Changes in persistence, spurious regressions and the Fisher hypothesis

DEFF Research Database (Denmark)

Kruse, Robinson; Ventosa-Santaulària, Daniel; Noriega, Antonio E.

Declining inflation persistence has been documented in numerous studies. When such series are analyzed in a regression framework in conjunction with other persistent time series, spurious regressions are likely to occur. We propose to use the coefficient of determination R2 as a test statistic to...

Towards molecular design using 2D-molecular contour maps obtained from PLS regression coefficients

Science.gov (United States)

Borges, Cleber N.; Barigye, Stephen J.; Freitas, Matheus P.

2017-12-01

The multivariate image analysis descriptors used in quantitative structure-activity relationships are direct representations of chemical structures as they are simply numerical decodifications of pixels forming the 2D chemical images. These MDs have found great utility in the modeling of diverse properties of organic molecules. Given the multicollinearity and high dimensionality of the data matrices generated with the MIA-QSAR approach, modeling techniques that involve the projection of the data space onto orthogonal components e.g. Partial Least Squares (PLS) have been generally used. However, the chemical interpretation of the PLS-based MIA-QSAR models, in terms of the structural moieties affecting the modeled bioactivity has not been straightforward. This work describes the 2D-contour maps based on the PLS regression coefficients, as a means of assessing the relevance of single MIA predictors to the response variable, and thus allowing for the structural, electronic and physicochemical interpretation of the MIA-QSAR models. A sample study to demonstrate the utility of the 2D-contour maps to design novel drug-like molecules is performed using a dataset of some anti-HIV-1 2-amino-6-arylsulfonylbenzonitriles and derivatives, and the inferences obtained are consistent with other reports in the literature. In addition, the different schemes for encoding atomic properties in molecules are discussed and evaluated.

Local heat transfer coefficients during boiling of R22 and R407C in horizontal smooth and microfin tubes; Coefficients d'echange locaux au cours de l'ebullition du R22 et du R407C dans des tubes horizontaux, lisse ou micro-ailete

Energy Technology Data Exchange (ETDEWEB)

Lallemand, M; Branescu, C; Haberschill, P [Centre de Thermique, INSA-CNRS, UMR 5008, Villeurbanne (France)

2001-07-01

The purpose of this study is to experimentally investigate forced convective boiling. The heat transfer coefficients of pure refrigerant R22 and non azeotropic refrigerant mixture R407C were measured in both a smooth tube and a microfin tube. The tests have been carried out with a uniform heat flux all along the tube length. The refrigerant mass flux was varied from 100 to 300 kg m{sup -2} s{sup -1} and heat fluxes from 10 to 30 kW m{sup -2}. Local heat transfer coefficient depend strongly on heat flux at a low quality and on mass fluxes at a high quality. When compared to smooth tube, the microfin tubes exhibit a significant heat transfer enhancement, up to 180%. In comparison to R22, the R407C heat transfer coefficients of smooth and microfin tubes are 15 to 35% lower, respectively. The best heat transfer enhancement is obtained at low heat flux and mass flow rate. (Author)

Comparing Regression Coefficients between Nested Linear Models for Clustered Data with Generalized Estimating Equations

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Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer

2013-01-01

Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…

Biostatistics Series Module 6: Correlation and Linear Regression.

Science.gov (United States)

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

[Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

Science.gov (United States)

Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi

2018-04-01

Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.

Partial F-tests with multiply imputed data in the linear regression framework via coefficient of determination.

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Chaurasia, Ashok; Harel, Ofer

2015-02-10

Tests for regression coefficients such as global, local, and partial F-tests are common in applied research. In the framework of multiple imputation, there are several papers addressing tests for regression coefficients. However, for simultaneous hypothesis testing, the existing methods are computationally intensive because they involve calculation with vectors and (inversion of) matrices. In this paper, we propose a simple method based on the scalar entity, coefficient of determination, to perform (global, local, and partial) F-tests with multiply imputed data. The proposed method is evaluated using simulated data and applied to suicide prevention data. Copyright © 2014 John Wiley & Sons, Ltd.

SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients.

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Weaver, Bruce; Wuensch, Karl L

2013-09-01

Several procedures that use summary data to test hypotheses about Pearson correlations and ordinary least squares regression coefficients have been described in various books and articles. To our knowledge, however, no single resource describes all of the most common tests. Furthermore, many of these tests have not yet been implemented in popular statistical software packages such as SPSS and SAS. In this article, we describe all of the most common tests and provide SPSS and SAS programs to perform them. When they are applicable, our code also computes 100 × (1 - α)% confidence intervals corresponding to the tests. For testing hypotheses about independent regression coefficients, we demonstrate one method that uses summary data and another that uses raw data (i.e., Potthoff analysis). When the raw data are available, the latter method is preferred, because use of summary data entails some loss of precision due to rounding.

[From clinical judgment to linear regression model.

Science.gov (United States)

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Flow condensation heat transfer coefficients of R22, R134a, R407C, and R410A inside plain and microfin tubes

Energy Technology Data Exchange (ETDEWEB)

Jung, Dongsoo; Cho, Youngmok; Park, Kiho [Inha Univ., Incheon (Korea). Dept. of Mechanical Engineering

2004-01-01

Flow condensation heat transfer coefficients (HTCs) of R22, R134a, R407C, and R410A inside horizontal plain and microfin tubes of 9.52 mm outside diameter and 1 m length were measured at the condensation temperature of 40{sup o}C with mass fluxes of 100, 200, and 300 kg m{sup -2} s{sup -1} and a heat flux of 7.7-7.9 kW m{sup -2}. For a plain tube, HTCs of R134a and R410A were similar to those of R22 while HTCs of R407C are 11-15% lower than those of R22. For a microfin tube, HTCs of R134a were similar to those of R22 while HTCs of R407C and R410A were 23-53% and 10-21% lower than those of R22. For a plain tube, our correlation agreed well with the present data for all refrigerants exhibiting a mean deviation of 11.6%. Finally, HTCs of a microfin tube were 2-3 times higher than those of a plain tube and the heat transfer enhancement factor decreased as the mass flux increased for all refrigerants tested. (Author)

The performance of random coefficient regression in accounting for residual confounding.

Science.gov (United States)

Gustafson, Paul; Greenland, Sander

2006-09-01

Greenland (2000, Biometrics 56, 915-921) describes the use of random coefficient regression to adjust for residual confounding in a particular setting. We examine this setting further, giving theoretical and empirical results concerning the frequentist and Bayesian performance of random coefficient regression. Particularly, we compare estimators based on this adjustment for residual confounding to estimators based on the assumption of no residual confounding. This devolves to comparing an estimator from a nonidentified but more realistic model to an estimator from a less realistic but identified model. The approach described by Gustafson (2005, Statistical Science 20, 111-140) is used to quantify the performance of a Bayesian estimator arising from a nonidentified model. From both theoretical calculations and simulations we find support for the idea that superior performance can be obtained by replacing unrealistic identifying constraints with priors that allow modest departures from those constraints. In terms of point-estimator bias this superiority arises when the extent of residual confounding is substantial, but the advantage is much broader in terms of interval estimation. The benefit from modeling residual confounding is maintained when the prior distributions employed only roughly correspond to reality, for the standard identifying constraints are equivalent to priors that typically correspond much worse.

Mixed Frequency Data Sampling Regression Models: The R Package midasr

Directory of Open Access Journals (Sweden)

Eric Ghysels

2016-08-01

Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.

Isothermal (vapour + liquid) equilibrium for the binary {l_brace}1,1,2,2-tetrafluoroethane (R134) + propane (R290){r_brace} and {l_brace}1,1,2,2-tetrafluoroethane (R134) + isobutane (R600a){r_brace} systems

Energy Technology Data Exchange (ETDEWEB)

Dong Xueqiang [Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, P.O. Box 2711, Beijing 100190 (China); Graduate University of Chinese Academy of Sciences, Beijing 100039 (China); Gong Maoqiong, E-mail: gongmq@mail.ipc.ac.c [Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, P.O. Box 2711, Beijing 100190 (China); Liu Junsheng [Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, P.O. Box 2711, Beijing 100190 (China); Graduate University of Chinese Academy of Sciences, Beijing 100039 (China); Wu Jianfeng, E-mail: jfwu@mail.ipc.ac.c [Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, P.O. Box 2711, Beijing 100190 (China)

2010-09-15

(Vapour + liquid) equilibrium (VLE) data for the binary systems of {l_brace}1,1,2,2-tetrafluoroethane (R134) + propane (R290){r_brace} and {l_brace}1,1,2,2-tetrafluoroethane (R134) + isobutane (R600a){r_brace} were measured with a recirculation method at the temperatures ranging from (263.15 to 278.15) K and (268.15 to 288.15) K, respectively. All of the data were correlated by the Peng-Robinson (PR) equation of state (EoS) with the Huron-Vidal (HV) mixing rules utilizing the non-random two-liquid (NRTL) activity coefficient model. Good agreement can be found between the experimental data and the correlated results. Azeotropic behaviour can be found at the measured temperature ranges for these two mixtures.

Synthesis of linear regression coefficients by recovering the within-study covariance matrix from summary statistics.

Science.gov (United States)

Yoneoka, Daisuke; Henmi, Masayuki

2017-06-01

Recently, the number of regression models has dramatically increased in several academic fields. However, within the context of meta-analysis, synthesis methods for such models have not been developed in a commensurate trend. One of the difficulties hindering the development is the disparity in sets of covariates among literature models. If the sets of covariates differ across models, interpretation of coefficients will differ, thereby making it difficult to synthesize them. Moreover, previous synthesis methods for regression models, such as multivariate meta-analysis, often have problems because covariance matrix of coefficients (i.e. within-study correlations) or individual patient data are not necessarily available. This study, therefore, proposes a brief explanation regarding a method to synthesize linear regression models under different covariate sets by using a generalized least squares method involving bias correction terms. Especially, we also propose an approach to recover (at most) threecorrelations of covariates, which is required for the calculation of the bias term without individual patient data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

Science.gov (United States)

Iacobucci, Dawn; Schneider, Matthew J; Popovich, Deidre L; Bakamitsos, Georgios A

2017-02-01

In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R 2 will remain undisturbed (which is also good).

Retro-regression--another important multivariate regression improvement.

Science.gov (United States)

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Overcoming multicollinearity in multiple regression using correlation coefficient

Science.gov (United States)

Zainodin, H. J.; Yap, S. J.

2013-09-01

Multicollinearity happens when there are high correlations among independent variables. In this case, it would be difficult to distinguish between the contributions of these independent variables to that of the dependent variable as they may compete to explain much of the similar variance. Besides, the problem of multicollinearity also violates the assumption of multiple regression: that there is no collinearity among the possible independent variables. Thus, an alternative approach is introduced in overcoming the multicollinearity problem in achieving a well represented model eventually. This approach is accomplished by removing the multicollinearity source variables on the basis of the correlation coefficient values based on full correlation matrix. Using the full correlation matrix can facilitate the implementation of Excel function in removing the multicollinearity source variables. It is found that this procedure is easier and time-saving especially when dealing with greater number of independent variables in a model and a large number of all possible models. Hence, in this paper detailed insight of the procedure is shown, compared and implemented.

MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units.

Science.gov (United States)

Juttukonda, Meher R; Mersereau, Bryant G; Chen, Yasheng; Su, Yi; Rubin, Brian G; Benzinger, Tammie L S; Lalush, David S; An, Hongyu

2015-05-15

MR-based correction for photon attenuation in PET/MRI remains challenging, particularly for neurological applications requiring quantitation of data. Existing methods are either not sufficiently accurate or are limited by the computation time required. The goal of this study was to develop an MR-based attenuation correction method that accurately separates bone tissue from air and provides continuous-valued attenuation coefficients for bone. PET/MRI and CT datasets were obtained from 98 subjects (mean age [±SD]: 66yrs [±9.8], 57 females) using an IRB-approved protocol and with informed consent. Subjects were injected with 352±29MBq of (18)F-Florbetapir tracer, and PET acquisitions were begun either immediately or 50min after injection. CT images of the head were acquired separately using a PET/CT system. Dual echo ultrashort echo-time (UTE) images and two-point Dixon images were acquired. Regions of air were segmented via a threshold of the voxel-wise multiplicative inverse of the UTE echo 1 image. Regions of bone were segmented via a threshold of the R2* image computed from the UTE echo 1 and UTE echo 2 images. Regions of fat and soft tissue were segmented using fat and water images decomposed from the Dixon images. Air, fat, and soft tissue were assigned linear attenuation coefficients (LACs) of 0, 0.092, and 0.1cm(-1), respectively. LACs for bone were derived from a regression analysis between corresponding R2* and CT values. PET images were reconstructed using the gold standard CT method and the proposed CAR-RiDR method. The RiDR segmentation method produces mean Dice coefficient±SD across subjects of 0.75±0.05 for bone and 0.60±0.08 for air. The CAR model for bone LACs greatly improves accuracy in estimating CT values (28.2%±3.0 mean error) compared to the use of a constant CT value (46.9%±5.8, punits. From our analysis, we conclude that the proposed method closely approaches (<3% error) the gold standard CT-scaled method in PET reconstruction accuracy

Molecular Descriptors Family on Structure Activity Relationships 6. Octanol-Water Partition Coefficient of Polychlorinated Biphenyls

Directory of Open Access Journals (Sweden)

Lorentz JÄNTSCHI

2006-01-01

Full Text Available Octanol-water partition coefficient of two hundred and six polychlorinated biphenyls was model by the use of an original method based on complex information obtained from compounds structure. The regression analysis shows that best results are obtained in four-varied model (r2 = 0.9168. The prediction ability of the model was studied through leave-one-out analysis (r2cv(loo = 0.9093 and in training and test sets analysis. Modeling the octanol-water partition coefficient of polychlorinated biphenyls by integration of complex structural information provide a stable and performing four-varied model, allowing us to make remarks about relationship between structure of polychlorinated biphenyls and associated octanol-water partition coefficients.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

Science.gov (United States)

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

The microcomputer scientific software series 2: general linear model--regression.

Science.gov (United States)

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

QSAR Modeling of COX -2 Inhibitory Activity of Some Dihydropyridine and Hydroquinoline Derivatives Using Multiple Linear Regression (MLR) Method.

Science.gov (United States)

Akbari, Somaye; Zebardast, Tannaz; Zarghi, Afshin; Hajimahdi, Zahra

2017-01-01

COX-2 inhibitory activities of some 1,4-dihydropyridine and 5-oxo-1,4,5,6,7,8-hexahydroquinoline derivatives were modeled by quantitative structure-activity relationship (QSAR) using stepwise-multiple linear regression (SW-MLR) method. The built model was robust and predictive with correlation coefficient (R 2 ) of 0.972 and 0.531 for training and test groups, respectively. The quality of the model was evaluated by leave-one-out (LOO) cross validation (LOO correlation coefficient (Q 2 ) of 0.943) and Y-randomization. We also employed a leverage approach for the defining of applicability domain of model. Based on QSAR models results, COX-2 inhibitory activity of selected data set had correlation with BEHm6 (highest eigenvalue n. 6 of Burden matrix/weighted by atomic masses), Mor03u (signal 03/unweighted) and IVDE (Mean information content on the vertex degree equality) descriptors which derived from their structures.

pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms.

Science.gov (United States)

Molnos, Sophie; Baumbach, Clemens; Wahl, Simone; Müller-Nurasyid, Martina; Strauch, Konstantin; Wang-Sattler, Rui; Waldenberger, Melanie; Meitinger, Thomas; Adamski, Jerzy; Kastenmüller, Gabi; Suhre, Karsten; Peters, Annette; Grallert, Harald; Theis, Fabian J; Gieger, Christian

2017-09-29

Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different "omics" layers. Existing tools only consider single-nucleotide polymorphism (SNP)-SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different "omics" layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/ .

Insilico study of the A(2A)R-D (2)R kinetics and interfacial contact surface for heteromerization.

Science.gov (United States)

Prakash, Amresh; Luthra, Pratibha Mehta

2012-10-01

G-protein-coupled receptors (GPCRs) are cell surface receptors. The dynamic property of receptor-receptor interactions in GPCRs modulates the kinetics of G-protein signaling and stability. In the present work, the structural and dynamic study of A(2A)R-D(2)R interactions was carried to acquire the understanding of the A(2A)R-D(2)R receptor activation and deactivation process, facilitating the design of novel drugs and therapeutic target for Parkinson's disease. The structure-based features (Alpha, Beta, SurfAlpha, and SurfBeta; GapIndex, Leakiness and Gap Volume) and slow mode model (ENM) facilitated the prediction of kinetics (K (off), K (on), and K (d)) of A(2A)R-D(2)R interactions. The results demonstrated the correlation coefficient 0.294 for K (d) and K (on) and the correlation coefficient 0.635 for K (d) and K (off), and indicated stable interfacial contacts in the formation of heterodimer. The coulombic interaction involving the C-terminal tails of the A(2A)R and intracellular loops (ICLs) of D(2)R led to the formation of interfacial contacts between A(2A)R-D(2)R. The properties of structural dynamics, ENM and KFC server-based hot-spot analysis illustrated the stoichiometry of A(2A)R-D(2)R contact interfaces as dimer. The propensity of amino acid residues involved in A(2A)R-D(2)R interaction revealed the presence of positively (R, H and K) and negatively (E and D) charged structural motif of TMs and ICL3 of A(2A)R and D(2)R at interface of dimer contact. Essentially, in silico structural and dynamic study of A(2A)R-D(2)R interactions will provide the basic understanding of the A(2A)R-D(2)R interfacial contact surface for activation and deactivation processes, and could be used as constructive model to recognize the protein-protein interactions in receptor assimilations.

Rate coefficients of open shell molecules and radicals: R-matrix ...

Indian Academy of Sciences (India)

2017-04-07

Apr 7, 2017 ... Rate coefficients of open shell molecules and radicals: R-matrix method. JASMEET SINGH1 ... lasers, study of structure of DNA and astrophysics which require a ..... [6] CCPForge, http://ccpforge.cse.rl.ac.uk/projects/ukrmol-in/.

On two flexible methods of 2-dimensional regression analysis

Czech Academy of Sciences Publication Activity Database

Volf, Petr

2012-01-01

Roč. 18, č. 4 (2012), s. 154-164 ISSN 1803-9782 Grant - others:GA ČR(CZ) GAP209/10/2045 Institutional support: RVO:67985556 Keywords : regression analysis * Gordon surface * prediction error * projection pursuit Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/SI/volf-on two flexible methods of 2-dimensional regression analysis.pdf

Quantitative structure-property relationship study of n-octanol-water partition coefficients of some of diverse drugs using multiple linear regression

International Nuclear Information System (INIS)

Ghasemi, Jahanbakhsh; Saaidpour, Saadi

2007-01-01

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structures of 150 drug organic compounds to their n-octanol-water partition coefficients (log P o/w ). Molecular descriptors derived solely from 3D structures of the molecular drugs. A genetic algorithm was also applied as a variable selection tool in QSPR analysis. The models were constructed using 110 molecules as training set, and predictive ability tested using 40 compounds. Modeling of log P o/w of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR). Four descriptors for these compounds molecular volume (MV) (geometrical), hydrophilic-lipophilic balance (HLB) (constitutional), hydrogen bond forming ability (HB) (electronic) and polar surface area (PSA) (electrostatic) are taken as inputs for the model. The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of log P o/w for molecules not yet synthesized. Application of the developed model to a testing set of 40 drug organic compounds demonstrates that the model is reliable with good predictive accuracy and simple formulation. The prediction results are in good agreement with the experimental value. The root mean square error of prediction (RMSEP) and square correlation coefficient (R 2 ) for MLR model were 0.22 and 0.99 for the prediction set log P o/w

Measurements of Heat-Transfer and Friction Coefficients for Helium Flowing in a Tube at Surface Temperatures up to 5900 Deg R

Science.gov (United States)

Taylor, Maynard F.; Kirchgessner, Thomas A.

