any greater capacity of MUAC to predict mortality among infants can be explained by the overall variability in body composition. (author)

Analysis of Margin Index as a Method for Predicting Residual Disease After Breast-Conserving Surgery in a European Cancer Center.

LENUS (Irish Health Repository)

Bolger, Jarlath C

2011-06-03

INTRODUCTION: Breast-conserving surgery (BCS), followed by appropriate adjuvant therapies is established as a standard treatment option for women with early-stage invasive breast cancers. A number of factors have been shown to correlate with local and regional disease recurrence. Although margin status is a strong predictor of disease recurrence, consensus is yet to be established on the optimum margin necessary. Margenthaler et al. recently proposed the use of a "margin index," combining tumor size and margin status as a predictor of residual disease after BCS. We applied this new predictive tool to a population of patients with primary breast cancer who presented to a symptomatic breast unit to determine its suitability in predicting those who require reexcision surgery. METHODS: Retrospective analysis of our breast cancer database from January 1, 2000 to June 30, 2010 was performed, including all patients who underwent BCS. Of 531 patients who underwent BCS, 27.1% (144\\/531) required further reexcision procedures, and 55 were eligible for inclusion in the study. Margin index was calculated as: margin index = closest margin (mm)\\/tumor size (mm) × 100, with index >5 considered optimum. RESULTS: Of the 55 patients included, 31% (17\\/55) had residual disease. Fisher\\'s exact test showed margin index not to be a significant predictor of residual disease on reexcision specimen (P = 0.57). Of note, a significantly higher proportion of our patients presented with T2\\/3 tumors (60% vs. 38%). CONCLUSIONS: Although an apparently elegant tool for predicting residual disease after BCS, we have shown that it is not applicable to a symptomatic breast unit in Ireland.

Analysis of margin index as a method for predicting residual disease after breast-conserving surgery in a European cancer center.

LENUS (Irish Health Repository)

Bolger, Jarlath C

2012-02-01

INTRODUCTION: Breast-conserving surgery (BCS), followed by appropriate adjuvant therapies is established as a standard treatment option for women with early-stage invasive breast cancers. A number of factors have been shown to correlate with local and regional disease recurrence. Although margin status is a strong predictor of disease recurrence, consensus is yet to be established on the optimum margin necessary. Margenthaler et al. recently proposed the use of a "margin index," combining tumor size and margin status as a predictor of residual disease after BCS. We applied this new predictive tool to a population of patients with primary breast cancer who presented to a symptomatic breast unit to determine its suitability in predicting those who require reexcision surgery. METHODS: Retrospective analysis of our breast cancer database from January 1, 2000 to June 30, 2010 was performed, including all patients who underwent BCS. Of 531 patients who underwent BCS, 27.1% (144\\/531) required further reexcision procedures, and 55 were eligible for inclusion in the study. Margin index was calculated as: margin index = closest margin (mm)\\/tumor size (mm) x 100, with index >5 considered optimum. RESULTS: Of the 55 patients included, 31% (17\\/55) had residual disease. Fisher\\'s exact test showed margin index not to be a significant predictor of residual disease on reexcision specimen (P = 0.57). Of note, a significantly higher proportion of our patients presented with T2\\/3 tumors (60% vs. 38%). CONCLUSIONS: Although an apparently elegant tool for predicting residual disease after BCS, we have shown that it is not applicable to a symptomatic breast unit in Ireland.

Prediction error variance and expected response to selection, when selection is based on the best predictor - for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits

DEFF Research Database (Denmark)

Andersen, Anders Holst; Korsgaard, Inge Riis; Jensen, Just

2002-01-01

In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed...... or random effects). In the different models, expressions are given (when these can be found - otherwise unbiased estimates are given) for prediction error variance, accuracy of selection and expected response to selection on the additive genetic scale and on the observed scale. The expressions given for non...... Gaussian traits are generalisations of the well-known formulas for Gaussian traits - and reflect, for Poisson mixed models and frailty models for survival data, the hierarchal structure of the models. In general the ratio of the additive genetic variance to the total variance in the Gaussian part...

A Numerical Model for Prediction of Residual Stress Using Rayleigh Waves

International Nuclear Information System (INIS)

Yuan, Mao Dan; Kang, To; Kim, Hak Joon; Song, Sung Jin

2011-01-01

In this work, a numerical model is proposed for the relation between the magnitudes and the depth residual stress with the velocity of Rayleigh wave. Three cases, stress-free, uniform stress and layered stress, are investigated for the change tendency of the Rayleigh wave speed. Using the simulated signal with variation of residual stress magnitude and depth, investigation of the parameters for fitting residual stress and velocity change are performed. The speed change of Rayleigh wave shows a linear relation with the magnitude and an exponential relation with the depth of residual stress. The combination of these two effects could be used for the depth profile evaluation of the residual stress

A zero-variance-based scheme for variance reduction in Monte Carlo criticality

Energy Technology Data Exchange (ETDEWEB)

Christoforou, S.; Hoogenboom, J. E. [Delft Univ. of Technology, Mekelweg 15, 2629 JB Delft (Netherlands)

2006-07-01

A zero-variance scheme is derived and proven theoretically for criticality cases, and a simplified transport model is used for numerical demonstration. It is shown in practice that by appropriate biasing of the transition and collision kernels, a significant reduction in variance can be achieved. This is done using the adjoint forms of the emission and collision densities, obtained from a deterministic calculation, according to the zero-variance scheme. By using an appropriate algorithm, the figure of merit of the simulation increases by up to a factor of 50, with the possibility of an even larger improvement. In addition, it is shown that the biasing speeds up the convergence of the initial source distribution. (authors)

A zero-variance-based scheme for variance reduction in Monte Carlo criticality

International Nuclear Information System (INIS)

Christoforou, S.; Hoogenboom, J. E.

2006-01-01

A zero-variance scheme is derived and proven theoretically for criticality cases, and a simplified transport model is used for numerical demonstration. It is shown in practice that by appropriate biasing of the transition and collision kernels, a significant reduction in variance can be achieved. This is done using the adjoint forms of the emission and collision densities, obtained from a deterministic calculation, according to the zero-variance scheme. By using an appropriate algorithm, the figure of merit of the simulation increases by up to a factor of 50, with the possibility of an even larger improvement. In addition, it is shown that the biasing speeds up the convergence of the initial source distribution. (authors)

Seeing the signs: Using the course of residual depressive symptomatology to predict patterns of relapse and recurrence of major depressive disorder.

Science.gov (United States)

Verhoeven, Floor E A; Wardenaar, Klaas J; Ruhé, Henricus G Eric; Conradi, Henk Jan; de Jonge, Peter

2018-02-01

Major depressive disorder (MDD) is characterized by high relapse/recurrence rates. Predicting individual patients' relapse/recurrence risk has proven hard, possibly due to course heterogeneity among patients. This study aimed to (1) identify homogeneous data-driven subgroups with different patterns of relapse/recurrence and (2) identify associated predictors. For a year, we collected weekly depressive symptom ratings in 213 primary care MDD patients. Latent class growth analyses (LCGA), based on symptom-severity during the 24 weeks after no longer fulfilling criteria for the initial major depressive episode (MDE), were used to identify groups with different patterns of relapse/recurrence. Associations of baseline predictors with these groups were investigated, as were the groups' associations with 3- and 11-year follow-up depression outcomes. LCGA showed that heterogeneity in relapse/recurrence after no longer fulfilling criteria for the initial MDE was best described by four classes: "quick symptom decline" (14.0%), "slow symptom decline" (23.3%), "steady residual symptoms" (38.7%), and "high residual symptoms" (24.1%). The latter two classes showed lower self-esteem at baseline, and more recurrences and higher severity at 3-year follow-up than the first two classes. Moreover, the high residual symptom class scored higher on neuroticism and lower on extraversion and self-esteem at baseline. Interestingly, the steady residual symptoms and high residual symptoms classes still showed higher severity of depressive symptoms after 11 years. Some measures were associated with specific patterns of relapse/recurrence. Moreover, the data-driven relapse/recurrence groups were predictive of long-term outcomes, suggesting that patterns of residual symptoms could be of prognostic value in clinical practice. © 2017 Wiley Periodicals, Inc.

Cutoff value of pharyngeal residue in prognosis prediction after neuromuscular electrical stimulation therapy for Dysphagia in subacute stroke patients.

Science.gov (United States)

Park, Jeong Mee; Yong, Sang Yeol; Kim, Ji Hyun; Jung, Hong Sun; Chang, Sei Jin; Kim, Ki Young; Kim, Hee

2014-10-01

To determine the cutoff value of the pharyngeal residue for predicting reduction of aspiration, by measuring the residue of valleculae and pyriformis sinuses through videofluoroscopic swallowing studies (VFSS) after treatment with neuromuscular electrical stimulator (VitalStim) in stroke patients with dysphagia. VFSS was conducted on first-time stroke patients before and after the VitalStim therapy. The results were analyzed for comparison of the pharyngeal residue in the improved group and the non-improved group. A total of 59 patients concluded the test, in which 42 patients improved well enough to change the dietary methods while 17 did not improve sufficiently. Remnant area to total area (R/T) ratios of the valleculae before treatment in the improved group were 0.120, 0.177, and 0.101 for solid, soft, and liquid foods, respectively, whereas the ratios for the non-improved group were 0.365, 0.396, and 0.281, respectively. The ratios of the pyriformis sinuses were 0.126, 0.159, and 0.121 for the improved group and 0.315, 0.338, and 0.244 for the non-improved group. The R/T ratios of valleculae and pyriformis sinus were significantly lower in the improved group than the non-improved group in all food types before treatment. The R/T ratio cutoff values were 0.267, 0.250, and 0.185 at valleculae and 0.228, 0.218, and 0.185 at pyriformis sinuses. In dysphagia after stroke, less pharyngeal residue before treatment serves as a factor for predicting greater improvement after VitalStim treatment.

Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data.

Science.gov (United States)

Dazard, Jean-Eudes; Rao, J Sunil

2012-07-01

The paper addresses a common problem in the analysis of high-dimensional high-throughput "omics" data, which is parameter estimation across multiple variables in a set of data where the number of variables is much larger than the sample size. Among the problems posed by this type of data are that variable-specific estimators of variances are not reliable and variable-wise tests statistics have low power, both due to a lack of degrees of freedom. In addition, it has been observed in this type of data that the variance increases as a function of the mean. We introduce a non-parametric adaptive regularization procedure that is innovative in that : (i) it employs a novel "similarity statistic"-based clustering technique to generate local-pooled or regularized shrinkage estimators of population parameters, (ii) the regularization is done jointly on population moments, benefiting from C. Stein's result on inadmissibility, which implies that usual sample variance estimator is improved by a shrinkage estimator using information contained in the sample mean. From these joint regularized shrinkage estimators, we derived regularized t-like statistics and show in simulation studies that they offer more statistical power in hypothesis testing than their standard sample counterparts, or regular common value-shrinkage estimators, or when the information contained in the sample mean is simply ignored. Finally, we show that these estimators feature interesting properties of variance stabilization and normalization that can be used for preprocessing high-dimensional multivariate data. The method is available as an R package, called 'MVR' ('Mean-Variance Regularization'), downloadable from the CRAN website.

Modality-Driven Classification and Visualization of Ensemble Variance

Energy Technology Data Exchange (ETDEWEB)

Bensema, Kevin; Gosink, Luke; Obermaier, Harald; Joy, Kenneth I.

2016-10-01

Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no information about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. We apply a similar method to time-varying ensembles to illustrate the relationship between peak variance and bimodal or multimodal behavior. These classification schemes enable a deeper understanding of the behavior of the ensemble members by distinguishing between distributions that can be described by a single tendency and distributions which reflect divergent trends in the ensemble.

Portfolio optimization using median-variance approach

Science.gov (United States)

Wan Mohd, Wan Rosanisah; Mohamad, Daud; Mohamed, Zulkifli

2013-04-01

Optimization models have been applied in many decision-making problems particularly in portfolio selection. Since the introduction of Markowitz's theory of portfolio selection, various approaches based on mathematical programming have been introduced such as mean-variance, mean-absolute deviation, mean-variance-skewness and conditional value-at-risk (CVaR) mainly to maximize return and minimize risk. However most of the approaches assume that the distribution of data is normal and this is not generally true. As an alternative, in this paper, we employ the median-variance approach to improve the portfolio optimization. This approach has successfully catered both types of normal and non-normal distribution of data. With this actual representation, we analyze and compare the rate of return and risk between the mean-variance and the median-variance based portfolio which consist of 30 stocks from Bursa Malaysia. The results in this study show that the median-variance approach is capable to produce a lower risk for each return earning as compared to the mean-variance approach.

Prediction of residual stress distributions due to surface machining and welding and crack growth simulation under residual stress distribution

International Nuclear Information System (INIS)

Ihara, Ryohei; Katsuyama, JInya; Onizawa, Kunio; Hashimoto, Tadafumi; Mikami, Yoshiki; Mochizuki, Masahito

2011-01-01

Research highlights: → Residual stress distributions due to welding and machining are evaluated by XRD and FEM. → Residual stress due to machining shows higher tensile stress than welding near the surface. → Crack growth analysis is performed using calculated residual stress. → Crack growth result is affected machining rather than welding. → Machining is an important factor for crack growth. - Abstract: In nuclear power plants, stress corrosion cracking (SCC) has been observed near the weld zone of the core shroud and primary loop recirculation (PLR) pipes made of low-carbon austenitic stainless steel Type 316L. The joining process of pipes usually includes surface machining and welding. Both processes induce residual stresses, and residual stresses are thus important factors in the occurrence and propagation of SCC. In this study, the finite element method (FEM) was used to estimate residual stress distributions generated by butt welding and surface machining. The thermoelastic-plastic analysis was performed for the welding simulation, and the thermo-mechanical coupled analysis based on the Johnson-Cook material model was performed for the surface machining simulation. In addition, a crack growth analysis based on the stress intensity factor (SIF) calculation was performed using the calculated residual stress distributions that are generated by welding and surface machining. The surface machining analysis showed that tensile residual stress due to surface machining only exists approximately 0.2 mm from the machined surface, and the surface residual stress increases with cutting speed. The crack growth analysis showed that the crack depth is affected by both surface machining and welding, and the crack length is more affected by surface machining than by welding.

Efficient Cardinality/Mean-Variance Portfolios

OpenAIRE

Brito, R. Pedro; Vicente, Luís Nunes

2014-01-01

International audience; We propose a novel approach to handle cardinality in portfolio selection, by means of a biobjective cardinality/mean-variance problem, allowing the investor to analyze the efficient tradeoff between return-risk and number of active positions. Recent progress in multiobjective optimization without derivatives allow us to robustly compute (in-sample) the whole cardinality/mean-variance efficient frontier, for a variety of data sets and mean-variance models. Our results s...

Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives

Directory of Open Access Journals (Sweden)

Alexov Emil G

2006-11-01

Full Text Available Abstract Background Predicting residues' contacts using primary amino acid sequence alone is an important task that can guide 3D structure modeling and can verify the quality of the predicted 3D structures. The correlated mutations (CM method serves as the most promising approach and it has been used to predict amino acids pairs that are distant in the primary sequence but form contacts in the native 3D structure of homologous proteins. Results Here we report a new implementation of the CM method with an added set of selection rules (filters. The parameters of the algorithm were optimized against fifteen high resolution crystal structures with optimization criterion that maximized the confidentiality of the predictions. The optimization resulted in a true positive ratio (TPR of 0.08 for the CM without filters and a TPR of 0.14 for the CM with filters. The protocol was further benchmarked against 65 high resolution structures that were not included in the optimization test. The benchmarking resulted in a TPR of 0.07 for the CM without filters and to a TPR of 0.09 for the CM with filters. Conclusion Thus, the inclusion of selection rules resulted to an overall improvement of 30%. In addition, the pair-wise comparison of TPR for each protein without and with filters resulted in an average improvement of 1.7. The methodology was implemented into a web server http://www.ces.clemson.edu/compbio/recon that is freely available to the public. The purpose of this implementation is to provide the 3D structure predictors with a tool that can help with ranking alternative models by satisfying the largest number of predicted contacts, as well as it can provide a confidence score for contacts in cases where structure is known.

Circulating microRNA levels predict residual beta cell function and glycaemic control in children with type 1 diabetes mellitus

DEFF Research Database (Denmark)

Samandari, Nasim; Mirza, Aashiq H; Nielsen, Lotte B

2017-01-01

AIMS/HYPOTHESIS: We aimed to identify circulating microRNA (miRNA) that predicts clinical progression in a cohort of 123 children with new-onset type 1 diabetes mellitus. METHODS: Plasma samples were prospectively obtained at 1, 3, 6, 12 and 60 months after diagnosis from a subset of 40 children......RNAs revealed significant enrichment for pathways related to gonadotropin-releasing hormone receptor and angiogenesis pathways. CONCLUSIONS/INTERPRETATION: The miRNA hsa-miR-197-3p at 3 months was the strongest predictor of residual beta cell function 1 year after diagnosis in children with type 1 diabetes...... from the Danish Remission Phase Cohort, and profiled for miRNAs. At the same time points, meal-stimulated C-peptide and HbA1c levels were measured and insulin-dose adjusted HbA1c (IDAA1c) calculated. miRNAs that at 3 months after diagnosis predicted residual beta cell function and glycaemic control...

Modeling Residual NAPL in Water-Wet Porous Media

Directory of Open Access Journals (Sweden)

R.J. Lenhard

2002-06-01

Full Text Available A model is outlined that predicts NAPL which is held in pore wedges and as films or lenses on solid and water surfaces and contributes negligibly to NAPL advection. This is conceptually referred to as residual NAPL. Since residual NAPL is immobile, it remains in the vadose zone after all free NAPL has drained. Residual NAPL is very important because it is a long-term source for groundwater contamination. Recent laboratory experiments have demonstrated that current models for predicting subsurface NAPL behavior are inadequate because they do not correctly predict residual NAPL. The main reason for the failure is a deficiency in the current constitutive theories for multiphase flow that are used in numerical simulators. Multiphase constitutive theory governs the relations among relative permeability, saturation, and pressure for fluid systems (i.e., air, NAPL, water. In this paper, we outline a model describing relations between fluid saturations and pressures that can be combined with existing multiphase constitutive theory to predict residual NAPL. We test the revised constitutive theory by applying it to a scenario involving NAPL imbibition and drainage, as well as water imbibition and drainage. The results suggest that the revised constitutive theory is able to predict the distribution of residual NAPL in the vadose zone as a function of saturation-path history. The revised model describing relations between fluid saturation and pressures will help toward developing or improving numerical multiphase flow simulators.

Approximation errors during variance propagation

International Nuclear Information System (INIS)

Dinsmore, Stephen

1986-01-01

Risk and reliability analyses are often performed by constructing and quantifying large fault trees. The inputs to these models are component failure events whose probability of occuring are best represented as random variables. This paper examines the errors inherent in two approximation techniques used to calculate the top event's variance from the inputs' variance. Two sample fault trees are evaluated and several three dimensional plots illustrating the magnitude of the error over a wide range of input means and variances are given

Identification of mannose interacting residues using local composition.

Directory of Open Access Journals (Sweden)

Sandhya Agarwal

Full Text Available BACKGROUND: Mannose binding proteins (MBPs play a vital role in several biological functions such as defense mechanisms. These proteins bind to mannose on the surface of a wide range of pathogens and help in eliminating these pathogens from our body. Thus, it is important to identify mannose interacting residues (MIRs in order to understand mechanism of recognition of pathogens by MBPs. RESULTS: This paper describes modules developed for predicting MIRs in a protein. Support vector machine (SVM based models have been developed on 120 mannose binding protein chains, where no two chains have more than 25% sequence similarity. SVM models were developed on two types of datasets: 1 main dataset consists of 1029 mannose interacting and 1029 non-interacting residues, 2 realistic dataset consists of 1029 mannose interacting and 10320 non-interacting residues. In this study, firstly, we developed standard modules using binary and PSSM profile of patterns and got maximum MCC around 0.32. Secondly, we developed SVM modules using composition profile of patterns and achieved maximum MCC around 0.74 with accuracy 86.64% on main dataset. Thirdly, we developed a model on a realistic dataset and achieved maximum MCC of 0.62 with accuracy 93.08%. Based on this study, a standalone program and web server have been developed for predicting mannose interacting residues in proteins (http://www.imtech.res.in/raghava/premier/. CONCLUSIONS: Compositional analysis of mannose interacting and non-interacting residues shows that certain types of residues are preferred in mannose interaction. It was also observed that residues around mannose interacting residues have a preference for certain types of residues. Composition of patterns/peptide/segment has been used for predicting MIRs and achieved reasonable high accuracy. It is possible that this novel strategy may be effective to predict other types of interacting residues. This study will be useful in annotating the function

Numerical experiment on variance biases and Monte Carlo neutronics analysis with thermal hydraulic feedback

International Nuclear Information System (INIS)

Hyung, Jin Shim; Beom, Seok Han; Chang, Hyo Kim

2003-01-01

Monte Carlo (MC) power method based on the fixed number of fission sites at the beginning of each cycle is known to cause biases in the variances of the k-eigenvalue (keff) and the fission reaction rate estimates. Because of the biases, the apparent variances of keff and the fission reaction rate estimates from a single MC run tend to be smaller or larger than the real variances of the corresponding quantities, depending on the degree of the inter-generational correlation of the sample. We demonstrate this through a numerical experiment involving 100 independent MC runs for the neutronics analysis of a 17 x 17 fuel assembly of a pressurized water reactor (PWR). We also demonstrate through the numerical experiment that Gelbard and Prael's batch method and Ueki et al's covariance estimation method enable one to estimate the approximate real variances of keff and the fission reaction rate estimates from a single MC run. We then show that the use of the approximate real variances from the two-bias predicting methods instead of the apparent variances provides an efficient MC power iteration scheme that is required in the MC neutronics analysis of a real system to determine the pin power distribution consistent with the thermal hydraulic (TH) conditions of individual pins of the system. (authors)

Modeling the subfilter scalar variance for large eddy simulation in forced isotropic turbulence

Science.gov (United States)

Cheminet, Adam; Blanquart, Guillaume

2011-11-01

Static and dynamic model for the subfilter scalar variance in homogeneous isotropic turbulence are investigated using direct numerical simulations (DNS) of a lineary forced passive scalar field. First, we introduce a new scalar forcing technique conditioned only on the scalar field which allows the fluctuating scalar field to reach a statistically stationary state. Statistical properties, including 2nd and 3rd statistical moments, spectra, and probability density functions of the scalar field have been analyzed. Using this technique, we performed constant density and variable density DNS of scalar mixing in isotropic turbulence. The results are used in an a-priori study of scalar variance models. Emphasis is placed on further studying the dynamic model introduced by G. Balarac, H. Pitsch and V. Raman [Phys. Fluids 20, (2008)]. Scalar variance models based on Bedford and Yeo's expansion are accurate for small filter width but errors arise in the inertial subrange. Results suggest that a constant coefficient computed from an assumed Kolmogorov spectrum is often sufficient to predict the subfilter scalar variance.

The phenotypic variance gradient - a novel concept.

Science.gov (United States)

Pertoldi, Cino; Bundgaard, Jørgen; Loeschcke, Volker; Barker, James Stuart Flinton

2014-11-01

Evolutionary ecologists commonly use reaction norms, which show the range of phenotypes produced by a set of genotypes exposed to different environments, to quantify the degree of phenotypic variance and the magnitude of plasticity of morphometric and life-history traits. Significant differences among the values of the slopes of the reaction norms are interpreted as significant differences in phenotypic plasticity, whereas significant differences among phenotypic variances (variance or coefficient of variation) are interpreted as differences in the degree of developmental instability or canalization. We highlight some potential problems with this approach to quantifying phenotypic variance and suggest a novel and more informative way to plot reaction norms: namely "a plot of log (variance) on the y-axis versus log (mean) on the x-axis, with a reference line added". This approach gives an immediate impression of how the degree of phenotypic variance varies across an environmental gradient, taking into account the consequences of the scaling effect of the variance with the mean. The evolutionary implications of the variation in the degree of phenotypic variance, which we call a "phenotypic variance gradient", are discussed together with its potential interactions with variation in the degree of phenotypic plasticity and canalization.

Residual stresses in Inconel 718 engine disks

Directory of Open Access Journals (Sweden)

Dahan Yoann

2014-01-01

Full Text Available Aubert&Duval has developed a methodology to establish a residual stress model for Inconel 718 engine discs. To validate the thermal, mechanical and metallurgical parts of the model, trials on lab specimens with specific geometry were carried out. These trials allow a better understanding of the residual stress distribution and evolution during different processes (quenching, ageing, machining. A comparison between experimental and numerical results reveals the residual stresses model accuracy. Aubert&Duval has also developed a mechanical properties prediction model. Coupled with the residual stress prediction model, Aubert&Duval can now propose improvements to the process of manufacturing in Inconel 718 engine disks. This model enables Aubert&Duval customers and subcontractors to anticipate distortions issues during machining. It could also be usedt to optimise the engine disk life.