1959-01-01

Measurements of average heat transfer and friction coefficients and local heat transfer coefficients were made with helium flowing through electrically heated smooth tubes with length-diameter ratios of 60 and 92 for the following range of conditions: Average surface temperature from 1457 to 4533 R, Reynolds numbe r from 3230 to 60,000, heat flux up to 583,200 Btu per hr per ft2 of heat transfer area, and exit Mach numbe r up to 1.0. The results indicate that, in the turbulent range of Reynolds number, good correlation of the local heat transfer coefficients is obtained when the physical properties and density of helium are evaluated at the surface temperature. The average heat transfer coefficients are best correlated on the basis that the coefficient varies with [1 + (L/D))(sup -0,7)] and that the physical properties and density are evaluated at the surface temperature. The average friction coefficients for the tests with no heat addition are in complete agreement with the Karman-Nikuradse line. The average friction coefficients for heat addition are in poor agreement with the accepted line.

The adsorption coefficient (KOC) of chlorpyrifos in clay soil

International Nuclear Information System (INIS)

Halimah Muhamad; Nashriyah Mat; Tan Yew Ai; Ismail Sahid

2005-01-01

The purpose of this study was to determine the adsorption coefficient (KOC) of chlorpyrifos in clay soil by measuring the Freundlich adsorption coefficient (Kads(f)) and desorption coefficient (1/n value) of chlorpyrifos. It was found that the Freundlich adsorption coefficient (Kads(f)) and the linear regression (r2) of the Freundlich adsorption isotherm for chlorpyrifos in the clay soil were 52.6 L/kg and 0.5244, respectively. Adsorption equilibrium time was achieved within 24 hours for clay soil. This adsorption equilibrium time was used to determine the effect of concentration on adsorption. The adsorption coefficient (KOC) of clay soil was found to be 2783 L/kg with an initial concentration solution of 1 μg/g, soil-solution ratio (1:5) at 300 C when the equilibrium between the soil matrix and solution was 24 hours. The Kdes decreased over four repetitions of the desorption process. The chlorpyrifos residues may be strongly adsorbed onto the surface of clay. (Author)

Tutorial on Using Regression Models with Count Outcomes Using R

Directory of Open Access Journals (Sweden)

A. Alexander Beaujean

2016-02-01

Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.

MANCOVA for one way classification with homogeneity of regression coefficient vectors

Science.gov (United States)

Mokesh Rayalu, G.; Ravisankar, J.; Mythili, G. Y.

2017-11-01

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.

Octanol-air partition coefficients of polybrominated biphenyls.

Science.gov (United States)

Hongxia, Zhao; Jingwen, Chen; Xie, Quan; Baocheng, Qu; Xinmiao, Liang

2009-03-01

The octanol-air partition coefficients (K(OA)) for PBB15, PBB26, PBB31, PBB49, PBB103 and PBB153 were determined as a function of temperature using a gas chromatographic retention time technique with 1,1,1-trichloro-2,2-bis (4-chlorophenyl) ethane (p,p'-DDT) as a reference substance. The internal energies of phase change from octanol to air (Delta(OA)U) were calculated for the six compounds and were in the range from 74 to 116 kJ mol(-1). Simple regression equations of log K(OA) versus relative retention times (RRTs) on gas chromatography (GC), and log K(OA) versus molecular connectivity indexes (MCI) were obtained, for which the correlation coefficients (r(2)) were greater than 0.985 at 283.15K and 298.15K. Thus the K(OA) values of the remaining PBBs can be predicted by using their RRTs and MCI according to these relationships.

Path coefficient analysis of zinc dynamics in varying soil environment

International Nuclear Information System (INIS)

Rattan, R.K.; Phung, C.V.; Singhal, S.K.; Deb, D.L.; Singh, A.K.

1994-01-01

Influence of soil properties on labile zinc, as measured by diethylene-triamine pentaacetic acid (DTPA) and zinc-65, and self-diffusion coefficients of zinc was assessed on 22 surface soil samples varying widely in their characteristics following linear regression and path coefficient analysis techniques. DTPA extractable zinc could be predicted from organic carbon status and pH of the soil with a highly significant coefficient of determination (R 2 =0.84 ** ). Ninety seven per cent variation in isotopically exchangeable zinc was explained by pH, clay content and cation exchange capacity (CEC) of soil. The self-diffusion coefficients (DaZn and DpZn) and buffer power of zinc exhibited exponential relationship with soil properties, pH being the most dominant one. Soil properties like organic matter, clay content etc. exhibited indirect effects on zinc diffusion rates via pH only. (author). 13 refs., 6 tabs

Comparison of Utility of Histogram Apparent Diffusion Coefficient and R2* for Differentiation of Low-Grade From High-Grade Clear Cell Renal Cell Carcinoma.

Science.gov (United States)

Zhang, Yu-Dong; Wu, Chen-Jiang; Wang, Qing; Zhang, Jing; Wang, Xiao-Ning; Liu, Xi-Sheng; Shi, Hai-Bin

2015-08-01

The purpose of this study was to compare histogram analysis of apparent diffusion coefficient (ADC) and R2* for differentiating low-grade from high-grade clear cell renal cell carcinoma (RCC). Forty-six patients with pathologically confirmed clear cell RCC underwent preoperative BOLD and DWI MRI of the kidneys. ADCs based on the entire tumor volume were calculated with b value combinations of 0 and 800 s/mm(2). ROI-based R2* was calculated with eight TE combinations of 6.7-22.8 milliseconds. Histogram analysis of tumor ADCs and R2* values was performed to obtain mean; median; width; and fifth, 10th, 90th, and 95th percentiles and histogram inhomogeneity, kurtosis, and skewness for all lesions. Thirty-three low-grade and 13 high-grade clear cell RCCs were found at pathologic examination. The TNM classification and tumor volume of clear cell RCC significantly correlated with histogram ADC and R2* (ρ = -0.317 to 0.506; p histogram ADC and R2* indexes, 10th percentile ADC had the highest accuracy (91.3%) in discriminating low- from high-grade clear cell RCC. R2* in discriminating hemorrhage was achieved with a threshold of 68.95 Hz. At this threshold, high-grade clear cell RCC had a significantly higher prevalence of intratumor hemorrhage (high-grade, 76.9%; low-grade, 45.4%; p Histogram analysis of ADC and R2* allows differentiation of low- from high-grade clear cell RCC with high accuracy.

Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks

International Nuclear Information System (INIS)

Elliott Campbell, J.; Moen, Jeremie C.; Ney, Richard A.; Schnoor, Jerald L.

2008-01-01

Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively. - Large differences in estimates of soil organic carbon stocks and annual changes in stocks for Wisconsin forestlands indicate a need for validation from forthcoming forest surveys

Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

CERN Document Server

Faraway, Julian J

2005-01-01

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

Science.gov (United States)

Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

2016-03-01

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

Reduced Rank Regression

DEFF Research Database (Denmark)

Johansen, Søren

2008-01-01

The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

Science.gov (United States)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

THE DETERMINATION OF BETA COEFFICIENTS OF PUBLICLY-HELD COMPANIES BY A REGRESSION MODEL AND AN APPLICATION ON PRIVATE FIRMS

Directory of Open Access Journals (Sweden)

METİN KAMİL ERCAN

2013-06-01

Full Text Available It is possible to determine the value of private companies by means of suggestions and assumptions derived from their financial statements. However, there comes out a serious problem in the determination of equity costs of these private companies using Capital Assets Pricing Model (CAPM as beta coefficients are unknown or unavailable. In this study, firstly, a regression model that represents the relationship between the beta coefficients and financial statements’ Variables of publicly-held companies will be developed. Then, this model will be tested and applied on private companies.

H2 line-mixing coefficients in the ν2 and ν4 bands of PH3 at low temperature

International Nuclear Information System (INIS)

Salem, Jamel; Blanquet, Ghislain; Lepère, Muriel; Aroui, Hassen

2016-01-01

Using a tunable diode-laser spectrometer adapted with a low temperature cell, we have measured the H 2 line-mixing coefficients for 21 lines in the Q R branch of the ν 2 band and in the P P and R P branches of the ν 4 band of phosphine (PH 3 ) at low temperature. These coefficients were determined using a multi-pressure fitting procedure that accounts for the apparatus function, the Doppler and the collisional effects. These lines with J values ranging from 2 to 11 and K from 0 to 9 are located in the spectral range from 1016 to 1093 cm −1 . The variations of these parameters with the temperature, and the ro-vibrational quantum numbers are discussed. - Graphical abstract: Comparisons of the determined line-mixing coefficients (in atm −1 ) obtained in this study in the ν 2 and ν 4 bands of PH 3 at T=173.2 K with those measured at T=298 K for different values of the J quantum number. - Highlights: • The spectra have been recorded with a tunable diode-laser spectrometer at 173.2 K. • The line-mixing coefficients are determined by a multi-pressure fitting procedure. • The effect of the line-mixing in the spectra, appear to be important.

Distributed Monitoring of the R2 Statistic for Linear Regression

Data.gov (United States)

National Aeronautics and Space Administration — The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and...

Interpreting Bivariate Regression Coefficients: Going beyond the Average

Science.gov (United States)

Halcoussis, Dennis; Phillips, G. Michael

2010-01-01

Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…

A regression approach for Zircaloy-2 in-reactor creep constitutive equations

International Nuclear Information System (INIS)

Yung Liu, Y.; Bement, A.L.

1977-01-01

In this paper the methodology of multiple regressions as applied to Zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor Zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) When there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets. (2) Regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections. Multiple regression analysis performed on a set of carefully selected Zircaloy-2 in-reactor creep data leads to a model which provides excellent correlations for the data. (Auth.)

Moderation analysis using a two-level regression model.

Science.gov (United States)

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

A Quantitative Property-Property Relationship for the Internal Diffusion Coefficients of Organic Compounds in Solid Materials

DEFF Research Database (Denmark)

Huang, Lei; Fantke, Peter; Jolliet, Olivier

2017-01-01

of chemical-material combinations. This paper develops and evaluates a quantitative property-property relationship (QPPR) to predict diffusion coefficients for a wide range of organic chemicals and materials. We first compiled a training dataset of 1103 measured diffusion coefficients for 158 chemicals in 32......Indoor releases of organic chemicals encapsulated in solid materials are major contributors to human exposures and are directly related to the internal diffusion coefficient in solid materials. Existing correlations to estimate the diffusion coefficient are only valid for a limited number...... consolidated material types. Following a detailed analysis of the temperature influence, we developed a multiple linear regression model to predict diffusion coefficients as a function of chemical molecular weight (MW), temperature, and material type (adjusted R2 of 0.93). The internal validations showed...

The R Package threg to Implement Threshold Regression Models

Directory of Open Access Journals (Sweden)

Tao Xiao

2015-08-01

This new package includes four functions: threg, and the methods hr, predict and plot for threg objects returned by threg. The threg function is the model-fitting function which is used to calculate regression coefficient estimates, asymptotic standard errors and p values. The hr method for threg objects is the hazard-ratio calculation function which provides the estimates of hazard ratios at selected time points for specified scenarios (based on given categories or value settings of covariates. The predict method for threg objects is used for prediction. And the plot method for threg objects provides plots for curves of estimated hazard functions, survival functions and probability density functions of the first-hitting-time; function curves corresponding to different scenarios can be overlaid in the same plot for comparison to give additional research insights.

Vector regression introduced

Directory of Open Access Journals (Sweden)

Mok Tik

2014-06-01

Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

Discharge Coefficient of Rectangular Short-Crested Weir with Varying Slope Coefficients

Directory of Open Access Journals (Sweden)

Yuejun Chen

2018-02-01

Full Text Available Rectangular short-crested weirs are widely used for simple structure and high discharge capacity. As one of the most important and influential factors of discharge capacity, side slope can improve the hydraulic characteristics of weirs at special conditions. In order to systemically study the effects of upstream and downstream slope coefficients S1 and S2 on overflow discharge coefficient in a rectangular short-crested weir the Volume of Fluid (VOF method and the Renormalization Group (RNG κ-ε turbulence model are used. In this study, the slope coefficient ranges from V to 3H:1V and each model corresponds to five total energy heads of H0 ranging from 8.0 to 24.0 cm. Comparisons of discharge coefficients and free surface profiles between simulated and laboratory results display a good agreement. The simulated results show that the difference of discharge coefficients will decrease with upstream slopes and increase with downstream slopes as H0 increases. For a given H0, the discharge coefficient has a convex parabolic relation with S1 and a piecewise linearity relation with S2. The maximum discharge coefficient is always obtained at S2 = 0.8. There exists a difference between upstream and downstream slope coefficients in the influence range of free surface curvatures. Furthermore, a proposed discharge coefficient equation by nonlinear regression is a function of upstream and downstream slope coefficients.

Panel Smooth Transition Regression Models

DEFF Research Database (Denmark)

González, Andrés; Terasvirta, Timo; Dijk, Dick van

We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Science.gov (United States)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients.

Science.gov (United States)

Kim, Seongho

2015-11-01

Lack of a general matrix formula hampers implementation of the semi-partial correlation, also known as part correlation, to the higher-order coefficient. This is because the higher-order semi-partial correlation calculation using a recursive formula requires an enormous number of recursive calculations to obtain the correlation coefficients. To resolve this difficulty, we derive a general matrix formula of the semi-partial correlation for fast computation. The semi-partial correlations are then implemented on an R package ppcor along with the partial correlation. Owing to the general matrix formulas, users can readily calculate the coefficients of both partial and semi-partial correlations without computational burden. The package ppcor further provides users with the level of the statistical significance with its test statistic.

Assessing the reliability of the borderline regression method as a standard setting procedure for objective structured clinical examination

Directory of Open Access Journals (Sweden)

Sara Mortaz Hejri

2013-01-01

Full Text Available Background: One of the methods used for standard setting is the borderline regression method (BRM. This study aims to assess the reliability of BRM when the pass-fail standard in an objective structured clinical examination (OSCE was calculated by averaging the BRM standards obtained for each station separately. Materials and Methods: In nine stations of the OSCE with direct observation the examiners gave each student a checklist score and a global score. Using a linear regression model for each station, we calculated the checklist score cut-off on the regression equation for the global scale cut-off set at 2. The OSCE pass-fail standard was defined as the average of all station′s standard. To determine the reliability, the root mean square error (RMSE was calculated. The R2 coefficient and the inter-grade discrimination were calculated to assess the quality of OSCE. Results: The mean total test score was 60.78. The OSCE pass-fail standard and its RMSE were 47.37 and 0.55, respectively. The R2 coefficients ranged from 0.44 to 0.79. The inter-grade discrimination score varied greatly among stations. Conclusion: The RMSE of the standard was very small indicating that BRM is a reliable method of setting standard for OSCE, which has the advantage of providing data for quality assurance.

A review of a priori regression models for warfarin maintenance dose prediction.

Directory of Open Access Journals (Sweden)

Ben Francis

Full Text Available A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.

A review of a priori regression models for warfarin maintenance dose prediction.

Science.gov (United States)

Francis, Ben; Lane, Steven; Pirmohamed, Munir; Jorgensen, Andrea

2014-01-01

A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.

Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability.

Science.gov (United States)

Wu, Chih-Da; Chen, Yu-Cheng; Pan, Wen-Chi; Zeng, Yu-Ting; Chen, Mu-Jean; Guo, Yue Leon; Lung, Shih-Chun Candice

2017-05-01

This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples and Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM 2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM 2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM 2.5 measurements from 2013 were used for external data verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled with buffer analysis were used to assess the spatial-temporal variations of greenness surrounding the monitoring sites. The distribution of temples and Chinese restaurants were included to represent the emission contributions from incense and joss money burning, and gas cooking, respectively. Spearman correlation coefficient and stepwise regression were used for LUR model development, and 10-fold cross-validation and external data verification were applied to verify the model reliability. The results showed a strongly negative correlation (r: -0.71 to -0.77) between NDVI and PM 2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM 2.5 concentrations. With the adjusted model R 2 of 0.89, a cross-validated adj-R 2 of 0.90, and external validated R 2 of 0.83, the high explanatory power of the resultant model was confirmed. Moreover, the averaged NDVI within a 1750 m circular buffer (p < 0.01), the number of Chinese restaurants within a 1750 m buffer (p < 0.01), and the number of temples within a 750 m buffer (p = 0.06) were selected as important predictors during the stepwise selection procedures. According to the partial R 2 , NDVI explained 66% of PM 2.5 variation and was the dominant variable in the developed model. We suggest future studies

Osmotic and activity coefficients of {l_brace}y Na{sub 2}SO{sub 4} + (1 - y) ZnSO{sub 4}{r_brace}(aq) at T = 298.15 K

Energy Technology Data Exchange (ETDEWEB)

Marjanovic, V. [High Technical School, Trg Svetog Save 34, 31 000 Uzice (Serbia and Montenegro); Ninkovic, R. [Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11 000 Belgrade (Serbia and Montenegro); Miladinovic, J. [Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11 000 Belgrade (Serbia and Montenegro)]. E-mail: duma@elab.tmf.bg.ac.yu; Todorovic, M. [Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11 000 Belgrade (Serbia and Montenegro); Pavicevic, V. [Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11 000 Belgrade (Serbia and Montenegro)

2005-02-01

The osmotic coefficients of the mixed electrolyte solution {l_brace}y Na{sub 2}SO{sub 4} + (1 - y) ZnSO{sub 4}{r_brace}(aq) have been measured by the isopiestic method, at T = 298.5 K. The experimental results were treated by Scatchard's, Pitzer-Kim's and Clegg-Pitzer-Brimblecombe's methods for mixed-electrolyte solutions. By these methods, the activity coefficients for Na{sub 2}SO{sub 4} and ZnSO{sub 4} were calculated and compared. The Scatchard interaction parameters are used for calculation of the excess Gibbs free energy as a function of ionic strength and ionic-strength fraction of Na{sub 2}SO{sub 4}. Also, the Zdanovskii's rule of linearity is tested.

Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques

Energy Technology Data Exchange (ETDEWEB)

Deng, Yangyang; Parajuli, Prem B.

2011-08-10

Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.

Can multi-slice or navigator-gated R2* MRI replace single-slice breath-hold acquisition for hepatic iron quantification?

International Nuclear Information System (INIS)

Loeffler, Ralf B.; McCarville, M.B.; Song, Ruitian; Hillenbrand, Claudia M.; Wagstaff, Anne W.; Smeltzer, Matthew P.; Krafft, Axel J.; Hankins, Jane S.

2017-01-01

Liver R2* values calculated from multi-gradient echo (mGRE) magnetic resonance images (MRI) are strongly correlated with hepatic iron concentration (HIC) as shown in several independently derived biopsy calibration studies. These calibrations were established for axial single-slice breath-hold imaging at the location of the portal vein. Scanning in multi-slice mode makes the exam more efficient, since whole-liver coverage can be achieved with two breath-holds and the optimal slice can be selected afterward. Navigator echoes remove the need for breath-holds and allow use in sedated patients. To evaluate if the existing biopsy calibrations can be applied to multi-slice and navigator-controlled mGRE imaging in children with hepatic iron overload, by testing if there is a bias-free correlation between single-slice R2* and multi-slice or multi-slice navigator controlled R2*. This study included MRI data from 71 patients with transfusional iron overload, who received an MRI exam to estimate HIC using gradient echo sequences. Patient scans contained 2 or 3 of the following imaging methods used for analysis: single-slice images (n = 71), multi-slice images (n = 69) and navigator-controlled images (n = 17). Small and large blood corrected region of interests were selected on axial images of the liver to obtain R2* values for all data sets. Bland-Altman and linear regression analysis were used to compare R2* values from single-slice images to those of multi-slice images and navigator-controlled images. Bland-Altman analysis showed that all imaging method comparisons were strongly associated with each other and had high correlation coefficients (0.98 ≤ r ≤ 1.00) with P-values ≤0.0001. Linear regression yielded slopes that were close to 1. We found that navigator-gated or breath-held multi-slice R2* MRI for HIC determination measures R2* values comparable to the biopsy-validated single-slice, single breath-hold scan. We conclude that these three R2* methods can be

Can multi-slice or navigator-gated R2* MRI replace single-slice breath-hold acquisition for hepatic iron quantification?

Energy Technology Data Exchange (ETDEWEB)

Loeffler, Ralf B.; McCarville, M.B.; Song, Ruitian; Hillenbrand, Claudia M. [St. Jude Children' s Research Hospital, Diagnostic Imaging, Memphis, TN (United States); Wagstaff, Anne W. [St. Jude Children' s Research Hospital, Diagnostic Imaging, Memphis, TN (United States); Rhodes College, Memphis, TN (United States); University of Alabama at Birmingham School of Medicine, Birmingham, AL (United States); Smeltzer, Matthew P. [St. Jude Children' s Research Hospital, Department of Biostatistics, Memphis, TN (United States); University of Memphis, Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, Memphis, TN (United States); Krafft, Axel J. [St. Jude Children' s Research Hospital, Diagnostic Imaging, Memphis, TN (United States); University Hospital Center Freiburg, Department of Radiology, Freiburg (Germany); Hankins, Jane S. [St. Jude Children' s Research Hospital, Department of Hematology, Memphis, TN (United States)

2017-01-15

Liver R2* values calculated from multi-gradient echo (mGRE) magnetic resonance images (MRI) are strongly correlated with hepatic iron concentration (HIC) as shown in several independently derived biopsy calibration studies. These calibrations were established for axial single-slice breath-hold imaging at the location of the portal vein. Scanning in multi-slice mode makes the exam more efficient, since whole-liver coverage can be achieved with two breath-holds and the optimal slice can be selected afterward. Navigator echoes remove the need for breath-holds and allow use in sedated patients. To evaluate if the existing biopsy calibrations can be applied to multi-slice and navigator-controlled mGRE imaging in children with hepatic iron overload, by testing if there is a bias-free correlation between single-slice R2* and multi-slice or multi-slice navigator controlled R2*. This study included MRI data from 71 patients with transfusional iron overload, who received an MRI exam to estimate HIC using gradient echo sequences. Patient scans contained 2 or 3 of the following imaging methods used for analysis: single-slice images (n = 71), multi-slice images (n = 69) and navigator-controlled images (n = 17). Small and large blood corrected region of interests were selected on axial images of the liver to obtain R2* values for all data sets. Bland-Altman and linear regression analysis were used to compare R2* values from single-slice images to those of multi-slice images and navigator-controlled images. Bland-Altman analysis showed that all imaging method comparisons were strongly associated with each other and had high correlation coefficients (0.98 ≤ r ≤ 1.00) with P-values ≤0.0001. Linear regression yielded slopes that were close to 1. We found that navigator-gated or breath-held multi-slice R2* MRI for HIC determination measures R2* values comparable to the biopsy-validated single-slice, single breath-hold scan. We conclude that these three R2* methods can be

Crystal-fields at rare-earth sites in R2Fe14B compounds

International Nuclear Information System (INIS)

Adam, S.; Adam, G.; Burzo, E.

1985-12-01

Crystal-field effects are expected to be important in R 2 Fe 14 B compounds. Within a model-independent approach, it is proved that four distinct rare-earth sites exist with respect to the crystalline electric fields, namely, R(4f; z=0), R(4f; z=0.5 c), R(4g; z=0), and R(4g; z=0.5 c), and relationships are established between the corresponding crystal-fields coefficients. Further, generalized Stevens parametrizations of the crystal field coefficients are derived at three levels of approximation for the interatomic forces inside the crystal. A crystal lattice dressing effect upon the radial electronic integrals is found to occur, the magnitude of which depends on the deviation of the interatomic forces from Coulombian. Finally, computation of crystal-field coefficients in Nd 2 Fe 14 B leads to results which raise questions about the validity of the simple Coulomb point-charge model. (author)

Calculation of the dispersion-dipole coefficients for interactions between H, He, and H2

International Nuclear Information System (INIS)

Bishop, D.M.; Pipin, J.

1993-01-01

Collisions between atoms and molecules create an induced dipole moment which, at long range separations, stems, in part, from the van der Waals interactions between the colliding species. This contribution is known as the dispersion dipole moment and is of the order R -7 , where R is the separation between particles. Although there have been several approximate calculations of the dispersion-dipole coefficients which govern this contribution, and are the counterparts to the van der Waals dispersion-energy coefficients, there have been few ab initio calculations. In this article we present highly accurate results, based on explicitly electron-correlated wave functions, for the dispersion-dipole coefficients pertaining to interactions between pairs chosen from H, He, and H 2 . We also obtain values with some of the currently used approximate formulas. A comparison shows that these values differ, in general, by a significant amount (∼20--∼40 %) from the accurate ones. We also tabulate values of the dipole--dipole-quadrupole polarizability tensor (B) for imaginary frequency (iω) for a range of frequencies appropriate to a 64-point Gauss--Legendre quadrature for H, He, and H 2 . These values were used in certain numerical integrations we made to verify our original results which had been obtained by analytic integration---they may, however, be useful in other contexts. For H--H 2 and H 2 --H 2 , these are the only ab initio calculations of the dispersion-dipole coefficients of which we are aware

Relative Importance for Linear Regression in R: The Package relaimpo

Directory of Open Access Journals (Sweden)

Ulrike Gromping

2006-09-01

Full Text Available Relative importance is a topic that has seen a lot of interest in recent years, particularly in applied work. The R package relaimpo implements six different metrics for assessing relative importance of regressors in the linear model, two of which are recommended - averaging over orderings of regressors and a newly proposed metric (Feldman 2005 called pmvd. Apart from delivering the metrics themselves, relaimpo also provides (exploratory bootstrap confidence intervals. This paper offers a brief tutorial introduction to the package. The methods and relaimpo’s functionality are illustrated using the data set swiss that is generally available in R. The paper targets readers who have a basic understanding of multiple linear regression. For the background of more advanced aspects, references are provided.

Geographically weighted regression model on poverty indicator

Science.gov (United States)

Slamet, I.; Nugroho, N. F. T. A.; Muslich

2017-12-01

In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.

Regression Model to Predict Global Solar Irradiance in Malaysia

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Hairuniza Ahmed Kutty

2015-01-01

Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia

2018-04-10

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.

ISO/IEC 17025 Sysmex R-500 hematology reticulocyte analyzer validation.

Science.gov (United States)

Dimopoulou, H A; Theodoridis, T; Galea, V; Christopoulou-Cokkinou, V; Spyridaki, M-H E; Georgakopoulos, C G

2007-01-01

The Sysmex R-500 (R-500) Hematology Analyzer is a bench-top system appropriate for the analysis of limited batches of blood samples. The R-500 provides percentage proportional (RET%), absolute reticulocyte (RET#), and absolute red blood cell (RBC#) counts. The system was validated at the Doping Control Laboratory of Athens, according to the International Committee for Standardization in Hematology, International Standards Organization (ISO/IEC) 17025, and World Antidoping Agency (WADA) specifications. The instrument calibration was performed according to the manufacturer and validation parameters comprised linearity, precision, uncertainty (intermediate and long-term precision), comparability, effect of drift, carryover, stability, and accuracy. The linearity and the comparability studies for RET#, RET%, and RBC# were expressed in regression factors (R2) and coefficients of correlation [r(x, y)], respectively. For the precision studies, the coefficients of variation for RET#, RET%, and RBC# were 9.49%, 9.83%, and ISO/IEC 17025 and WADA specifications.

Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

Science.gov (United States)

Buchner, Florian; Wasem, Jürgen; Schillo, Sonja

2017-01-01

Risk equalization formulas have been refined since their introduction about two decades ago. Because of the complexity and the abundance of possible interactions between the variables used, hardly any interactions are considered. A regression tree is used to systematically search for interactions, a methodologically new approach in risk equalization. Analyses are based on a data set of nearly 2.9 million individuals from a major German social health insurer. A two-step approach is applied: In the first step a regression tree is built on the basis of the learning data set. Terminal nodes characterized by more than one morbidity-group-split represent interaction effects of different morbidity groups. In the second step the 'traditional' weighted least squares regression equation is expanded by adding interaction terms for all interactions detected by the tree, and regression coefficients are recalculated. The resulting risk adjustment formula shows an improvement in the adjusted R 2 from 25.43% to 25.81% on the evaluation data set. Predictive ratios are calculated for subgroups affected by the interactions. The R 2 improvement detected is only marginal. According to the sample level performance measures used, not involving a considerable number of morbidity interactions forms no relevant loss in accuracy. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Significance testing in ridge regression for genetic data

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De Iorio Maria

2011-09-01

Full Text Available Abstract Background Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. Results We develop and evaluate a test of significance for ridge regression coefficients. Using simulation studies, we demonstrate that the performance of the test is comparable to that of a permutation test, with the advantage of a much-reduced computational cost. We introduce the p-value trace, a plot of the negative logarithm of the p-values of ridge regression coefficients with increasing shrinkage parameter, which enables the visualisation of the change in p-value of the regression coefficients with increasing penalisation. We apply the proposed method to a lung cancer case-control data set from EPIC, the European Prospective Investigation into Cancer and Nutrition. Conclusions The proposed test is a useful alternative to a permutation test for the estimation of the significance of ridge regression coefficients, at a much-reduced computational cost. The p-value trace is an informative graphical tool for evaluating the results of a test of significance of ridge regression coefficients as the shrinkage parameter increases, and the proposed test makes its production computationally feasible.

Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network

Energy Technology Data Exchange (ETDEWEB)

Jorjani, E.; Poorali, H.A.; Sam, A.; Chelgani, S.C.; Mesroghli, S.; Shayestehfar, M.R. [Islam Azad University, Tehran (Iran). Dept. of Mining Engineering

2009-10-15

In this paper, the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate were predicted by regression and artificial neural network based on proximate and group macerals analysis. The regression method shows that the relationships between (a) in (ash), volatile matter and moisture (b) in (ash), in (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R{sup 2}) of 0.8 and 0.79, respectively. In addition, the input sets of (c) ash, volatile matter and moisture (d) ash, liptinite and fusinite can predict the combustible recovery with the correlation coefficients of 0.84 and 0.63, respectively. Feed-forward artificial neural network with 6-8-12-11-2-1 arrangement for moisture, ash and volatile matter input set was capable to estimate both combustible value and combustible recovery with correlation of 0.95. It was shown that the proposed neural network model could accurately reproduce all the effects of proximate and group macerals analysis on coal flotation system.

Support vector regression methodology for estimating global solar radiation in Algeria

Science.gov (United States)

Guermoui, Mawloud; Rabehi, Abdelaziz; Gairaa, Kacem; Benkaciali, Said

2018-01-01

Accurate estimation of Daily Global Solar Radiation (DGSR) has been a major goal for solar energy applications. In this paper we show the possibility of developing a simple model based on the Support Vector Regression (SVM-R), which could be used to estimate DGSR on the horizontal surface in Algeria based only on sunshine ratio as input. The SVM model has been developed and tested using a data set recorded over three years (2005-2007). The data was collected at the Applied Research Unit for Renewable Energies (URAER) in Ghardaïa city. The data collected between 2005-2006 are used to train the model while the 2007 data are used to test the performance of the selected model. The measured and the estimated values of DGSR were compared during the testing phase statistically using the Root Mean Square Error (RMSE), Relative Square Error (rRMSE), and correlation coefficient (r2), which amount to 1.59(MJ/m2), 8.46 and 97,4%, respectively. The obtained results show that the SVM-R is highly qualified for DGSR estimation using only sunshine ratio.

Multiple regression analysis in modelling of carbon dioxide emissions by energy consumption use in Malaysia

Science.gov (United States)

Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat

2015-04-01

Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.

Simple and multiple linear regression: sample size considerations.

Science.gov (United States)

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

Advanced statistics: linear regression, part II: multiple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Heat transfer coefficient between UO2 and Zircaloy-2

International Nuclear Information System (INIS)

Ross, A.M.; Stoute, R.L.

1962-06-01

This paper provides some experimental values of the heat-transfer coefficient between UO 2 and Zircaloy-2 surfaces in contact under conditions of interfacial pressure, temperature, surface roughness and interface atmosphere, that are relevant to UO 2 /Zircaloy-2 fuel elements operating in pressurized-water power reactors. Coefficients were obtained from eight UO 2 / Zircaloy-2 pairs in atmospheres of helium, argon, krypton or xenon, at atmosphere pressure and in vacuum. Interfacial pressures were varied from 50 to 550 kgf/cm 2 while surface roughness heights were in the range 0.2 x 10 -4 to 3.5 x 10 -4 cm. The effect on the coefficients of cycling the interfacial pressure, of interface gas pressure and of temperature were examined. The experimental values of the coefficients were used to test the predictions of expressions for the heat-transfer between two solids in contact. For the particular UO 2 / Zircaloy-2 pairs examined, numerical values were assigned to several parameters that related the surface roughnesses to either the radius of solid/solid contact spots or to the mean thickness of the interface voids and that accounted for the imperfect accommodation of the void gas on the test surfaces. (author)

Weyl q-coefficients for uq(3) and Racah q -coefficients for suq(2)

International Nuclear Information System (INIS)

Asherova, R.M.; Smirnov, Yu.F.; Tolstoy, V.N.

1996-01-01

With the aid of the projection-operator technique, the general analytic expression for the elements of the matrix that relates the U and T bases of an arbitrary finite-dimensional irreducible representation of the uq(3) quantum algebra (Weyl q-coefficients) is obtained for the case where the deformation parameter q is not equal to a square root of unity. The procedure for resummation of q-factorial expressions is used to prove that, modulo phase factors, these Weyl q-coefficients coincide with Racah q-coefficients for the suq(2) quantum algebra. It is also shown that, on the basis of one general formula, the q-analogs of all known general analytic expressions for the 6j symbols (and Racah coefficients) of the Lie algebras of the angular momentum can be obtained by using this resummation procedure. The symmetry properties of these q coefficients are discussed. The result is formulated in the following way: the general formulas for the q-6j symbols (Racah q-coefficients) of the suq(2) quantum algebra are obtained from the general formulas for the conventional 6j symbols (Racah coefficients) of the su(2) Lie algebra by replacing directly all factorials with q-factorials, the symmetry properties of the q-6j symbols being completely coincident with the symmetry properties of the conventional 6j symbols

Determination of drying kinetics and convective heat transfer coefficients of ginger slices

Science.gov (United States)

Akpinar, Ebru Kavak; Toraman, Seda

2016-10-01

In the present work, the effects of some parametric values on convective heat transfer coefficients and the thin layer drying process of ginger slices were investigated. Drying was done in the laboratory by using cyclone type convective dryer. The drying air temperature was varied as 40, 50, 60 and 70 °C and the air velocity is 0.8, 1.5 and 3 m/s. All drying experiments had only falling rate period. The drying data were fitted to the twelve mathematical models and performance of these models was investigated by comparing the determination of coefficient ( R 2), reduced Chi-square ( χ 2) and root mean square error between the observed and predicted moisture ratios. The effective moisture diffusivity and activation energy were calculated using an infinite series solution of Fick's diffusion equation. The average effective moisture diffusivity values and activation energy values varied from 2.807 × 10-10 to 6.977 × 10-10 m2/s and 19.313-22.722 kJ/mol over the drying air temperature and velocity range, respectively. Experimental data was used to evaluate the values of constants in Nusselt number expression by using linear regression analysis and consequently, convective heat transfer coefficients were determined in forced convection mode. Convective heat transfer coefficient of ginger slices showed changes in ranges 0.33-2.11 W/m2 °C.

Research on the multiple linear regression in non-invasive blood glucose measurement.

Science.gov (United States)

Zhu, Jianming; Chen, Zhencheng

2015-01-01

A non-invasive blood glucose measurement sensor and the data process algorithm based on the metabolic energy conservation (MEC) method are presented in this paper. The physiological parameters of human fingertip can be measured by various sensing modalities, and blood glucose value can be evaluated with the physiological parameters by the multiple linear regression analysis. Five methods such as enter, remove, forward, backward and stepwise in multiple linear regression were compared, and the backward method had the best performance. The best correlation coefficient was 0.876 with the standard error of the estimate 0.534, and the significance was 0.012 (sig. regression equation was valid. The Clarke error grid analysis was performed to compare the MEC method with the hexokinase method, using 200 data points. The correlation coefficient R was 0.867 and all of the points were located in Zone A and Zone B, which shows the MEC method provides a feasible and valid way for non-invasive blood glucose measurement.

Earning on Response Coefficient in Automobile and Go Public Companies

Directory of Open Access Journals (Sweden)

Lisdawati Arifin

2017-09-01

Full Text Available This study aims to analyze factors that influence earnings response coefficients (ERC, simultaneously and partially, composed of leverage, the systematic risk (beta, growth opportunities (market to book value ratio, and the size of the firm (firm size, selection of the sample in this study the author take 12 automakers and components that meet the criteria of completeness of the data from the year 2008 to 2012, entirely based on consideration of the following criteria: (1 the company's automotive and components are listed on the stock exchange, (2 have the financial statements years 2008-2012 (3 has a return data (closing price the first day after the date of issuance of the financial statements. This study uses secondary data applying multiple linear regression models to analyze and test the effect of independent variables on the dependent variable partially (t-test, simultaneous (f-test, and the goodness of fit (R-square on a research model. The result shows that leverage, beta, growth opportunities (market to book value ratio and size along with (simultaneously the effect on the dependent variable (dependent variable earnings response coefficients. Partially leverage negatively affect earnings response coefficients, partially beta negatively correlated earnings response coefficients, partially growth opportunities (market to book value ratio significant effect on earnings response coefficients, partially sized companies (firm size significantly influence earnings response coefficients.

Fungible weights in logistic regression.

Science.gov (United States)

Jones, Jeff A; Waller, Niels G

2016-06-01

In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Using R2* values to evaluate brain tumours on magnetic resonance imaging: Preliminary results

International Nuclear Information System (INIS)

Liu, Zhenghua; Liao, Haibo; Yin, Jianhua; Li, Yanfang

2014-01-01

To determine the usefulness of the R2* value in assessing the histopathological grade of glioma at magnetic resonance imaging and differentiating various brain tumours. Sixty-four patients with brain tumours underwent R2* mapping and diffusion-weighted imaging examinations. ANOVA was performed to analyse R2* values among four groups of glioma and among high-grade gliomas (grades III and IV), low-grade gliomas (grades I and II), meningiomas, and brain metastasis. Spearman's correlation coefficients were used to determine the relationships between the R2* values or apparent diffusion coefficient (ADC) and the histopathological grade of gliomas. R2* values of low- and high-grade gliomas were analysed with the receiver-operator characteristic curve. R2* values were significantly different among high-grade gliomas, low-grade gliomas, meningiomas, and brain metastasis, but not between grade I and grade II or between grade III and grade IV. The R2* value (18.73) of high-grade gliomas provided a very high sensitivity and specificity for differentiating low-grade gliomas. A strong correlation existed between the R2* value and the pathological grade of gliomas. R2* mapping is a useful sequence for determining grade of gliomas and in distinguishing benign from malignant tumours. R2* values are better than ADC for characterising gliomas. (orig.)