Evolution of Genetic Variance during Adaptive Radiation.

Science.gov (United States)

Walter, Greg M; Aguirre, J David; Blows, Mark W; Ortiz-Barrientos, Daniel

2018-04-01

Genetic correlations between traits can concentrate genetic variance into fewer phenotypic dimensions that can bias evolutionary trajectories along the axis of greatest genetic variance and away from optimal phenotypes, constraining the rate of evolution. If genetic correlations limit adaptation, rapid adaptive divergence between multiple contrasting environments may be difficult. However, if natural selection increases the frequency of rare alleles after colonization of new environments, an increase in genetic variance in the direction of selection can accelerate adaptive divergence. Here, we explored adaptive divergence of an Australian native wildflower by examining the alignment between divergence in phenotype mean and divergence in genetic variance among four contrasting ecotypes. We found divergence in mean multivariate phenotype along two major axes represented by different combinations of plant architecture and leaf traits. Ecotypes also showed divergence in the level of genetic variance in individual traits and the multivariate distribution of genetic variance among traits. Divergence in multivariate phenotypic mean aligned with divergence in genetic variance, with much of the divergence in phenotype among ecotypes associated with changes in trait combinations containing substantial levels of genetic variance. Overall, our results suggest that natural selection can alter the distribution of genetic variance underlying phenotypic traits, increasing the amount of genetic variance in the direction of natural selection and potentially facilitating rapid adaptive divergence during an adaptive radiation.

Residual-strength determination in polymetric materials

Energy Technology Data Exchange (ETDEWEB)

Christensen, R.M.

1981-10-01

Kinetic theory of crack growth is used to predict the residual strength of polymetric materials acted upon by a previous history. Specifically, the kinetic theory is used to characterize the state of growing damage that occurs under a constant-stress (load) state. The load is removed before failure under creep-rupture conditions, and the residual instantaneous strength is determined from the theory by taking account of the damage accumulation under the preceding constant-load history. The rate of change of residual strength is found to be strongest when the duration of the preceding load history is near the ultimate lifetime under that condition. Physical explanations for this effect are given, as are numerical examples. Also, the theoretical prediction is compared with experimental data.

Residual-strength determination in polymetric materials

International Nuclear Information System (INIS)

Christensen, R.M.

1981-01-01

Kinetic theory of crack growth is used to predict the residual strength of polymetric materials acted upon by a previous history. Specifically, the kinetic theory is used to characterize the state of growing damage that occurs under a constant-stress (load) state. The load is removed before failure under creep-rupture conditions, and the residual instantaneous strength is determined from the theory by taking account of the damage accumulation under the preceding constant-load history. The rate of change of residual strength is found to be strongest when the duration of the preceding load history is near the ultimate lifetime under that condition. Physical explanations for this effect are given, as are numerical examples. Also, the theoretical prediction is compared with experimental data

Neuroticism explains unwanted variance in Implicit Association Tests of personality: Possible evidence for an affective valence confound

Directory of Open Access Journals (Sweden)

Monika eFleischhauer

2013-09-01

Full Text Available Meta-analytic data highlight the value of the Implicit Association Test (IAT as an indirect measure of personality. Based on evidence suggesting that confounding factors such as cognitive abilities contribute to the IAT effect, this study provides a first investigation of whether basic personality traits explain unwanted variance in the IAT. In a gender-balanced sample of 204 volunteers, the Big-Five dimensions were assessed via self-report, peer-report, and IAT. By means of structural equation modeling, latent Big-Five personality factors (based on self- and peer-report were estimated and their predictive value for unwanted variance in the IAT was examined. In a first analysis, unwanted variance was defined in the sense of method-specific variance which may result from differences in task demands between the two IAT block conditions and which can be mirrored by the absolute size of the IAT effects. In a second analysis, unwanted variance was examined in a broader sense defined as those systematic variance components in the raw IAT scores that are not explained by the latent implicit personality factors. In contrast to the absolute IAT scores, this also considers biases associated with the direction of IAT effects (i.e., whether they are positive or negative in sign, biases that might result, for example, from the IAT’s stimulus or category features. None of the explicit Big-Five factors was predictive for method-specific variance in the IATs (first analysis. However, when considering unwanted variance that goes beyond pure method-specific variance (second analysis, a substantial effect of neuroticism occurred that may have been driven by the affective valence of IAT attribute categories and the facilitated processing of negative stimuli, typically associated with neuroticism. The findings thus point to the necessity of using attribute category labels and stimuli of similar affective valence in personality IATs to avoid confounding due to

On the Structural Context and Identification of Enzyme Catalytic Residues

Directory of Open Access Journals (Sweden)

Yu-Tung Chien

2013-01-01

Full Text Available Enzymes play important roles in most of the biological processes. Although only a small fraction of residues are directly involved in catalytic reactions, these catalytic residues are the most crucial parts in enzymes. The study of the fundamental and unique features of catalytic residues benefits the understanding of enzyme functions and catalytic mechanisms. In this work, we analyze the structural context of catalytic residues based on theoretical and experimental structure flexibility. The results show that catalytic residues have distinct structural features and context. Their neighboring residues, whether sequence or structure neighbors within specific range, are usually structurally more rigid than those of noncatalytic residues. The structural context feature is combined with support vector machine to identify catalytic residues from enzyme structure. The prediction results are better or comparable to those of recent structure-based prediction methods.

UTILIZAÇÃO DE DIFERENTES ESTRUTURAS DE VARIÂNCIA RESIDUAL EM MODELOS DE REGRESSÃO ALEATÓRIA PARA DESCRIÇÃO DA CURVA DE CRESCIMENTO DE PERDIZES (Rhynchotus rufescens CRIADAS EM CATIVEIRO

Directory of Open Access Journals (Sweden)

Patrícia Tholon

2008-01-01

Full Text Available Random regression models (RRM allows considering heterogeneous residual variances to describe the growth for each age. However, this feature increases the number of parameters to be estimated in the maximization likelihood function process. Searching for more parsimonious RRM, several approaches have been suggested. One of them is the use of different structures of residual variances modelled through step function in different classes with similar variance or through variance functions. A total of 7,369 records of body weight of partridges, measured from birth to 210 days of partridges born from 2000 to 2004 were used in this research. The random regression models applied to the data set considered different structures of residual variances and were performed by the restricted maximum likelihood method. The residual variances were modeled using classes of 210 (R210 and 30 (R30 ages and variance functions with orders ranging from quadratic (VF2 to nine (VF9. The R30 considered birds weighted in the same week. The random effects included were the genetic additive direct and the permanent environment effects of the animal. It was not possible to include the maternal effects in the models. All random effects were modelled by sixth order regression on Legendre polynomials. The models were compared by the likelihood ratio test, the Akaike's information criterion and the Schwarz's Bayesian information criterion. Best results were showed by the models R210 and VF5. In conclusion, the most parsimonious model was VF5 and should be applied to fit growth records of partridges.

Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels

Directory of Open Access Journals (Sweden)

McDermott Drew

2009-08-01

Full Text Available Abstract Background Proteins interact through specific binding interfaces that contain many residues in domains. Protein interactions thus occur on three different levels of a concept hierarchy: whole-proteins, domains, and residues. Each level offers a distinct and complementary set of features for computationally predicting interactions, including functional genomic features of whole proteins, evolutionary features of domain families and physical-chemical features of individual residues. The predictions at each level could benefit from using the features at all three levels. However, it is not trivial as the features are provided at different granularity. Results To link up the predictions at the three levels, we propose a multi-level machine-learning framework that allows for explicit information flow between the levels. We demonstrate, using representative yeast interaction networks, that our algorithm is able to utilize complementary feature sets to make more accurate predictions at the three levels than when the three problems are approached independently. To facilitate application of our multi-level learning framework, we discuss three key aspects of multi-level learning and the corresponding design choices that we have made in the implementation of a concrete learning algorithm. 1 Architecture of information flow: we show the greater flexibility of bidirectional flow over independent levels and unidirectional flow; 2 Coupling mechanism of the different levels: We show how this can be accomplished via augmenting the training sets at each level, and discuss the prevention of error propagation between different levels by means of soft coupling; 3 Sparseness of data: We show that the multi-level framework compounds data sparsity issues, and discuss how this can be dealt with by building local models in information-rich parts of the data. Our proof-of-concept learning algorithm demonstrates the advantage of combining levels, and opens up

Portfolio optimization with mean-variance model

Science.gov (United States)

Hoe, Lam Weng; Siew, Lam Weng

2016-06-01

Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.

European Network of Excellence on NPP residual lifetime prediction methodologies (NULIFE)

International Nuclear Information System (INIS)

Badea, M.; Vidican, D.

2006-01-01

Within Europe massive investments in nuclear power have been made to meet present and future energy needs. The majority of nuclear reactors have been operating for longer than 20 years and their continuing safe operation depends crucially on effective lifetime management. Furthermore, to extend the economic return on investment and environmental benefits, it is necessary to ensure in advance the safe operation of nuclear reactors for 60 years, a period which is typically 20 years in excess of nominal design life. This depends on a clear understanding of, and predictive capability for, how safety margins may be maintained as components degrade under operational conditions. Ageing mechanisms, environment effects and complex loadings increase the likelihood of damage to safety relevant systems, structures and components. The ability to claim increased benefits from reduced conservatism via improved assessments is therefore of great value. Harmonisation and qualification are essential for industrial exploitation of approaches developed for life prediction methodology. Several European organisations and networks have been at the forefront of the development of advanced methodologies in this area. However, these efforts have largely been made at national level and their overall impact and benefit (in comparison to the situation in the USA) has been reduced by fragmentation. There is a need to restructure the networking approach in order to create a single organisational entity capable of working at European level to produce and exploit R and D in support of the safe and competitive operation of nuclear power plants. It is also critical to ensure the competitiveness of European plant life management (PLIM) services at international level, in particular with the USA and Asian countries. To the above challenges the European Network on European research in residual lifetime prediction methodologies (NULIFE) will: - Create a Europe-wide body in order to achieve scientific and

Residual life of technical systems; diagnosis, prediction and life extension

International Nuclear Information System (INIS)

Reinertsen, Rune

1996-01-01

The paper presents and discusses research related to residual life of non-repairable and repairable technical systems. Diagnosis of systems and extension of residual life of technical systems are also presented and discussed. This paper concludes that research published describing determination and extension of residual life as well as methods for diagnosis of non-repairable and repairable technical systems, is somewhat limited. Many papers have a rather pragmatic approach. The authors only describe special cases from their own plant and do not provide any explanation of a more academical nature. The other papers are mainly describing very specific applications of statistical models, leaving the more general case out of consideration. One of the main results of this paper is to point out these facts, and thereby identify the need for future research in this area

Least-squares variance component estimation

NARCIS (Netherlands)

Teunissen, P.J.G.; Amiri-Simkooei, A.R.

2007-01-01

Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive method for the estimation of unknown variance and covariance components. LS-VCE is simple because it is based on the well-known principle of LS; it is flexible because it works with a user-defined weight

Variance-based selection may explain general mating patterns in social insects.

Science.gov (United States)

Rueppell, Olav; Johnson, Nels; Rychtár, Jan

2008-06-23

Female mating frequency is one of the key parameters of social insect evolution. Several hypotheses have been suggested to explain multiple mating and considerable empirical research has led to conflicting results. Building on several earlier analyses, we present a simple general model that links the number of queen matings to variance in colony performance and this variance to average colony fitness. The model predicts selection for multiple mating if the average colony succeeds in a focal task, and selection for single mating if the average colony fails, irrespective of the proximate mechanism that links genetic diversity to colony fitness. Empirical support comes from interspecific comparisons, e.g. between the bee genera Apis and Bombus, and from data on several ant species, but more comprehensive empirical tests are needed.

New applications of partial residual methodology

International Nuclear Information System (INIS)

Uslu, V.R.

1999-12-01

The formulation of a problem of interest in the framework of a statistical analysis starts with collecting the data, choosing a model, making certain assumptions as described in the basic paradigm by Box (1980). This stage is is called model building. Then the estimation stage is in order by pretending as if the formulation of the problem was true to obtain estimates, to make tests and inferences. In the final stage, called diagnostic checking, checking of whether there are some disagreements between the data and the model fitted is done by using diagnostic measures and diagnostic plots. It is well known that statistical methods perform best under the condition that all assumptions related to the methods are satisfied. However it is true that having the ideal case in practice is very difficult. Diagnostics are therefore becoming important so are diagnostic plots because they provide a immediate assessment. Partial residual plots that are the main interest of the present study are playing the major role among the diagnostic plots in multiple regression analysis. In statistical literature it is admitted that partial residual plots are more useful than ordinary residual plots in detecting outliers, nonconstant variance, and especially discovering curvatures. In this study we consider the partial residual methodology in statistical methods rather than multiple regression. We have shown that for the same purpose as in the multiple regression the use of partial residual plots is possible particularly in autoregressive time series models, transfer function models, linear mixed models and ridge regression. (author)

Genetic variants influencing phenotypic variance heterogeneity.

Science.gov (United States)

Ek, Weronica E; Rask-Andersen, Mathias; Karlsson, Torgny; Enroth, Stefan; Gyllensten, Ulf; Johansson, Åsa

2018-03-01

Most genetic studies identify genetic variants associated with disease risk or with the mean value of a quantitative trait. More rarely, genetic variants associated with variance heterogeneity are considered. In this study, we have identified such variance single-nucleotide polymorphisms (vSNPs) and examined if these represent biological gene × gene or gene × environment interactions or statistical artifacts caused by multiple linked genetic variants influencing the same phenotype. We have performed a genome-wide study, to identify vSNPs associated with variance heterogeneity in DNA methylation levels. Genotype data from over 10 million single-nucleotide polymorphisms (SNPs), and DNA methylation levels at over 430 000 CpG sites, were analyzed in 729 individuals. We identified vSNPs for 7195 CpG sites (P mean DNA methylation levels. We further showed that variance heterogeneity between genotypes mainly represents additional, often rare, SNPs in linkage disequilibrium (LD) with the respective vSNP and for some vSNPs, multiple low frequency variants co-segregating with one of the vSNP alleles. Therefore, our results suggest that variance heterogeneity of DNA methylation mainly represents phenotypic effects by multiple SNPs, rather than biological interactions. Such effects may also be important for interpreting variance heterogeneity of more complex clinical phenotypes.

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

Science.gov (United States)

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Residual stress analysis in reactor pressure vessel attachments

International Nuclear Information System (INIS)

Dexter, R.J.; Pont, D.

1991-08-01

Residual stresses in cladding and welded attachments could contribute to the problem of stress-corrosion cracking in boiling-water reactors (BWR). As part of a larger program aimed at quantifying residual stress in BWR components, models that would be applicable for predicting residual stress in BWR components are reviewed and documented. The review includes simple methods of estimating residual stresses as well as advanced finite-element software. In general, simple methods are capable of predicting peak magnitudes of residual stresses but are incapable of adequately characterizing the distribution of residual stresses. Ten groups of researchers using finite-element software are reviewed in detail. For each group, the assumptions of the model, possible simplifications, material property data, and specific applications are discussed. The most accurate results are obtained when a metallurgical simulation is performed, transformation plasticity effects are included, and the heating and cooling parts of the welding thermal cycle are simulated. Two models are identified which can provide these features. The present state of these models and the material property data available in the literature are adequate to quantify residual stress in BWR components

Investigation of residual stress in laser welding dissimilar materials

International Nuclear Information System (INIS)

Mirim, Denilson de Camargo; Oliveira, Rene Ramos de; Berretta, Jose Roberto; Rossi, Wagner de; Lima, Nelson Batista de; Delijaicov, Sergio; Gomes, Diego Oliva

2010-01-01

One of the most critical problems found in the different materials welding is the residual stress formation, that happens mainly for the fact of those materials they possess coefficients of thermal expansion and different thermal conductivities. Like this in this work the residual tension was evaluated in the technique of welding laser among the steel low carbon, AISI 1010 and AISI 304. The materials were united for it welds autogenous of top with a laser of continuous Nd:YAG in that they were varied the potency, speed and the focus of the laser stayed constant in relation to surface of the sample. The main objective of the study went identification and to analysis of the residual stress in HAZ on both sides of seem. Um planning factorial of two factors at two levels each it was executed for optimization the combination of the factors potency and speed. The obtained answers were the residual stress in different depths in HAZ. In the surface of the sample measures of residual stress were accomplished by the technique of X-ray diffraction. The hole drilling strain gage method it was applied to measure the residual stress on both sides of the union. The results were analyzed using the variance analysis and the statistical regression based on the different influences of the entrance and combination of the factors in the residual stress generated in that union. The results indicate that the development of models can foresee the answers satisfactorily. (author)

Prediction error variance and expected response to selection, when selection is based on the best predictor – for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits

Directory of Open Access Journals (Sweden)

Jensen Just

2002-05-01

Full Text Available Abstract In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed or random effects. In the different models, expressions are given (when these can be found – otherwise unbiased estimates are given for prediction error variance, accuracy of selection and expected response to selection on the additive genetic scale and on the observed scale. The expressions given for non Gaussian traits are generalisations of the well-known formulas for Gaussian traits – and reflect, for Poisson mixed models and frailty models for survival data, the hierarchal structure of the models. In general the ratio of the additive genetic variance to the total variance in the Gaussian part of the model (heritability on the normally distributed level of the model or a generalised version of heritability plays a central role in these formulas.

Measurement of residual stresses using fracture mechanics weight functions

International Nuclear Information System (INIS)

Fan, Y.

2000-01-01

A residual stress measurement method has been developed to quantify through-the-thickness residual stresses. Accurate measurement of residual stresses is crucial for many engineering structures. Fabrication processes such as welding and machining generate residual stresses that are difficult to predict. Residual stresses affect the integrity of structures through promoting failures due to brittle fracture, fatigue, stress corrosion cracking, and wear. In this work, the weight function theory of fracture mechanics is used to measure residual stresses. The weight function theory is an important development in computational fracture mechanics. Stress intensity factors for arbitrary stress distribution on the crack faces can be accurately and efficiently computed for predicting crack growth. This paper demonstrates that the weight functions are equally useful in measuring residual stresses. In this method, an artificial crack is created by a thin cut in a structure containing residual stresses. The cut relieves the residual stresses normal to the crack-face and allows the relieved residual stresses to deform the structure. Strain gages placed adjacent to the cut measure the relieved strains corresponding to incrementally increasing depths of the cut. The weight functions of the cracked body relate the measured strains to the residual stresses normal to the cut within the structure. The procedure details, such as numerical integration of the singular functions in applying the weight function method, will be discussed

Measurement of residual stresses using fracture mechanics weight functions

International Nuclear Information System (INIS)

Fan, Y.

2001-01-01

A residual stress measurement method has been developed to quantify through-the-thickness residual stresses. Accurate measurement of residual stresses is crucial for many engineering structures. Fabrication processes such as welding and machining generate residual stresses that are difficult to predict. Residual stresses affect the integrity of structures through promoting failures due to brittle fracture, fatigue, stress corrosion cracking, and wear. In this work, the weight function theory of fracture mechanics is used to measure residual stresses. The weight function theory is an important development in computational fracture mechanics. Stress intensity factors for arbitrary stress distribution on the crack faces can be accurately and efficiently computed for predicting crack growth. This paper demonstrates that the weight functions are equally useful in measuring residual stresses. In this method, an artificial crack is created by a thin cut in a structure containing residual stresses. The cut relieves the residual stresses normal to the crack-face and allows the relieved residual stresses to deform the structure. Strain gages placed adjacent to the cut measure the relieved strains corresponding to incrementally increasing depths of the cut. The weight functions of the cracked body relate the measured strains to the residual stresses normal to the cut within the structure. The procedure details, such as numerical integration of the singular functions in applying the weight function method, will be discussed. (author)

Is Romantic Desire Predictable? Machine Learning Applied to Initial Romantic Attraction.

Science.gov (United States)

Joel, Samantha; Eastwick, Paul W; Finkel, Eli J

2017-10-01

Matchmaking companies and theoretical perspectives on close relationships suggest that initial attraction is, to some extent, a product of two people's self-reported traits and preferences. We used machine learning to test how well such measures predict people's overall tendencies to romantically desire other people (actor variance) and to be desired by other people (partner variance), as well as people's desire for specific partners above and beyond actor and partner variance (relationship variance). In two speed-dating studies, romantically unattached individuals completed more than 100 self-report measures about traits and preferences that past researchers have identified as being relevant to mate selection. Each participant met each opposite-sex participant attending a speed-dating event for a 4-min speed date. Random forests models predicted 4% to 18% of actor variance and 7% to 27% of partner variance; crucially, however, they were unable to predict relationship variance using any combination of traits and preferences reported before the dates. These results suggest that compatibility elements of human mating are challenging to predict before two people meet.

Speed Variance and Its Influence on Accidents.

Science.gov (United States)

Garber, Nicholas J.; Gadirau, Ravi

A study was conducted to investigate the traffic engineering factors that influence speed variance and to determine to what extent speed variance affects accident rates. Detailed analyses were carried out to relate speed variance with posted speed limit, design speeds, and other traffic variables. The major factor identified was the difference…

On the Rule of Mixtures for Predicting Stress-Softening and Residual Strain Effects in Biological Tissues and Biocompatible Materials

Directory of Open Access Journals (Sweden)

Alex Elías-Zúñiga

2014-01-01

Full Text Available In this work, we use the rule of mixtures to develop an equivalent material model in which the total strain energy density is split into the isotropic part related to the matrix component and the anisotropic energy contribution related to the fiber effects. For the isotropic energy part, we select the amended non-Gaussian strain energy density model, while the energy fiber effects are added by considering the equivalent anisotropic volumetric fraction contribution, as well as the isotropized representation form of the eight-chain energy model that accounts for the material anisotropic effects. Furthermore, our proposed material model uses a phenomenological non-monotonous softening function that predicts stress softening effects and has an energy term, derived from the pseudo-elasticity theory, that accounts for residual strain deformations. The model’s theoretical predictions are compared with experimental data collected from human vaginal tissues, mice skin, poly(glycolide-co-caprolactone (PGC25 3-0 and polypropylene suture materials and tracheal and brain human tissues. In all cases examined here, our equivalent material model closely follows stress-softening and residual strain effects exhibited by experimental data.

On the Rule of Mixtures for Predicting Stress-Softening and Residual Strain Effects in Biological Tissues and Biocompatible Materials

Science.gov (United States)

Elías-Zúñiga, Alex; Baylón, Karen; Ferrer, Inés; Serenó, Lídia; Garcia-Romeu, Maria Luisa; Bagudanch, Isabel; Grabalosa, Jordi; Pérez-Recio, Tania; Martínez-Romero, Oscar; Ortega-Lara, Wendy; Elizalde, Luis Ernesto

2014-01-01

In this work, we use the rule of mixtures to develop an equivalent material model in which the total strain energy density is split into the isotropic part related to the matrix component and the anisotropic energy contribution related to the fiber effects. For the isotropic energy part, we select the amended non-Gaussian strain energy density model, while the energy fiber effects are added by considering the equivalent anisotropic volumetric fraction contribution, as well as the isotropized representation form of the eight-chain energy model that accounts for the material anisotropic effects. Furthermore, our proposed material model uses a phenomenological non-monotonous softening function that predicts stress softening effects and has an energy term, derived from the pseudo-elasticity theory, that accounts for residual strain deformations. The model’s theoretical predictions are compared with experimental data collected from human vaginal tissues, mice skin, poly(glycolide-co-caprolactone) (PGC25 3-0) and polypropylene suture materials and tracheal and brain human tissues. In all cases examined here, our equivalent material model closely follows stress-softening and residual strain effects exhibited by experimental data. PMID:28788466

Evaluation of residue-residue contact prediction in CASP10

KAUST Repository

Monastyrskyy, Bohdan; D'Andrea, Daniel; Fidelis, Krzysztof; Tramontano, Anna; Kryshtafovych, Andriy

2013-01-01

not take part in the experiment likely because they require a very deep sequence alignment not available for any of the 114 CASP10 targets. The performance of contact prediction methods was evaluated with the measures used in previous CASPs (i

Volatility and variance swaps : A comparison of quantitative models to calculate the fair volatility and variance strike

OpenAIRE

Röring, Johan

2017-01-01

Volatility is a common risk measure in the field of finance that describes the magnitude of an asset’s up and down movement. From only being a risk measure, volatility has become an asset class of its own and volatility derivatives enable traders to get an isolated exposure to an asset’s volatility. Two kinds of volatility derivatives are volatility swaps and variance swaps. The problem with volatility swaps and variance swaps is that they require estimations of the future variance and volati...

FE-simulation of hot forging with an integrated heat treatment with the objective of residual stress prediction

Science.gov (United States)

Behrens, Bernd-Arno; Chugreeva, Anna; Chugreev, Alexander

2018-05-01

Hot forming as a coupled thermo-mechanical process comprises numerous material phenomena with a corresponding impact on the material behavior during and after the forming process as well as on the final component performance. In this context, a realistic FE-simulation requires reliable mathematical models as well as detailed thermo-mechanical material data. This paper presents experimental and numerical results focused on the FE-based simulation of a hot forging process with a subsequent heat treatment step aiming at the prediction of the final mechanical properties and residual stress state in the forged component made of low alloy CrMo-steel DIN 42CrMo4. For this purpose, hot forging experiments of connecting rod geometry with a corresponding metallographic analysis and x-ray residual stress measurements have been carried out. For the coupled thermo-mechanical-metallurgical FE-simulations, a special user-defined material model based on the additive strain decomposition method and implemented in Simufact Forming via MSC.Marc solver features has been used.

Local variances in biomonitoring

International Nuclear Information System (INIS)

Wolterbeek, H.T.

1999-01-01

The present study deals with the (larger-scaled) biomonitoring survey and specifically focuses on the sampling site. In most surveys, the sampling site is simply selected or defined as a spot of (geographical) dimensions which is small relative to the dimensions of the total survey area. Implicitly it is assumed that the sampling site is essentially homogeneous with respect to the investigated variation in survey parameters. As such, the sampling site is mostly regarded as 'the basic unit' of the survey. As a logical consequence, the local (sampling site) variance should also be seen as a basic and important characteristic of the survey. During the study, work is carried out to gain more knowledge of the local variance. Multiple sampling is carried out at a specific site (tree bark, mosses, soils), multi-elemental analyses are carried out by NAA, and local variances are investigated by conventional statistics, factor analytical techniques, and bootstrapping. Consequences of the outcomes are discussed in the context of sampling, sample handling and survey quality. (author)

Effect of Filament Fineness on Composite Yarn Residual Torque

Directory of Open Access Journals (Sweden)

Sarıoğlu Esin

2018-03-01

Full Text Available Yarn residual torque or twist liveliness occurs when the twist is imparted to spin the fibers during yarn formation. It causes yarn snarling, which is an undesirable property and can lead the problems for further processes such as weaving and knitting. It affects the spirality of knitted fabrics and skewness of woven fabrics. Generally, yarn residual torque depends on yarn twist, yarn linear density, and fiber properties used. Composite yarns are widely produced to exploit two yarns with different properties such on optimum way at the same time and these yarns can be produced by wrapping sheath fibers around filament core fiber with a certain twist. In this study, the effect of filament fineness used as core component of composite yarn on residual torque was analyzed. Thus, the false twist textured polyester filament yarns with different filament fineness were used to produce composite yarns with different yarn count. The variance analysis was performed to determine the significance of twist liveliness of filament yarns and yarn count on yarn twist liveliness. Results showed that there is a statistically significant differences at significance level of α=0.05 between filament fineness and yarn residual torque of composite yarns.

Weighted-Average Least Squares Prediction

NARCIS (Netherlands)

Magnus, Jan R.; Wang, Wendun; Zhang, Xinyu

2016-01-01

Prediction under model uncertainty is an important and difficult issue. Traditional prediction methods (such as pretesting) are based on model selection followed by prediction in the selected model, but the reported prediction and the reported prediction variance ignore the uncertainty from the

Male size composition affects male reproductive variance in Atlantic cod Gadus morhua L. spawning aggregations

DEFF Research Database (Denmark)

Bekkevold, Dorte

2006-01-01

Estimates of Atlantic cod Gadus morhua reproductive success, determined using experimental spawning groups and genetic paternity assignment of offspring, showed that within-group variance in male size correlated positively with the degree of male mating skew, predicting a decrease in male reprodu...

Residual stress analysis in BWR pressure vessel attachments

International Nuclear Information System (INIS)

Dexter, R.J.; Leung, C.P.; Pont, D.

1992-06-01

Residual stresses from welding processes can be the primary driving force for stress corrosion cracking (SCC) in BWR components. Thus, a better understanding of the causes and nature of these residual stresses can help assess and remedy SCC. Numerical welding simulation software, such as SYSWELD, and material property data have been used to quantify residual stresses for application to SCC assessments in BWR components. Furthermore, parametric studies using SYSWELD have revealed which variables significantly affect predicted residual stress. Overall, numerical modeling techniques can be used to evaluate residual stress for SCC assessments of BWR components and to identify and plan future SCC research

Long-term stabilization of crop residues and soil organic carbon affected by residue quality and initial soil pH.

Science.gov (United States)

Wang, Xiaojuan; Butterly, Clayton R; Baldock, Jeff A; Tang, Caixian

2017-06-01

Residues differing in quality and carbon (C) chemistry are presumed to contribute differently to soil pH change and long-term soil organic carbon (SOC) pools. This study examined the liming effect of different crop residues (canola, chickpea and wheat) down the soil profile (0-30cm) in two sandy soils differing in initial pH as well as the long-term stability of SOC at the amended layer (0-10cm) using mid-infrared (MIR) and solid-state 13 C nuclear magnetic resonance (NMR) spectroscopy. A field column experiment was conducted for 48months. Chickpea- and canola-residue amendments increased soil pH at 0-10cm in the Podzol by up to 0.47 and 0.36units, and in the Cambisol by 0.31 and 0.18units, respectively, at 48months when compared with the non-residue-amended control. The decomposition of crop residues was greatly retarded in the Podzol with lower initial soil pH during the first 9months. The MIR-predicted particulate organic C (POC) acted as the major C sink for residue-derived C in the Podzol. In contrast, depletion of POC and recovery of residue C in MIR-predicted humic organic C (HOC) were detected in the Cambisol within 3months. Residue types showed little impact on total SOC and its chemical composition in the Cambisol at 48months, in contrast to the Podzol. The final HOC and resistant organic C (ROC) pools in the Podzol amended with canola and chickpea residues were about 25% lower than the control. This apparent priming effect might be related to the greater liming effect of these two residues in the Podzol. Copyright © 2017 Elsevier B.V. All rights reserved.

Dynamic Mean-Variance Asset Allocation

OpenAIRE

Basak, Suleyman; Chabakauri, Georgy

2009-01-01

Mean-variance criteria remain prevalent in multi-period problems, and yet not much is known about their dynamically optimal policies. We provide a fully analytical characterization of the optimal dynamic mean-variance portfolios within a general incomplete-market economy, and recover a simple structure that also inherits several conventional properties of static models. We also identify a probability measure that incorporates intertemporal hedging demands and facilitates much tractability in ...

Tail Risk Premia and Return Predictability

DEFF Research Database (Denmark)

Bollerslev, Tim; Todorov, Viktor; Xu, Lai

The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may be attribu......The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may......-varying economic uncertainty and changes in risk aversion, or market fears, respectively....

The Variance Composition of Firm Growth Rates

Directory of Open Access Journals (Sweden)

Luiz Artur Ledur Brito

2009-04-01

Full Text Available Firms exhibit a wide variability in growth rates. This can be seen as another manifestation of the fact that firms are different from one another in several respects. This study investigated this variability using the variance components technique previously used to decompose the variance of financial performance. The main source of variation in growth rates, responsible for more than 40% of total variance, corresponds to individual, idiosyncratic firm aspects and not to industry, country, or macroeconomic conditions prevailing in specific years. Firm growth, similar to financial performance, is mostly unique to specific firms and not an industry or country related phenomenon. This finding also justifies using growth as an alternative outcome of superior firm resources and as a complementary dimension of competitive advantage. This also links this research with the resource-based view of strategy. Country was the second source of variation with around 10% of total variance. The analysis was done using the Compustat Global database with 80,320 observations, comprising 13,221 companies in 47 countries, covering the years of 1994 to 2002. It also compared the variance structure of growth to the variance structure of financial performance in the same sample.

The scope and control of attention: Sources of variance in working memory capacity.

Science.gov (United States)

Chow, Michael; Conway, Andrew R A

2015-04-01

Working memory capacity is a strong positive predictor of many cognitive abilities, across various domains. The pattern of positive correlations across domains has been interpreted as evidence for a unitary source of inter-individual differences in behavior. However, recent work suggests that there are multiple sources of variance contributing to working memory capacity. The current study (N = 71) investigates individual differences in the scope and control of attention, in addition to the number and resolution of items maintained in working memory. Latent variable analyses indicate that the scope and control of attention reflect independent sources of variance and each account for unique variance in general intelligence. Also, estimates of the number of items maintained in working memory are consistent across tasks and related to general intelligence whereas estimates of resolution are task-dependent and not predictive of intelligence. These results provide insight into the structure of working memory, as well as intelligence, and raise new questions about the distinction between number and resolution in visual short-term memory.

Estimating the encounter rate variance in distance sampling

Science.gov (United States)

Fewster, R.M.; Buckland, S.T.; Burnham, K.P.; Borchers, D.L.; Jupp, P.E.; Laake, J.L.; Thomas, L.

2009-01-01

The dominant source of variance in line transect sampling is usually the encounter rate variance. Systematic survey designs are often used to reduce the true variability among different realizations of the design, but estimating the variance is difficult and estimators typically approximate the variance by treating the design as a simple random sample of lines. We explore the properties of different encounter rate variance estimators under random and systematic designs. We show that a design-based variance estimator improves upon the model-based estimator of Buckland et al. (2001, Introduction to Distance Sampling. Oxford: Oxford University Press, p. 79) when transects are positioned at random. However, if populations exhibit strong spatial trends, both estimators can have substantial positive bias under systematic designs. We show that poststratification is effective in reducing this bias. ?? 2008, The International Biometric Society.

BLAST-based structural annotation of protein residues using Protein Data Bank.

Science.gov (United States)

Singh, Harinder; Raghava, Gajendra P S

2016-01-25

In the era of next-generation sequencing where thousands of genomes have been already sequenced; size of protein databases is growing with exponential rate. Structural annotation of these proteins is one of the biggest challenges for the computational biologist. Although, it is easy to perform BLAST search against Protein Data Bank (PDB) but it is difficult for a biologist to annotate protein residues from BLAST search. A web-server StarPDB has been developed for structural annotation of a protein based on its similarity with known protein structures. It uses standard BLAST software for performing similarity search of a query protein against protein structures in PDB. This server integrates wide range modules for assigning different types of annotation that includes, Secondary-structure, Accessible surface area, Tight-turns, DNA-RNA and Ligand modules. Secondary structure module allows users to predict regular secondary structure states to each residue in a protein. Accessible surface area predict the exposed or buried residues in a protein. Tight-turns module is designed to predict tight turns like beta-turns in a protein. DNA-RNA module developed for predicting DNA and RNA interacting residues in a protein. Similarly, Ligand module of server allows one to predicted ligands, metal and nucleotides ligand interacting residues in a protein. In summary, this manuscript presents a web server for comprehensive annotation of a protein based on similarity search. It integrates number of visualization tools that facilitate users to understand structure and function of protein residues. This web server is available freely for scientific community from URL http://crdd.osdd.net/raghava/starpdb .

Towards the ultimate variance-conserving convection scheme

International Nuclear Information System (INIS)

Os, J.J.A.M. van; Uittenbogaard, R.E.

2004-01-01

In the past various arguments have been used for applying kinetic energy-conserving advection schemes in numerical simulations of incompressible fluid flows. One argument is obeying the programmed dissipation by viscous stresses or by sub-grid stresses in Direct Numerical Simulation and Large Eddy Simulation, see e.g. [Phys. Fluids A 3 (7) (1991) 1766]. Another argument is that, according to e.g. [J. Comput. Phys. 6 (1970) 392; 1 (1966) 119], energy-conserving convection schemes are more stable i.e. by prohibiting a spurious blow-up of volume-integrated energy in a closed volume without external energy sources. In the above-mentioned references it is stated that nonlinear instability is due to spatial truncation rather than to time truncation and therefore these papers are mainly concerned with the spatial integration. In this paper we demonstrate that discretized temporal integration of a spatially variance-conserving convection scheme can induce non-energy conserving solutions. In this paper the conservation of the variance of a scalar property is taken as a simple model for the conservation of kinetic energy. In addition, the derivation and testing of a variance-conserving scheme allows for a clear definition of kinetic energy-conserving advection schemes for solving the Navier-Stokes equations. Consequently, we first derive and test a strictly variance-conserving space-time discretization for the convection term in the convection-diffusion equation. Our starting point is the variance-conserving spatial discretization of the convection operator presented by Piacsek and Williams [J. Comput. Phys. 6 (1970) 392]. In terms of its conservation properties, our variance-conserving scheme is compared to other spatially variance-conserving schemes as well as with the non-variance-conserving schemes applied in our shallow-water solver, see e.g. [Direct and Large-eddy Simulation Workshop IV, ERCOFTAC Series, Kluwer Academic Publishers, 2001, pp. 409-287

Prediction method of long-term reliability in improving residual stresses by means of surface finishing

International Nuclear Information System (INIS)

Sera, Takehiko; Hirano, Shinro; Chigusa, Naoki; Okano, Shigetaka; Saida, Kazuyoshi; Mochizuki, Masahito; Nishimoto, Kazutoshi

2012-01-01

Surface finishing methods, such as Water Jet Peening (WJP), have been applied to welds in some major components of nuclear power plants as a counter measure to Primary Water Stress Corrosion Cracking (PWSCC). In addition, the methods of surface finishing (buffing treatment) is being standardized, and thus the buffing treatment has been also recognized as the well-established method of improving stress. On the other hand, the long-term stability of peening techniques has been confirmed by accelerated test. However, the effectiveness of stress improvement by surface treatment is limited to thin layers and the effect of complicated residual stress distribution in the weld metal beneath the surface is not strictly taken into account for long-term stability. This paper, therefore, describes the accelerated tests, which confirmed that the long-term stability of the layer subjected to buffing treatment was equal to that subjected to WJP. The long-term reliability of very thin stress improved layer was also confirmed through a trial evaluation by thermal elastic-plastic creep analysis, even if the effect of complicated residual stress distribution in the weld metal was excessively taken into account. Considering the above findings, an approach is proposed for constructing the prediction method of the long-term reliability of stress improvement by surface finishing. (author)

Residual stress effects in LMFBR fracture assessment procedures

International Nuclear Information System (INIS)

Hooton, D.G.

1984-01-01

Two post-yield fracture mechanics methods, which have been developed into fully detailed failure assessment procedures for ferritic structures, have been reviewed from the point of view of the manner in which as-welded residual stress effects are incorporated, and comparisons then made with finite element and theoretical models of centre-cracked plates containing residual/thermal stresses in the form of crack-driving force curves. Applying the procedures to austenitic structures, comparisons are made in terms of failure assessment curves and it is recommended that the preferred method for the prediction of critical crack sizes in LMFBR austenitic structures containing as-welded residual stresses is the CEGB-R6 procedure based on a flow stress defined at 3% strain in the parent plate. When the prediction of failure loads in such structures is required, it is suggested that the CEGB-R6 procedure be used with residual/thermal stresses factored to give a maximum total stress of flow stress magnitude

The Distribution of the Sample Minimum-Variance Frontier

OpenAIRE

Raymond Kan; Daniel R. Smith

2008-01-01

In this paper, we present a finite sample analysis of the sample minimum-variance frontier under the assumption that the returns are independent and multivariate normally distributed. We show that the sample minimum-variance frontier is a highly biased estimator of the population frontier, and we propose an improved estimator of the population frontier. In addition, we provide the exact distribution of the out-of-sample mean and variance of sample minimum-variance portfolios. This allows us t...

Minimum variance and variance of outgoing quality limit MDS-1(c1, c2) plans

Science.gov (United States)

Raju, C.; Vidya, R.

2016-06-01

In this article, the outgoing quality (OQ) and total inspection (TI) of multiple deferred state sampling plans MDS-1(c1,c2) are studied. It is assumed that the inspection is rejection rectification. Procedures for designing MDS-1(c1,c2) sampling plans with minimum variance of OQ and TI are developed. A procedure for obtaining a plan for a designated upper limit for the variance of the OQ (VOQL) is outlined.

Predicting Variations in Math Performancein Four Countries Using TIMSS

Directory of Open Access Journals (Sweden)

Daniel Koretz

2001-09-01

Full Text Available Although international comparisons of average student performance are a staple of U.S. educational debate, little attention has been paid to cross-national differences in the variability of performance. It is often assumed that the performance of U.S. students is unusually variable or that the distribution of U.S. scores is left-skewed – that is, that it has an unusually long ‘tail' of low-scoring students – but data from international studies are rarely brought to bear on these questions. This study used data from the Third International Mathematics and Science Study (TIMSS to compare the variability of performance in the U.S., Australia, France, Germany, Hong Kong, Korea, and Japan; investigate how this performance variation is distributed within and between classrooms; and explore how well background variables predict performance at both levels. TIMSS shows that the U.S. is not anomalous in terms of the amount, distribution, or prediction of performance variation. Nonetheless, some striking differences appear between countries that are potentially important for both research and policy. In the U.S., Germany, Hong Kong, and Australia, between 42 and 47 percent of score variance was between classrooms. At the other extreme, Japan and Korea both had less than 10 percent of score variance between classrooms. Two-level models (student and classroom were used to explore the prediction of performance by social background variables in four of these countries (the U.S., Hong Kong, France, and Korea. The final models included only a few variables; TIMSS lacked some important background variables, such as income, and other variables were dropped either because of problems revealed by exploratory data analysis or because of a lack of significance in the models. In all four countries, these sparse models predicted most of the between-classroom score variance (from 59 to 94 percent but very little of the within-classroom variance. Korea was the only

Development of Analytical Method for Predicting Residual Mechanical Properties of Corroded Steel Plates

Directory of Open Access Journals (Sweden)

J. M. R. S. Appuhamy

2011-01-01

Full Text Available Bridge infrastructure maintenance and assurance of adequate safety is of paramount importance in transportation engineering and maintenance management industry. Corrosion causes strength deterioration, leading to impairment of its operation and progressive weakening of the structure. Since the actual corroded surfaces are different from each other, only experimental approach is not enough to estimate the remaining strength of corroded members. However, in modern practices, numerical simulation is being used to replace the time-consuming and expensive experimental work and to comprehend on the lack of knowledge on mechanical behavior, stress distribution, ultimate behavior, and so on. This paper presents the nonlinear FEM analyses results of many corroded steel plates and compares them with their respective tensile coupon tests. Further, the feasibility of establishing an accurate analytical methodology to predict the residual strength capacities of a corroded steel member with lesser number of measuring points is also discussed.

Genotypic-specific variance in Caenorhabditis elegans lifetime fecundity.

Science.gov (United States)

Diaz, S Anaid; Viney, Mark

2014-06-01

Organisms live in heterogeneous environments, so strategies that maximze fitness in such environments will evolve. Variation in traits is important because it is the raw material on which natural selection acts during evolution. Phenotypic variation is usually thought to be due to genetic variation and/or environmentally induced effects. Therefore, genetically identical individuals in a constant environment should have invariant traits. Clearly, genetically identical individuals do differ phenotypically, usually thought to be due to stochastic processes. It is now becoming clear, especially from studies of unicellular species, that phenotypic variance among genetically identical individuals in a constant environment can be genetically controlled and that therefore, in principle, this can be subject to selection. However, there has been little investigation of these phenomena in multicellular species. Here, we have studied the mean lifetime fecundity (thus a trait likely to be relevant to reproductive success), and variance in lifetime fecundity, in recently-wild isolates of the model nematode Caenorhabditis elegans. We found that these genotypes differed in their variance in lifetime fecundity: some had high variance in fecundity, others very low variance. We find that this variance in lifetime fecundity was negatively related to the mean lifetime fecundity of the lines, and that the variance of the lines was positively correlated between environments. We suggest that the variance in lifetime fecundity may be a bet-hedging strategy used by this species.

Spectrum Fatigue Lifetime and Residual Strength for Fiberglass Laminates; TOPICAL

International Nuclear Information System (INIS)

WAHL, NEIL K.; MANDELL, JOHN F.; SAMBORSKY, DANIEL D.

2002-01-01

This report addresses the effects of spectrum loading on lifetime and residual strength of a typical fiberglass laminate configuration used in wind turbine blade construction. Over 1100 tests have been run on laboratory specimens under a variety of load sequences. Repeated block loading at two or more load levels, either tensile-tensile, compressive-compressive, or reversing, as well as more random standard spectra have been studied. Data have been obtained for residual strength at various stages of the lifetime. Several lifetime prediction theories have been applied to the results. The repeated block loading data show lifetimes that are usually shorter than predicted by the most widely used linear damage accumulation theory, Miner's sum. Actual lifetimes are in the range of 10 to 20 percent of predicted lifetime in many cases. Linear and nonlinear residual strength models tend to fit the data better than Miner's sum, with the nonlinear providing a better fit of the two. Direct tests of residual strength at various fractions of the lifetime are consistent with the residual strength models. Load sequencing effects are found to be insignificant. The more a spectrum deviates from constant amplitude, the more sensitive predictions are to the damage law used. The nonlinear model provided improved correlation with test data for a modified standard wind turbine spectrum. When a single, relatively high load cycle was removed, all models provided similar, though somewhat non-conservative correlation with the experimental results. Predictions for the full spectrum, including tensile and compressive loads were slightly non-conservative relative to the experimental data, and accurately captured the trend with varying maximum load. The nonlinear residual strength based prediction with a power law S-N curve extrapolation provided the best fit to the data in most cases. The selection of the constant amplitude fatigue regression model becomes important at the lower stress, higher

A two step Bayesian approach for genomic prediction of breeding values.

Science.gov (United States)

Shariati, Mohammad M; Sørensen, Peter; Janss, Luc

2012-05-21

In genomic models that assign an individual variance to each marker, the contribution of one marker to the posterior distribution of the marker variance is only one degree of freedom (df), which introduces many variance parameters with only little information per variance parameter. A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size p on the posterior distribution of the marker variances will be p df. The simulated data from the 15th QTL-MAS workshop were analyzed such that SNP markers were ranked based on their effects and markers with similar estimated effects were grouped together. In step 1, all markers with minor allele frequency more than 0.01 were included in a SNP-BLUP prediction model. In step 2, markers were ranked based on their estimated variance on the trait in step 1 and each 150 markers were assigned to one group with a common variance. In further analyses, subsets of 1500 and 450 markers with largest effects in step 2 were kept in the prediction model. Grouping markers outperformed SNP-BLUP model in terms of accuracy of predicted breeding values. However, the accuracies of predicted breeding values were lower than Bayesian methods with marker specific variances. Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker variances increases. A prior knowledge of the genetic architecture of the trait is necessary for clustering markers and appropriate prior parameterization.

Discrete and continuous time dynamic mean-variance analysis

OpenAIRE

Reiss, Ariane

1999-01-01

Contrary to static mean-variance analysis, very few papers have dealt with dynamic mean-variance analysis. Here, the mean-variance efficient self-financing portfolio strategy is derived for n risky assets in discrete and continuous time. In the discrete setting, the resulting portfolio is mean-variance efficient in a dynamic sense. It is shown that the optimal strategy for n risky assets may be dominated if the expected terminal wealth is constrained to exactly attain a certain goal instead o...

Decomposing variation in male reproductive success: age-specific variances and covariances through extra-pair and within-pair reproduction.

Science.gov (United States)

Lebigre, Christophe; Arcese, Peter; Reid, Jane M

2013-07-01

Age-specific variances and covariances in reproductive success shape the total variance in lifetime reproductive success (LRS), age-specific opportunities for selection, and population demographic variance and effective size. Age-specific (co)variances in reproductive success achieved through different reproductive routes must therefore be quantified to predict population, phenotypic and evolutionary dynamics in age-structured populations. While numerous studies have quantified age-specific variation in mean reproductive success, age-specific variances and covariances in reproductive success, and the contributions of different reproductive routes to these (co)variances, have not been comprehensively quantified in natural populations. We applied 'additive' and 'independent' methods of variance decomposition to complete data describing apparent (social) and realised (genetic) age-specific reproductive success across 11 cohorts of socially monogamous but genetically polygynandrous song sparrows (Melospiza melodia). We thereby quantified age-specific (co)variances in male within-pair and extra-pair reproductive success (WPRS and EPRS) and the contributions of these (co)variances to the total variances in age-specific reproductive success and LRS. 'Additive' decomposition showed that within-age and among-age (co)variances in WPRS across males aged 2-4 years contributed most to the total variance in LRS. Age-specific (co)variances in EPRS contributed relatively little. However, extra-pair reproduction altered age-specific variances in reproductive success relative to the social mating system, and hence altered the relative contributions of age-specific reproductive success to the total variance in LRS. 'Independent' decomposition showed that the (co)variances in age-specific WPRS, EPRS and total reproductive success, and the resulting opportunities for selection, varied substantially across males that survived to each age. Furthermore, extra-pair reproduction increased

Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania.

Science.gov (United States)

Mauya, Ernest William; Hansen, Endre Hofstad; Gobakken, Terje; Bollandsås, Ole Martin; Malimbwi, Rogers Ernest; Næsset, Erik

2015-12-01

Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework. The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R 2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m 2 . The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7. This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.