Shape optimization in 2D contact problems with given friction and a solution-dependent coefficient of friction

Czech Academy of Sciences Publication Activity Database

Haslinger, J.; Outrata, Jiří; Pathó, R.

2012-01-01

Roč. 20, č. 1 (2012), s. 31-59 ISSN 1877-0533 R&D Projects: GA AV ČR IAA100750802 Institutional research plan: CEZ:AV0Z10750506 Institutional support: RVO:67985556 Keywords : shape optimization * Signorini problem * model with given frinction * solution-dependent coefficient of friction * mathematical probrams with equilibrium constraints Subject RIV: BA - General Mathematics Impact factor: 1.036, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/outrata-shape optimization in 2d contact problems with given friction and a solution-dependent coefficient of friction .pdf

Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model

Science.gov (United States)

Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei

2014-10-01

Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.

[Quantitative magnetic resonance imaging of brain iron deposition: comparison between quantitative susceptibility mapping and transverse relaxation rate (R2*) mapping].

Science.gov (United States)

Guan, Ji-Jing; Feng, Yan-Qiu

2018-03-20

To evaluate the accuracy and sensitivity of quantitative susceptibility mapping (QSM) and transverse relaxation rate (R2*) mapping in the measurement of brain iron deposition. Super paramagnetic iron oxide (SPIO) phantoms and mouse models of Parkinson's disease (PD) related to iron deposition in the substantia nigra (SN) underwent 7.0 T magnetic resonance (MR) scans (Bruker, 70/16) with a multi-echo 3D gradient echo sequence, and the acquired data were processed to obtain QSM and R2*. Linear regression analysis was performed for susceptibility and R2* in the SPIO phantoms containing 5 SPIO concentrations (30, 15, 7.5, 3.75 and 1.875 µg/mL) to evaluate the accuracy of QSM and R2* in quantitative iron analysis. The sensitivities of QSM and R2* mapping in quantitative detection of brain iron deposition were assessed using mouse models of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahy-dropyridine (MPTP) in comparison with the control mice. In SPIO phantoms, QSM provided a higher accuracy than R2* mapping and their goodness-of-fit coefficients (R 2 ) were 0.98 and 0.89, respectively. In the mouse models of PD and control mice, the susceptibility of the SN was significantly higher in the PD models (5.19∓1.58 vs 2.98∓0.88, n=5; Pbrain iron deposition than R2*, and the susceptibility derived by QSM can be a potentially useful biomarker for studying PD.

A comparison of R-22, R-134a, R-410a, and R-407c condensation performance in smooth and enhanced tubes: Part 1, Heat transfer

Energy Technology Data Exchange (ETDEWEB)

Eckels, S J; Tesene, B A

1999-07-01

Local and average heat transfer coefficients during condensation are reported for R-22, R-134a, R-410a, and R-407c in one smooth tube and three enhanced surface tubes. The test tubes included a 3/8 inch outer diameter smooth tube, a 3/8 inch outer diameter microfin tube, a 5/16 inch outer diameter microfin tube, and a 5/8 inch outer diameter microfin tube. The local and average heat transfer coefficients were measured over a mass flux range of 92,100 lb/ft{sup 2}{center_dot}h to 442,200 lb/ft{sup 2}{center_dot}h and at saturation temperatures of 104 F and 122 F. A comparison of the performance of the different refrigerants reveals that R-134a has the highest heat transfer performance followed by R-22 and R-410a, which have similar performances. In general, R-407c had the lowest performance of the refrigerants tested. The microfin tube more than doubles the heat transfer coefficient compared to the smooth tube for all refrigerants at the low mass fluxes, but only increases the heat transfer coefficients by 50% at the highest mass flux tested. The measured heat transfer coefficients are also compared with a number of correlations for condensation.

Application of nonlinear regression analysis for ammonium exchange by natural (Bigadic) clinoptilolite

International Nuclear Information System (INIS)

Gunay, Ahmet

2007-01-01

The experimental data of ammonium exchange by natural Bigadic clinoptilolite was evaluated using nonlinear regression analysis. Three two-parameters isotherm models (Langmuir, Freundlich and Temkin) and three three-parameters isotherm models (Redlich-Peterson, Sips and Khan) were used to analyse the equilibrium data. Fitting of isotherm models was determined using values of standard normalization error procedure (SNE) and coefficient of determination (R 2 ). HYBRID error function provided lowest sum of normalized error and Khan model had better performance for modeling the equilibrium data. Thermodynamic investigation indicated that ammonium removal by clinoptilolite was favorable at lower temperatures and exothermic in nature

On weighted and locally polynomial directional quantile regression

Czech Academy of Sciences Publication Activity Database

Boček, Pavel; Šiman, Miroslav

2017-01-01

Roč. 32, č. 3 (2017), s. 929-946 ISSN 0943-4062 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : Quantile regression * Nonparametric regression * Nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.434, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0458380.pdf

Testing the equality of nonparametric regression curves based on ...

African Journals Online (AJOL)

Abstract. In this work we propose a new methodology for the comparison of two regression functions f1 and f2 in the case of homoscedastic error structure and a fixed design. Our approach is based on the empirical Fourier coefficients of the regression functions f1 and f2 respectively. As our main results we obtain the ...

Skeletal height estimation from regression analysis of sternal lengths in a Northwest Indian population of Chandigarh region: a postmortem study.

Science.gov (United States)

Singh, Jagmahender; Pathak, R K; Chavali, Krishnadutt H

2011-03-20

Skeletal height estimation from regression analysis of eight sternal lengths in the subjects of Chandigarh zone of Northwest India is the topic of discussion in this study. Analysis of eight sternal lengths (length of manubrium, length of mesosternum, combined length of manubrium and mesosternum, total sternal length and first four intercostals lengths of mesosternum) measured from 252 male and 91 female sternums obtained at postmortems revealed that mean cadaver stature and sternal lengths were more in North Indians and males than the South Indians and females. Except intercostal lengths, all the sternal lengths were positively correlated with stature of the deceased in both sexes (P regression analysis of sternal lengths was found more useful than the linear regression for stature estimation. Using multivariate regression analysis, the combined length of manubrium and mesosternum in both sexes and the length of manubrium along with 2nd and 3rd intercostal lengths of mesosternum in males were selected as best estimators of stature. Nonetheless, the stature of males can be predicted with SEE of 6.66 (R(2) = 0.16, r = 0.318) from combination of MBL+BL_3+LM+BL_2, and in females from MBL only, it can be estimated with SEE of 6.65 (R(2) = 0.10, r = 0.318), whereas from the multiple regression analysis of pooled data, stature can be known with SEE of 6.97 (R(2) = 0.387, r = 575) from the combination of MBL+LM+BL_2+TSL+BL_3. The R(2) and F-ratio were found to be statistically significant for almost all the variables in both the sexes, except 4th intercostal length in males and 2nd to 4th intercostal lengths in females. The 'major' sternal lengths were more useful than the 'minor' ones for stature estimation The universal regression analysis used by Kanchan et al. [39] when applied to sternal lengths, gave satisfactory estimates of stature for males only but female stature was comparatively better estimated from simple linear regressions. But they are not proposed for the

Rate Coefficient Determinations for H + NO2 → OH + NO from High Pressure Flow Reactor Measurements.

Science.gov (United States)

Haas, Francis M; Dryer, Frederick L

2015-07-16

Rate coefficients for the reaction H + NO2 → OH + NO (R1) have been determined over the nominal temperature and pressure ranges of 737-882 K and 10-20 atm, respectively, from measurements in two different flow reactor facilities: one laminar and one turbulent. Considering the existing database of experimental k1 measurements, the present conditions add measurements of k1 at previously unconsidered temperatures between ∼820-880 K, as well as at pressures that exceed existing measurements by over an order of magnitude. Experimental measurements of NOx-perturbed H2 oxidation have been interpreted by a quasi-steady state NOx plateau (QSSP) method. At the QSSP conditions considered here, overall reactivity is sensitive only to the rates of R1 and H + O2 + M → HO2 + M (R2.M). Consequently, the ratio of k1 to k2.M may be extracted as a simple algebraic function of measured NO2, O2, and total gas concentrations with only minimal complication (within measurement uncertainty) due to treatment of overall gas composition M that differs slightly from pure bath gas B. Absolute values of k1 have been determined with reference to the relatively well-known, pressure-dependent rate coefficients of R2.B for B = Ar and N2. Rate coefficients for the title reaction determined from present experimental interpretation of both laminar and turbulent flow reactor results appear to be in very good agreement around a representative value of 1.05 × 10(14) cm(3) mol(-1) s(-1) (1.74 × 10(-10) cm(3) molecule(-1) s(-1)). Further, the results of this study agree both with existing low pressure flash photolysis k1 determinations of Ko and Fontijn (J. Phys. Chem. 95 3984) near 760 K as well as a present fit to the theoretical expression of Su et al. (J. Phys. Chem. A 106 8261). These results indicate that, over the temperature range considered in this study and up to at least 20 atm, net chemistry due to stabilization of the H-NO2 reaction intermediate to form isomers of HNO2 may proceed at

Multiple regression models for energy use in air-conditioned office buildings in different climates

International Nuclear Information System (INIS)

Lam, Joseph C.; Wan, Kevin K.W.; Liu Dalong; Tsang, C.L.

2010-01-01

An attempt was made to develop multiple regression models for office buildings in the five major climates in China - severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R 2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89-97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.

Early cost estimating for road construction projects using multiple regression techniques

Directory of Open Access Journals (Sweden)

Ibrahim Mahamid

2011-12-01

Full Text Available The objective of this study is to develop early cost estimating models for road construction projects using multiple regression techniques, based on 131 sets of data collected in the West Bank in Palestine. As the cost estimates are required at early stages of a project, considerations were given to the fact that the input data for the required regression model could be easily extracted from sketches or scope definition of the project. 11 regression models are developed to estimate the total cost of road construction project in US dollar; 5 of them include bid quantities as input variables and 6 include road length and road width. The coefficient of determination r2 for the developed models is ranging from 0.92 to 0.98 which indicate that the predicted values from a forecast models fit with the real-life data. The values of the mean absolute percentage error (MAPE of the developed regression models are ranging from 13% to 31%, the results compare favorably with past researches which have shown that the estimate accuracy in the early stages of a project is between ±25% and ±50%.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

Science.gov (United States)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Determination of the rate coefficients of the CH{sub 4} + O{sub 2} → HO{sub 2}+CH{sub 3} and HCO+O{sub 2} → HO{sub 2} + CO reactions at high temperatures

Energy Technology Data Exchange (ETDEWEB)

Ryu, Si Ok [School of Chemical Engineering, Yeungnam University, Gyeongsan (Korea, Republic of); Shin, Kuan Soo [Dept. of Chemistry, Soongsil University, Seoul (Korea, Republic of); Hwang, Soon Muk [Science Applications International Corp oration, 3000 Aerospace Park way, Brook Park, Ohio (United States)

2017-02-15

Rate coefficients of the title reactions, R1 (CH{sub 4} + O{sub 2} → HO{sub 2}+CH{sub 3}) and R{sub 2} (HCO+O{sub 2} → HO{sub 2} + CO) were obtained over T = 1610 ⁓ 1810 K and T = 200 ⁓ 1760 K, respectively, and at ρ = 7.1 μmol/cm{sup 3}. A lean CH{sub 4}/O{sub 2}/Ar mixture (0.1% CH{sub 4}, ϕ = 0.02) was heated behind reflected shock waves and the temporal OH absorption profiles were measured using a laser absorption spectroscopy. Reaction rate coefficients were elucidated by matching the experimental profiles via optimization of k1 and k2 values in the reaction simulation. The rate coefficient expressions derived are k{sub 1} = 1.46 × 10{sup 14} exp (−26 210 K/T) cm{sup 3}/mol/s, T = 1610 ⁓ 1810 K and k{sub 2} = 1.9 × 10{sup 12} T{sup 0.1{sup 6}} exp (−245 K/T) cm{sup 3}/mol/s, T = 200 ⁓ 1760 K.

A regression approach for zircaloy-2 in-reactor creep constitutive equations

International Nuclear Information System (INIS)

Yung Liu, Y.; Bement, A.L.

1977-01-01

In this paper the methodology of multiple regressions as applied to zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. From data analysis and model development point of views, both the assumption of independence and prior committment to specific model forms are unacceptable. One would desire means which can not only estimate the required parameters directly from data but also provide basis for model selections, viz., one model against others. Basic understanding of the physics of deformation is important in choosing the forms of starting physical model equations, but the justifications must rely on their abilities in correlating the overall data. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) when there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets, (2) regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections

Impact of multicollinearity on small sample hydrologic regression models

Science.gov (United States)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin

2015-04-03

© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

Pearson's correlation coefficient in the theory of networks: A comment

OpenAIRE

Ahmed, Zafar; Kumar, Sachin

2018-01-01

In statistics, the Pearson correlation coefficient $r_{x,y}$ determines the degree of linear correlation between two variables and it is known that $-1 \\le r_{x,y} \\le 1$. In the theory of networks, a curious expression proposed in [PRL {\\bf 89} 208701 (2002)] for degree-degree correlation coefficient $r_{j_i,k_i}, i\\in [1,M]$ has been in use. We realize that the suggested form is the conventional Pearson's coefficient for $\\{(j_i,k_i), (k_i,j_i)\\}$ for $2M$ data points and hence it is rightl...

Recursive Algorithm For Linear Regression

Science.gov (United States)

Varanasi, S. V.

1988-01-01

Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.

The influence of precipitates on the low-field Hall coefficient of Cu-Be 2

International Nuclear Information System (INIS)

Sachslehner, F.

1988-01-01

The Hall coefficient R H , electrical resistivity, and transverse magnetoresistance of aged Cu-Be 2 samples (commercial alloy) are measured between 5 and 300 K. The temperature curves R H (T) show an interesting effect. There are monotonous curves being in qualitative accordance with the two group model but at certain ageing times or particle sizes a minimum appears in R H (T) in the region of 40 to 60 K. It is suggested that a minimum always appears if the mean free path of the conduction electrons becomes comparable to dimensions of the precipitates. A change to 'two phase boundary scattering' could cause the minima. (author)

truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models

Directory of Open Access Journals (Sweden)

Maria Karlsson

2014-05-01

Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

Clebsch-Gordan and Racah coefficients of SUpq(2)

International Nuclear Information System (INIS)

Kachurik, I.I.

1993-01-01

Explicit expressions for the Clebsch-Gordan coefficients and for the Racah coefficients of the two-parametric quantum algebra SU pq (2) are derived. They are given as finite sums and as terminating basic hypergeometric functions 3 φ 2 and 4 φ 3 . It is indicated how other expressions for these coefficients can be derived with the help of basic hypergeometric functions. (author). 11 refs

Multicollinearity and Regression Analysis

Science.gov (United States)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland.

Science.gov (United States)

Kolasa-Wiecek, Alicja

2015-04-01

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R2=0.90. For N2O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2O emission is the peat and wood fuel consumption. Copyright © 2015. Published by Elsevier B.V.

Isotherms and thermodynamics by linear and non-linear regression analysis for the sorption of methylene blue onto activated carbon: Comparison of various error functions

International Nuclear Information System (INIS)

Kumar, K. Vasanth; Porkodi, K.; Rocha, F.

2008-01-01

A comparison of linear and non-linear regression method in selecting the optimum isotherm was made to the experimental equilibrium data of methylene blue sorption by activated carbon. The r 2 was used to select the best fit linear theoretical isotherm. In the case of non-linear regression method, six error functions, namely coefficient of determination (r 2 ), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), average relative error (ARE), sum of the errors squared (ERRSQ) and sum of the absolute errors (EABS) were used to predict the parameters involved in the two and three parameter isotherms and also to predict the optimum isotherm. For two parameter isotherm, MPSD was found to be the best error function in minimizing the error distribution between the experimental equilibrium data and predicted isotherms. In the case of three parameter isotherm, r 2 was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. The present study showed that the size of the error function alone is not a deciding factor to choose the optimum isotherm. In addition to the size of error function, the theory behind the predicted isotherm should be verified with the help of experimental data while selecting the optimum isotherm. A coefficient of non-determination, K 2 was explained and was found to be very useful in identifying the best error function while selecting the optimum isotherm

Regression and artificial neural network modeling for the prediction of gray leaf spot of maize.

Science.gov (United States)

Paul, P A; Munkvold, G P

2005-04-01

ABSTRACT Regression and artificial neural network (ANN) modeling approaches were combined to develop models to predict the severity of gray leaf spot of maize, caused by Cercospora zeae-maydis. In all, 329 cases consisting of environmental, cultural, and location-specific variables were collected for field plots in Iowa between 1998 and 2002. Disease severity on the ear leaf at the dough to dent plant growth stage was used as the response variable. Correlation and regression analyses were performed to select potentially useful predictor variables. Predictors from the best 9 of 80 regression models were used to develop ANN models. A random sample of 60% of the cases was used to train the networks, and 20% each for testing and validation. Model performance was evaluated based on coefficient of determination (R(2)) and mean square error (MSE) for the validation data set. The best models had R(2) ranging from 0.70 to 0.75 and MSE ranging from 174.7 to 202.8. The most useful predictor variables were hours of daily temperatures between 22 and 30 degrees C (85.50 to 230.50 h) and hours of nightly relative humidity >/=90% (122 to 330 h) for the period between growth stages V4 and V12, mean nightly temperature (65.26 to 76.56 degrees C) for the period between growth stages V12 and R2, longitude (90.08 to 95.14 degrees W), maize residue on the soil surface (0 to 100%), planting date (in day of the year; 112 to 182), and gray leaf spot resistance rating (2 to 7; based on a 1-to-9 scale, where 1 = most susceptible to 9 = most resistant).

Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient.