Nonlinear Epigenetic Variance: Review and Simulations

Science.gov (United States)

Kan, Kees-Jan; Ploeger, Annemie; Raijmakers, Maartje E. J.; Dolan, Conor V.; van Der Maas, Han L. J.

2010-01-01

We present a review of empirical evidence that suggests that a substantial portion of phenotypic variance is due to nonlinear (epigenetic) processes during ontogenesis. The role of such processes as a source of phenotypic variance in human behaviour genetic studies is not fully appreciated. In addition to our review, we present simulation studies…

Partitioning of the variance in the growth parameters of Erwinia carotovora on vegetable products.

Science.gov (United States)

Shorten, P R; Membré, J-M; Pleasants, A B; Kubaczka, M; Soboleva, T K

2004-06-01

The objective of this paper was to estimate and partition the variability in the microbial growth model parameters describing the growth of Erwinia carotovora on pasteurised and non-pasteurised vegetable juice from laboratory experiments performed under different temperature-varying conditions. We partitioned the model parameter variance and covariance components into effects due to temperature profile and replicate using a maximum likelihood technique. Temperature profile and replicate were treated as random effects and the food substrate was treated as a fixed effect. The replicate variance component was small indicating a high level of control in this experiment. Our analysis of the combined E. carotovora growth data sets used the Baranyi primary microbial growth model along with the Ratkowsky secondary growth model. The variability in the microbial growth parameters estimated from these microbial growth experiments is essential for predicting the mean and variance through time of the E. carotovora population size in a product supply chain and is the basis for microbiological risk assessment and food product shelf-life estimation. The variance partitioning made here also assists in the management of optimal product distribution networks by identifying elements of the supply chain contributing most to product variability. Copyright 2003 Elsevier B.V.

Revision: Variance Inflation in Regression

Directory of Open Access Journals (Sweden)

D. R. Jensen

2013-01-01

the intercept; and (iv variance deflation may occur, where ill-conditioned data yield smaller variances than their orthogonal surrogates. Conventional VIFs have all regressors linked, or none, often untenable in practice. Beyond these, our models enable the unlinking of regressors that can be unlinked, while preserving dependence among those intrinsically linked. Moreover, known collinearity indices are extended to encompass angles between subspaces of regressors. To reaccess ill-conditioned data, we consider case studies ranging from elementary examples to data from the literature.

A neural measure of behavioral engagement: task-residual low-frequency blood oxygenation level-dependent activity in the precuneus.

Science.gov (United States)

Zhang, Sheng; Li, Chiang-Shan Ray

2010-01-15

Brain imaging has provided a useful tool to examine the neural processes underlying human cognition. A critical question is whether and how task engagement influences the observed regional brain activations. Here we highlighted this issue and derived a neural measure of task engagement from the task-residual low-frequency blood oxygenation level-dependent (BOLD) activity in the precuneus. Using independent component analysis, we identified brain regions in the default circuit - including the precuneus and medial prefrontal cortex (mPFC) - showing greater activation during resting as compared to task residuals in 33 individuals. Time series correlations with the posterior cingulate cortex as the seed region showed that connectivity with the precuneus was significantly stronger during resting as compared to task residuals. We hypothesized that if the task-residual BOLD activity in the precuneus reflects engagement, it should account for a certain amount of variance in task-related regional brain activation. In an additional experiment of 59 individuals performing a stop signal task, we observed that the fractional amplitude of low-frequency fluctuation (fALFF) of the precuneus but not the mPFC accounted for approximately 10% of the variance in prefrontal activation related to attentional monitoring and response inhibition. Taken together, these results suggest that task-residual fALFF in the precuneus may be a potential indicator of task engagement. This measurement may serve as a useful covariate in identifying motivation-independent neural processes that underlie the pathogenesis of a psychiatric or neurological condition.

Characterization of residual stresses generated during inhomogeneous plastic deformation

DEFF Research Database (Denmark)

Lorentzen, T.; Faurholdt, T.; Clausen, B.

1998-01-01

Residual stresses generated by macroscopic inhomogeneous plastic deformation are predicted by an explicit finite element (FE) technique. The numerical predictions are evaluated by characterizing the residual elastic strains by neutron diffraction using two different (hkl) reflections. Intergranular...... compare well and verify the capability of the numerical technique as well as the possibilities of experimental validation using neutron diffraction. The presented experimental and numerical approach will subsequently be utilized for the evaluation of more complicated plastic deformation processes...

Residual tumor size and IGCCCG risk classification predict additional vascular procedures in patients with germ cell tumors and residual tumor resection: a multicenter analysis of the German Testicular Cancer Study Group.

Science.gov (United States)

Winter, Christian; Pfister, David; Busch, Jonas; Bingöl, Cigdem; Ranft, Ulrich; Schrader, Mark; Dieckmann, Klaus-Peter; Heidenreich, Axel; Albers, Peter

2012-02-01

Residual tumor resection (RTR) after chemotherapy in patients with advanced germ cell tumors (GCT) is an important part of the multimodal treatment. To provide a complete resection of residual tumor, additional surgical procedures are sometimes necessary. In particular, additional vascular interventions are high-risk procedures that require multidisciplinary planning and adequate resources to optimize outcome. The aim was to identify parameters that predict additional vascular procedures during RTR in GCT patients. A retrospective analysis was performed in 402 GCT patients who underwent 414 RTRs in 9 German Testicular Cancer Study Group (GTCSG) centers. Overall, 339 of 414 RTRs were evaluable with complete perioperative data sets. The RTR database was queried for additional vascular procedures (inferior vena cava [IVC] interventions, aortic prosthesis) and correlated to International Germ Cell Cancer Collaborative Group (IGCCCG) classification and residual tumor volume. In 40 RTRs, major vascular procedures (23 IVC resections with or without prosthesis, 11 partial IVC resections, and 6 aortic prostheses) were performed. In univariate analysis, the necessity of IVC intervention was significantly correlated with IGCCCG (14.1% intermediate/poor vs 4.8% good; p=0.0047) and residual tumor size (3.7% size risk features must initially be identified as high-risk patients for vascular procedures and therefore should be referred to specialized surgical centers with the ad hoc possibility of vascular interventions. Copyright © 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Variance estimation for generalized Cavalieri estimators

OpenAIRE

Johanna Ziegel; Eva B. Vedel Jensen; Karl-Anton Dorph-Petersen

2011-01-01

The precision of stereological estimators based on systematic sampling is of great practical importance. This paper presents methods of data-based variance estimation for generalized Cavalieri estimators where errors in sampling positions may occur. Variance estimators are derived under perturbed systematic sampling, systematic sampling with cumulative errors and systematic sampling with random dropouts. Copyright 2011, Oxford University Press.

Influence of Family Structure on Variance Decomposition

DEFF Research Database (Denmark)

Edwards, Stefan McKinnon; Sarup, Pernille Merete; Sørensen, Peter

Partitioning genetic variance by sets of randomly sampled genes for complex traits in D. melanogaster and B. taurus, has revealed that population structure can affect variance decomposition. In fruit flies, we found that a high likelihood ratio is correlated with a high proportion of explained ge...... capturing pure noise. Therefore it is necessary to use both criteria, high likelihood ratio in favor of a more complex genetic model and proportion of genetic variance explained, to identify biologically important gene groups...

Multiperiod Mean-Variance Portfolio Optimization via Market Cloning

International Nuclear Information System (INIS)

Ankirchner, Stefan; Dermoune, Azzouz

2011-01-01

The problem of finding the mean variance optimal portfolio in a multiperiod model can not be solved directly by means of dynamic programming. In order to find a solution we therefore first introduce independent market clones having the same distributional properties as the original market, and we replace the portfolio mean and variance by their empirical counterparts. We then use dynamic programming to derive portfolios maximizing a weighted sum of the empirical mean and variance. By letting the number of market clones converge to infinity we are able to solve the original mean variance problem.

Multiperiod Mean-Variance Portfolio Optimization via Market Cloning

Energy Technology Data Exchange (ETDEWEB)

Ankirchner, Stefan, E-mail: ankirchner@hcm.uni-bonn.de [Rheinische Friedrich-Wilhelms-Universitaet Bonn, Institut fuer Angewandte Mathematik, Hausdorff Center for Mathematics (Germany); Dermoune, Azzouz, E-mail: Azzouz.Dermoune@math.univ-lille1.fr [Universite des Sciences et Technologies de Lille, Laboratoire Paul Painleve UMR CNRS 8524 (France)

2011-08-15

The problem of finding the mean variance optimal portfolio in a multiperiod model can not be solved directly by means of dynamic programming. In order to find a solution we therefore first introduce independent market clones having the same distributional properties as the original market, and we replace the portfolio mean and variance by their empirical counterparts. We then use dynamic programming to derive portfolios maximizing a weighted sum of the empirical mean and variance. By letting the number of market clones converge to infinity we are able to solve the original mean variance problem.

Variance components estimation for farrowing traits of three purebred pigs in Korea

Directory of Open Access Journals (Sweden)

Bryan Irvine Lopez

2017-09-01

Full Text Available Objective This study was conducted to estimate breed-specific variance components for total number born (TNB, number born alive (NBA and mortality rate from birth through weaning including stillbirths (MORT of three main swine breeds in Korea. In addition, the importance of including maternal genetic and service sire effects in estimation models was evaluated. Methods Records of farrowing traits from 6,412 Duroc, 18,020 Landrace, and 54,254 Yorkshire sows collected from January 2001 to September 2016 from different farms in Korea were used in the analysis. Animal models and the restricted maximum likelihood method were used to estimate variances in animal genetic, permanent environmental, maternal genetic, service sire and residuals. Results The heritability estimates ranged from 0.072 to 0.102, 0.090 to 0.099, and 0.109 to 0.121 for TNB; 0.087 to 0.110, 0.088 to 0.100, and 0.099 to 0.107 for NBA; and 0.027 to 0.031, 0.050 to 0.053, and 0.073 to 0.081 for MORT in the Duroc, Landrace and Yorkshire breeds, respectively. The proportion of the total variation due to permanent environmental effects, maternal genetic effects, and service sire effects ranged from 0.042 to 0.088, 0.001 to 0.031, and 0.001 to 0.021, respectively. Spearman rank correlations among models ranged from 0.98 to 0.99, demonstrating that the maternal genetic and service sire effects have small effects on the precision of the breeding value. Conclusion Models that include additive genetic and permanent environmental effects are suitable for farrowing traits in Duroc, Landrace, and Yorkshire populations in Korea. This breed-specific variance components estimates for litter traits can be utilized for pig improvement programs in Korea.

Minimum Variance Portfolios in the Brazilian Equity Market

Directory of Open Access Journals (Sweden)

Alexandre Rubesam

2013-03-01

Full Text Available We investigate minimum variance portfolios in the Brazilian equity market using different methods to estimate the covariance matrix, from the simple model of using the sample covariance to multivariate GARCH models. We compare the performance of the minimum variance portfolios to those of the following benchmarks: (i the IBOVESPA equity index, (ii an equally-weighted portfolio, (iii the maximum Sharpe ratio portfolio and (iv the maximum growth portfolio. Our results show that the minimum variance portfolio has higher returns with lower risk compared to the benchmarks. We also consider long-short 130/30 minimum variance portfolios and obtain similar results. The minimum variance portfolio invests in relatively few stocks with low βs measured with respect to the IBOVESPA index, being easily replicable by individual and institutional investors alike.

Statistically generated weighted curve fit of residual functions for modal analysis of structures

Science.gov (United States)

Bookout, P. S.

1995-01-01

A statistically generated weighting function for a second-order polynomial curve fit of residual functions has been developed. The residual flexibility test method, from which a residual function is generated, is a procedure for modal testing large structures in an external constraint-free environment to measure the effects of higher order modes and interface stiffness. This test method is applicable to structures with distinct degree-of-freedom interfaces to other system components. A theoretical residual function in the displacement/force domain has the characteristics of a relatively flat line in the lower frequencies and a slight upward curvature in the higher frequency range. In the test residual function, the above-mentioned characteristics can be seen in the data, but due to the present limitations in the modal parameter evaluation (natural frequencies and mode shapes) of test data, the residual function has regions of ragged data. A second order polynomial curve fit is required to obtain the residual flexibility term. A weighting function of the data is generated by examining the variances between neighboring data points. From a weighted second-order polynomial curve fit, an accurate residual flexibility value can be obtained. The residual flexibility value and free-free modes from testing are used to improve a mathematical model of the structure. The residual flexibility modal test method is applied to a straight beam with a trunnion appendage and a space shuttle payload pallet simulator.

Surgical treatment for residual or recurrent strabismus

Directory of Open Access Journals (Sweden)

Tao Wang

2014-12-01

Full Text Available Although the surgical treatment is a relatively effective and predictable method for correcting residual or recurrent strabismus, such as posterior fixation sutures, medial rectus marginal myotomy, unilateral or bilateral rectus re-recession and resection, unilateral lateral rectus recession and adjustable suture, no standard protocol is established for the surgical style. Different surgical approaches have been recommended for correcting residual or recurrent strabismus. The choice of the surgical procedure depends on the former operation pattern and the surgical dosages applied on the patients, residual or recurrent angle of deviation and the operator''s preference and experience. This review attempts to outline recent publications and current opinion in the management of residual or recurrent esotropia and exotropia.

Simultaneous estimation of cross-validation errors in least squares collocation applied for statistical testing and evaluation of the noise variance components

Science.gov (United States)

Behnabian, Behzad; Mashhadi Hossainali, Masoud; Malekzadeh, Ahad

2018-02-01

The cross-validation technique is a popular method to assess and improve the quality of prediction by least squares collocation (LSC). We present a formula for direct estimation of the vector of cross-validation errors (CVEs) in LSC which is much faster than element-wise CVE computation. We show that a quadratic form of CVEs follows Chi-squared distribution. Furthermore, a posteriori noise variance factor is derived by the quadratic form of CVEs. In order to detect blunders in the observations, estimated standardized CVE is proposed as the test statistic which can be applied when noise variances are known or unknown. We use LSC together with the methods proposed in this research for interpolation of crustal subsidence in the northern coast of the Gulf of Mexico. The results show that after detection and removing outliers, the root mean square (RMS) of CVEs and estimated noise standard deviation are reduced about 51 and 59%, respectively. In addition, RMS of LSC prediction error at data points and RMS of estimated noise of observations are decreased by 39 and 67%, respectively. However, RMS of LSC prediction error on a regular grid of interpolation points covering the area is only reduced about 4% which is a consequence of sparse distribution of data points for this case study. The influence of gross errors on LSC prediction results is also investigated by lower cutoff CVEs. It is indicated that after elimination of outliers, RMS of this type of errors is also reduced by 19.5% for a 5 km radius of vicinity. We propose a method using standardized CVEs for classification of dataset into three groups with presumed different noise variances. The noise variance components for each of the groups are estimated using restricted maximum-likelihood method via Fisher scoring technique. Finally, LSC assessment measures were computed for the estimated heterogeneous noise variance model and compared with those of the homogeneous model. The advantage of the proposed method is the

Why risk is not variance: an expository note.

Science.gov (United States)

Cox, Louis Anthony Tony

2008-08-01

Variance (or standard deviation) of return is widely used as a measure of risk in financial investment risk analysis applications, where mean-variance analysis is applied to calculate efficient frontiers and undominated portfolios. Why, then, do health, safety, and environmental (HS&E) and reliability engineering risk analysts insist on defining risk more flexibly, as being determined by probabilities and consequences, rather than simply by variances? This note suggests an answer by providing a simple proof that mean-variance decision making violates the principle that a rational decisionmaker should prefer higher to lower probabilities of receiving a fixed gain, all else being equal. Indeed, simply hypothesizing a continuous increasing indifference curve for mean-variance combinations at the origin is enough to imply that a decisionmaker must find unacceptable some prospects that offer a positive probability of gain and zero probability of loss. Unlike some previous analyses of limitations of variance as a risk metric, this expository note uses only simple mathematics and does not require the additional framework of von Neumann Morgenstern utility theory.

Partial least squares modeling of combined infrared, 1H NMR and 13C NMR spectra to predict long residue properties of crude oils

NARCIS (Netherlands)

de Peinder, P.; Visser, T.; Petrauskas, D.D.; Salvatori, F.; Soulimani, F.; Weckhuysen, B.M.

2009-01-01

Research has been carried out to determine the potential of partial least squares (PLS) modeling of mid-infrared (IR) spectra of crude oils combined with the corresponding 1H and 13C nuclear magnetic resonance (NMR) data, to predict the long residue (LR) properties of these substances. The study

Variance bias analysis for the Gelbard's batch method

Energy Technology Data Exchange (ETDEWEB)

Seo, Jae Uk; Shim, Hyung Jin [Seoul National Univ., Seoul (Korea, Republic of)

2014-05-15

In this paper, variances and the bias will be derived analytically when the Gelbard's batch method is applied. And then, the real variance estimated from this bias will be compared with the real variance calculated from replicas. Variance and the bias were derived analytically when the batch method was applied. If the batch method was applied to calculate the sample variance, covariance terms between tallies which exist in the batch were eliminated from the bias. With the 2 by 2 fission matrix problem, we could calculate real variance regardless of whether or not the batch method was applied. However as batch size got larger, standard deviation of real variance was increased. When we perform a Monte Carlo estimation, we could get a sample variance as the statistical uncertainty of it. However, this value is smaller than the real variance of it because a sample variance is biased. To reduce this bias, Gelbard devised the method which is called the Gelbard's batch method. It has been certificated that a sample variance get closer to the real variance when the batch method is applied. In other words, the bias get reduced. This fact is well known to everyone in the MC field. However, so far, no one has given the analytical interpretation on it.

Modelling of the Residual Stress State in a new Type of Residual Stress Specimen

DEFF Research Database (Denmark)

Jakobsen, Johnny; Andreasen, Jens Henrik

2014-01-01

forms the experimental case which is analysed. A FE model of the specimen is used for analysing the curing history and the residual stress build up. The model is validated against experimental strain data which are recorded by a Fibre Brag Grating sensor and good agreement has been achieved.......The paper presents a study on a new type residual stress specimen which is proposed as a simple way to conduct experimental validation for model predictions. A specimen comprising of a steel plate with circular hole embedded into a stack of CSM glass fibre and further infused with an epoxy resin...

Model determination in a case of heterogeneity of variance using sampling techniques.

Science.gov (United States)

Varona, L; Moreno, C; Garcia-Cortes, L A; Altarriba, J

1997-01-12

A sampling determination procedure has been described in a case of heterogeneity of variance. The procedure makes use of the predictive distributions of each data given the rest of the data and the structure of the assumed model. The computation of these predictive distributions is carried out using a Gibbs Sampling procedure. The final criterion to compare between models is the Mean Square Error between the expectation of predictive distributions and real data. The procedure has been applied to a data set of weight at 210 days in the Spanish Pirenaica beef cattle breed. Three proposed models have been compared: (a) Single Trait Animal Model; (b) Heterogeneous Variance Animal Model; and (c) Multiple Trait Animal Model. After applying the procedure, the most adjusted model was the Heterogeneous Variance Animal Model. This result is probably due to a compromise between the complexity of the model and the amount of available information. The estimated heritabilities under the preferred model have been 0.489 ± 0.076 for males and 0.331 ± 0.082 for females. RESUMEN: Contraste de modelos en un caso de heterogeneidad de varianzas usando métodos de muestreo Se ha descrito un método de contraste de modelos mediante técnicas de muestreo en un caso de heterogeneidad de varianza entre sexos. El procedimiento utiliza las distribucviones predictivas de cada dato, dado el resto de datos y la estructura del modelo. El criterio para coparar modelos es el error cuadrático medio entre la esperanza de las distribuciones predictivas y los datos reales. El procedimiento se ha aplicado en datos de peso a los 210 días en la raza bovina Pirenaica. Se han propuesto tres posibles modelos: (a) Modelo Animal Unicaracter; (b) Modelo Animal con Varianzas Heterogéneas; (c) Modelo Animal Multicaracter. El modelo mejor ajustado fue el Modelo Animal con Varianzas Heterogéneas. Este resultado es probablemente debido a un compromiso entre la complejidad del modelo y la cantidad de datos

Prediction of long-residue properties of potential blends from mathematically mixed infrared spectra of pure crude oils by partial least-squares regression models

NARCIS (Netherlands)

de Peinder, P.; Visser, T.; Petrauskas, D.D.; Salvatori, F.; Soulimani, F.; Weckhuysen, B.M.

2009-01-01

Research has been carried out to determine the feasibility of partial least-squares (PLS) regression models to predict the long-residue (LR) properties of potential blends from infrared (IR) spectra that have been created by linearly co-adding the IR spectra of crude oils. The study is the follow-up

Integrating Variances into an Analytical Database

Science.gov (United States)

Sanchez, Carlos

2010-01-01

For this project, I enrolled in numerous SATERN courses that taught the basics of database programming. These include: Basic Access 2007 Forms, Introduction to Database Systems, Overview of Database Design, and others. My main job was to create an analytical database that can handle many stored forms and make it easy to interpret and organize. Additionally, I helped improve an existing database and populate it with information. These databases were designed to be used with data from Safety Variances and DCR forms. The research consisted of analyzing the database and comparing the data to find out which entries were repeated the most. If an entry happened to be repeated several times in the database, that would mean that the rule or requirement targeted by that variance has been bypassed many times already and so the requirement may not really be needed, but rather should be changed to allow the variance's conditions permanently. This project did not only restrict itself to the design and development of the database system, but also worked on exporting the data from the database to a different format (e.g. Excel or Word) so it could be analyzed in a simpler fashion. Thanks to the change in format, the data was organized in a spreadsheet that made it possible to sort the data by categories or types and helped speed up searches. Once my work with the database was done, the records of variances could be arranged so that they were displayed in numerical order, or one could search for a specific document targeted by the variances and restrict the search to only include variances that modified a specific requirement. A great part that contributed to my learning was SATERN, NASA's resource for education. Thanks to the SATERN online courses I took over the summer, I was able to learn many new things about computers and databases and also go more in depth into topics I already knew about.

Optimal design criteria - prediction vs. parameter estimation

Science.gov (United States)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

Regional sensitivity analysis using revised mean and variance ratio functions

International Nuclear Information System (INIS)

Wei, Pengfei; Lu, Zhenzhou; Ruan, Wenbin; Song, Jingwen

2014-01-01

The variance ratio function, derived from the contribution to sample variance (CSV) plot, is a regional sensitivity index for studying how much the output deviates from the original mean of model output when the distribution range of one input is reduced and to measure the contribution of different distribution ranges of each input to the variance of model output. In this paper, the revised mean and variance ratio functions are developed for quantifying the actual change of the model output mean and variance, respectively, when one reduces the range of one input. The connection between the revised variance ratio function and the original one is derived and discussed. It is shown that compared with the classical variance ratio function, the revised one is more suitable to the evaluation of model output variance due to reduced ranges of model inputs. A Monte Carlo procedure, which needs only a set of samples for implementing it, is developed for efficiently computing the revised mean and variance ratio functions. The revised mean and variance ratio functions are compared with the classical ones by using the Ishigami function. At last, they are applied to a planar 10-bar structure

A logging residue "yield" table for Appalachian hardwoods

Science.gov (United States)

A. Jeff Martin

1976-01-01

An equation for predicting logging-residue volume per acre for Appalachian hardwoods was developed from data collected on 20 timber sales in national forests in West Virginia and Virginia. The independent variables of type-of-cut, products removed, basal area per acre, and stand age explained 95 percent of the variation in residue volume per acre. A "yield"...

Residual stresses due to weld repairs, cladding and electron beam welds and effect of residual stresses on fracture behavior. Annual report, September 1, 1977--November 30, 1978

International Nuclear Information System (INIS)

Rybicki, E.F.

1978-11-01

The study is divided into three tasks. Task I is concerned with predicting and understanding the effects of residual stresses due to weld repairs of pressure vessels. Task II examines residual stresses due to an electron beam weld. Task III addresses the problem of residual stresses produced by weld cladding at a nozzle vessel intersection. The objective of Task I is to develop a computational model for predicting residual stress states due to a weld repair of pressure vessel and thereby gain an understanding of the mechanisms involved in the creation of the residual stresses. Experimental data from the Heavy Section Steel Technology (HSST) program at Oak Ridge National Laboratories (ORNL) is used to validate the computational model. In Task II, the residual stress model is applied to the case of an electron beam weld of a compact tension freacture specimen. The results in the form of residual stresses near the weld are then used to explain unexpected fracture behavior which is observed in the testing of the specimen. For Task III, the residual stress model is applied to the cladding process used in nozzle regions of nuclear pressure vessels. The residual stresses obtained from this analysis are evaluated to determine their effect on the phenomena of under-clad cracking

Pharmacokinetics, efficacy prediction indexes and residue depletion of antibacterial drugs.

Directory of Open Access Journals (Sweden)

Arturo Anadón

2016-06-01

Full Text Available Pharmacokinetics behaviour of the antibacterial in food producing animals, provides information on the rates of absorption and elimination, half-life in plasma and tissue, elimination pathways and metabolism. The dose and the dosing interval of the antimicrobial can be justified by considering the pharmacokinetic/pharmacodynamic (PK/PD relationship, if established, as well as the severity of the disease, whereas the number of administrations should be in line with the nature of the disease. The target population for therapy should be well defined and possible to identify under field conditions. Based on in vitro susceptibility data, and target animal PK data, an analysis for the PK/PD relationship may be used to support dose regimen selection and interpretation criteria for a clinical breakpoint. Therefore, for all antibacterials with systemic activity, the MIC data collected should be compared with the concentration of the compound at the relevant biophase following administration at the assumed therapeutic dose as recorded in the pharmacokinetic studies. Currently, the most frequently used parameters to express the PK/PD relationship are Cmax/MIC (maximum serum concentration/MIC, %T > MIC (fraction of time in which concentration exceeds MIC and AUC/MIC (area under the inhibitory concentration– time curve/MIC. Furthermore, the pharmacokinetic parameters provide the first indication of the potential for persistent residues and the tissues in which they may occur. The information on residue depletion in food-producing animals, provides the data on which MRL recommendations will be based. A critical factor in the antibacterial medication of all food-producing animals is the mandatory withdrawal period, defined as the time during which drug must not be administered prior to the slaughter of the animal for consumption. The withdrawal period is an integral part of the regulatory authorities’ approval process and is designed to ensure that no

Prediction of residual stresses in the heat affected zone

International Nuclear Information System (INIS)

Taleb, L.; Petit, S.; Jullien, J.F.

2004-01-01

In this paper the behavior of a disc made up of carbon manganese steel and subjected to an axisymmetric heating in its middle zone is considered. The applied thermal cycle generates localized metallurgical solid-solid phase transformations. Contrary to the study performed some years ago, the present work is concerned with relatively thick discs that lead to variable behavior according to axial direction. Experimentally, temperature and axial displacement of the face below have continuously been measured during tests. At the end of tests, the nature and the proportions of the final phases as well as residual stresses on both faces of the discs has also been assessed. These experimental results have been compared to numerical simulations using the finite element code ASTER, developed by EDF (Electricity of France), ASTER enables us to take into account the main mechanical consequences of phase transformations. From the obtained results it can be pointed out the significant importance to take into account the transformation induced plasticity (TRIP) phenomenon for better estimation of residual stresses. (authors)

Mixed model with spatial variance-covariance structure for accommodating of local stationary trend and its influence on multi-environmental crop variety trial assessment

Energy Technology Data Exchange (ETDEWEB)

Negash, A. W.; Mwambi, H.; Zewotir, T.; Eweke, G.

2014-06-01

The most common procedure for analyzing multi-environmental trials is based on the assumption that the residual error variance is homogenous across all locations considered. However, this may often be unrealistic, and therefore limit the accuracy of variety evaluation or the reliability of variety recommendations. The objectives of this study were to show the advantages of mixed models with spatial variance-covariance structures, and direct implications of model choice on the inference of varietal performance, ranking and testing based on two multi-environmental data sets from realistic national trials. A model comparison with a {chi}{sup 2}-test for the trials in the two data sets (wheat data set BW00RVTI and barley data set BW01RVII) suggested that selected spatial variance-covariance structures fitted the data significantly better than the ANOVA model. The forms of optimally-fitted spatial variance-covariance, ranking and consistency ratio test were not the same from one trial (location) to the other. Linear mixed models with single stage analysis including spatial variance-covariance structure with a group factor of location on the random model also improved the real estimation of genotype effect and their ranking. The model also improved varietal performance estimation because of its capacity to handle additional sources of variation, location and genotype by location (environment) interaction variation and accommodating of local stationary trend. (Author)

The genotype-environment interaction variance in rice-seed protein determination

International Nuclear Information System (INIS)

Ismachin, M.

1976-01-01

Many environmental factors influence the protein content of cereal seed. This fact procured difficulties in breeding for protein. Yield is another example on which so many environmental factors are of influence. The length of time required by the plant to reach maturity, is also affected by the environmental factors; even though its effect is not too decisive. In this investigation the genotypic variance and the genotype-environment interaction variance which contribute to the total variance or phenotypic variance was analysed, with purpose to give an idea to the breeder how selection should be made. It was found that genotype-environment interaction variance is larger than the genotypic variance in contribution to total variance of protein-seed determination or yield. In the analysis of the time required to reach maturity it was found that genotypic variance is larger than the genotype-environment interaction variance. It is therefore clear, why selection for time required to reach maturity is much easier than selection for protein or yield. Selected protein in one location may be different from that to other locations. (author)

Parsimony in personality: predicting sexual prejudice.

Science.gov (United States)

Miller, Audrey K; Wagner, Maverick M; Hunt, Amy N

2012-01-01

Extant research has established numerous demographic, personal-history, attitudinal, and ideological correlates of sexual prejudice, also known as homophobia. The present study investigated whether Five-Factor Model (FFM) personality domains, particularly Openness, and FFM facets, particularly Openness to Values, contribute independent and incremental variance to the prediction of sexual prejudice beyond these established correlates. Participants were 117 college students who completed a comprehensive FFM measure, measures of sexual prejudice, and a demographics, personal-history, and attitudes-and-ideologies questionnaire. Results of stepwise multiple regression analyses demonstrated that, whereas Openness domain score predicted only marginal incremental variance in sexual prejudice, Openness facet scores (particularly Openness to Values) predicted independent and substantial incremental variance beyond numerous other zero-order correlates of sexual prejudice. The importance of integrating FFM personality variables, especially facet-level variables, into conceptualizations of sexual prejudice is highlighted. Study strengths and weaknesses are discussed as are potential implications for prejudice-reduction interventions.

Estimation of measurement variances

International Nuclear Information System (INIS)

Jaech, J.L.

1984-01-01

The estimation of measurement error parameters in safeguards systems is discussed. Both systematic and random errors are considered. A simple analysis of variances to characterize the measurement error structure with biases varying over time is presented

Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model

Science.gov (United States)

Wadoux, Alexandre M. J.-C.; Brus, Dick J.; Rico-Ramirez, Miguel A.; Heuvelink, Gerard B. M.

2017-09-01

The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform.

Thermo-mechanical characterization of a thermoplastic composite and prediction of the residual stresses and lamina curvature during cooling

Science.gov (United States)

Péron, Mael; Jacquemin, Frédéric; Casari, Pascal; Orange, Gilles; Bailleul, Jean-Luc; Boyard, Nicolas

2017-10-01

The prediction of process induced stresses during the cooling of thermoplastic composites still represents a challenge for the scientific community. However, a precise determination of these stresses is necessary in order to optimize the process conditions and thus lower the stresses effects on the final part health. A model is presented here, that permits the estimation of residual stresses during cooling. It relies on the nonlinear laminate theory, which has been adapted to arbitrary layup sequences. The developed model takes into account the heat transfers through the thickness of the laminate, together with the crystallization kinetics. The development of the composite mechanical properties during cooling is addressed by an incremental linear elastic constitutive law, which also considers thermal and crystallization strains. In order to feed the aforementioned model, a glass fiber and PA6.6 matrix unidirectional (UD) composite has been characterized. This work finally focuses on the identification of the material and process related parameters that lower the residual stresses level, including the ply sequence, the fiber volume fraction and the cooling rate.

29 CFR 1905.5 - Effect of variances.

Science.gov (United States)

2010-07-01

...-STEIGER OCCUPATIONAL SAFETY AND HEALTH ACT OF 1970 General § 1905.5 Effect of variances. All variances... Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR... concerning a proposed penalty or period of abatement is pending before the Occupational Safety and Health...

Realized range-based estimation of integrated variance

DEFF Research Database (Denmark)

Christensen, Kim; Podolskij, Mark

2007-01-01

We provide a set of probabilistic laws for estimating the quadratic variation of continuous semimartingales with the realized range-based variance-a statistic that replaces every squared return of the realized variance with a normalized squared range. If the entire sample path of the process is a...

Prediction of retained residual stresses in laboratory fracture mechanics specimens extracted from welded components

International Nuclear Information System (INIS)

Hurlston, R.G.; Sherry, A.H.; James, P.; Sharples, J.K.

2015-01-01

The measurement of weld material fracture toughness properties is important for the structural integrity assessment of engineering components. However, welds can contain high levels of residual stress and these can be retained in fracture mechanics specimens, particularly when machined from non-stress relieved welds. Retained residual stresses can make the measurement of valid fracture toughness properties difficult. This paper describes the results of analytical work undertaken to investigate factors that can influence the magnitude and distribution of residual stresses retained in fracture mechanics specimen blanks extracted from as-welded ferritic and austenitic stainless steel plates. The results indicate that significant levels of residual stress can be retained in specimen blanks prior to notching, and that the magnitude and distribution of stress is dependent upon material properties, specimen geometry and size, and extraction location through the thickness of the weld. Finite element modelling is shown to provide a useful approach for estimating the level and distributions of retained residual stresses. A new stress partitioning approach has been developed to estimate retained stress levels and results compare favourably with FE analysis and available experimental data. The approach can help guide the selection of specimen geometry and machining strategies to minimise the level of residual stresses retained in fracture mechanics specimen blanks extracted from non stress-relieved welds and thus improve the measurement of weld fracture toughness properties. - Highlights: • A simplified method for generating realistic weld residual stresses has been developed. • It has been shown that significant levels of residual stress can be retained within laboratory fracture mechanics specimens. • The level and distribution is dependant upon material, specimen type, specimen size and extraction location. • A method has been developed to allow estimates of the

Variance Function Partially Linear Single-Index Models1.

Science.gov (United States)

Lian, Heng; Liang, Hua; Carroll, Raymond J

2015-01-01

We consider heteroscedastic regression models where the mean function is a partially linear single index model and the variance function depends upon a generalized partially linear single index model. We do not insist that the variance function depend only upon the mean function, as happens in the classical generalized partially linear single index model. We develop efficient and practical estimation methods for the variance function and for the mean function. Asymptotic theory for the parametric and nonparametric parts of the model is developed. Simulations illustrate the results. An empirical example involving ozone levels is used to further illustrate the results, and is shown to be a case where the variance function does not depend upon the mean function.

RBscore&NBench: a high-level web server for nucleic acid binding residues prediction with a large-scale benchmarking database.

Science.gov (United States)

Miao, Zhichao; Westhof, Eric

2016-07-08

RBscore&NBench combines a web server, RBscore and a database, NBench. RBscore predicts RNA-/DNA-binding residues in proteins and visualizes the prediction scores and features on protein structures. The scoring scheme of RBscore directly links feature values to nucleic acid binding probabilities and illustrates the nucleic acid binding energy funnel on the protein surface. To avoid dataset, binding site definition and assessment metric biases, we compared RBscore with 18 web servers and 3 stand-alone programs on 41 datasets, which demonstrated the high and stable accuracy of RBscore. A comprehensive comparison led us to develop a benchmark database named NBench. The web server is available on: http://ahsoka.u-strasbg.fr/rbscorenbench/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A proposal of parameter determination method in the residual strength degradation model for the prediction of fatigue life (I)

International Nuclear Information System (INIS)

Kim, Sang Tae; Jang, Seong Soo

2001-01-01

The static and fatigue tests have been carried out to verify the validity of a generalized residual strength degradation model. And a new method of parameter determination in the model is verified experimentally to account for the effect of tension-compression fatigue loading of spheroidal graphite cast iron. It is shown that the correlation between the experimental results and the theoretical prediction on the statistical distribution of fatigue life by using the proposed method is very reasonable. Furthermore, it is found that the correlation between the theoretical prediction and the experimental results of fatigue life in case of tension-tension fatigue data in composite material appears to be reasonable. Therefore, the proposed method is more adjustable in the determination of the parameter than maximum likelihood method and minimization technique

Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs.

Science.gov (United States)

Lado, Bettina; Battenfield, Sarah; Guzmán, Carlos; Quincke, Martín; Singh, Ravi P; Dreisigacker, Susanne; Peña, R Javier; Fritz, Allan; Silva, Paula; Poland, Jesse; Gutiérrez, Lucía

2017-07-01

The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (V) or assuming linkage equilibrium (V). After predicting the mean and the variance of each cross, we selected crosses based on mid-parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat ( L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses. Copyright © 2017 Crop Science Society of America.

Sources of variance in BC mass measurements from a small marine engine: Influence of the instruments, fuels and loads

Science.gov (United States)

Jiang, Yu; Yang, Jiacheng; Gagné, Stéphanie; Chan, Tak W.; Thomson, Kevin; Fofie, Emmanuel; Cary, Robert A.; Rutherford, Dan; Comer, Bryan; Swanson, Jacob; Lin, Yue; Van Rooy, Paul; Asa-Awuku, Akua; Jung, Heejung; Barsanti, Kelley; Karavalakis, Georgios; Cocker, David; Durbin, Thomas D.; Miller, J. Wayne; Johnson, Kent C.

2018-06-01

Knowledge of black carbon (BC) emission factors from ships is important from human health and environmental perspectives. A study of instruments measuring BC and fuels typically used in marine operation was carried out on a small marine engine. Six analytical methods measured the BC emissions in the exhaust of the marine engine operated at two load points (25% and 75%) while burning one of three fuels: a distillate marine (DMA), a low sulfur, residual marine (RMB-30) and a high-sulfur residual marine (RMG-380). The average emission factors with all instruments increased from 0.08 to 1.88 gBC/kg fuel in going from 25 to 75% load. An analysis of variance (ANOVA) tested BC emissions against instrument, load, and combined fuel properties and showed that both engine load and fuels had a statistically significant impact on BC emission factors. While BC emissions were impacted by the fuels used, none of the fuel properties investigated (sulfur content, viscosity, carbon residue and CCAI) was a primary driver for BC emissions. Of the two residual fuels, RMB-30 with the lower sulfur content, lower viscosity and lower residual carbon, had the highest BC emission factors. BC emission factors determined with the different instruments showed a good correlation with the PAS values with correlation coefficients R2 >0.95. A key finding of this research is the relative BC measured values were mostly independent of load and fuel, except for some instruments in certain fuel and load combinations.

Discrete time and continuous time dynamic mean-variance analysis

OpenAIRE

Reiss, Ariane

1999-01-01

Contrary to static mean-variance analysis, very few papers have dealt with dynamic mean-variance analysis. Here, the mean-variance efficient self-financing portfolio strategy is derived for n risky assets in discrete and continuous time. In the discrete setting, the resulting portfolio is mean-variance efficient in a dynamic sense. It is shown that the optimal strategy for n risky assets may be dominated if the expected terminal wealth is constrained to exactly attain a certain goal instead o...

Dominance genetic variance for traits under directional selection in Drosophila serrata.

Science.gov (United States)

Sztepanacz, Jacqueline L; Blows, Mark W

2015-05-01

In contrast to our growing understanding of patterns of additive genetic variance in single- and multi-trait combinations, the relative contribution of nonadditive genetic variance, particularly dominance variance, to multivariate phenotypes is largely unknown. While mechanisms for the evolution of dominance genetic variance have been, and to some degree remain, subject to debate, the pervasiveness of dominance is widely recognized and may play a key role in several evolutionary processes. Theoretical and empirical evidence suggests that the contribution of dominance variance to phenotypic variance may increase with the correlation between a trait and fitness; however, direct tests of this hypothesis are few. Using a multigenerational breeding design in an unmanipulated population of Drosophila serrata, we estimated additive and dominance genetic covariance matrices for multivariate wing-shape phenotypes, together with a comprehensive measure of fitness, to determine whether there is an association between directional selection and dominance variance. Fitness, a trait unequivocally under directional selection, had no detectable additive genetic variance, but significant dominance genetic variance contributing 32% of the phenotypic variance. For single and multivariate morphological traits, however, no relationship was observed between trait-fitness correlations and dominance variance. A similar proportion of additive and dominance variance was found to contribute to phenotypic variance for single traits, and double the amount of additive compared to dominance variance was found for the multivariate trait combination under directional selection. These data suggest that for many fitness components a positive association between directional selection and dominance genetic variance may not be expected. Copyright © 2015 by the Genetics Society of America.

Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction

Directory of Open Access Journals (Sweden)

Ling Huang

2017-02-01

Full Text Available Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 1016 electrons/m2 with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the

Expected Stock Returns and Variance Risk Premia

DEFF Research Database (Denmark)

Bollerslev, Tim; Zhou, Hao

risk premium with the P/E ratio results in an R2 for the quarterly returns of more than twenty-five percent. The results depend crucially on the use of "model-free", as opposed to standard Black-Scholes, implied variances, and realized variances constructed from high-frequency intraday, as opposed...

Allowable variance set on left ventricular function parameter

International Nuclear Information System (INIS)

Zhou Li'na; Qi Zhongzhi; Zeng Yu; Ou Xiaohong; Li Lin

2010-01-01

Purpose: To evaluate the influence of allowable Variance settings on left ventricular function parameter of the arrhythmia patients during gated myocardial perfusion imaging. Method: 42 patients with evident arrhythmia underwent myocardial perfusion SPECT, 3 different allowable variance with 20%, 60%, 100% would be set before acquisition for every patients,and they will be acquired simultaneously. After reconstruction by Astonish, end-diastole volume(EDV) and end-systolic volume (ESV) and left ventricular ejection fraction (LVEF) would be computed with Quantitative Gated SPECT(QGS). Using SPSS software EDV, ESV, EF values of analysis of variance. Result: there is no statistical difference between three groups. Conclusion: arrhythmia patients undergo Gated myocardial perfusion imaging, Allowable Variance settings on EDV, ESV, EF value does not have a statistical meaning. (authors)

Deviation of the Variances of Classical Estimators and Negative Integer Moment Estimator from Minimum Variance Bound with Reference to Maxwell Distribution

Directory of Open Access Journals (Sweden)

G. R. Pasha

2006-07-01

Full Text Available In this paper, we present that how much the variances of the classical estimators, namely, maximum likelihood estimator and moment estimator deviate from the minimum variance bound while estimating for the Maxwell distribution. We also sketch this difference for the negative integer moment estimator. We note the poor performance of the negative integer moment estimator in the said consideration while maximum likelihood estimator attains minimum variance bound and becomes an attractive choice.

SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids.

Science.gov (United States)

López, Yosvany; Dehzangi, Abdollah; Lal, Sunil Pranit; Taherzadeh, Ghazaleh; Michaelson, Jacob; Sattar, Abdul; Tsunoda, Tatsuhiko; Sharma, Alok

2017-06-15

Post-Translational Modification (PTM) is a biological reaction which contributes to diversify the proteome. Despite many modifications with important roles in cellular activity, lysine succinylation has recently emerged as an important PTM mark. It alters the chemical structure of lysines, leading to remarkable changes in the structure and function of proteins. In contrast to the huge amount of proteins being sequenced in the post-genome era, the experimental detection of succinylated residues remains expensive, inefficient and time-consuming. Therefore, the development of computational tools for accurately predicting succinylated lysines is an urgent necessity. To date, several approaches have been proposed but their sensitivity has been reportedly poor. In this paper, we propose an approach that utilizes structural features of amino acids to improve lysine succinylation prediction. Succinylated and non-succinylated lysines were first retrieved from 670 proteins and characteristics such as accessible surface area, backbone torsion angles and local structure conformations were incorporated. We used the k-nearest neighbors cleaning treatment for dealing with class imbalance and designed a pruned decision tree for classification. Our predictor, referred to as SucStruct (Succinylation using Structural features), proved to significantly improve performance when compared to previous predictors, with sensitivity, accuracy and Mathew's correlation coefficient equal to 0.7334-0.7946, 0.7444-0.7608 and 0.4884-0.5240, respectively. Copyright © 2017 Elsevier Inc. All rights reserved.

Towards a mathematical foundation of minimum-variance theory

Energy Technology Data Exchange (ETDEWEB)

Feng Jianfeng [COGS, Sussex University, Brighton (United Kingdom); Zhang Kewei [SMS, Sussex University, Brighton (United Kingdom); Wei Gang [Mathematical Department, Baptist University, Hong Kong (China)

2002-08-30

The minimum-variance theory which accounts for arm and eye movements with noise signal inputs was proposed by Harris and Wolpert (1998 Nature 394 780-4). Here we present a detailed theoretical analysis of the theory and analytical solutions of the theory are obtained. Furthermore, we propose a new version of the minimum-variance theory, which is more realistic for a biological system. For the new version we show numerically that the variance is considerably reduced. (author)

Quantitative genetic variance and multivariate clines in the Ivyleaf morning glory, Ipomoea hederacea.

Science.gov (United States)

Stock, Amanda J; Campitelli, Brandon E; Stinchcombe, John R

2014-08-19

Clinal variation is commonly interpreted as evidence of adaptive differentiation, although clines can also be produced by stochastic forces. Understanding whether clines are adaptive therefore requires comparing clinal variation to background patterns of genetic differentiation at presumably neutral markers. Although this approach has frequently been applied to single traits at a time, we have comparatively fewer examples of how multiple correlated traits vary clinally. Here, we characterize multivariate clines in the Ivyleaf morning glory, examining how suites of traits vary with latitude, with the goal of testing for divergence in trait means that would indicate past evolutionary responses. We couple this with analysis of genetic variance in clinally varying traits in 20 populations to test whether past evolutionary responses have depleted genetic variance, or whether genetic variance declines approaching the range margin. We find evidence of clinal differentiation in five quantitative traits, with little evidence of isolation by distance at neutral loci that would suggest non-adaptive or stochastic mechanisms. Within and across populations, the traits that contribute most to population differentiation and clinal trends in the multivariate phenotype are genetically variable as well, suggesting that a lack of genetic variance will not cause absolute evolutionary constraints. Our data are broadly consistent theoretical predictions of polygenic clines in response to shallow environmental gradients. Ecologically, our results are consistent with past findings of natural selection on flowering phenology, presumably due to season-length variation across the range. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Dynamical Predictability of Monthly Means.

Science.gov (United States)

Shukla, J.

1981-12-01

We have attempted to determine the theoretical upper limit of dynamical predictability of monthly means for prescribed nonfluctuating external forcings. We have extended the concept of `classical' predictability, which primarily refers to the lack of predictability due mainly to the instabilities of synoptic-scale disturbances, to the predictability of time averages, which are determined by the predictability of low-frequency planetary waves. We have carded out 60-day integrations of a global general circulation model with nine different initial conditions but identical boundary conditions of sea surface temperature, snow, sea ice and soil moisture. Three of these initial conditions are the observed atmospheric conditions on 1 January of 1975, 1976 and 1977. The other six initial conditions are obtained by superimposing over the observed initial conditions a random perturbation comparable to the errors of observation. The root-mean-square (rms) error of random perturbations at all the grid points and all the model levels is 3 m s1 in u and v components of wind. The rms vector wind error between the observed initial conditions is >15 m s1.It is hypothesized that for a given averaging period, if the rms error among the time averages predicted from largely different initial conditions becomes comparable to the rms error among the time averages predicted from randomly perturbed initial conditions, the time averages are dynamically unpredictable. We have carried out the analysis of variance to compare the variability, among the three groups, due to largely different initial conditions, and within each group due to random perturbations.It is found that the variances among the first 30-day means, predicted from largely different initial conditions, are significantly different from the variances due to random perturbations in the initial conditions, whereas the variances among 30-day means for days 31-60 are not distinguishable from the variances due to random initial

Direct encoding of orientation variance in the visual system.

Science.gov (United States)

Norman, Liam J; Heywood, Charles A; Kentridge, Robert W

2015-01-01

Our perception of regional irregularity, an example of which is orientation variance, seems effortless when we view two patches of texture that differ in this attribute. Little is understood, however, of how the visual system encodes a regional statistic like orientation variance, but there is some evidence to suggest that it is directly encoded by populations of neurons tuned broadly to high or low levels. The present study shows that selective adaptation to low or high levels of variance results in a perceptual aftereffect that shifts the perceived level of variance of a subsequently viewed texture in the direction away from that of the adapting stimulus (Experiments 1 and 2). Importantly, the effect is durable across changes in mean orientation, suggesting that the encoding of orientation variance is independent of global first moment orientation statistics (i.e., mean orientation). In Experiment 3 it was shown that the variance-specific aftereffect did not show signs of being encoded in a spatiotopic reference frame, similar to the equivalent aftereffect of adaptation to the first moment orientation statistic (the tilt aftereffect), which is represented in the primary visual cortex and exists only in retinotopic coordinates. Experiment 4 shows that a neuropsychological patient with damage to ventral areas of the cortex but spared intact early areas retains sensitivity to orientation variance. Together these results suggest that orientation variance is encoded directly by the visual system and possibly at an early cortical stage.

Variance-reduction technique for Coulomb-nuclear thermalization of energetic fusion products in hot plasmas

International Nuclear Information System (INIS)

DeVeaux, J.C.; Miley, G.H.