Science.gov (United States)

Wu, Xia; Zhu, Jian-Cheng; Zhang, Yu; Li, Wei-Min; Rong, Xiang-Lu; Feng, Yi-Fan

2016-08-25

Potential impact of lipid research has been increasingly realized both in disease treatment and prevention. An effective metabolomics approach based on ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) along with multivariate statistic analysis has been applied for investigating the dynamic change of plasma phospholipids compositions in early type 2 diabetic rats after the treatment of an ancient prescription of Chinese Medicine Huang-Qi-San. The exported UPLC/Q-TOF-MS data of plasma samples were subjected to SIMCA-P and processed by bioMark, mixOmics, Rcomdr packages with R software. A clear score plots of plasma sample groups, including normal control group (NC), model group (MC), positive medicine control group (Flu) and Huang-Qi-San group (HQS), were achieved by principal-components analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Biomarkers were screened out using student T test, principal component regression (PCR), partial least-squares regression (PLS) and important variable method (variable influence on projection, VIP). Structures of metabolites were identified and metabolic pathways were deduced by correlation coefficient. The relationship between compounds was explained by the correlation coefficient diagram, and the metabolic differences between similar compounds were illustrated. Based on KEGG database, the biological significances of identified biomarkers were described. The correlation coefficient was firstly applied to identify the structure and deduce the metabolic pathways of phospholipids metabolites, and the study provided a new methodological cue for further understanding the molecular mechanisms of metabolites in the process of regulating Huang-Qi-San for treating early type 2 diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Crystal structure, magnetization, {sup 125}Te NMR, and Seebeck coefficient of Ge{sub 49}Te{sub 50}R{sub 1} (R = La, Pr, Gd, Dy, and Yb)

Energy Technology Data Exchange (ETDEWEB)

Levin, E.M., E-mail: levin@iastate.edu [Division of Materials Sciences and Engineering, US Department of Energy Ames Laboratory, Ames, IA 50011 (United States); Department of Physics and Astronomy, Iowa State University, Ames, IA 50011 (United States); Cooling, C. [Division of Materials Sciences and Engineering, US Department of Energy Ames Laboratory, Ames, IA 50011 (United States); Bud’ko, S.L. [Division of Materials Sciences and Engineering, US Department of Energy Ames Laboratory, Ames, IA 50011 (United States); Department of Physics and Astronomy, Iowa State University, Ames, IA 50011 (United States); Straszheim, W.E. [Division of Materials Sciences and Engineering, US Department of Energy Ames Laboratory, Ames, IA 50011 (United States); Lograsso, T.A. [Division of Materials Sciences and Engineering, US Department of Energy Ames Laboratory, Ames, IA 50011 (United States); Department of Materials Sciences and Engineering, Iowa State University, Ames, IA 50011 (United States)

2017-05-01

GeTe, a self-doping semiconductor, is a well-known base compound for thermoelectric and phase-change materials. It is known, that replacement of Ge in Ag{sub 6.5}Sb{sub 6.5}Ge{sub 37}Te{sub 50} (TAGS-85) material by rare earth Dy significantly enhances both the power factor and thermoelectric figure of merit. Here we demonstrate how replacement of Ge in GeTe by rare earths with different atomic size and localized magnetic moments affect XRD patterns, magnetization, {sup 125}Te NMR spectra and spin-lattice relaxation, and the Seebeck coefficient of the alloys with a nominal composition of Ge{sub 49}Te{sub 50}R{sub 1} (R = La, Pr, Gd, Dy, and Yb). SEM, EDS and WDS data show that rare earth atoms in the matrix are present at smaller extent compared to a nominal composition, whereas rare earth also is present in inclusions. Rare earths affect the Seebeck coefficient, which is a result of interplay between the reduction due to higher carrier concentration and enhancement due to magnetic contribution. The effect of replacement of Ge in GeTe by Dy on the Seebeck coefficient is smaller than that observed in Ag{sub 6.5}Sb{sub 6.5}Ge{sub 36} Te{sub 50}Dy{sub 1}. This can be explained by larger amount of rare earth, which can be embedded into the lattice of materials containing [Ag + Sb] atomic pairs and possible effect from these pairs. - Highlights: • The effects of rare earth in Ge{sub 49}Te{sub 50}R{sub 1} (R = La, Pr, Gd, Dy, and Yb) are studied. • Rare earth atoms in the matrix are present at smaller extent compared to a nominal composition. • The effect on the Seebeck coefficient is a result from carrier concentration and magnetic contribution.

Multiple linear regression analysis

Science.gov (United States)

Edwards, T. R.

1980-01-01

Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network

International Nuclear Information System (INIS)

Konoz, Elahe; Golmohammadi, Hassan

2008-01-01

An artificial neural network (ANN) was constructed and trained for the prediction of air-to-blood partition coefficients of volatile organic compounds. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (MLR) features selection techniques. These descriptors are: R maximal autocorrelation of lag 1 weighted by atomic Sanderson electronegativities (R1E+), electron density on the most negative atom in molecule (EDNA), maximum partial charge for C atom (MXPCC), surface weighted charge partial surface area (WNSA1), fractional charge partial surface area (FNSA2) and atomic charge weighted partial positive surface area (PPSA3). The standard errors of training, test and validation sets for the ANN model are 0.095, 0.148 and 0.120, respectively. Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the partition coefficients of the molecules in data set accurately

Conformal anomaly c-coefficients of superconformal 6d theories

Energy Technology Data Exchange (ETDEWEB)

Beccaria, Matteo [Dipartimento di Matematica e Fisica Ennio De Giorgi, Università del Salento & INFN,Via Arnesano, 73100 Lecce (Italy); Tseytlin, Arkady A. [The Blackett Laboratory, Imperial College,London SW7 2AZ (United Kingdom)

2016-01-04

We propose general relations between the conformal anomaly and the chiral (R-symmetry and gravitational) anomaly coefficients in 6d (1,0) superconformal theories. The suggested expressions for the three type B conformal anomaly c{sub i}-coefficients complement the expression for the type A anomaly a-coefficient found in http://arxiv.org/abs/1506.03807. We check them on several examples — the standard (1,0) hyper and tensor multiplets as well as some higher derivative short multiplets containing vector fields that generalize the superconformal 6d vector multiplet discussed in http://arxiv.org/abs/1506.08727. We also consider a family of higher derivative superconformal (2,0) 6d multiplets associated to 7d multiplets in the KK spectrum of 11d supergravity compactified on S{sup 4}. In particular, we prove that (2,0) 6d conformal supergravity coupled to 26 tensor multiplets is free of all chiral and conformal anomalies. We discuss some interacting (1,0) superconformal theories, predicting the c{sub i}-coefficients for the “E-string” theory on multiple M5-branes at E{sub 8} 9-brane and for the theory describing M5-branes at an orbifold singularity ℂ{sup 2}/Γ. Finally, we elaborate on holographic computation of subleading corrections to conformal anomaly coefficients coming from R{sup 2}+R{sup 3} terms in 7d effective action, revisiting, in particular, the (2,0) theory case.

Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

Science.gov (United States)

Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

2016-01-01

Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

Correlation and simple linear regression.

Science.gov (United States)

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

Bias in regression coefficient estimates upon different treatments of ...

African Journals Online (AJOL)

MS and PW consistently overestimated the population parameter. EM and RI, on the other hand, tended to consistently underestimate the population parameter under non-monotonic pattern. Keywords: Missing data, bias, regression, percent missing, non-normality, missing pattern > East African Journal of Statistics Vol.

Reducing Monte Carlo error in the Bayesian estimation of risk ratios using log-binomial regression models.

Science.gov (United States)

Salmerón, Diego; Cano, Juan A; Chirlaque, María D

2015-08-30

In cohort studies, binary outcomes are very often analyzed by logistic regression. However, it is well known that when the goal is to estimate a risk ratio, the logistic regression is inappropriate if the outcome is common. In these cases, a log-binomial regression model is preferable. On the other hand, the estimation of the regression coefficients of the log-binomial model is difficult owing to the constraints that must be imposed on these coefficients. Bayesian methods allow a straightforward approach for log-binomial regression models and produce smaller mean squared errors in the estimation of risk ratios than the frequentist methods, and the posterior inferences can be obtained using the software WinBUGS. However, Markov chain Monte Carlo methods implemented in WinBUGS can lead to large Monte Carlo errors in the approximations to the posterior inferences because they produce correlated simulations, and the accuracy of the approximations are inversely related to this correlation. To reduce correlation and to improve accuracy, we propose a reparameterization based on a Poisson model and a sampling algorithm coded in R. Copyright © 2015 John Wiley & Sons, Ltd.

Characteristics of surface acoustic waves in (11\\bar 2 0)ZnO film/ R-sapphire substrate structures

Science.gov (United States)

Wang, Yan; Zhang, ShuYi; Xu, Jing; Xie, YingCai; Lan, XiaoDong

2018-02-01

(11\\bar 2 0)ZnO film/ R-sapphire substrate structure is promising for high frequency acoustic wave devices. The propagation characteristics of SAWs, including the Rayleigh waves along [0001] direction and Love waves along [1ī00] direction, are investigated by using 3 dimensional finite element method (3D-FEM). The phase velocity ( v p), electromechanical coupling coefficient ( k 2), temperature coefficient of frequency ( TCF) and reflection coefficient ( r) of Rayleigh wave and Love wave devices are theoretically analyzed. Furthermore, the influences of ZnO films with different crystal orientation on SAW properties are also investigated. The results show that the 1st Rayleigh wave has an exceedingly large k 2 of 4.95% in (90°, 90°, 0°) (11\\bar 2 0)ZnO film/ R-sapphire substrate associated with a phase velocity of 5300 m/s; and the 0th Love wave in (0°, 90°, 0°) (11\\bar 2 0)ZnO film/ R-sapphire substrate has a maximum k 2 of 3.86% associated with a phase velocity of 3400 m/s. And (11\\bar 2 0)ZnO film/ R-sapphire substrate structures can be used to design temperature-compensated and wide-band SAW devices. All of the results indicate that the performances of SAW devices can be optimized by suitably selecting ZnO films with different thickness and crystal orientations deposited on R-sapphire substrates.

Estimation of the simple correlation coefficient.

Science.gov (United States)

Shieh, Gwowen

2010-11-01

This article investigates some unfamiliar properties of the Pearson product-moment correlation coefficient for the estimation of simple correlation coefficient. Although Pearson's r is biased, except for limited situations, and the minimum variance unbiased estimator has been proposed in the literature, researchers routinely employ the sample correlation coefficient in their practical applications, because of its simplicity and popularity. In order to support such practice, this study examines the mean squared errors of r and several prominent formulas. The results reveal specific situations in which the sample correlation coefficient performs better than the unbiased and nearly unbiased estimators, facilitating recommendation of r as an effect size index for the strength of linear association between two variables. In addition, related issues of estimating the squared simple correlation coefficient are also considered.

[Methodology of the description of atmospheric air pollution by nitrogen dioxide by land use regression method in Ekaterinburg].

Science.gov (United States)

Antropov, K M; Varaksin, A N

2013-01-01

This paper provides the description of Land Use Regression (LUR) modeling and the result of its application in the study of nitrogen dioxide air pollution in Ekaterinburg. The paper describes the difficulties of the modeling for air pollution caused by motor vehicles exhaust, and the ways to address these challenges. To create LUR model of the NO2 air pollution in Ekaterinburg, concentrations of NO2 were measured, data on factors affecting air pollution were collected, a statistical analysis of the data were held. A statistical model of NO2 air pollution (coefficient of determination R2 = 0.70) and a map of pollution were created.

The significance level and repeatability for isotope-temperature coefficient of precipitation in China

International Nuclear Information System (INIS)

Wang Dongsheng; Wang Jinglan

2003-01-01

The good linear relationship with significance level α = 0.01 exists between isotope in precipitation and surface air temperature with multi-year average in 32 stations of China, and the yearly δD-temperature coefficient = 3.1‰/1℃ and the yearly δ 18 O-temperature coefficient = 0.36‰/1℃, and its determination coefficient R 2 = 0.67 and 0.64 respectively. So the isotope-temperature coefficient with yearly average can serve as the temperature yearly measure. But the monthly average isotope-temperature coefficient in each station is variable according to both of space and time, and its repeatability is determined by the meteorological regimes. According to the monthly isotope-temperature coefficient (B) and the coefficient of determination (R 2 ) and its α, all of China can be zoned the following three belts: (1) In the North Belt, B>O, R 2 ≈ 0.3-0.65, α = 0.01, the relation between monthly isotope in precipitation and surface air temperature (RMIT) belongs to a direct correlation and is closer in 99% probability; (2) In the South Belt, BR 2 ≈ 0.3-0.5, α = 0.01, the RMIT belongs to a weak negative correlation; (3) In the Middle Belt the RMIP is a non-correlation. The isotope-temperature coefficient with both of yearly average and monthly average and its statistical attribution is site-specific, it may be used to reconstruct past surface air temperatures or to diagnose regional climate models. (authors)

Determination of corneal elasticity coefficient using the ORA database.

Science.gov (United States)

Avetisov, Sergei E; Novikov, Ivan A; Bubnova, Irina A; Antonov, Alexei A; Siplivyi, Vladimir I

2010-07-01

To propose a new approach for the study of corneal biomechanics using the Reichert Ocular Response Analyzer (ORA) database, which is based on changes in velocity retardation in the central cornea at the peak of flattening. The ORA applanation curve was analyzed using a mathematical technique, which allowed calculation of the elasticity coefficient (Ke), which is primarily characteristic of the elastic properties of the cornea. Elasticity coefficient values were obtained in patients with presumably different biomechanical properties of the cornea: "normal" cornea (71 eyes, normal group), keratoconus (34 eyes, keratoconus group), LASIK (36 eyes, LASIK group), and glaucoma with elevated and compensated intraocular pressure (lOP) (38 eyes, glaucoma group). The mean Ke value in the normal group was 11.05 +/- 1.6, and the corneal thickness correlation coefficient r2 was 0.48. In the keratoconus group, the mean Ke value was 4.91 +/- 1.87 and the corneal thickness correlation coefficient r2 was 0.47. In the LASIK group, Ke and r2 were 5.99 +/- 1.18 and 0.39, respectively. In the glaucoma group, the same eyes that experienced a two-fold reduction in lOP developed a statistically significant reduction in the Ke (1.06 times lower), whereas their corneal hysteresis value increased 1.25 times. The elasticity coefficient calculated using the ORA applanation curve can be used in the evaluation of corneal biomechanical properties.

Isopiestic determination of the osmotic and activity coefficients of the {l_brace}yKCl + (1 - y)K{sub 2}HPO{sub 4}{r_brace}(aq) system at T = 298.15 K

Energy Technology Data Exchange (ETDEWEB)

Popovic, Daniela Z. [Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11 001 Belgrade (Serbia); Miladinovic, Jelena, E-mail: duma@tmf.bg.ac.rs [Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11 001 Belgrade (Serbia); Todorovic, Milica D.; Zrilic, Milorad M. [Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11 001 Belgrade (Serbia); Rard, Joseph A., E-mail: solution_chemistry2@comcast.net [4363 Claremont Way, Livermore, CA 94550 (United States)

2011-12-15

Highlights: > Isopiestic measurements were made for {l_brace}yKCl + (1 - y)K{sub 2}HPO{sub 4}{r_brace}(aq) at T = 298.15 K. > The resulting osmotic coefficients were represented by three thermodynamic models. > Activity coefficients from Pitzer model with Scatchard mixing terms are recommended. - Abstract: The osmotic coefficients of aqueous mixtures of KCl and K{sub 2}HPO{sub 4} have been measured at T = (298.15 {+-} 0.01) K by the isopiestic vapor pressure method over the range of ionic strengths from (2.3700 to 11.250) mol . kg{sup -1} using CaCl{sub 2}(aq) as the reference solution. Our new experimental results were modeled with an extended form of Pitzer's ion-interaction model equations, both with the usual mixing terms and with Scatchard's neutral-electrolyte mixing terms, and with the Clegg-Pitzer-Brimblecombe equations based on the mole-fraction-composition scale. There is a dearth of previously published isopiestic data for mixtures containing salts of HPO{sub 4}{sup 2-}(aq) and, consequently, no previous measurements are available for comparison with the present results. The present study yields Cl{sup -}HPO{sub 4}{sup 2-} mixing parameters for these three models that are needed for modeling the thermodynamic activities of solute components of natural waters and other complex aqueous electrolyte mixtures.

Introduction to regression graphics

CERN Document Server

Cook, R Dennis

2009-01-01

Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava

General regression and representation model for classification.

Directory of Open Access Journals (Sweden)

Jianjun Qian

Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.

Methodology update for determination of the erosion coefficient(Z

Directory of Open Access Journals (Sweden)

Tošić Radislav

2012-01-01

Full Text Available The research and mapping the intensity of mechanical water erosion that have begun with the empirical methodology of S. Gavrilović during the mid-twentieth century last, by various intensity, until the present time. A many decades work on the research of these issues pointed to some shortcomings of the existing methodology, and thus the need for its innovation. In this sense, R. Lazarević made certain adjustments of the empirical methodology of S. Gavrilović by changing the tables for determination of the coefficients Φ, X and Y, that is, the tables for determining the mean erosion coefficient (Z. The main objective of this paper is to update the existing methodology for determining the erosion coefficient (Z with the empirical methodology of S. Gavrilović and amendments made by R. Lazarević (1985, but also with better adjustments to the information technologies and the needs of modern society. The proposed procedure, that is, the model to determine the erosion coefficient (Z in this paper is the result of ten years of scientific research and project work in mapping the intensity of mechanical water erosion and its modeling using various models of erosion in the Republic of Srpska and Serbia. By analyzing the correlation of results obtained by regression models and results obtained during the mapping of erosion on the territory of the Republic of Srpska, a high degree of correlation (R² = 0.9963 was established, which is essentially a good assessment of the proposed models.

A comparison of random forest regression and multiple linear regression for prediction in neuroscience.

Science.gov (United States)

Smith, Paul F; Ganesh, Siva; Liu, Ping

2013-10-30

Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.

Synthesis, Characterization and Application of 2-Line and 6-Line ...

African Journals Online (AJOL)

Michael Horsfall

Corresponding author; E-Mail: nasalami2002@yahoo.co.uk ... ferrihydrite are 0.02, 1.99 and 0.911 for b, Qm and the regression coefficient R2 ... order that give broad range X-ray diffraction (XRD) .... Atomic Absorption Spectroscopy (AAS) to.

Interpretation of commonly used statistical regression models.

Science.gov (United States)

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Statistical Analysis of Manning's roughness Coefficients in Non-vegetated Canals for Irrigation and Drainage Network of Moghan

Directory of Open Access Journals (Sweden)

Abolfazl Nasseri

2017-03-01

software’s of SPSS and Minitab were used in statistical analysis. Results and Discussion: Roughness coefficients averaged 0.06. Results revealed that RC varied from 0.014 to 0.050 (and more than for 90 to 40% probabilities in non-vegetated canals. Also, flow velocity, hydraulic radius, cross section area, wetted perimeter and roughness coefficient were lognormal in distributions. Results also showed that flow regimes were turbulent and with increase in Reynolds numbers, roughness coefficients decrease. Sensitivity analysis of flow rate to roughness coefficient showed that with increase as 200 and 300 percent in roughness coefficients, flow rates were 0.50 and 0.33 of flow rate from average roughness coefficient. Moreover, A simple regression model was developed based on effective variables (viz. flow velocity and canal slope on roughness coefficient by omitting non-effective variables in non-vegetated canals. Developed model was as follows: (2 R2=0.99 The variables of the model were previously introduced earlier. The coefficient of determination (R2 shows that more than 99% variations in RC could be explained by flow velocity and canal slope. Conclusion: Roughness coefficient in the earth non-vegetated canals was successfully and precisely evaluated for irrigation and drainage network of Moghan (in North-west of Iran by statistical methods. Roughness coefficients averaged 0.06. The sensitivity of canal discharge to roughness coefficient was significant. It is recommended to select and apply actual values of this coefficient in engineering or computing purposes. By omitting non-effective variables in roughness coefficient in non-vegetated canals, a simple regression model with R2 of 0.99 was developed based on effective variables. In this study, the role of vegetation in channel for roughness coefficient was not evaluated. Therefore, it is recommended that the effect of different vegetation on roughness coefficient tobe evaluated with models such as hydrodynamic and zero-inertia.

Application of least squares support vector regression and linear multiple regression for modeling removal of methyl orange onto tin oxide nanoparticles loaded on activated carbon and activated carbon prepared from Pistacia atlantica wood.