1982-01-01

A variance-reduction technique involving use of exponential transform and angular-biasing methods has been developed. Its purpose is to minimize the variance and computer time involved in estimating the mean fusion product (fp) energy deposited in a hot, multi-region plasma under the influence of small-energy transfer Coulomb collisions and large-energy transfer nuclear elastic scattering (NES) events. This technique is applicable to high-temperature D- 3 He, Cat. D and D-T plasmas which have highly energetic fps capable of undergoing NES. A first application of this technique is made to a D- 3 He Field Reversed Mirror (FRM) where the Larmor radius of the 14.7 MeV protons are typically comparable to the plasma radius (plasma radius approx. 2 fp gyroradii) and the optimistic fp confinement (approx. 45% of 14.7 MeV protons) previously predicted is vulnerable to large orbit perturbations induced by NES. In the FRM problem, this variance reduction technique is used to estimate the fractional difference in the average fp energy deposited in the closed-field region, E/sub cf/, with and without NES collisions

Network Structure and Biased Variance Estimation in Respondent Driven Sampling.

Science.gov (United States)

Verdery, Ashton M; Mouw, Ted; Bauldry, Shawn; Mucha, Peter J

2015-01-01

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.

Local variances in biomonitoring

International Nuclear Information System (INIS)

Wolterbeek, H.Th; Verburg, T.G.

2001-01-01

The present study was undertaken to explore possibilities to judge survey quality on basis of a limited and restricted number of a-priori observations. Here, quality is defined as the ratio between survey and local variance (signal-to-noise ratio). The results indicate that the presented surveys do not permit such judgement; the discussion also suggests that the 5-fold local sampling strategies do not merit any sound judgement. As it stands, uncertainties in local determinations may largely obscure possibilities to judge survey quality. The results further imply that surveys will benefit from procedures, controls and approaches in sampling and sample handling, to assess both average, variance and the nature of the distribution of elemental concentrations in local sites. This reasoning is compatible with the idea of the site as a basic homogeneous survey unit, which is implicitly and conceptually underlying any survey performed. (author)

Application of Higher Order Fission Matrix for Real Variance Estimation in McCARD Monte Carlo Eigenvalue Calculation

Energy Technology Data Exchange (ETDEWEB)

Park, Ho Jin [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Shim, Hyung Jin [Seoul National University, Seoul (Korea, Republic of)

2015-05-15

In a Monte Carlo (MC) eigenvalue calculation, it is well known that the apparent variance of a local tally such as pin power differs from the real variance considerably. The MC method in eigenvalue calculations uses a power iteration method. In the power iteration method, the fission matrix (FM) and fission source density (FSD) are used as the operator and the solution. The FM is useful to estimate a variance and covariance because the FM can be calculated by a few cycle calculations even at inactive cycle. Recently, S. Carney have implemented the higher order fission matrix (HOFM) capabilities into the MCNP6 MC code in order to apply to extend the perturbation theory to second order. In this study, the HOFM capability by the Hotelling deflation method was implemented into McCARD and used to predict the behavior of a real and apparent SD ratio. In the simple 1D slab problems, the Endo's theoretical model predicts well the real to apparent SD ratio. It was noted that the Endo's theoretical model with the McCARD higher mode FS solutions by the HOFM yields much better the real to apparent SD ratio than that with the analytic solutions. In the near future, the application for a high dominance ratio problem such as BEAVRS benchmark will be conducted.

Energy and variance budgets of a diffusive staircase with implications for heat flux scaling

Science.gov (United States)

Hieronymus, M.; Carpenter, J. R.

2016-02-01

Diffusive convection, the mode of double-diffusive convection that occur when both temperature and salinity increase with increasing depth, is commonplace throughout the high latitude oceans and diffusive staircases constitute an important heat transport process in the Arctic Ocean. Heat and buoyancy fluxes through these staircases are often estimated using flux laws deduced either from laboratory experiments, or from simplified energy or variance budgets. We have done direct numerical simulations of double-diffusive convection at a range of Rayleigh numbers and quantified the energy and variance budgets in detail. This allows us to compare the fluxes in our simulations to those derived using known flux laws and to quantify how well the simplified energy and variance budgets approximate the full budgets. The fluxes are found to agree well with earlier estimates at high Rayleigh numbers, but we find large deviations at low Rayleigh numbers. The close ties between the heat and buoyancy fluxes and the budgets of thermal variance and energy have been utilized to derive heat flux scaling laws in the field of thermal convection. The result is the so called GL-theory, which has been found to give accurate heat flux scaling laws in a very wide parameter range. Diffusive convection has many similarities to thermal convection and an extension of the GL-theory to diffusive convection is also presented and its predictions are compared to the results from our numerical simulations.

Some variance reduction methods for numerical stochastic homogenization.

Science.gov (United States)

Blanc, X; Le Bris, C; Legoll, F

2016-04-28

We give an overview of a series of recent studies devoted to variance reduction techniques for numerical stochastic homogenization. Numerical homogenization requires that a set of problems is solved at the microscale, the so-called corrector problems. In a random environment, these problems are stochastic and therefore need to be repeatedly solved, for several configurations of the medium considered. An empirical average over all configurations is then performed using the Monte Carlo approach, so as to approximate the effective coefficients necessary to determine the macroscopic behaviour. Variance severely affects the accuracy and the cost of such computations. Variance reduction approaches, borrowed from other contexts in the engineering sciences, can be useful. Some of these variance reduction techniques are presented, studied and tested here. © 2016 The Author(s).

variance components and genetic parameters for live weight

African Journals Online (AJOL)

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Against this background the present study estimated the (co)variance .... Starting values for the (co)variance components of two-trait models were ..... Estimates of genetic parameters for weaning weight of beef accounting for direct-maternal.

Restricted Variance Interaction Effects

DEFF Research Database (Denmark)

Cortina, Jose M.; Köhler, Tine; Keeler, Kathleen R.

2018-01-01

Although interaction hypotheses are increasingly common in our field, many recent articles point out that authors often have difficulty justifying them. The purpose of this article is to describe a particular type of interaction: the restricted variance (RV) interaction. The essence of the RV int...

Variance Swaps in BM&F: Pricing and Viability of Hedge

Directory of Open Access Journals (Sweden)

Richard John Brostowicz Junior

2010-07-01

Full Text Available A variance swap can theoretically be priced with an infinite set of vanilla calls and puts options considering that the realized variance follows a purely diffusive process with continuous monitoring. In this article we willanalyze the possible differences in pricing considering discrete monitoring of realized variance. It will analyze the pricing of variance swaps with payoff in dollars, since there is a OTC market that works this way and thatpotentially serve as a hedge for the variance swaps traded in BM&F. Additionally, will be tested the feasibility of hedge of variance swaps when there is liquidity in just a few exercise prices, as is the case of FX optionstraded in BM&F. Thus be assembled portfolios containing variance swaps and their replicating portfolios using the available exercise prices as proposed in (DEMETERFI et al., 1999. With these portfolios, the effectiveness of the hedge was not robust in mostly of tests conducted in this work.

Prediction of residual stresses and distortions due to laser beam welding of butt joints in pressure vessels

International Nuclear Information System (INIS)

Moraitis, G.A.; Labeas, G.N.

2009-01-01

A two-level three-dimensional Finite Element (FE) model has been developed to predict keyhole formation and thermo-mechanical response during Laser Beam Welding (LBW) of steel and aluminium pressure vessel or pipe butt-joints. A very detailed and localized (level-1) non-linear three-dimensional transient thermal model is initially developed, which simulates the mechanisms of keyhole formation, calculates the temperature distribution in the local weld area and predicts the keyhole size and shape. Subsequently, using a laser beam heat source model based on keyhole assumptions, a global (level-2) thermo-mechanical analysis of the LBW butt-joint is performed, from which the joint residual stresses and distortions are calculated. All the major physical phenomena associated to LBW, such as laser heat input via radiation, heat losses through convection and radiation, as well as latent heat are accounted for in the numerical model. Material properties and particularly enthalpy, which is very important due to significant material phase changes, are introduced as temperature-dependent functions. The main advantages of the developed model are its efficiency, flexibility and applicability to a wide range of LBW problems (e.g. welding for pressure vessel or pipework construction, welding of automotive, marine or aircraft components, etc). The model efficiency arises from the two-scale approach applied. Minimal or no experimental data are required for the keyhole size and shape computation by the level-1 model, while the thermo-mechanical response calculation by the level-2 model requires only process and material data. Therefore, it becomes possible to efficiently apply the developed simulation model to different material types and varying welding parameters (i.e. welding speed, heat source power, joint geometry, etc.) in order to control residual stresses and distortions within the welded structure

A Theoretical Study on Quantitative Prediction and Evaluation of Thermal Residual Stresses in Metal Matrix Composite (Case 1 : Two-Dimensional In-Plane Fiber Distribution)

International Nuclear Information System (INIS)

Lee, Joon Hyun; Son, Bong Jin

1997-01-01

Although discontinuously reinforced metal matrix composite(MMC) is one of the most promising materials for applications of aerospace, automotive industries, the thermal residual stresses developed in the MMC due to the mismatch in coefficients of thermal expansion between the matrix and the fiber under a temperature change has been pointed out as one of the serious problem in practical applications. There are very limited nondestructive techniques to measure the residual stress of composite materials. However, many difficulties have been reported in their applications. Therefore it is important to establish analytical model to evaluate the thermal residual stress of MMC for practical engineering application. In this study, an elastic model is developed to predict the average thermal residual stresses in the matrix and fiber of a misoriented short fiber composite. The thermal residual stresses are induced by the mismatch in the coefficient of the thermal expansion of the matrix and fiber when the composite is subjected to a uniform temperature change. The model considers two-dimensional in-plane fiber misorientation. The analytical formulation of the model is based on Eshelby's equivalent inclusion method and is unique in that it is able to account for interactions among fibers. This model is more general than past models to investigate the effect of parameters which might influence thermal residual stress in composites. The present model is to investigate the effects of fiber volume fraction, distribution type, distribution cut-off angle, and aspect ratio on thermal residual stress for in-plane fiber misorientation. Fiber volume fraction, aspect ratio, and distribution cut-off angle are shown to have more significant effects on the magnitude of the thermal residual stresses than fiber distribution type for in-plane misorientation

Utility of Urinary Biomarkers in Predicting Loss of Residual Renal Function: The balANZ Trial

Science.gov (United States)

Cho, Yeoungjee; Johnson, David W.; Vesey, David A.; Hawley, Carmel M.; Clarke, Margaret; Topley, Nicholas

2015-01-01

♦ Background: The ability of urinary biomarkers to predict residual renal function (RRF) decline in peritoneal dialysis (PD) patients has not been defined. The present study aimed to explore the utility of established biomarkers from kidney injury models for predicting loss of RRF in incident PD patients, and to evaluate the impact on RRF of using neutral-pH PD solution low in glucose degradation products. ♦ Methods: The study included 50 randomly selected participants from the balANZ trial who had completed 24 months of follow-up. A change in glomerular filtration rate (GFR) was used as the primary clinical outcome measure. In a mixed-effects general linear model, baseline measurements of 18 novel urinary biomarkers and albumin were used to predict GFR change. The model was further used to evaluate the impact of biocompatible PD solution on RRF, adjusted for each biomarker. ♦ Results: Baseline albuminuria was not a useful predictor of change in RRF in PD patients (p = 0.84). Only clusterin was a significant predictor of GFR decline in the whole population (p = 0.04, adjusted for baseline GFR and albuminuria). However, the relationship was no longer apparent when albuminuria was removed from the model (p = 0.31). When the effect of the administered PD solutions was examined using a model adjusted for PD solution type, baseline albuminuria, and GFR, higher baseline urinary concentrations of trefoil factor 3 (TFF3, p = 0.02), kidney injury molecule 1 (KIM-1, p = 0.04), and interferon γ-induced protein 10 (IP-10, p = 0.03) were associated with more rapid decline of RRF in patients receiving conventional PD solution compared with biocompatible PD solution. ♦ Conclusions: Higher urinary levels of kidney injury biomarkers (TFF3, KIM-1, IP-10) at baseline predicted significantly slower RRF decline in patients receiving biocompatible PD solutions. Findings from the present investigation should help to guide future studies to validate the utility of urinary

Integrating mean and variance heterogeneities to identify differentially expressed genes.

Science.gov (United States)

Ouyang, Weiwei; An, Qiang; Zhao, Jinying; Qin, Huaizhen

2016-12-06

In functional genomics studies, tests on mean heterogeneity have been widely employed to identify differentially expressed genes with distinct mean expression levels under different experimental conditions. Variance heterogeneity (aka, the difference between condition-specific variances) of gene expression levels is simply neglected or calibrated for as an impediment. The mean heterogeneity in the expression level of a gene reflects one aspect of its distribution alteration; and variance heterogeneity induced by condition change may reflect another aspect. Change in condition may alter both mean and some higher-order characteristics of the distributions of expression levels of susceptible genes. In this report, we put forth a conception of mean-variance differentially expressed (MVDE) genes, whose expression means and variances are sensitive to the change in experimental condition. We mathematically proved the null independence of existent mean heterogeneity tests and variance heterogeneity tests. Based on the independence, we proposed an integrative mean-variance test (IMVT) to combine gene-wise mean heterogeneity and variance heterogeneity induced by condition change. The IMVT outperformed its competitors under comprehensive simulations of normality and Laplace settings. For moderate samples, the IMVT well controlled type I error rates, and so did existent mean heterogeneity test (i.e., the Welch t test (WT), the moderated Welch t test (MWT)) and the procedure of separate tests on mean and variance heterogeneities (SMVT), but the likelihood ratio test (LRT) severely inflated type I error rates. In presence of variance heterogeneity, the IMVT appeared noticeably more powerful than all the valid mean heterogeneity tests. Application to the gene profiles of peripheral circulating B raised solid evidence of informative variance heterogeneity. After adjusting for background data structure, the IMVT replicated previous discoveries and identified novel experiment

Simultaneous Monte Carlo zero-variance estimates of several correlated means

International Nuclear Information System (INIS)

Booth, T.E.

1998-01-01

Zero-variance biasing procedures are normally associated with estimating a single mean or tally. In particular, a zero-variance solution occurs when every sampling is made proportional to the product of the true probability multiplied by the expected score (importance) subsequent to the sampling; i.e., the zero-variance sampling is importance weighted. Because every tally has a different importance function, a zero-variance biasing for one tally cannot be a zero-variance biasing for another tally (unless the tallies are perfectly correlated). The way to optimize the situation when the required tallies have positive correlation is shown

Comparing estimates of genetic variance across different relationship models.

Science.gov (United States)

Legarra, Andres

2016-02-01

Use of relationships between individuals to estimate genetic variances and heritabilities via mixed models is standard practice in human, plant and livestock genetics. Different models or information for relationships may give different estimates of genetic variances. However, comparing these estimates across different relationship models is not straightforward as the implied base populations differ between relationship models. In this work, I present a method to compare estimates of variance components across different relationship models. I suggest referring genetic variances obtained using different relationship models to the same reference population, usually a set of individuals in the population. Expected genetic variance of this population is the estimated variance component from the mixed model times a statistic, Dk, which is the average self-relationship minus the average (self- and across-) relationship. For most typical models of relationships, Dk is close to 1. However, this is not true for very deep pedigrees, for identity-by-state relationships, or for non-parametric kernels, which tend to overestimate the genetic variance and the heritability. Using mice data, I show that heritabilities from identity-by-state and kernel-based relationships are overestimated. Weighting these estimates by Dk scales them to a base comparable to genomic or pedigree relationships, avoiding wrong comparisons, for instance, "missing heritabilities". Copyright © 2015 Elsevier Inc. All rights reserved.

Variance estimation in the analysis of microarray data

KAUST Repository

Wang, Yuedong

2009-04-01

Microarrays are one of the most widely used high throughput technologies. One of the main problems in the area is that conventional estimates of the variances that are required in the t-statistic and other statistics are unreliable owing to the small number of replications. Various methods have been proposed in the literature to overcome this lack of degrees of freedom problem. In this context, it is commonly observed that the variance increases proportionally with the intensity level, which has led many researchers to assume that the variance is a function of the mean. Here we concentrate on estimation of the variance as a function of an unknown mean in two models: the constant coefficient of variation model and the quadratic variance-mean model. Because the means are unknown and estimated with few degrees of freedom, naive methods that use the sample mean in place of the true mean are generally biased because of the errors-in-variables phenomenon. We propose three methods for overcoming this bias. The first two are variations on the theme of the so-called heteroscedastic simulation-extrapolation estimator, modified to estimate the variance function consistently. The third class of estimators is entirely different, being based on semiparametric information calculations. Simulations show the power of our methods and their lack of bias compared with the naive method that ignores the measurement error. The methodology is illustrated by using microarray data from leukaemia patients.

Developing an Integrated Model Framework for the Assessment of Sustainable Agricultural Residue Removal Limits for Bioenergy Systems

Energy Technology Data Exchange (ETDEWEB)

David Muth, Jr.; Jared Abodeely; Richard Nelson; Douglas McCorkle; Joshua Koch; Kenneth Bryden

2011-08-01

Agricultural residues have significant potential as a feedstock for bioenergy production, but removing these residues can have negative impacts on soil health. Models and datasets that can support decisions about sustainable agricultural residue removal are available; however, no tools currently exist capable of simultaneously addressing all environmental factors that can limit availability of residue. The VE-Suite model integration framework has been used to couple a set of environmental process models to support agricultural residue removal decisions. The RUSLE2, WEPS, and Soil Conditioning Index models have been integrated. A disparate set of databases providing the soils, climate, and management practice data required to run these models have also been integrated. The integrated system has been demonstrated for two example cases. First, an assessment using high spatial fidelity crop yield data has been run for a single farm. This analysis shows the significant variance in sustainably accessible residue across a single farm and crop year. A second example is an aggregate assessment of agricultural residues available in the state of Iowa. This implementation of the integrated systems model demonstrates the capability to run a vast range of scenarios required to represent a large geographic region.

76 FR 78698 - Proposed Revocation of Permanent Variances

Science.gov (United States)

2011-12-19

... Administration (``OSHA'' or ``the Agency'') granted permanent variances to 24 companies engaged in the... DEPARTMENT OF LABOR Occupational Safety and Health Administration [Docket No. OSHA-2011-0054] Proposed Revocation of Permanent Variances AGENCY: Occupational Safety and Health Administration (OSHA...

Dissipation and residue of clothianidin in granules and pesticide fertilizers used in cabbage and soil under field conditions.

Science.gov (United States)

Zhang, P W; Wang, S Y; Huang, C L; Fu, J T; Huang, R L; Li, Z H; Zhang, Z X

2018-01-01

The single application of 0.5 % clothianidin granules, a novel formulation, was used to control pests in vegetables under a high dose. In this article, residues of clothianidin in cabbage and soil samples under field conditions from Guangzhou, Nanning, and Qianjiang were determined by HPLC. The terminal residues of clothianidin in cabbage were less than the limit of detection (clothianidin residual, clothianidin granules and fertilizers of chicken manure, urea, and organic fertilizer were mixed into different pesticide fertilizers through their normal field using dosage and evaluate residual influence of clothianidin in different formula. After analysis of variance of the effect factors, the effect of different pesticide types on half-life was not significant, but the effect of sample types was significant. Clothianidin granules and pesticide fertilizers could be safely applied in cabbage under a single high-dose administration.

Text mining improves prediction of protein functional sites.

Directory of Open Access Journals (Sweden)

Karin M Verspoor

Full Text Available We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites. The structure analysis was carried out using Dynamics Perturbation Analysis (DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

Text Mining Improves Prediction of Protein Functional Sites

Science.gov (United States)

Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

Polycrystalline models for the calculation of residual stresses in zirconium alloys tubes

International Nuclear Information System (INIS)

Signorelli, J.W.; Turner, P.A.; Lebensohn, R.A.; Pochettino, A.A.

1995-01-01

Tubes made of different Zirconium alloys are used in various types of reactors. The final texture of tubes as well as the distribution of residual stresses depend on the mechanical treatments done during their manufacturing process. The knowledge and prediction of both the final texture and the distribution of residual stresses in a tube for nuclear applications are of outstanding importance in relation with in-reactor performance of the tube, especially in what concerns to its irradiation creep and growth behaviour. The viscoplastic and the elastoplastic self consistent polycrystal models are used to investigate the influence of different mechanical treatments, performed during rolling processes on the final distribution of intergranular residual stresses of zirconium alloys tubes. The residual strains predictions with both formulations show a non linear dependence with the orientation, but they are qualitatively different. This discrepancy could be explain in terms of the relative plastic activity between the -type and -type deformation modes predicted with the viscoplastic and elastoplastic models. (author). 10 refs., 4 figs., 1 tab

RR-Interval variance of electrocardiogram for atrial fibrillation detection

Science.gov (United States)

Nuryani, N.; Solikhah, M.; Nugoho, A. S.; Afdala, A.; Anzihory, E.

2016-11-01

Atrial fibrillation is a serious heart problem originated from the upper chamber of the heart. The common indication of atrial fibrillation is irregularity of R peak-to-R-peak time interval, which is shortly called RR interval. The irregularity could be represented using variance or spread of RR interval. This article presents a system to detect atrial fibrillation using variances. Using clinical data of patients with atrial fibrillation attack, it is shown that the variance of electrocardiographic RR interval are higher during atrial fibrillation, compared to the normal one. Utilizing a simple detection technique and variances of RR intervals, we find a good performance of atrial fibrillation detection.

Continuous-Time Mean-Variance Portfolio Selection under the CEV Process

OpenAIRE

Ma, Hui-qiang

2014-01-01

We consider a continuous-time mean-variance portfolio selection model when stock price follows the constant elasticity of variance (CEV) process. The aim of this paper is to derive an optimal portfolio strategy and the efficient frontier. The mean-variance portfolio selection problem is formulated as a linearly constrained convex program problem. By employing the Lagrange multiplier method and stochastic optimal control theory, we obtain the optimal portfolio strategy and mean-variance effici...

Variance based OFDM frame synchronization

Directory of Open Access Journals (Sweden)

Z. Fedra

2012-04-01

Full Text Available The paper deals with a new frame synchronization scheme for OFDM systems and calculates the complexity of this scheme. The scheme is based on the computing of the detection window variance. The variance is computed in two delayed times, so a modified Early-Late loop is used for the frame position detection. The proposed algorithm deals with different variants of OFDM parameters including guard interval, cyclic prefix, and has good properties regarding the choice of the algorithm's parameters since the parameters may be chosen within a wide range without having a high influence on system performance. The verification of the proposed algorithm functionality has been performed on a development environment using universal software radio peripheral (USRP hardware.

Residual Strength of In-plane Loaded Debonded Sandwich Panels

DEFF Research Database (Denmark)

Berggreen, Carl Christian; Simonsen, Bo Cerup

2005-01-01

This paper presents a FEM based numerical model for prediction of residual strength of damaged sandwich panels. As demonstrated, the model can predict the maximum load carrying capacity of real-life panels with debond damages, where the failure is governed by face-sheet buckling followed by debond...

Means and Variances without Calculus

Science.gov (United States)

Kinney, John J.

2005-01-01

This article gives a method of finding discrete approximations to continuous probability density functions and shows examples of its use, allowing students without calculus access to the calculation of means and variances.

Diverse effects of distance cutoff and residue interval on the performance of distance-dependent atom-pair potential in protein structure prediction.

Science.gov (United States)

Yao, Yuangen; Gui, Rong; Liu, Quan; Yi, Ming; Deng, Haiyou

2017-12-08

As one of the most successful knowledge-based energy functions, the distance-dependent atom-pair potential is widely used in all aspects of protein structure prediction, including conformational search, model refinement, and model assessment. During the last two decades, great efforts have been made to improve the reference state of the potential, while other factors that also strongly affect the performance of the potential have been relatively less investigated. Based on different distance cutoffs (from 5 to 22 Å) and residue intervals (from 0 to 15) as well as six different reference states, we constructed a series of distance-dependent atom-pair potentials and tested them on several groups of structural decoy sets collected from diverse sources. A comprehensive investigation has been performed to clarify the effects of distance cutoff and residue interval on the potential's performance. Our results provide a new perspective as well as a practical guidance for optimizing distance-dependent statistical potentials. The optimal distance cutoff and residue interval are highly related with the reference state that the potential is based on, the measurements of the potential's performance, and the decoy sets that the potential is applied to. The performance of distance-dependent statistical potential can be significantly improved when the best statistical parameters for the specific application environment are adopted.

Jensen's Inequality Predicts Effects of Environmental Variation

Science.gov (United States)

Jonathan J. Ruel; Matthew P. Ayres

1999-01-01

Many biologists now recognize that environmental variance can exert important effects on patterns and processes in nature that are independent of average conditions. Jenson's inequality is a mathematical proof that is seldom mentioned in the ecological literature but which provides a powerful tool for predicting some direct effects of environmental variance in...

Evaluation of Mean and Variance Integrals without Integration

Science.gov (United States)

Joarder, A. H.; Omar, M. H.