Science.gov (United States)

Ghaedi, M; Rahimi, Mahmoud Reza; Ghaedi, A M; Tyagi, Inderjeet; Agarwal, Shilpi; Gupta, Vinod Kumar

2016-01-01

Two novel and eco friendly adsorbents namely tin oxide nanoparticles loaded on activated carbon (SnO2-NP-AC) and activated carbon prepared from wood tree Pistacia atlantica (AC-PAW) were used for the rapid removal and fast adsorption of methyl orange (MO) from the aqueous phase. The dependency of MO removal with various adsorption influential parameters was well modeled and optimized using multiple linear regressions (MLR) and least squares support vector regression (LSSVR). The optimal parameters for the LSSVR model were found based on γ value of 0.76 and σ(2) of 0.15. For testing the data set, the mean square error (MSE) values of 0.0010 and the coefficient of determination (R(2)) values of 0.976 were obtained for LSSVR model, and the MSE value of 0.0037 and the R(2) value of 0.897 were obtained for the MLR model. The adsorption equilibrium and kinetic data was found to be well fitted and in good agreement with Langmuir isotherm model and second-order equation and intra-particle diffusion models respectively. The small amount of the proposed SnO2-NP-AC and AC-PAW (0.015 g and 0.08 g) is applicable for successful rapid removal of methyl orange (>95%). The maximum adsorption capacity for SnO2-NP-AC and AC-PAW was 250 mg g(-1) and 125 mg g(-1) respectively. Copyright © 2015 Elsevier Inc. All rights reserved.

Regression modeling methods, theory, and computation with SAS

CERN Document Server

Panik, Michael

2009-01-01

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

Isopiestic Investigation of the Osmotic and Activity Coefficients of {yMgCl2 + (1 - y)MgSO4}(aq) and the Osmotic Coefficients of Na2SO4.MgSO4(aq) at 298.15 K

Energy Technology Data Exchange (ETDEWEB)

Miladinovic, J; Ninkovic, R; Todorovic, M; Rard, J A

2007-06-06

Isopiestic vapor pressure measurements were made for {l_brace}yMgCl{sub 2} + (1-y)MgSO{sub 4}{r_brace}(aq) solutions with MgCl{sub 2} ionic strength fractions of y = 0, 0.1997, 0.3989, 0.5992, 0.8008, and (1) at the temperature 298.15 K, using KCl(aq) as the reference standard. These measurements for the mixtures cover the ionic strength range I = 0.9794 to 9.4318 mol {center_dot} kg{sup -1}. In addition, isopiestic measurements were made with NaCl(aq) as reference standard for mixtures of {l_brace}xNa{sub 2}SO{sub 4} + (1-x)MgSO{sub 4}{r_brace}(aq) with the molality fraction x = 0.50000 that correspond to solutions of the evaporite mineral bloedite (astrakanite), Na{sub 2}Mg(SO{sub 4}){sub 2} {center_dot} 4H{sub 2}O(cr). The total molalities, m{sub T} = m(Na{sub 2}SO{sub 4}) + m(MgSO{sub 4}), range from m{sub T} = 1.4479 to 4.4312 mol {center_dot} kg{sup -1} (I = 5.0677 to 15.509 mol {center_dot} kg{sup -1}), where the uppermost concentration is the highest oversaturation molality that could be achieved by isothermal evaporation of the solvent at 298.15 K. The parameters of an extended ion-interaction (Pitzer) model for MgCl2(aq) at 298.15 K, which were required for an analysis of the {l_brace}yMgCl{sub 2} + (1-y)MgSO{sub 4}{r_brace}(aq) mixture results, were evaluated up to I = 12.025 mol {center_dot} kg{sup -1} from published isopiestic data together with the six new osmotic coefficients obtained in this study. Osmotic coefficients of {l_brace}yMgCl{sub 2} + (1-y)MgSO{sub 4}{r_brace}(aq) solutions from the present study, along with critically-assessed values from previous studies, were used to evaluate the mixing parameters of the extended ion-interaction model.

Determination of regression functions for the charging and discharging processes of valve regulated lead-acid batteries

Directory of Open Access Journals (Sweden)

Vukić Vladimir Đ.

2012-01-01

Full Text Available Following a deep discharge of AGM SVT 300 valve-regulated lead-acid batteries using the ten-hour discharge current, the batteries were charged using variable current. In accordance with the obtained results, exponential and polynomial functions for the approximation of the specified processes were analyzed. The main evaluation instrument for the quality of the implemented approximations was the adjusted coefficient of determination R-2. It was perceived that the battery discharge process might be successfully approximated with both an exponential and the second order polynomial function. On all the occasions analyzed, values of the adjusted coefficient of determination were greater than 0.995. The charging process of the deeply discharged batteries was successfully approximated with the exponential function; the measured values of the adjusted coefficient of determination being nearly 0.95. Apart from the high measured values of the adjusted coefficient of determination, polynomial approximations of the second and third order did not provide satisfactory results regarding the interpolation of the battery charging characteristics. A possibility for a practical implementation of the procured regression functions in uninterruptible power supply systems was described.

Density-scaling exponents and virial potential-energy correlation coefficients for the (2n, n) Lennard-Jones system

DEFF Research Database (Denmark)

Friisberg, Ida Marie; Costigliola, Lorenzo; Dyre, Jeppe C.

2017-01-01

This paper investigates the relation between the density-scaling exponent γ and the virial potentialenergy coefficient R at several thermodynamic state points in three dimensions for the generalized (2n, n) Lennard-Jones (LJ) system for n = 4, 9, 12, 18, as well as for the standard n = 6 LJ syste...

Prediction of beef marblingusing Hyperspectral Imaging (HSI and Partial Least Squares Regression (PLSR

Directory of Open Access Journals (Sweden)

Victor Aredo

2017-01-01

Full Text Available The aim of this study was to build a model to predict the beef marbling using HSI and Partial Least Squares Regression (PLSR. Totally 58 samples of longissmus dorsi muscle were scanned by a HSI system (400 - 1000 nm in reflectance mode, using 44 samples to build t he PLSR model and 14 samples to model validation. The Japanese Beef Marbling Standard (BMS was used as reference by 15 middle - trained judges for the samples evaluation. The scores were assigned as continuous values and varied from 1.2 to 5.3 BMS. The PLSR model showed a high correlation coefficient in the prediction (r = 0.95, a low Standard Error of Calibration (SEC of 0.2 BMS score, and a low Standard Error of Prediction (SEP of 0.3 BMS score.

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

Science.gov (United States)

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

Intermediate and advanced topics in multilevel logistic regression analysis.

Science.gov (United States)

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

The characteristic of rBMD distribution in lumbar vertebral body

International Nuclear Information System (INIS)

Wang Chenguang; Xiao Xiangsheng; Chen Xingrong; Shen Tianzhen; Liu Guanghua; Hong Qingjian; Ji Rongming; Zhou Weiming

1998-01-01

Purpose: To determine the distribution and variation of rBMD in human lumbar vertebral body. Methods: The BMD and rBMD of 28 samples of lumbar body were measured with QCT. The rBMD was measured in the regions of anterior, anterolateral, posterolateral and central, superior-level, middle-level and inferior-level of the vertebral bodies. The relationship between BMD and rBMD were statistically analysed with multiple regression. Results: The rBMD of the inferior vertebral body was higher than that of the superior and middle portions (P<0.05); the central and posterolateral higher than the anterior and anterolateral (P<0.05). The rBMD of posterioinferior vertebral body was the highest. The multiple regression showed that the standard partial regression coefficient of inferior was larger than the superior and middle; the anterior and central were larger than the other regions of the vertebra. Variations of the BMD of vertebral body were mostly related to the rBMD of anterior and central parts. Conclusion: The distribution of BMD are heterogeneous in vertebral body. The anterior and central part of vertebral body are most sensitive to bone loss in osteoporosis. It is emphasized that the rBMD of anterior and central part of vertebral body should be measured for following the osteoporosis

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing.

Science.gov (United States)

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-02-01

A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R 2 ), using R 2 as the primary metric of assay agreement. However, the use of R 2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Solar tidal variations of coefficients of second harmonic of gravitational potential of Mercury

Science.gov (United States)

Ferrandiz, Jose; Barkin, Yury

2010-05-01

Variations of coefficients of the second harmonic of Mercury potential caused by the solar tides have been studied. In the paper we use analytical expressions for tidal variations of Stoks coefficients obtained for model of the elastic celestial body with concentric distributions of masses and elastic parameters (Love numbers) and their reduced form with using fundamental elastic parameter k2 of the Mercury. Taking into account the resonant properties of the Mercury motion variations of the Mercury potential coefficients we present in the form of Fourier series on the multiple of corresponding arguments of the Mercury orbital theory. Evaluations of the amplitudes and periods of observed variations of Mercury potential have been tabulated for base elastic model of the Mercury characterized by hypothetic elastic parameter (Love number) k2=0.37 (Dehant et al., 2005). Tidal variations of polar moment of inertia of the Mercury (due to tidal deformations) lead to remarkable variations of the Mercury rotation. Tidal variations of the Mercury axial rotation also have been determined and tabulated. From our results it follows that the tide periodic variations of gravitational coefficients of the Mercury in a few orders bigger then corresponding tidal variations of Earth's geopotential coefficients (Ferrandiz, Getino, 1993). Variations coefficients of the second harmonic of Mercury potential. These variations are determined by the known formulae for variations of coefficients of the second harmonic of geopotential (Ferrandiz, Getino, 1993). Here we present these formulae in some special form as applied to the considered problem about the Mercury tidal deformations: ( ) δJ2 = - 3Tα23-2, δC22 = T α21 - α22 -4, δS22 = T α1α2-2, δC21 = Tα1α3, δS21 = T α2α3. Here T = k2(M R3 -ma3 ) = 1.667 × 10-7 is a estimation of some conditional coefficient of tidal deformation of Mercury. m and Rare the mass and the mean radius of Mercury. Here we have used standard values of

tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models

Directory of Open Access Journals (Sweden)

Robert B. Gramacy

2007-06-01

Full Text Available The tgp package for R is a tool for fully Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes with jumps to the limiting linear model. Special cases also implemented include Bayesian linear models, linear CART, stationary separable and isotropic Gaussian processes. In addition to inference and posterior prediction, the package supports the (sequential design of experiments under these models paired with several objective criteria. 1-d and 2-d plotting, with higher dimension projection and slice capabilities, and tree drawing functions (requiring maptree and combinat packages, are also provided for visualization of tgp objects.

Correlated linear response calculations of the C6 dispersion coefficients of hydrogen halides

Czech Academy of Sciences Publication Activity Database

Sauer, S. P. A.; Paidarová, Ivana

2007-01-01

Roč. 3, 2-4 (2007), s. 399-421 ISSN 1574-0404 R&D Projects: GA AV ČR IAA401870702 Institutional research plan: CEZ:AV0Z40400503 Keywords : hydrogen halides * C6 dospersion coefficients * van der Waals coefficients * polarizability at imaginary frequences * SOPPA Subject RIV: CF - Physical ; Theoretical Chemistry

Measurement of the friction coefficient between UO2 and cladding tube

International Nuclear Information System (INIS)

Tachibana, Toshimichi; Narita, Daisuke; Kaneko, Hiromitsu; Honda, Yutaka

1978-01-01

Most of fuel rods used for light water reactors or fast reactors consist of the cladding tubes filled with UO 2 -PuO 2 pellets. The measurement was made on the coefficient of static friction and the coefficient of dynamic friction in helium under high contact load on UO 2 /Zry-2 and UO 2 /SUS 316 combined samples at the temperature ranging from room temperature to 400 deg. C and from room temperature to 600 deg. C, respectively. The coefficient of static friction for Zry-2 tube and UO 2 pellets was 0.32 +- 0.08 at room temperature and 0.47 +- 0.07 at 400 deg. C, and increased with temperature rise in this temperature range. The coefficient of static friction between 316 stainless steel tube and UO 2 pellets was 0.29 +- 0.04 at room temperature and 1.2 +- 0.2 at 600 deg. C, and increased with temperature rise in this temperature range. The coefficient of dynamic friction for both UO 2 /Zry-2 and UO 2 /SUS 316 combinations seems to be equal to or about 10% excess of the coefficient of static friction. The coefficient of static friction for UO 2 /SUS 316 combination decreased with the increasing number of repetition, when repeating slip several times on the same contact surfaces. (Kobatake, H.)

Evaluation of Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) for Water Quality Monitoring: A Case Study for the Estimation of Salinity

Science.gov (United States)

Nazeer, Majid; Bilal, Muhammad

2018-04-01

Landsat-5 Thematic Mapper (TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square (OLS) regression and Geographically Weighted Regression (GWR) based on in situ data of October 2009. Results show that the coefficient of determination ( R 2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher ( R 2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay (north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant (32 practical salinity units) towards the open sea.

EOS7R: Radionuclide transport for TOUGH2

International Nuclear Information System (INIS)

Oldenburg, C.M.; Pruess, K.

1995-11-01

EOS7R provides radionuclide transport capability for TOUGH2. EOS7R extends the EOS7 module (water, brine, and optional air) to model water, brine, parent component, daughter component, and optional air and heat. The radionuclide components follow a first-order decay law, and may adsorb onto the solid grains. Volatilization of the decaying components is modeled by Henry's Law. The decaying components are normally referred to as radionuclides, but they may in fact by any trace components that decay, adsorb, and volatilize. The decay process need not be radioactive decay, but could be any process that follows a first-order decay law, such as biodegradation. EOS7R includes molecular diffusion for all components in gaseous and aqueous phases using a simplified binary diffusion model. When EOS7R is used with standard TOUGH2, transport occurs by advection and molecular diffusion in all phases. When EOS7R is coupled with the dispersion module T2DM, one obtains T2DMR, the radionuclide transport version of T2DM. T2DMR models advection, diffusion, and hydrodynamic dispersion in rectangular two-dimensional regions. Modeling of radionuclide transport requires input parameters specifying the half-life for first-order decay, distribution coefficients for each rock type for adsorption, and inverse Henry's constants for volatilization. Options can be specified in the input file to model decay in inactive grid blocks and to read from standard EOS7 INCON files. The authors present a number of example problems to demonstrate application and accuracy of TOUGH2/EOS7R. One-dimensional simulation results agree well with analytical solutions. For a two-dimensional salt-dome flow problem, the final distribution of daughter radionuclide component is complicated by the presence of weak recirculation caused by density effects due to salinity

R2 dark energy in the laboratory

Science.gov (United States)

Brax, Philippe; Valageas, Patrick; Vanhove, Pierre

2018-05-01

We analyze the role, on large cosmological scales and laboratory experiments, of the leading curvature squared contributions to the low-energy effective action of gravity. We argue for a natural relationship c0λ2≃1 at low energy between the R2 coefficients c0 of the Ricci scalar squared term in this expansion and the dark energy scale Λ =(λ MPl)4 in four-dimensional Planck mass units. We show how the compatibility between the acceleration of the expansion rate of the Universe, local tests of gravity and the quantum stability of the model all converge to select such a relationship up to a coefficient which should be determined experimentally. When embedding this low-energy theory of gravity into candidates for its ultraviolet completion, we find that the proposed relationship is guaranteed in string-inspired supergravity models with modulus stabilization and supersymmetry breaking leading to de Sitter compactifications. In this case, the scalar degree of freedom of R2 gravity is associated to a volume modulus. Once written in terms of a scalar-tensor theory, the effective theory corresponds to a massive scalar field coupled with the universal strength β =1 /√{6 } to the matter stress-energy tensor. When the relationship c0λ2≃1 is realized, we find that on astrophysical scales and in cosmology the scalar field is ultralocal and therefore no effect arises on such large scales. On the other hand, the scalar field mass is tightly constrained by the nonobservation of fifth forces in torsion pendulum experiments such as Eöt-Wash. It turns out that the observation of the dark energy scale in cosmology implies that the scalar field could be detectable by fifth-force experiments in the near future.

Calculations of the magnetic properties of R2M14B intermetallic compounds (R=rare earth, M=Fe, Co)

International Nuclear Information System (INIS)

Ito, Masaaki; Yano, Masao; Dempsey, Nora M.; Givord, Dominique

2016-01-01

The hard magnetic properties of “R–M–B” (R=rare earth, M=mainly Fe) magnets derive from the specific intrinsic magnetic properties encountered in Fe-rich R 2 M 14 B compounds. Exchange interactions are dominated by the 3d elements, Fe and Co, and may be modeled at the macroscopic scale with good accuracy. Based on classical formulae that relate the anisotropy coefficients to the crystalline electric field parameters and exchange interactions, a simple numerical approach is used to derive the temperature dependence of anisotropy in various R 2 Fe 14 B compounds (R=Pr, Nd, Dy). Remarkably, a unique set of crystal field parameters give fair agreement with the experimentally measured properties of all compounds. This implies reciprocally that the properties of compounds that incorporate a mixture of different rare-earth elements may be predicted accurately. This is of special interest for material optimization that often involves the partial replacement of Nd with another R element and also the substitution of Co for Fe. - Highlights: • Anisotropy constants derived from CEF parameters of R 2 M 14 B compounds (M=Fe, Co). • Anisotropy constants of all R 2 Fe 14 B compounds using unique set of CEF parameters. • Moment non-collinearity in magnetization processes under B app along hard axis.

Development of a Watershed-Scale Long-Term Hydrologic Impact Assessment Model with the Asymptotic Curve Number Regression Equation

Directory of Open Access Journals (Sweden)

Jichul Ryu

2016-04-01

Full Text Available In this study, 52 asymptotic Curve Number (CN regression equations were developed for combinations of representative land covers and hydrologic soil groups. In addition, to overcome the limitations of the original Long-term Hydrologic Impact Assessment (L-THIA model when it is applied to larger watersheds, a watershed-scale L-THIA Asymptotic CN (ACN regression equation model (watershed-scale L-THIA ACN model was developed by integrating the asymptotic CN regressions and various modules for direct runoff/baseflow/channel routing. The watershed-scale L-THIA ACN model was applied to four watersheds in South Korea to evaluate the accuracy of its streamflow prediction. The coefficient of determination (R2 and Nash–Sutcliffe Efficiency (NSE values for observed versus simulated streamflows over intervals of eight days were greater than 0.6 for all four of the watersheds. The watershed-scale L-THIA ACN model, including the asymptotic CN regression equation method, can simulate long-term streamflow sufficiently well with the ten parameters that have been added for the characterization of streamflow.

Multiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis.

Science.gov (United States)

Ennouri, Karim; Ben Ayed, Rayda; Triki, Mohamed Ali; Ottaviani, Ennio; Mazzarello, Maura; Hertelli, Fathi; Zouari, Nabil

2017-07-01

The aim of the present work was to develop a model that supplies accurate predictions of the yields of delta-endotoxins and proteases produced by B. thuringiensis var. kurstaki HD-1. Using available medium ingredients as variables, a mathematical method, based on Plackett-Burman design (PB), was employed to analyze and compare data generated by the Bootstrap method and processed by multiple linear regressions (MLR) and artificial neural networks (ANN) including multilayer perceptron (MLP) and radial basis function (RBF) models. The predictive ability of these models was evaluated by comparison of output data through the determination of coefficient (R 2 ) and mean square error (MSE) values. The results demonstrate that the prediction of the yields of delta-endotoxin and protease was more accurate by ANN technique (87 and 89% for delta-endotoxin and protease determination coefficients, respectively) when compared with MLR method (73.1 and 77.2% for delta-endotoxin and protease determination coefficients, respectively), suggesting that the proposed ANNs, especially MLP, is a suitable new approach for determining yields of bacterial products that allow us to make more appropriate predictions in a shorter time and with less engineering effort.