2007-01-01

The mean and variance of some continuous distributions, in particular the exponentially decreasing probability distribution and the normal distribution, are considered. Since they involve integration by parts, many students do not feel comfortable. In this note, a technique is demonstrated for deriving mean and variance through differential…

Residual nilpotence and residual solubility of groups

International Nuclear Information System (INIS)

Mikhailov, R V

2005-01-01

The properties of the residual nilpotence and the residual solubility of groups are studied. The main objects under investigation are the class of residually nilpotent groups such that each central extension of these groups is also residually nilpotent and the class of residually soluble groups such that each Abelian extension of these groups is residually soluble. Various examples of groups not belonging to these classes are constructed by homological methods and methods of the theory of modules over group rings. Several applications of the theory under consideration are presented and problems concerning the residual nilpotence of one-relator groups are considered.

Approximate zero-variance Monte Carlo estimation of Markovian unreliability

International Nuclear Information System (INIS)

Delcoux, J.L.; Labeau, P.E.; Devooght, J.

1997-01-01

Monte Carlo simulation has become an important tool for the estimation of reliability characteristics, since conventional numerical methods are no more efficient when the size of the system to solve increases. However, evaluating by a simulation the probability of occurrence of very rare events means playing a very large number of histories of the system, which leads to unacceptable computation times. Acceleration and variance reduction techniques have to be worked out. We show in this paper how to write the equations of Markovian reliability as a transport problem, and how the well known zero-variance scheme can be adapted to this application. But such a method is always specific to the estimation of one quality, while a Monte Carlo simulation allows to perform simultaneously estimations of diverse quantities. Therefore, the estimation of one of them could be made more accurate while degrading at the same time the variance of other estimations. We propound here a method to reduce simultaneously the variance for several quantities, by using probability laws that would lead to zero-variance in the estimation of a mean of these quantities. Just like the zero-variance one, the method we propound is impossible to perform exactly. However, we show that simple approximations of it may be very efficient. (author)

Continuous-Time Mean-Variance Portfolio Selection under the CEV Process

Directory of Open Access Journals (Sweden)

Hui-qiang Ma

2014-01-01

Full Text Available We consider a continuous-time mean-variance portfolio selection model when stock price follows the constant elasticity of variance (CEV process. The aim of this paper is to derive an optimal portfolio strategy and the efficient frontier. The mean-variance portfolio selection problem is formulated as a linearly constrained convex program problem. By employing the Lagrange multiplier method and stochastic optimal control theory, we obtain the optimal portfolio strategy and mean-variance efficient frontier analytically. The results show that the mean-variance efficient frontier is still a parabola in the mean-variance plane, and the optimal strategies depend not only on the total wealth but also on the stock price. Moreover, some numerical examples are given to analyze the sensitivity of the efficient frontier with respect to the elasticity parameter and to illustrate the results presented in this paper. The numerical results show that the price of risk decreases as the elasticity coefficient increases.

Variance in centrality within rock hyrax social networks predicts adult longevity.

Directory of Open Access Journals (Sweden)

Adi Barocas

Full Text Available BACKGROUND: In communal mammals the levels of social interaction among group members vary considerably. In recent years, biologists have realized that within-group interactions may affect survival of the group members. Several recent studies have demonstrated that the social integration of adult females is positively associated with infant survival, and female longevity is affected by the strength and stability of the individual social bonds. Our aim was to determine the social factors that influence adult longevity in social mammals. METHODOLOGY/PRINCIPAL FINDINGS: As a model system, we studied the social rock hyrax (Procavia capensis, a plural breeder with low reproductive skew, whose groups are mainly composed of females. We applied network theory using 11 years of behavioral data to quantify the centrality of individuals within groups, and found adult longevity to be inversely correlated to the variance in centrality. In other words, animals in groups with more equal associations lived longer. Individual centrality was not correlated with longevity, implying that social tension may affect all group members and not only the weakest or less connected ones. CONCLUSIONS/SIGNIFICANCE: Our novel findings support previous studies emphasizing the adaptive value of social associations and the consequences of inequality among adults within social groups. However, contrary to previous studies, we suggest that it is not the number or strength of associations that an adult individual has (i.e. centrality that is important, but the overall configuration of social relationships within the group (i.e. centrality SD that is a key factor in influencing longevity.

Predicting Persuasion-Induced Behavior Change from the Brain

Science.gov (United States)

Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

2011-01-01

Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889

Variance in binary stellar population synthesis

Science.gov (United States)

Breivik, Katelyn; Larson, Shane L.

2016-03-01

In the years preceding LISA, Milky Way compact binary population simulations can be used to inform the science capabilities of the mission. Galactic population simulation efforts generally focus on high fidelity models that require extensive computational power to produce a single simulated population for each model. Each simulated population represents an incomplete sample of the functions governing compact binary evolution, thus introducing variance from one simulation to another. We present a rapid Monte Carlo population simulation technique that can simulate thousands of populations in less than a week, thus allowing a full exploration of the variance associated with a binary stellar evolution model.

Gender effects in gaming research: a case for regression residuals?

Science.gov (United States)

Pfister, Roland

2011-10-01

Numerous recent studies have examined the impact of video gaming on various dependent variables, including the players' affective reactions, positive as well as detrimental cognitive effects, and real-world aggression. These target variables are typically analyzed as a function of game characteristics and player attributes-especially gender. However, findings on the uneven distribution of gaming experience between males and females, on the one hand, and the effect of gaming experience on several target variables, on the other hand, point at a possible confound when gaming experiments are analyzed with a standard analysis of variance. This study uses simulated data to exemplify analysis of regression residuals as a potentially beneficial data analysis strategy for such datasets. As the actual impact of gaming experience on each of the various dependent variables differs, the ultimate benefits of analysis of regression residuals entirely depend on the research question, but it offers a powerful statistical approach to video game research whenever gaming experience is a confounding factor.

Recent advances in residual stress measurement

International Nuclear Information System (INIS)

Withers, P.J.; Turski, M.; Edwards, L.; Bouchard, P.J.; Buttle, D.J.

2008-01-01

Until recently residual stresses have been included in structural integrity assessments of nuclear pressure vessels and piping in a very primitive manner due to the lack of reliable residual stress measurement or prediction tools. This situation is changing the capabilities of newly emerging destructive (i.e. the contour method) and non-destructive (i.e. magnetic and high-energy synchrotron X-ray strain mapping) residual stress measurement techniques for evaluating ferritic and austenitic pressure vessel components are contrasted against more well-established methods. These new approaches offer the potential for obtaining area maps of residual stress or strain in welded plants, mock-up components or generic test-pieces. The mapped field may be used directly in structural integrity calculations, or indirectly to validate finite element process/structural models on which safety cases for pressurised nuclear systems are founded. These measurement methods are complementary in terms of application to actual plant, cost effectiveness and measurements in thick sections. In each case an exemplar case study is used to illustrate the method and to highlight its particular capabilities

Importance of the macroeconomic variables for variance prediction: A GARCH-MIDAS approach

DEFF Research Database (Denmark)

Asgharian, Hossein; Hou, Ai Jun; Javed, Farrukh

2013-01-01

This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term compone......This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long...

Construction of a predictive model for concentration of nickel and vanadium in vacuum residues of crude oils using artificial neural networks and LIBS.

Science.gov (United States)

Tarazona, José L; Guerrero, Jáder; Cabanzo, Rafael; Mejía-Ospino, E

2012-03-01

A predictive model to determine the concentration of nickel and vanadium in vacuum residues of Colombian crude oils using laser-induced breakdown spectroscopy (LIBS) and artificial neural networks (ANNs) with nodes distributed in multiple layers (multilayer perceptron) is presented. ANN inputs are intensity values in the vicinity of the emission lines 300.248, 301.200 and 305.081 nm of the Ni(I), and 309.310, 310.229, and 311.070 nm of the V(II). The effects of varying number of nodes and the initial weights and biases in the ANNs were systematically explored. Average relative error of calibration/prediction (REC/REP) and average relative standard deviation (RSD) metrics were used to evaluate the performance of the ANN in the prediction of concentrations of two elements studied here. © 2012 Optical Society of America

A Mean variance analysis of arbitrage portfolios

Science.gov (United States)

Fang, Shuhong

2007-03-01

Based on the careful analysis of the definition of arbitrage portfolio and its return, the author presents a mean-variance analysis of the return of arbitrage portfolios, which implies that Korkie and Turtle's results ( B. Korkie, H.J. Turtle, A mean-variance analysis of self-financing portfolios, Manage. Sci. 48 (2002) 427-443) are misleading. A practical example is given to show the difference between the arbitrage portfolio frontier and the usual portfolio frontier.

Mean-Variance Optimization in Markov Decision Processes

OpenAIRE

Mannor, Shie; Tsitsiklis, John N.

2011-01-01

We consider finite horizon Markov decision processes under performance measures that involve both the mean and the variance of the cumulative reward. We show that either randomized or history-based policies can improve performance. We prove that the complexity of computing a policy that maximizes the mean reward under a variance constraint is NP-hard for some cases, and strongly NP-hard for others. We finally offer pseudo-polynomial exact and approximation algorithms.

Capturing Option Anomalies with a Variance-Dependent Pricing Kernel

DEFF Research Database (Denmark)

Christoffersen, Peter; Heston, Steven; Jacobs, Kris

2013-01-01

We develop a GARCH option model with a new pricing kernel allowing for a variance premium. While the pricing kernel is monotonic in the stock return and in variance, its projection onto the stock return is nonmonotonic. A negative variance premium makes it U shaped. We present new semiparametric...... evidence to confirm this U-shaped relationship between the risk-neutral and physical probability densities. The new pricing kernel substantially improves our ability to reconcile the time-series properties of stock returns with the cross-section of option prices. It provides a unified explanation...... for the implied volatility puzzle, the overreaction of long-term options to changes in short-term variance, and the fat tails of the risk-neutral return distribution relative to the physical distribution....

Gender Variance and Educational Psychology: Implications for Practice

Science.gov (United States)

Yavuz, Carrie

2016-01-01

The area of gender variance appears to be more visible in both the media and everyday life. Within educational psychology literature gender variance remains underrepresented. The positioning of educational psychologists working across the three levels of child and family, school or establishment and education authority/council, means that they are…

Demonstration of a zero-variance based scheme for variance reduction to a mini-core Monte Carlo calculation

Energy Technology Data Exchange (ETDEWEB)

Christoforou, Stavros, E-mail: stavros.christoforou@gmail.com [Kirinthou 17, 34100, Chalkida (Greece); Hoogenboom, J. Eduard, E-mail: j.e.hoogenboom@tudelft.nl [Department of Applied Sciences, Delft University of Technology (Netherlands)

2011-07-01

A zero-variance based scheme is implemented and tested in the MCNP5 Monte Carlo code. The scheme is applied to a mini-core reactor using the adjoint function obtained from a deterministic calculation for biasing the transport kernels. It is demonstrated that the variance of the k{sub eff} estimate is halved compared to a standard criticality calculation. In addition, the biasing does not affect source distribution convergence of the system. However, since the code lacked optimisations for speed, we were not able to demonstrate an appropriate increase in the efficiency of the calculation, because of the higher CPU time cost. (author)

Variance-in-Mean Effects of the Long Forward-Rate Slope

DEFF Research Database (Denmark)

Christiansen, Charlotte

2005-01-01

This paper contains an empirical analysis of the dependence of the long forward-rate slope on the long-rate variance. The long forward-rate slope and the long rate are described by a bivariate GARCH-in-mean model. In accordance with theory, a negative long-rate variance-in-mean effect for the long...... forward-rate slope is documented. Thus, the greater the long-rate variance, the steeper the long forward-rate curve slopes downward (the long forward-rate slope is negative). The variance-in-mean effect is both statistically and economically significant....

Residual stress effects on the impact resistance and strength of fiber composites

Science.gov (United States)

Chamis, C. C.

1973-01-01

Equations have been derived to predict degradation effects of microresidual stresses on impact resistance of unidirectional fiber composites. Equations also predict lamination residual stresses in multilayered angle ply composites.

Simultaneous Monte Carlo zero-variance estimates of several correlated means

International Nuclear Information System (INIS)

Booth, T.E.

1997-08-01

Zero variance procedures have been in existence since the dawn of Monte Carlo. Previous works all treat the problem of zero variance solutions for a single tally. One often wants to get low variance solutions to more than one tally. When the sets of random walks needed for two tallies are similar, it is more efficient to do zero variance biasing for both tallies in the same Monte Carlo run, instead of two separate runs. The theory presented here correlates the random walks of particles by the similarity of their tallies. Particles with dissimilar tallies rapidly become uncorrelated whereas particles with similar tallies will stay correlated through most of their random walk. The theory herein should allow practitioners to make efficient use of zero-variance biasing procedures in practical problems

The lesser known challenge of climate change: thermal variance and sex-reversal in vertebrates with temperature-dependent sex determination.

Directory of Open Access Journals (Sweden)

Jennifer L Neuwald

Full Text Available Climate change is expected to disrupt biological systems. Particularly susceptible are species with temperature-dependent sex determination (TSD, as in many reptiles. While the potentially devastating effect of rising mean temperatures on sex ratios in TSD species is appreciated, the consequences of increased thermal variance predicted to accompany climate change remain obscure. Surprisingly, no study has tested if the effect of thermal variance around high-temperatures (which are particularly relevant given climate change predictions has the same or opposite effects as around lower temperatures. Here we show that sex ratios of the painted turtle (Chrysemys picta were reversed as fluctuations increased around low and high unisexual mean-temperatures. Unexpectedly, the developmental and sexual responses around female-producing temperatures were decoupled in a more complex manner than around male-producing values. Our novel observations are not fully explained by existing ecological models of development and sex determination, and provide strong evidence that thermal fluctuations are critical for shaping the biological outcomes of climate change.

CT volumetry is superior to nuclear renography for prediction of residual kidney function in living donors.

Science.gov (United States)

Barbas, Andrew S; Li, Yanhong; Zair, Murtuza; Van, Julie A; Famure, Olusegun; Dib, Martin J; Laurence, Jerome M; Kim, S Joseph; Ghanekar, Anand

2016-09-01

Living kidney donor evaluation commonly includes nuclear renography to assess split kidney function and computed tomography (CT) scan to evaluate anatomy. To streamline donor workup and minimize exposure to radioisotopes, we sought to assess the feasibility of using proportional kidney volume from CT volumetry in lieu of nuclear renography. We examined the correlation between techniques and assessed their ability to predict residual postoperative kidney function following live donor nephrectomy. In a cohort of 224 live kidney donors, we compared proportional kidney volume derived by CT volumetry with split kidney function derived from nuclear renography and found only modest correlation (left kidney R(2) =26.2%, right kidney R(2) =26.7%). In a subset of 88 live kidney donors with serum creatinine measured 6 months postoperatively, we compared observed estimated glomerular filtration rate (eGFR) at 6 months with predicted eGFR from preoperative imaging. Compared to nuclear renography, CT volumetry more closely approximated actual observed postoperative eGFR for Chronic Kidney Disease Epidemiology Collaboration (J-test: P=.02, Cox-Pesaran test: P=.01) and Mayo formulas (J-test: P=.004, Cox-Pesaran test: Pvolumetry for estimation of split kidney function in healthy individuals with normal kidney function and morphology. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Systematic Review of Uit Parameters on Residual Stresses of Sensitized AA5456 and Field Based Residual Stress Measurements for Predicting and Mitigating Stress Corrosion Cracking

Science.gov (United States)

2014-03-01

University Press, 2009, pp. 820–824. [30] S. Kou, Welding Metallurgy , 2nd ed. Hoboken, NJ: John Wiley and Sons, Inc., 2003. [31] M. N.James et al...around welds in aluminum ship structures both in the laboratory and in the field. Tensile residual stresses are often generated during welding and, in...mitigate and even reverse these tensile residual stresses. This research uses x-ray diffraction to measure residual stresses around welds in AA5456 before

Estimating quadratic variation using realized variance

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Shephard, N.

2002-01-01

with a rather general SV model - which is a special case of the semimartingale model. Then QV is integrated variance and we can derive the asymptotic distribution of the RV and its rate of convergence. These results do not require us to specify a model for either the drift or volatility functions, although we...... have to impose some weak regularity assumptions. We illustrate the use of the limit theory on some exchange rate data and some stock data. We show that even with large values of M the RV is sometimes a quite noisy estimator of integrated variance. Copyright © 2002 John Wiley & Sons, Ltd....

A note on minimum-variance theory and beyond

Energy Technology Data Exchange (ETDEWEB)

Feng Jianfeng [Department of Informatics, Sussex University, Brighton, BN1 9QH (United Kingdom); Tartaglia, Giangaetano [Physics Department, Rome University ' La Sapienza' , Rome 00185 (Italy); Tirozzi, Brunello [Physics Department, Rome University ' La Sapienza' , Rome 00185 (Italy)

2004-04-30

We revisit the minimum-variance theory proposed by Harris and Wolpert (1998 Nature 394 780-4), discuss the implications of the theory on modelling the firing patterns of single neurons and analytically find the optimal control signals, trajectories and velocities. Under the rate coding assumption, input control signals employed in the minimum-variance theory should be Fitts processes rather than Poisson processes. Only if information is coded by interspike intervals, Poisson processes are in agreement with the inputs employed in the minimum-variance theory. For the integrate-and-fire model with Fitts process inputs, interspike intervals of efferent spike trains are very irregular. We introduce diffusion approximations to approximate neural models with renewal process inputs and present theoretical results on calculating moments of interspike intervals of the integrate-and-fire model. Results in Feng, et al (2002 J. Phys. A: Math. Gen. 35 7287-304) are generalized. In conclusion, we present a complete picture on the minimum-variance theory ranging from input control signals, to model outputs, and to its implications on modelling firing patterns of single neurons.

A note on minimum-variance theory and beyond

International Nuclear Information System (INIS)

Feng Jianfeng; Tartaglia, Giangaetano; Tirozzi, Brunello

2004-01-01

We revisit the minimum-variance theory proposed by Harris and Wolpert (1998 Nature 394 780-4), discuss the implications of the theory on modelling the firing patterns of single neurons and analytically find the optimal control signals, trajectories and velocities. Under the rate coding assumption, input control signals employed in the minimum-variance theory should be Fitts processes rather than Poisson processes. Only if information is coded by interspike intervals, Poisson processes are in agreement with the inputs employed in the minimum-variance theory. For the integrate-and-fire model with Fitts process inputs, interspike intervals of efferent spike trains are very irregular. We introduce diffusion approximations to approximate neural models with renewal process inputs and present theoretical results on calculating moments of interspike intervals of the integrate-and-fire model. Results in Feng, et al (2002 J. Phys. A: Math. Gen. 35 7287-304) are generalized. In conclusion, we present a complete picture on the minimum-variance theory ranging from input control signals, to model outputs, and to its implications on modelling firing patterns of single neurons

Leptogenesis and residual CP symmetry

International Nuclear Information System (INIS)

Chen, Peng; Ding, Gui-Jun; King, Stephen F.

2016-01-01

We discuss flavour dependent leptogenesis in the framework of lepton flavour models based on discrete flavour and CP symmetries applied to the type-I seesaw model. Working in the flavour basis, we analyse the case of two general residual CP symmetries in the neutrino sector, which corresponds to all possible semi-direct models based on a preserved Z 2 in the neutrino sector, together with a CP symmetry, which constrains the PMNS matrix up to a single free parameter which may be fixed by the reactor angle. We systematically study and classify this case for all possible residual CP symmetries, and show that the R-matrix is tightly constrained up to a single free parameter, with only certain forms being consistent with successful leptogenesis, leading to possible connections between leptogenesis and PMNS parameters. The formalism is completely general in the sense that the two residual CP symmetries could result from any high energy discrete flavour theory which respects any CP symmetry. As a simple example, we apply the formalism to a high energy S 4 flavour symmetry with a generalized CP symmetry, broken to two residual CP symmetries in the neutrino sector, recovering familiar results for PMNS predictions, together with new results for flavour dependent leptogenesis.

The genetic variance but not the genetic covariance of life-history traits changes towards the north in a time-constrained insect.

Science.gov (United States)

Sniegula, Szymon; Golab, Maria J; Drobniak, Szymon M; Johansson, Frank

2018-03-22

Seasonal time constraints are usually stronger at higher than lower latitudes and can exert strong selection on life-history traits and the correlations among these traits. To predict the response of life-history traits to environmental change along a latitudinal gradient, information must be obtained about genetic variance in traits and also genetic correlation between traits, that is the genetic variance-covariance matrix, G. Here, we estimated G for key life-history traits in an obligate univoltine damselfly that faces seasonal time constraints. We exposed populations to simulated native temperatures and photoperiods and common garden environmental conditions in a laboratory set-up. Despite differences in genetic variance in these traits between populations (lower variance at northern latitudes), there was no evidence for latitude-specific covariance of the life-history traits. At simulated native conditions, all populations showed strong genetic and phenotypic correlations between traits that shaped growth and development. The variance-covariance matrix changed considerably when populations were exposed to common garden conditions compared with the simulated natural conditions, showing the importance of environmentally induced changes in multivariate genetic structure. Our results highlight the importance of estimating variance-covariance matrixes in environments that mimic selection pressures and not only trait variances or mean trait values in common garden conditions for understanding the trait evolution across populations and environments. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer.

Science.gov (United States)

Janco, Jo Marie Tran; Glaser, Gretchen; Kim, Bohyun; McGree, Michaela E; Weaver, Amy L; Cliby, William A; Dowdy, Sean C; Bakkum-Gamez, Jamie N

2015-07-01

To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC). Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index. 279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685). The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated. Copyright © 2015 Elsevier Inc. All rights reserved.

Bioenergy from agricultural residues in Ghana

DEFF Research Database (Denmark)

Thomsen, Sune Tjalfe

and biomethane under Ghanaian conditions. Detailed characterisations of thirteen of the most common agricultural residues in Ghana are presented, enabling estimations of theoretical bioenergy potentials and identifying specific residues for future biorefinery applications. When aiming at residue-based ethanol...... to pursue increased implementation of anaerobic digestion in Ghana, as the first bioenergy option, since anaerobic digestion is more flexible than ethanol production with regard to both feedstock and scale of production. If possible, the available manure and municipal liquid waste should be utilised first....... A novel model for estimating BMP from compositional data of lignocellulosic biomasses is derived. The model is based on a statistical method not previously used in this area of research and the best prediction of BMP is: BMP = 347 xC+H+R – 438 xL + 63 DA , where xC+H+R is the combined content of cellulose...

Estimating High-Frequency Based (Co-) Variances: A Unified Approach

DEFF Research Database (Denmark)

Voev, Valeri; Nolte, Ingmar

We propose a unified framework for estimating integrated variances and covariances based on simple OLS regressions, allowing for a general market microstructure noise specification. We show that our estimators can outperform, in terms of the root mean squared error criterion, the most recent...... and commonly applied estimators, such as the realized kernels of Barndorff-Nielsen, Hansen, Lunde & Shephard (2006), the two-scales realized variance of Zhang, Mykland & Aït-Sahalia (2005), the Hayashi & Yoshida (2005) covariance estimator, and the realized variance and covariance with the optimal sampling...

Demonstration of a zero-variance based scheme for variance reduction to a mini-core Monte Carlo calculation

International Nuclear Information System (INIS)

Christoforou, Stavros; Hoogenboom, J. Eduard

2011-01-01

A zero-variance based scheme is implemented and tested in the MCNP5 Monte Carlo code. The scheme is applied to a mini-core reactor using the adjoint function obtained from a deterministic calculation for biasing the transport kernels. It is demonstrated that the variance of the k_e_f_f estimate is halved compared to a standard criticality calculation. In addition, the biasing does not affect source distribution convergence of the system. However, since the code lacked optimisations for speed, we were not able to demonstrate an appropriate increase in the efficiency of the calculation, because of the higher CPU time cost. (author)

EDOVE: Energy and Depth Variance-Based Opportunistic Void Avoidance Scheme for Underwater Acoustic Sensor Networks.

Science.gov (United States)

Bouk, Safdar Hussain; Ahmed, Syed Hassan; Park, Kyung-Joon; Eun, Yongsoon

2017-09-26

Underwater Acoustic Sensor Network (UASN) comes with intrinsic constraints because it is deployed in the aquatic environment and uses the acoustic signals to communicate. The examples of those constraints are long propagation delay, very limited bandwidth, high energy cost for transmission, very high signal attenuation, costly deployment and battery replacement, and so forth. Therefore, the routing schemes for UASN must take into account those characteristics to achieve energy fairness, avoid energy holes, and improve the network lifetime. The depth based forwarding schemes in literature use node's depth information to forward data towards the sink. They minimize the data packet duplication by employing the holding time strategy. However, to avoid void holes in the network, they use two hop node proximity information. In this paper, we propose the Energy and Depth variance-based Opportunistic Void avoidance (EDOVE) scheme to gain energy balancing and void avoidance in the network. EDOVE considers not only the depth parameter, but also the normalized residual energy of the one-hop nodes and the normalized depth variance of the second hop neighbors. Hence, it avoids the void regions as well as balances the network energy and increases the network lifetime. The simulation results show that the EDOVE gains more than 15 % packet delivery ratio, propagates 50 % less copies of data packet, consumes less energy, and has more lifetime than the state of the art forwarding schemes.