Determination of the Rate Coefficients of the SO2 plus O plus M yields SO3 plus M Reaction

Science.gov (United States)

Hwang, S. M.; Cooke, J. A.; De Witt, K. J.; Rabinowitz, M. J.

2010-01-01

Rate coefficients of the title reaction R(sub 31) (SO2 +O+M yields SO3 +M) and R(sub 56) (SO2 + HO2 yields SO3 +OH), important in the conversion of S(IV) to S(VI),were obtained at T =970-1150 K and rho (sub ave) = 16.2 micro mol/cubic cm behind reflected shock waves by a perturbation method. Shock-heated H2/ O2/Ar mixtures were perturbed by adding small amounts of SO2 (1%, 2%, and 3%) and the OH temporal profiles were then measured using laser absorption spectroscopy. Reaction rate coefficients were elucidated by matching the characteristic reaction times acquired from the individual experimental absorption profiles via simultaneous optimization of k(sub 31) and k(sub 56) values in the reaction modeling (for satisfactory matches to the observed characteristic times, it was necessary to take into account R(sub 56)). In the experimental conditions of this study, R(sub 31) is in the low-pressure limit. The rate coefficient expressions fitted using the combined data of this study and the previous experimental results are k(sub 31,0)/[Ar] = 2.9 10(exp 35) T(exp ?6.0) exp(?4780 K/T ) + 6.1 10(exp 24) T(exp ?3.0) exp(?1980 K/T ) cm(sup 6) mol(exp ?2)/ s at T = 300-2500 K; k(sub 56) = 1.36 10(exp 11) exp(?3420 K/T ) cm(exp 3)/mol/s at T = 970-1150 K. Computer simulations of typical aircraft engine environments, using the reaction mechanism with the above k(sub 31,0) and k(sub 56) expressions, gave the maximum S(IV) to S(VI) conversion yield of ca. 3.5% and 2.5% for the constant density and constant pressure flow condition, respectively. Moreover, maximum conversions occur at rather higher temperatures (?1200 K) than that where the maximum k(sub 31,0) value is located (approximately 800 K). This is because the conversion yield is dependent upon not only the k(sup 31,0) and k(sup 56) values (production flux) but also the availability of H, O, and HO2 in the system (consumption flux).

Predicting the cross-reactivities of polycyclic aromatic hydrocarbons in ELISA by regression analysis and CoMFA methods

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yan-Feng; Dai, Shu-Gui [College of Environmental Science and Engineering, Nankai University, Key Laboratory for Pollution Process and Environmental Criteria of Ministry of Education, Tianjin (China); Ma, Yi [College of Chemistry, Nankai University, Institute of Elemento-Organic Chemistry, Tianjin (China); Gao, Zhi-Xian [Institute of Hygiene and Environmental Medicine, Tianjin (China)

2010-07-15

Immunoassays have been regarded as a possible alternative or supplement for measuring polycyclic aromatic hydrocarbons (PAHs) in the environment. Since there are too many potential cross-reactants for PAH immunoassays, it is difficult to determine all the cross-reactivities (CRs) by experimental tests. The relationship between CR and the physical-chemical properties of PAHs and related compounds was investigated using the CR data from a commercial enzyme-linked immunosorbent assay (ELISA) kit test. Two quantitative structure-activity relationship (QSAR) techniques, regression analysis and comparative molecular field analysis (CoMFA), were applied for predicting the CR of PAHs in this ELISA kit. Parabolic regression indicates that the CRs are significantly correlated with the logarithm of the partition coefficient for the octanol-water system (log K{sub ow}) (r{sup 2}=0.643, n=23, P<0.0001), suggesting that hydrophobic interactions play an important role in the antigen-antibody binding and the cross-reactions in this ELISA test. The CoMFA model obtained shows that the CRs of the PAHs are correlated with the 3D structure of the molecules (r{sub cv}{sup 2}=0.663, r{sup 2}=0.873, F{sub 4,32}=55.086). The contributions of the steric and electrostatic fields to CR were 40.4 and 59.6%, respectively. Both of the QSAR models satisfactorily predict the CR in this PAH immunoassay kit, and help in understanding the mechanisms of antigen-antibody interaction. (orig.)

Tools to support interpreting multiple regression in the face of multicollinearity.

Science.gov (United States)

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

Estimating Longitudinal Dispersion Coefficient of Pollutants Using Adaptive Neuro-Fuzzy Inference System

Directory of Open Access Journals (Sweden)

Hossein Riahi Modvar

2008-09-01

Full Text Available Longitudinal dispersion coefficient in rivers and natural streams is usually estimated by simple inaccurate empirical relations because of the complexity of the phenomenon. In this study, the adaptive neuro-fuzzy inference system (ANFIS is used to develop a new flexible tool for predicting the longitudinal dispersion coefficient. The system has the ability to understand and realize the phenomenon without the need for mathematical governing equations.. The training and testing of this new model are accomplished using a set of available published filed data. Several statistical and graphical criteria are used to check the accuracy of the model. The dispersion coefficient values predicted by the ANFIS model compares satisfactorily with the measured data. The predicted values are also compared with those predicted by existing empirical equations reported in the literature to find that the ANFIS model with R2=0.99 and RMSE=15.18 in training stage and R2=0.91 and RMSE=187.8 in testing stage is superior in predicting the dispersion coefficient to the most accurate empirical equation with R2=0.48 and RMSE=295.7. The proposed methodology is a new approach to estimating dispersion coefficient in streams and can be combined with mathematical models of pollutant transfer or real-time updating of these models.

An improved multiple linear regression and data analysis computer program package

Science.gov (United States)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Evaporating heat transfer of R22 and R410A in horizontal smooth and microfin tubes

Energy Technology Data Exchange (ETDEWEB)

Kim, Man-Hoe; Shin, Joeng-Seob [Korea Advanced Institute of Science and Technology, Daejeon (Korea). Department of Mechanical Engineering

2005-09-01

An experimental investigation of evaporating heat transfer in 9.52 mm O.D. horizontal copper tubes was conducted. The refrigerants tested were R22 and the near-azeotropic mixture, R410A. The test rig had a straight, horizontal test section with an active length of 0.92 m and was heated by the heat transfer fluid (hot water) circulated in a surrounding annulus. Constant heat flux of 11.0 kW/m{sup 2} was maintained and refrigerant quality varied from 0.2 to 0.8.. The results were reported for evaporation at 15 {sup o}C in a 0.92 m long test section for 30-60 kg/h mass flow rate. The local and average heat transfer coefficients for seven microfin tubes were presented compared to those for a smooth tube. The average evaporation heat transfer coefficients of R22 and R410A for the microfin tubes were 1.86-3.27 and 1.64-2.99 times higher than those for the smooth tube, respectively. When compared to R22 at the same test conditions, the evaporating heat transfer coefficients for R410A were 97-129% of R22. (author)

Predictive power of Brazilian equity fund performance using R2 as a measure of selectivity

Directory of Open Access Journals (Sweden)

Marcelo dos Santos Guzella

2017-03-01

Full Text Available ABSTRACT This paper aimed to investigate the impact of levels of selectivity on the performance of equity funds using a methodology applied for the first time ever (as far as we know in the Brazilian market. As an indicator of the activity level of a fund, we proposed the coefficient of determination (R2 of the regression of its returns over market returns. In total, 867 funds were analyzed in the period between November 2004 and October 2014. The hypothesis tested is that more selective funds perform better to compensate for their higher operating costs. This hypothesis was confirmed in the Brazilian market. Dynamic equally-weighted portfolios of funds were simulated, according to their past R2 and alphas, with monthly rebalancing and 12-month moving windows. The portfolio of the most selective funds had a Sharpe ratio of 0.0494, on a monthly basis, while the portfolio of the least selective funds had a Sharpe ratio of -0.0314. Performance was also higher in evaluations involving excess returns, Jensen’s alpha, and accumulated returns, as well as when compared to randomly selected portfolios. Moreover, past performance (as measured by Jensen’s alpha was also a predictor of future performance. Particularly, the portfolio composed by funds with a higher past alpha and lower past R2 presented a Sharpe ratio of 0.1483 and a Jensen’s alpha of 0.87% (significant at 1%, while the one composed of funds with a lower past alpha and lower activity level presented a Sharpe ratio of -0.0673 and an alpha of -0.32% (also significant at 1%.

Constrained statistical inference : sample-size tables for ANOVA and regression

NARCIS (Netherlands)

Vanbrabant, Leonard; Van De Schoot, Rens; Rosseel, Yves

2015-01-01

Researchers in the social and behavioral sciences often have clear expectations about the order/direction of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β1 is larger than β2 and β3. The corresponding hypothesis is H: β1 > {β2, β3} and

Sirt2 suppresses glioma cell growth through targeting NF-κB–miR-21 axis

International Nuclear Information System (INIS)

Li, Ya’nan; Dai, Dongwei; Lu, Qiong; Fei, Mingyu; Li, Mengmeng; Wu, Xi

2013-01-01

Highlights: •Sirt2 expression is down-regulated in human glioma tissues and cell lines. •Sirt2 regresses glioma cell growth and colony formation via inducing apoptosis. •miR-21 is essential for the functions of Sirt2 in glioma cells. •Sirt2 deacetylates p65 to decrease miR-21 expression. -- Abstract: Sirtuins are NAD + -dependent deacetylases that regulate numerous cellular processes including aging, DNA repair, cell cycle, metabolism, and survival under stress conditions. The roles of sirtuin family members are widely studied in carcinogenesis. However, their roles in glioma remain unclear. Here we report that Sir2 was under expressed in human glioma tissues and cell lines. We found that Sirt2 overexpression decreased cell proliferation and colony formation capacity. In addition, Sirt2 overexpression induced cellular apoptosis via up-regulating cleaved caspase 3 and Bax, and down-regulating anti-apoptotic protein Bcl-2. Sirt2 knockdown obtained opposing results. We showed that Sirt2 overexpression inhibited miR-21 expression, and Sirt2 was not sufficient to reduce cell proliferation and colony formation as well as to induce apoptosis when miR-21 was knocked down in glioma cells. Mechanically, we demonstrated that Sirt2 deacetylated p65 at K310 and blocked p65 binding to the promoter region of miR-21, thus regressing the transcription of miR-21. In summary, Sirt2 is critical in human glioma via NF-κB–miR-21 pathway and Sirt2 activator may serve as candidate drug for glioma therapy

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

Science.gov (United States)

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Estimation of Stature from Foot Dimensions and Stature among South Indian Medical Students Using Regression Models

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Rajesh D. R

2015-01-01

Full Text Available Background: At times fragments of soft tissues are found disposed off in the open, in ditches at the crime scene and the same are brought to forensic experts for the purpose of identification and such type of cases pose a real challenge. Objectives: This study was aimed at developing a methodology which could help in personal identification by studying the relation between foot dimensions and stature among south subjects using regression models. Material and Methods: Stature and foot length of 100 subjects (age range 18-22 years were measured. Linear regression equations for stature estimation were calculated. Result: The correlation coefficients between stature and foot lengths were found to be positive and statistically significant. Height = 98.159 + 3.746 × FLRT (r = 0.821 and Height = 91.242 + 3.284 × FLRT (r = 0.837 are the regression formulas from foot lengths for males and females respectively. Conclusion: The regression equation derived in the study can be used reliably for estimation of stature in a diverse population group thus would be of immense value in the field of personal identification especially from mutilated bodies or fragmentary remains.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

Science.gov (United States)

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, pregression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Diagnosing cysts with correlation coefficient images from 2-dimensional freehand elastography.

Science.gov (United States)

Booi, Rebecca C; Carson, Paul L; O'Donnell, Matthew; Richards, Michael S; Rubin, Jonathan M

2007-09-01

We compared the diagnostic potential of using correlation coefficient images versus elastograms from 2-dimensional (2D) freehand elastography to characterize breast cysts. In this preliminary study, which was approved by the Institutional Review Board and compliant with the Health Insurance Portability and Accountability Act, we imaged 4 consecutive human subjects (4 cysts, 1 biopsy-verified benign breast parenchyma) with freehand 2D elastography. Data were processed offline with conventional 2D phase-sensitive speckle-tracking algorithms. The correlation coefficient in the cyst and surrounding tissue was calculated, and appearances of the cysts in the correlation coefficient images and elastograms were compared. The correlation coefficient in the cysts was considerably lower (14%-37%) than in the surrounding tissue because of the lack of sufficient speckle in the cysts, as well as the prominence of random noise, reverberations, and clutter, which decorrelated quickly. Thus, the cysts were visible in all correlation coefficient images. In contrast, the elastograms associated with these cysts each had different elastographic patterns. The solid mass in this study did not have the same high decorrelation rate as the cysts, having a correlation coefficient only 2.1% lower than that of surrounding tissue. Correlation coefficient images may produce a more direct, reliable, and consistent method for characterizing cysts than elastograms.

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu

2015-01-01

predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths

Moisture-Dependent Physical Properties of Àbèèrè (Picralima nitida Seeds

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A. K. Aremu

2015-08-01

Full Text Available Some relevant engineering properties of Àbèèrè (Picralima nitida seeds are needed for the design of its processing equipments. The geometric, gravimetric and frictional properties of Àbèèrè seed in the moisture content ranges of 7.98% - 47.77% (wb were investigated. The average length, width, thickness, arithmetic and geometric mean diameters, sphericity, surface area, volume, true and bulk densities and angles of repose increased from 28.76 – 30.75mm, 16.18 – 19.62mm, 5.75 – 7.15mm, 16.90 – 19.17mm, 13.81 – 16.11mm,0.48 – 0.53, 600.94 – 817.83mm2, 443.00 – 717.92mm3, 2.49×10-3 – 2.60×10-3g/mm3, 1.14×10-3 – 1.50×10-3g/mm3and 27.97o – 30.26o respectively as the moisture content increased from 7.98% to 47.77%. However, values for porosity decreased from 0.54 – 0.42. The static coefficient of friction of Àbèèrè increased linearly over the three material surfaces – plywood, stainless steel and glass – with increasing moisture content from 0.445 – 0.468, 0.286 – 0.384 and 0.357 – 0.389 respectively. The steel surface had the lowest static coefficient of friction whereas the plywood gave the highest value at all moisture content levels. The regression models developed for all the physical properties of the seeds had high coefficient of determination, R2.

Modeling maximum daily temperature using a varying coefficient regression model

Science.gov (United States)

Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith

2014-01-01

Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...

Evaporation of R407C and R410A in a horizontal herringbone microfin tube: heat transfer and pressure drop

Energy Technology Data Exchange (ETDEWEB)

Wellsandt, S; Vamling, L [Chalmers University of Technology, Gothenburg (Sweden). Department of Chemical Engineering and Environmental Science, Heat and Power Technology

2005-09-01

An experimental investigation of in-tube evaporation of R410A and R407C has been carried out for a 4 m long herringbone microfin tube with an outer diameter of 9.53 mm. Measured local heat transfer coefficients and pressure losses are reported for evaporation temperatures for R410A and R407C between -2.2 and 9.5 {sup o}C and between -5.5 and 13.8 {sup o}C, respectively. Mass flow rates between 162 and 366 kg m{sup -2} s{sup -1} have been investigated. Results from this work are compared to R134a data from an earlier work by the present authors, and also to predicted values from some available helical microfin correlations. Compared to R134a data, heat transfer coefficients for the investigated mixtures are generally lower, especially at low mass flow rates. No major effect of heat flux on heat transfer coefficients was found, with the exception of the high quality region. Predicted heat transfer coefficients from helical microfin correlations strongly overpredict the present data. Global pressure losses are predicted well, even though local deviations are found. (author)

Isopiestic determination of the osmotic coefficient and vapour pressure of N-R-4-(N,N-dimethylamino)pyridinium tetrafluoroborate (R = C4H9, C5H11, C6H13) in the ethanol solution at T = 298.15 K

International Nuclear Information System (INIS)

Sardroodi, Jaber Jahanbin; Atabay, Maryam; Azamat, Jafar

2012-01-01

Highlights: ► The osmotic coefficients of the solutions of ionic liquid in ethanol have been measured. ► Measured osmotic coefficients were correlated using Pitzer, e-NRTL and NRF models and polynomial equation. ► Vapour pressures were evaluated from the correlated osmotic coefficients. - Abstract: Osmotic coefficients of the solutions of room temperature ionic liquid N-R-4-(N,N-dimethylamino)pyridinium tetrafluoroborate (R = C 4 H 9 , C 5 H 11 , C 6 H 13 ) in ethanol have been measured at T = 298.15 K by the isopiestic method. The experimental osmotic coefficients have been correlated using the ion interaction model of Pitzer, electrolyte non-random two liquid (e-NRTL) model of Chen, non-random factor (NRF) and a fourth-order polynomial in terms of molality. The vapour pressures of the solutions studied have been evaluated from the osmotic coefficients.

Empirical estimation of the grades of hearing impairment among industrial workers based on new artificial neural networks and classical regression methods.

Science.gov (United States)

Farhadian, Maryam; Aliabadi, Mohsen; Darvishi, Ebrahim

2015-01-01

Prediction models are used in a variety of medical domains, and they are frequently built from experience which constitutes data acquired from actual cases. This study aimed to analyze the potential of artificial neural networks and logistic regression techniques for estimation of hearing impairment among industrial workers. A total of 210 workers employed in a steel factory (in West of Iran) were selected, and their occupational exposure histories were analyzed. The hearing loss thresholds of the studied workers were determined using a calibrated audiometer. The personal noise exposures were also measured using a noise dosimeter in the workstations. Data obtained from five variables, which can influence the hearing loss, were used as input features, and the hearing loss thresholds were considered as target feature of the prediction methods. Multilayer feedforward neural networks and logistic regression were developed using MATLAB R2011a software. Based on the World Health Organization classification for the grades of hearing loss, 74.2% of the studied workers have normal hearing thresholds, 23.4% have slight hearing loss, and 2.4% have moderate hearing loss. The accuracy and kappa coefficient of the best developed neural networks for prediction of the grades of hearing loss were 88.6 and 66.30, respectively. The accuracy and kappa coefficient of the logistic regression were also 84.28 and 51.30, respectively. Neural networks could provide more accurate predictions of the hearing loss than logistic regression. The prediction method can provide reliable and comprehensible information for occupational health and medicine experts.

System performance with R407A, R407B, R407C compared to R22

DEFF Research Database (Denmark)

Knudsen, Hans Jørgen Høgaard

1997-01-01

both the cooling capacity and COP are smaller then the cooling capacity and COP with R22. The cooling capacity for R407A and R407B is lower than the capacity for R22 for brine temperatures less than 0 C and higher then the cooling capacity for R22 for brine temperatures higher than 0 C. The COP for R......407A and R407B er lower than the COP for R22.The volumetric and isentropic efficiency of the compressor are with mixture higher than the volumetric and isentropic efficiency with R22.......The article presents the results obtained by substituting R22 with mixture of R32/R125/R134A (R407A, R407B and R407C) in an existing refrigeration plant. Cooling capacity, coefficient of performance and heat transfer coefficient in the evaporator have been measured.The results show that for R407C...