The Genealogical Consequences of Fecundity Variance Polymorphism

Science.gov (United States)

Taylor, Jesse E.

2009-01-01

The genealogical consequences of within-generation fecundity variance polymorphism are studied using coalescent processes structured by genetic backgrounds. I show that these processes have three distinctive features. The first is that the coalescent rates within backgrounds are not jointly proportional to the infinitesimal variance, but instead depend only on the frequencies and traits of genotypes containing each allele. Second, the coalescent processes at unlinked loci are correlated with the genealogy at the selected locus; i.e., fecundity variance polymorphism has a genomewide impact on genealogies. Third, in diploid models, there are infinitely many combinations of fecundity distributions that have the same diffusion approximation but distinct coalescent processes; i.e., in this class of models, ancestral processes and allele frequency dynamics are not in one-to-one correspondence. Similar properties are expected to hold in models that allow for heritable variation in other traits that affect the coalescent effective population size, such as sex ratio or fecundity and survival schedules. PMID:19433628

Stable "trait" variance of temperament as a predictor of the temporal course of depression and social phobia.

Science.gov (United States)

Naragon-Gainey, Kristin; Gallagher, Matthew W; Brown, Timothy A

2013-08-01

A large body of research has found robust associations between dimensions of temperament (e.g., neuroticism, extraversion) and the mood and anxiety disorders. However, mood-state distortion (i.e., the tendency for current mood state to bias ratings of temperament) likely confounds these associations, rendering their interpretation and validity unclear. This issue is of particular relevance to clinical populations who experience elevated levels of general distress. The current study used the "trait-state-occasion" latent variable model (D. A. Cole, N. C. Martin, & J. H. Steiger, 2005) to separate the stable components of temperament from transient, situational influences such as current mood state. We examined the predictive power of the time-invariant components of temperament on the course of depression and social phobia in a large, treatment-seeking sample with mood and/or anxiety disorders (N = 826). Participants were assessed 3 times over the course of 1 year, using interview and self-report measures; most participants received treatment during this time. Results indicated that both neuroticism/behavioral inhibition (N/BI) and behavioral activation/positive affect (BA/P) consisted largely of stable, time-invariant variance (57% to 78% of total variance). Furthermore, the time-invariant components of N/BI and BA/P were uniquely and incrementally predictive of change in depression and social phobia, adjusting for initial symptom levels. These results suggest that the removal of state variance bolsters the effect of temperament on psychopathology among clinically distressed individuals. Implications for temperament-psychopathology models, psychopathology assessment, and the stability of traits are discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved.

On the extension of multi-phase models to sub-residual saturations

International Nuclear Information System (INIS)

Lingineni, S.; Chen, Y.T.; Boehm, R.F.

1995-01-01

This paper focuses on the limitations of applying multi-phase flow and transport models to simulate the hydrothermal processes occurring when the liquid saturation falls below residual levels. A typical scenario of a heat-generating high-level waste package emplaced in a backfilled drift of a waste repository is presented. The hydrothermal conditions in the vicinity of the waste package as well as in the far-field are determined using multi-phase, non-isothermal codes such as TOUGH2 and FEHM. As the waste package temperature increases, heat-pipe effects are created and water is driven away from the package into colder regions where it condenses. The variations in the liquid saturations close to the waste package are determined using these models with extended capillary pressure-saturations relationships to sub-residual regime. The predictions indicate even at elevated temperatures, waste package surroundings are not completely dry. However, if transport based modeling is used to represent liquid saturation variations in the sub-residual regime, then complete dry conditions are predicted within the backfill for extended periods of time. The relative humidity conditions near the waste package are also found to be sensitive to the representation of capillary pressure-saturation relationship used for sub-residual regime. An experimental investigation is carried out to study the variations in liquid saturations and relative humidity conditions in sub-residual regimes. Experimental results indicated that extended multi-phase models without interphase transport can not predict dry-out conditions and the simulations underpredict the humidity conditions near the waste package

In-situ observation of dislocation and analysis of residual stresses by FEM/DDM modeling in water cavitation peening of pure titanium

International Nuclear Information System (INIS)

Ju, D Y; Han, B

2015-01-01

In this paper, in order to approach this problem, specimens of pure titanium were treated with WCP, and the subsequent changes in microstructure, residual stress, and surface morphologies were investigated as a function of WCP duration. The influence of water cavitation peening (WCP) treatment on the microstructure of pure titanium was investigated. A novel combined finite element and dislocation density method (FEM/DDM), proposed for predicting macro and micro residual stresses induced on the material subsurface treated with water cavitation peening, is also presented. A bilinear elastic-plastic finite element method was conducted to predict macro-residual stresses and a dislocation density method was conducted to predict micro-residual stresses. These approaches made possible the prediction of the magnitude and depth of residual stress fields in pure titanium. The effect of applied impact pressures on the residual stresses was also presented. The results of the FEM/DDM modeling were in good agreement with those of the experimental measurements. (paper)

On Mean-Variance Analysis

OpenAIRE

Li, Yang; Pirvu, Traian A

2011-01-01

This paper considers the mean variance portfolio management problem. We examine portfolios which contain both primary and derivative securities. The challenge in this context is due to portfolio's nonlinearities. The delta-gamma approximation is employed to overcome it. Thus, the optimization problem is reduced to a well posed quadratic program. The methodology developed in this paper can be also applied to pricing and hedging in incomplete markets.

Residual Negative Symptoms Differentiate Cognitive Performance in Clinically Stable Patients with Schizophrenia and Bipolar Disorder

Directory of Open Access Journals (Sweden)

Rajeev Krishnadas

2014-01-01

Full Text Available Cognitive deficits in various domains have been shown in patients with bipolar disorder and schizophrenia. The purpose of the present study was to examine if residual psychopathology explained the difference in cognitive function between clinically stable patients with schizophrenia and bipolar disorder. We compared the performance on tests of attention, visual and verbal memory, and executive function of 25 patients with schizophrenia in remission and 25 euthymic bipolar disorder patients with that of 25 healthy controls. Mediation analysis was used to see if residual psychopathology could explain the difference in cognitive function between the patient groups. Both patient groups performed significantly worse than healthy controls on most cognitive tests. Patients with bipolar disorder displayed cognitive deficits that were milder but qualitatively similar to those of patients with schizophrenia. Residual negative symptoms mediated the difference in performance on cognitive tests between the two groups. Neither residual general psychotic symptoms nor greater antipsychotic doses explained this relationship. The shared variance explained by the residual negative and cognitive deficits that the difference between patient groups may be explained by greater frontal cortical neurophysiological deficits in patients with schizophrenia, compared to bipolar disorder. Further longitudinal work may provide insight into pathophysiological mechanisms that underlie these deficits.

Modelling volatility by variance decomposition

DEFF Research Database (Denmark)

Amado, Cristina; Teräsvirta, Timo

In this paper, we propose two parametric alternatives to the standard GARCH model. They allow the variance of the model to have a smooth time-varying structure of either additive or multiplicative type. The suggested parameterisations describe both nonlinearity and structural change in the condit...

Variance heterogeneity in Saccharomyces cerevisiae expression data: trans-regulation and epistasis.

Science.gov (United States)

Nelson, Ronald M; Pettersson, Mats E; Li, Xidan; Carlborg, Örjan

2013-01-01

Here, we describe the results from the first variance heterogeneity Genome Wide Association Study (VGWAS) on yeast expression data. Using this forward genetics approach, we show that the genetic regulation of gene-expression in the budding yeast, Saccharomyces cerevisiae, includes mechanisms that can lead to variance heterogeneity in the expression between genotypes. Additionally, we performed a mean effect association study (GWAS). Comparing the mean and variance heterogeneity analyses, we find that the mean expression level is under genetic regulation from a larger absolute number of loci but that a higher proportion of the variance controlling loci were trans-regulated. Both mean and variance regulating loci cluster in regulatory hotspots that affect a large number of phenotypes; a single variance-controlling locus, mapping close to DIA2, was found to be involved in more than 10% of the significant associations. It has been suggested in the literature that variance-heterogeneity between the genotypes might be due to genetic interactions. We therefore screened the multi-locus genotype-phenotype maps for several traits where multiple associations were found, for indications of epistasis. Several examples of two and three locus genetic interactions were found to involve variance-controlling loci, with reports from the literature corroborating the functional connections between the loci. By using a new analytical approach to re-analyze a powerful existing dataset, we are thus able to both provide novel insights to the genetic mechanisms involved in the regulation of gene-expression in budding yeast and experimentally validate epistasis as an important mechanism underlying genetic variance-heterogeneity between genotypes.

Lifetime and residual strength of materials

DEFF Research Database (Denmark)

Nielsen, Lauge Fuglsang

1997-01-01

of load amplitude, load average, fractional time under maximum load, and load frequency.The analysis includes prediction of residual strength (re-cycle strength) during the process of load cycling. It is concluded that number of cycles to failure is a very poor design criterion. It is demonstrated how...... the theory developed can be generalized also to consider non-harmonic load variations.Algorithms are presented for design purposes which may be suggested as qualified alternatives to the Palmgren-Miner's methods normally used in fatigue analysis of materials under arbitrary load variations. Prediction...

Searching for concentric low variance circles in the cosmic microwave background

Energy Technology Data Exchange (ETDEWEB)

DeAbreu, Adam [Department of Physics, Simon Fraser University, Burnaby, BC, V5A 1S6 Canada (Canada); Contreras, Dagoberto; Scott, Douglas, E-mail: adeabreu@sfu.ca, E-mail: dagocont@phas.ubc.ca, E-mail: dscott@phas.ubc.ca [Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, V6T 1Z1 Canada (Canada)

2015-12-01

In a recent paper, Gurzadyan and Penrose claim to have found directions in the sky around which there are multiple concentric sets of annuli with anomalously low variance in the cosmic microwave background (CMB). These features are presented as evidence for a particular theory of the pre-Big Bang Universe. We are able to reproduce the analysis these authors presented for data from the WMAP satellite and we confirm the existence of these apparently special directions in the newer Planck data. However, we also find that these features are present at the same level of abundance in simulated Gaussian CMB skies, i.e., they are entirely consistent with the predictions of the standard cosmological model.

Scoring protein interaction decoys using exposed residues (SPIDER): a novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues.

Science.gov (United States)

Khashan, Raed; Zheng, Weifan; Tropsha, Alexander

2012-08-01

Accurate prediction of the structure of protein-protein complexes in computational docking experiments remains a formidable challenge. It has been recognized that identifying native or native-like poses among multiple decoys is the major bottleneck of the current scoring functions used in docking. We have developed a novel multibody pose-scoring function that has no theoretical limit on the number of residues contributing to the individual interaction terms. We use a coarse-grain representation of a protein-protein complex where each residue is represented by its side chain centroid. We apply a computational geometry approach called Almost-Delaunay tessellation that transforms protein-protein complexes into a residue contact network, or an undirectional graph where vertex-residues are nodes connected by edges. This treatment forms a family of interfacial graphs representing a dataset of protein-protein complexes. We then employ frequent subgraph mining approach to identify common interfacial residue patterns that appear in at least a subset of native protein-protein interfaces. The geometrical parameters and frequency of occurrence of each "native" pattern in the training set are used to develop the new SPIDER scoring function. SPIDER was validated using standard "ZDOCK" benchmark dataset that was not used in the development of SPIDER. We demonstrate that SPIDER scoring function ranks native and native-like poses above geometrical decoys and that it exceeds in performance a popular ZRANK scoring function. SPIDER was ranked among the top scoring functions in a recent round of CAPRI (Critical Assessment of PRedicted Interactions) blind test of protein-protein docking methods. Copyright © 2012 Wiley Periodicals, Inc.

Genetic Variance in Homophobia: Evidence from Self- and Peer Reports.

Science.gov (United States)

Zapko-Willmes, Alexandra; Kandler, Christian

2018-01-01

The present twin study combined self- and peer assessments of twins' general homophobia targeting gay men in order to replicate previous behavior genetic findings across different rater perspectives and to disentangle self-rater-specific variance from common variance in self- and peer-reported homophobia (i.e., rater-consistent variance). We hypothesized rater-consistent variance in homophobia to be attributable to genetic and nonshared environmental effects, and self-rater-specific variance to be partially accounted for by genetic influences. A sample of 869 twins and 1329 peer raters completed a seven item scale containing cognitive, affective, and discriminatory homophobic tendencies. After correction for age and sex differences, we found most of the genetic contributions (62%) and significant nonshared environmental contributions (16%) to individual differences in self-reports on homophobia to be also reflected in peer-reported homophobia. A significant genetic component, however, was self-report-specific (38%), suggesting that self-assessments alone produce inflated heritability estimates to some degree. Different explanations are discussed.

Acclimatization to high-variance habitats does not enhance physiological tolerance of two key Caribbean corals to future temperature and pH.

Science.gov (United States)

Camp, Emma F; Smith, David J; Evenhuis, Chris; Enochs, Ian; Manzello, Derek; Woodcock, Stephen; Suggett, David J

2016-05-25

Corals are acclimatized to populate dynamic habitats that neighbour coral reefs. Habitats such as seagrass beds exhibit broad diel changes in temperature and pH that routinely expose corals to conditions predicted for reefs over the next 50-100 years. However, whether such acclimatization effectively enhances physiological tolerance to, and hence provides refuge against, future climate scenarios remains unknown. Also, whether corals living in low-variance habitats can tolerate present-day high-variance conditions remains untested. We experimentally examined how pH and temperature predicted for the year 2100 affects the growth and physiology of two dominant Caribbean corals (Acropora palmata and Porites astreoides) native to habitats with intrinsically low (outer-reef terrace, LV) and/or high (neighbouring seagrass, HV) environmental variance. Under present-day temperature and pH, growth and metabolic rates (calcification, respiration and photosynthesis) were unchanged for HV versus LV populations. Superimposing future climate scenarios onto the HV and LV conditions did not result in any enhanced tolerance to colonies native to HV. Calcification rates were always lower for elevated temperature and/or reduced pH. Together, these results suggest that seagrass habitats may not serve as refugia against climate change if the magnitude of future temperature and pH changes is equivalent to neighbouring reef habitats. © 2016 The Author(s).

Environmental interpretation using insoluble residues within reef coral skeletons: problems, pitfalls, and preliminary results

Science.gov (United States)

Budd, Ann F.; Mann, Keith O.; Guzmán, Hector M.

1993-03-01

Insoluble residue concentrations have been measured within colonies of four massive reef corals from seven localities along the Caribbean coast of Panama to determine if detrital sediments, incorporated within the skeletal lattice during growth, record changes in sedimentation over the past twenty years. Amounts of resuspended sediment have increased to varying degrees at the seven localities over the past decades in response to increased deforestation in nearby terrestrial habitats. Preliminary results of correlation and regression analyses reveal few consistent temporal trends in the insoluble residue concentration. Analyses of variance suggest that amounts of insoluble residues, however, differ among environments within species, but that no consistent pattern of variation exists among species. D. strigosa and P. astreoides possess high concentrations at protected localities, S. siderea at localities with high amounts of resuspended sediment, and M. annularis at the least turbid localities. Little correlation exists between insoluble residue concentration and growth band width within species at each locality. Only in two more efficient suspension feeders ( S. siderea and D. strigosa) do weak negative correlations with growth band width exist overall. These results indicate that insoluble residue concentrations cannot be used unequivocally in environmental interpretation, until more is known about tissue damage, polyp behavior, and their effects on the incorporation of insolubles in the skeleton during growth in different coral species. Insoluble residue data are highly variable; therefore, large sample sizes and strong contrasts between environments are required to reveal significant trends.

Analysis of residual swirl in tangentially-fired natural gas-boiler

International Nuclear Information System (INIS)

Hasril Hasini; Muhammad Azlan Muad; Mohd Zamri Yusoff; Norshah Hafeez Shuaib

2010-01-01

This paper describes the investigation on residual swirl flow in a 120 MW natural gas, full-scale, tangential-fired boiler. Emphasis is given towards the understanding of the behavior of the combustion gas flow pattern and temperature distribution as a result of the tangential firing system of the boiler. The analysis was carried out based on three-dimensional computational modeling on full scale boiler with validation from key design parameter as well as practical observation. Actual operating parameters of the actual boiler are taken as the boundary conditions for this modeling. The prediction of total heat flux was found to be in agreement with the key design parameter while the residual swirl predicted at the upper furnace agrees qualitatively with the practical observation. Based on this comparison, detail analysis was carried out for comprehensive understanding on the generation and destruction of the residual swirl behavior in boiler especially those with high capacity. (author)

Decomposition of Variance for Spatial Cox Processes.

Science.gov (United States)

Jalilian, Abdollah; Guan, Yongtao; Waagepetersen, Rasmus

2013-03-01

Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive or log linear random intensity functions. We moreover consider a new and flexible class of pair correlation function models given in terms of normal variance mixture covariance functions. The proposed methodology is applied to point pattern data sets of locations of tropical rain forest trees.

Quadratic residues and non-residues selected topics

CERN Document Server

Wright, Steve

2016-01-01

This book offers an account of the classical theory of quadratic residues and non-residues with the goal of using that theory as a lens through which to view the development of some of the fundamental methods employed in modern elementary, algebraic, and analytic number theory. The first three chapters present some basic facts and the history of quadratic residues and non-residues and discuss various proofs of the Law of Quadratic Reciprosity in depth, with an emphasis on the six proofs that Gauss published. The remaining seven chapters explore some interesting applications of the Law of Quadratic Reciprocity, prove some results concerning the distribution and arithmetic structure of quadratic residues and non-residues, provide a detailed proof of Dirichlet’s Class-Number Formula, and discuss the question of whether quadratic residues are randomly distributed. The text is a valuable resource for graduate and advanced undergraduate students as well as for mathematicians interested in number theory.

Annotation-Based Whole Genomic Prediction and Selection

DEFF Research Database (Denmark)

Kadarmideen, Haja; Do, Duy Ngoc; Janss, Luc

Genomic selection is widely used in both animal and plant species, however, it is performed with no input from known genomic or biological role of genetic variants and therefore is a black box approach in a genomic era. This study investigated the role of different genomic regions and detected QTLs...... in their contribution to estimated genomic variances and in prediction of genomic breeding values by applying SNP annotation approaches to feed efficiency. Ensembl Variant Predictor (EVP) and Pig QTL database were used as the source of genomic annotation for 60K chip. Genomic prediction was performed using the Bayes...... classes. Predictive accuracy was 0.531, 0.532, 0.302, and 0.344 for DFI, RFI, ADG and BF, respectively. The contribution per SNP to total genomic variance was similar among annotated classes across different traits. Predictive performance of SNP classes did not significantly differ from randomized SNP...

Validation of Weld Residual Stress Modeling in the NRC International Round Robin Study

International Nuclear Information System (INIS)

Mullins, Jonathan; Gunnars, Jens

2013-01-01

Weld residual stresses (WRS) have a large influence on the behavior of cracks growing under normal operation loads and on the leakage flow from a through-wall crack. Accurate prediction on weld residual stresses is important to make proper decisions when cracks in weld joints are detected. During the latest years, there has been a strong development in both analytical procedures to numerically determine WRS and experimental measurements of WRS. The USNRC (United States Nuclear Regulatory Commission) has formed a program for validation of WRS predictions through comparison of numerically calculated residual stress fields in dissimilar welds measured by different methods. The present report describes the results of the project with special focus on the contribution from Inspecta Technology. Objectives: The principal objective of the project is to compare different WRS predictions for a dissimilar pipe weld with careful measurements on a mock-up weld. The results of the project will make it possible to make recommendations on computational procedures for WRS in dissimilar metal welds. Results: It is concluded that numerical analysis of weld residual stresses using the finite element method is very useful for the estimation of weld residual stresses in complex geometries and dissimilar metal welds. The validation study increases the understanding of uncertainties associated with different modeling approaches and helps to identify the most sensitive parameters

Assessing food allergy risks from residual peanut protein in highly refined vegetable oil

NARCIS (Netherlands)

Blom, W.M.; Kruizinga, A.G.; Rubingh, C.M.; Remington, B.C.; Crevel, R.W.R.; Houben, G.F.

2017-01-01

Refined vegetable oils including refined peanut oil are widely used in foods. Due to shared production processes, refined non-peanut vegetable oils can contain residual peanut proteins. We estimated the predicted number of allergic reactions to residual peanut proteins using probabilistic risk

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Directory of Open Access Journals (Sweden)

Pontil Massimiliano

2009-10-01

Full Text Available Abstract Background Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots" at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. Results We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which ΔΔG ≥ 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. Conclusion We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been

Estimation of residual stress distribution for pressurizer nozzle of Kori nuclear power plant considering safe end

Energy Technology Data Exchange (ETDEWEB)

Song, Tae Kwang; Bae, Hong Yeol; Chun, Yun Bae; Oh, Chang Young; Kim, Yun Jae [Korea University, Seoul (Korea, Republic of); Lee, Kyoung Soo; Park, Chi Yong [Korea Electric Power Research Institute, Daejeon (Korea, Republic of)

2008-08-15

In nuclear power plants, ferritic low alloy steel nozzle was connected with austenitic stainless steel piping system through alloy 82/182 butt weld. Accurate estimation of residual stress for weldment is important in the sense that alloy 82/182 is susceptible to stress corrosion cracking. There are many results which predict residual stress distribution for alloy 82/182 weld between nozzle and pipe. However, nozzle and piping system usually connected through safe end which has short length. In this paper, residual stress distribution for pressurizer nozzle of Kori nuclear power plant was predicted using FE analysis, which considered safe end. As a result, existing residual stress profile was redistributed and residual stress of inner surface was decreased specially. It means that safe end should be considered to reduce conservatism when estimating the piping system.

Grammatical and lexical variance in English

CERN Document Server

Quirk, Randolph

2014-01-01

Written by one of Britain's most distinguished linguists, this book is concerned with the phenomenon of variance in English grammar and vocabulary across regional, social, stylistic and temporal space.

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

Directory of Open Access Journals (Sweden)

Peng Lu

2018-01-01

Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

Variance decomposition in stochastic simulators.

Science.gov (United States)

Le Maître, O P; Knio, O M; Moraes, A

2015-06-28

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Variance decomposition in stochastic simulators

Science.gov (United States)

Le Maître, O. P.; Knio, O. M.; Moraes, A.

2015-06-01

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Variance decomposition in stochastic simulators

Energy Technology Data Exchange (ETDEWEB)

Le Maître, O. P., E-mail: olm@limsi.fr [LIMSI-CNRS, UPR 3251, Orsay (France); Knio, O. M., E-mail: knio@duke.edu [Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708 (United States); Moraes, A., E-mail: alvaro.moraesgutierrez@kaust.edu.sa [King Abdullah University of Science and Technology, Thuwal (Saudi Arabia)

2015-06-28

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Variance-based Salt Body Reconstruction

KAUST Repository

Ovcharenko, Oleg

2017-05-26

Seismic inversions of salt bodies are challenging when updating velocity models based on Born approximation- inspired gradient methods. We propose a variance-based method for velocity model reconstruction in regions complicated by massive salt bodies. The novel idea lies in retrieving useful information from simultaneous updates corresponding to different single frequencies. Instead of the commonly used averaging of single-iteration monofrequency gradients, our algorithm iteratively reconstructs salt bodies in an outer loop based on updates from a set of multiple frequencies after a few iterations of full-waveform inversion. The variance among these updates is used to identify areas where considerable cycle-skipping occurs. In such areas, we update velocities by interpolating maximum velocities within a certain region. The result of several recursive interpolations is later used as a new starting model to improve results of conventional full-waveform inversion. An application on part of the BP 2004 model highlights the evolution of the proposed approach and demonstrates its effectiveness.

Variance decomposition in stochastic simulators

KAUST Repository

Le Maî tre, O. P.; Knio, O. M.; Moraes, Alvaro

2015-01-01

This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

Fatigue life estimation considering welding residual stress and hot-spot stress of welded components

International Nuclear Information System (INIS)

Han, S. H.; Lee, T. K.; Shin, B. C.

2002-01-01

The fatigue life of welded joints is sensitive to welding residual stress and complexity of their geometric shapes. To predict the fatigue life more reasonably, the effects of welding residual stress and its relaxation have to be considered quantitatively which are equivalent to mean stress by external loads. The hot-spot stress concept should be also adopted which can be reduce the dependence of fatigue strengths for various welding details. Considering the factors mentioned above, a fatigue life prediction model using the modified Goodman's diagram was proposed. In this model, an equivalent stress was introduced which are composed of the mean stress based on the hot-spot stress concept and the relaxed welding residual stress. From the verification of the proposed model to real welding details, it is confirmed that this model can be applied to predict reasonably their fatigue lives

Protein-protein docking with dynamic residue protonation states.