Phase transition and conduction mechanism in Pb{sub 2}Na{sub 0.8}R{sub 0.2}Nb{sub 4.8}Fe{sub 0.2}O{sub 15} material (R=rare earth)

Energy Technology Data Exchange (ETDEWEB)

Bouziane, M. [Laboratoire de Chimie du Solide Appliquée, Faculté des Sciences, Université Mohammed V-Agdal, Avenue Ibn Batouta, BP 1014 Rabat (Morocco); Taibi, M., E-mail: taibiens@yahoo.fr [Laboratoire de Physico-Chimie des Matériaux (LAF 502), Ecole Normale Supérieure, Université Mohammed V-Agdal, BP 5118 Rabat (Morocco); Boukhari, A. [Laboratoire de Chimie du Solide Appliquée, Faculté des Sciences, Université Mohammed V-Agdal, Avenue Ibn Batouta, BP 1014 Rabat (Morocco)

2013-11-15

Electrical properties of Pb{sub 2}Na{sub 0.8}Eu{sub 0.2}Nb{sub 4.8}Fe{sub 0.2}O{sub 15} tungsten bronze compound were investigated. Ferroelectric phase transition of diffuse type is observed at 395 °C. Conductivity study as a function of temperature (RT-600 °C) and at three different frequencies (10, 100 and 1000 kHz) suggests the existence of dominant ionic conduction. The rise of ac conductivity on increasing temperature supports the NTCR (negative temperature coefficient of resistance) behaviour of the material. The activation energies have been evaluated from ac conductivity using Arrhenius equation and discussed. Different conduction mechanisms were identified. For comparison, the conducting properties of Pb{sub 2}Na{sub 0.8}R{sub 0.2}Nb{sub 4.8}Fe{sub 0.2}O{sub 15} (R=Dy, Nd, La) were also investigated. - Graphical abstract: Thermal evolution of lnσ{sub ac} of Pb{sub 2}Na{sub 0.8}Eu{sub 0.2}Nb{sub 4.8}Fe{sub 0.2}O{sub 15} at selected frequencies. Display Omitted - Highlights: • We found that TB compounds exhibit a diffuse type of first- order transition. • A negative temperature coefficient of resistance (NTCR) behaviour is observed. • Three conduction mechanisms were identified: n-and/or p-type at low temperatures. • The conduction mechanism in the studied compounds is very complex.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

Science.gov (United States)

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various

Quantitative Contribution of IL2Rγ to the Dynamic Formation of IL2-IL2R Complexes.

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Luis F Ponce

Full Text Available Interleukin-2 (IL2 is a growth factor for several immune cells and its function depends on its binding to IL2Rs in the cell membrane. The most accepted model for the assembling of IL2-IL2R complexes in the cell membrane is the Affinity Conversion Model (ACM. This model postulates that IL2R receptor association is sequential and dependent on ligand binding. Most likely free IL2 binds first to IL2Rα, and then this complex binds to IL2Rβ, and finally to IL2Rγ (γc. However, in previous mathematical models representing this process, the binding of γc has not been taken into account. In this work, the quantitative contribution of the number of IL2Rγ chain to the IL2-IL2R apparent binding affinity and signaling is studied. A mathematical model of the affinity conversion process including the γ chain in the dynamic, has been formulated. The model was calibrated by fitting it to experimental data, specifically, Scatchard plots obtained using human cell lines. This paper demonstrates how the model correctly explains available experimental observations. It was estimated, for the first time, the value of the kinetic coefficients of IL2-IL2R complexes interaction in the cell membrane. Moreover, the number of IL2R components in different cell lines was also estimated. It was obtained a variable distribution in the number of IL2R components depending on the cell type and the activation state. Of most significance, the study predicts that not only the number of IL2Rα and IL2Rβ, but also the number of γc determine the capacity of the cell to capture and retain IL2 in signalling complexes. Moreover, it is also showed that different cells might use different pathways to bind IL2 as consequence of its IL2R components distribution in the membrane.

Auxiliary variables in multiple imputation in regression with missing X: a warning against including too many in small sample research

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Hardt Jochen

2012-12-01

Full Text Available Abstract Background Multiple imputation is becoming increasingly popular. Theoretical considerations as well as simulation studies have shown that the inclusion of auxiliary variables is generally of benefit. Methods A simulation study of a linear regression with a response Y and two predictors X1 and X2 was performed on data with n = 50, 100 and 200 using complete cases or multiple imputation with 0, 10, 20, 40 and 80 auxiliary variables. Mechanisms of missingness were either 100% MCAR or 50% MAR + 50% MCAR. Auxiliary variables had low (r=.10 vs. moderate correlations (r=.50 with X’s and Y. Results The inclusion of auxiliary variables can improve a multiple imputation model. However, inclusion of too many variables leads to downward bias of regression coefficients and decreases precision. When the correlations are low, inclusion of auxiliary variables is not useful. Conclusion More research on auxiliary variables in multiple imputation should be performed. A preliminary rule of thumb could be that the ratio of variables to cases with complete data should not go below 1 : 3.

New Inference Procedures for Semiparametric Varying-Coefficient Partially Linear Cox Models

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Yunbei Ma

2014-01-01

Full Text Available In biomedical research, one major objective is to identify risk factors and study their risk impacts, as this identification can help clinicians to both properly make a decision and increase efficiency of treatments and resource allocation. A two-step penalized-based procedure is proposed to select linear regression coefficients for linear components and to identify significant nonparametric varying-coefficient functions for semiparametric varying-coefficient partially linear Cox models. It is shown that the penalized-based resulting estimators of the linear regression coefficients are asymptotically normal and have oracle properties, and the resulting estimators of the varying-coefficient functions have optimal convergence rates. A simulation study and an empirical example are presented for illustration.

On directional multiple-output quantile regression

Czech Academy of Sciences Publication Activity Database

Paindaveine, D.; Šiman, Miroslav

2011-01-01

Roč. 102, č. 2 (2011), s. 193-212 ISSN 0047-259X R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:Commision EC(BE) Fonds National de la Recherche Scientifique Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate quantile * quantile regression * multiple-output regression * halfspace depth * portfolio optimization * value-at risk Subject RIV: BA - General Mathematics Impact factor: 0.879, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/siman-0364128.pdf

QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES.

Science.gov (United States)

Toropov, A A; Toropova, A P; Raska, I

2008-04-01

Simplified molecular input line entry system (SMILES) has been utilized in constructing quantitative structure-property relationships (QSPR) for octanol/water partition coefficient of vitamins and organic compounds of different classes by optimal descriptors. Statistical characteristics of the best model (vitamins) are the following: n=17, R(2)=0.9841, s=0.634, F=931 (training set); n=7, R(2)=0.9928, s=0.773, F=690 (test set). Using this approach for modeling octanol/water partition coefficient for a set of organic compounds gives a model that is statistically characterized by n=69, R(2)=0.9872, s=0.156, F=5184 (training set) and n=70, R(2)=0.9841, s=0.179, F=4195 (test set).

Linear regression in astronomy. I

Science.gov (United States)

Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh

1990-01-01

Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.

A comprehensive investigation on static and dynamic friction coefficients of wheat grain with the adoption of statistical analysis.

Science.gov (United States)

Shafaei, S M; Kamgar, S

2017-07-01

This paper deals with studying and modeling static friction coefficient (SFC) and dynamic friction coefficient (DFC) of wheat grain as affected by several treatments. Significance of single effect (SE) and dual interaction effect (DIE) of treatments (moisture content and contact surface) on SFC and, SE, DIE, and triple interaction effect (TIE) of treatments (moisture content, contact surface and sliding velocity) on DFC were determined using statistical analysis methods. Multiple linear regression (MLR) modeling was employed to predict SFC and DFC on different contact surfaces. Predictive ability of developed MLR models was evaluated using some statistical parameters (coefficient of determination ( R 2 ), root mean square error (RMSE), and mean relative deviation modulus (MRDM)). Results indicated that significant increasing DIE of treatments on SFC was 3.2 and 3 times greater than significant increasing SE of moisture content and contact surface, respectively. In case of DFC, the significant increasing TIE of treatments was 8.8, 3.7, and 8.9 times greater than SE of moisture content, contact surface, and sliding velocity, respectively. It was also found that the SE of contact surface on SFC was 1.1 times greater than that of moisture content and the SE of contact surface on DFC was 2.4 times greater than that of moisture content or sliding velocity. According to the reasonable average of statistical parameters ( R 2  = 0.955, RMSE = 0.01788 and MRDM = 3.152%), the SFC and DFC could be successfully predicted by suggested MLR models. Practically, it is recommended to apply the models for direct prediction of SFC and DFC, respective to each contact surface, based on moisture content and sliding velocity.

An Investigation of the Fit of Linear Regression Models to Data from an SAT[R] Validity Study. Research Report 2011-3

Science.gov (United States)

Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael

2011-01-01

This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…

Evaluation of the Correlation Coefficient of Polyethylene Glycol Treated and Direct Prolactin Results and Comparability with Different Assay System Results.

Science.gov (United States)

Pal, Shyamali

2017-12-01

The presence of Macro prolactin is a significant cause of elevated prolactin resulting in misdiagnosis in all automated systems. Poly ethylene glycol (PEG) pretreatment is the preventive process but such process includes the probability of loss of a fraction of bioactive prolactin. Surprisingly, PEG treated EQAS & IQAS samples in Cobas e 411 are found out to be correlating with direct results of at least 3 immunoassay systems and treated and untreated Cobas e 411 results are comparable by a correlation coefficient. Comparison of EQAS, IQAS and patient samples were done to find out the trueness of such correlation factor. Study with patient's results have established the correlation coefficient is valid for very small concentration of prolactin also. EQAS, IQAS and 150 patient samples were treated with PEG and prolactin results of treated and untreated samples obtained from Roche Cobas e 411. 25 patient's results (treated) were compared with direct results in Advia Centaur, Architect I & Access2 systems. Correlation coefficient was obtained from trend line of the treated and untreated results. Two tailed p-value obtained from regression coefficient(r) and sample size. The correlation coefficient is in the range (0.761-0.771). Reverse correlation range is (1.289-1.301). r value of two sets of calculated results were 0.995. Two tailed p- value is zero approving dismissal of null hypothesis. The z-score of EQAS does not always assure authenticity of resultsPEG precipitation is correlated by the factor 0.761 even in very small concentrationsAbbreviationsGFCgel filtration chromatographyPEGpolyethylene glycolEQASexternal quality assurance systemM-PRLmacro prolactinPRLprolactinECLIAelectro-chemiluminescence immunoassayCLIAclinical laboratory improvement amendmentsIQASinternal quality assurance systemrregression coefficient.

A comparison of multiple regression and neural network techniques for mapping in situ pCO2 data

International Nuclear Information System (INIS)

Lefevre, Nathalie; Watson, Andrew J.; Watson, Adam R.

2005-01-01

Using about 138,000 measurements of surface pCO 2 in the Atlantic subpolar gyre (50-70 deg N, 60-10 deg W) during 1995-1997, we compare two methods of interpolation in space and time: a monthly distribution of surface pCO 2 constructed using multiple linear regressions on position and temperature, and a self-organizing neural network approach. Both methods confirm characteristics of the region found in previous work, i.e. the subpolar gyre is a sink for atmospheric CO 2 throughout the year, and exhibits a strong seasonal variability with the highest undersaturations occurring in spring and summer due to biological activity. As an annual average the surface pCO 2 is higher than estimates based on available syntheses of surface pCO 2 . This supports earlier suggestions that the sink of CO 2 in the Atlantic subpolar gyre has decreased over the last decade instead of increasing as previously assumed. The neural network is able to capture a more complex distribution than can be well represented by linear regressions, but both techniques agree relatively well on the average values of pCO 2 and derived fluxes. However, when both techniques are used with a subset of the data, the neural network predicts the remaining data to a much better accuracy than the regressions, with a residual standard deviation ranging from 3 to 11 μatm. The subpolar gyre is a net sink of CO 2 of 0.13 Gt-C/yr using the multiple linear regressions and 0.15 Gt-C/yr using the neural network, on average between 1995 and 1997. Both calculations were made with the NCEP monthly wind speeds converted to 10 m height and averaged between 1995 and 1997, and using the gas exchange coefficient of Wanninkhof

SPSS macros to compare any two fitted values from a regression model.

Science.gov (United States)

Weaver, Bruce; Dubois, Sacha

2012-12-01

In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.

Deep learning for biomarker regression: application to osteoporosis and emphysema on chest CT scans

Science.gov (United States)

González, Germán.; Washko, George R.; San José Estépar, Raúl

2018-03-01

Introduction: Biomarker computation using deep-learning often relies on a two-step process, where the deep learning algorithm segments the region of interest and then the biomarker is measured. We propose an alternative paradigm, where the biomarker is estimated directly using a regression network. We showcase this image-tobiomarker paradigm using two biomarkers: the estimation of bone mineral density (BMD) and the estimation of lung percentage of emphysema from CT scans. Materials and methods: We use a large database of 9,925 CT scans to train, validate and test the network for which reference standard BMD and percentage emphysema have been already computed. First, the 3D dataset is reduced to a set of canonical 2D slices where the organ of interest is visible (either spine for BMD or lungs for emphysema). This data reduction is performed using an automatic object detector. Second, The regression neural network is composed of three convolutional layers, followed by a fully connected and an output layer. The network is optimized using a momentum optimizer with an exponential decay rate, using the root mean squared error as cost function. Results: The Pearson correlation coefficients obtained against the reference standards are r = 0.940 (p < 0.00001) and r = 0.976 (p < 0.00001) for BMD and percentage emphysema respectively. Conclusions: The deep-learning regression architecture can learn biomarkers from images directly, without indicating the structures of interest. This approach simplifies the development of biomarker extraction algorithms. The proposed data reduction based on object detectors conveys enough information to compute the biomarkers of interest.

Estimation of Stature from Footprint Anthropometry Using Regression Analysis: A Study on the Bidayuh Population of East Malaysia

Directory of Open Access Journals (Sweden)

T. Nataraja Moorthy

2015-05-01

Full Text Available The human foot has been studied for a variety of reasons, i.e., for forensic as well as non-forensic purposes by anatomists, forensic scientists, anthropologists, physicians, podiatrists, and numerous other groups. An aspect of human identification that has received scant attention from forensic anthropologists is the study of human feet and the footprints made by the feet. The present study, conducted during 2013-2014, aimed to derive population specific regression equations to estimate stature from the footprint anthropometry of indigenous adult Bidayuhs in the east of Malaysia. The study sample consisted of 480 bilateral footprints collected using a footprint kit from 240 Bidayuhs (120 males and 120 females, who consented to taking part in the study. Their ages ranged from 18 to 70 years. Stature was measured using a portable body meter device (SECA model 206. The data were analyzed using PASW Statistics version 20. In this investigation, better results were obtained in terms of correlation coefficient (R between stature and various footprint measurements and regression analysis in estimating the stature. The (R values showed a positive and statistically significant (p < 0.001 relationship between the two parameters. The correlation coefficients in the pooled sample (0.861–0.882 were comparatively higher than those of an individual male (0.762-0.795 and female (0.722-0.765. This study provided regression equations to estimate stature from footprints in the Bidayuh population. The result showed that the regression equations without sex indicators performed significantly better than models with gender indications. The regression equations derived for a pooled sample can be used to estimate stature, even when the sex of the footprint is unknown, as in real crime scenes.

A new proposal for Lagrangian correlation coefficient

International Nuclear Information System (INIS)

Altinsoy, N.; Tugrul, A.B.

2002-01-01

The statistical description of dispersion in turbulent flow was first considered by Taylor (Proc. London Math. Soc. 20 (1921) 196) and the statistical properties of the field were determined by Lagrangian correlation coefficient R L (τ). Frenkiel (Adv. Appl. Mech. 3 (1953) 61) has proposed several simple forms for R L (τ). Some workers have investigated for a proper form of the Lagrangian correlation coefficient. In this work, a new proposal for the Lagrangian correlation coefficient is proposed and discussed. It can be written in general form with the one of the Frenkiel's (Adv. Appl. Mech. 3 (1953) 61) Lagrangian correlation coefficient. There is very satisfactory agreement between the new correlation and the experiment

(1R,2R,3R,4R,5S-2,3-Bis[(2S′-2-acetoxy-2-phenylacetoxy]-4-azido-1-[(2,4-dinitrophenylhydrazonomethyl]bicyclo[3.1.0]hexane

Directory of Open Access Journals (Sweden)

Robert McDonald

2008-02-01

Full Text Available In the title compound, C38H29N7O12, the five-membered ring adopts an envelope conformation in which the `flap' is cis to the cyclopropane group. This conformation is similar to those of other bicyclo[3.1.0]hexane analogues for which crystal structures have been reported. The absolute configuration of the stereogenic centers on the cyclopentane ring, as determined by comparison with the known configurations of the stereogenic centers in the (2S-2-acetoxy-2-phenylacetoxy groups, is 1(R, 2(R, 3(R, 4(R and 5(S. An intramolecular N—H...O hydrogen bond is present.

Estimation of octanol/water partition coefficients using LSER parameters

Science.gov (United States)

Luehrs, Dean C.; Hickey, James P.; Godbole, Kalpana A.; Rogers, Tony N.

1998-01-01

The logarithms of octanol/water partition coefficients, logKow, were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for logKow was 0.49. The regression equation was then used to estimate logKow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.

Coupling coefficients for tensor product representations of quantum SU(2)

International Nuclear Information System (INIS)

Groenevelt, Wolter

2014-01-01

We study tensor products of infinite dimensional irreducible * -representations (not corepresentations) of the SU(2) quantum group. We obtain (generalized) eigenvectors of certain self-adjoint elements using spectral analysis of Jacobi operators associated to well-known q-hypergeometric orthogonal polynomials. We also compute coupling coefficients between different eigenvectors corresponding to the same eigenvalue. Since the continuous spectrum has multiplicity two, the corresponding coupling coefficients can be considered as 2 × 2-matrix-valued orthogonal functions. We compute explicitly the matrix elements of these functions. The coupling coefficients can be considered as q-analogs of Bessel functions. As a results we obtain several q-integral identities involving q-hypergeometric orthogonal polynomials and q-Bessel-type functions

Coupling coefficients for tensor product representations of quantum SU(2)

Science.gov (United States)

Groenevelt, Wolter

2014-10-01

We study tensor products of infinite dimensional irreducible *-representations (not corepresentations) of the SU(2) quantum group. We obtain (generalized) eigenvectors of certain self-adjoint elements using spectral analysis of Jacobi operators associated to well-known q-hypergeometric orthogonal polynomials. We also compute coupling coefficients between different eigenvectors corresponding to the same eigenvalue. Since the continuous spectrum has multiplicity two, the corresponding coupling coefficients can be considered as 2 × 2-matrix-valued orthogonal functions. We compute explicitly the matrix elements of these functions. The coupling coefficients can be considered as q-analogs of Bessel functions. As a results we obtain several q-integral identities involving q-hypergeometric orthogonal polynomials and q-Bessel-type functions.

Soil moisture estimation using multi linear regression with terraSAR-X data

Directory of Open Access Journals (Sweden)

G. García

2016-06-01

Full Text Available The first five centimeters of soil form an interface where the main heat fluxes exchanges between the land surface and the atmosphere occur. Besides ground measurements, remote sensing has proven to be an excellent tool for the monitoring of spatial and temporal distributed data of the most relevant Earth surface parameters including soil’s parameters. Indeed, active microwave sensors (Synthetic Aperture Radar - SAR offer the opportunity to monitor soil moisture (HS at global, regional and local scales by monitoring involved processes. Several inversion algorithms, that derive geophysical information as HS from SAR data, were developed. Many of them use electromagnetic models for simulating the backscattering coefficient and are based on statistical techniques, such as neural networks, inversion methods and regression models. Recent studies have shown that simple multiple regression techniques yield satisfactory results. The involved geophysical variables in these methodologies are descriptive of the soil structure, microwave characteristics and land use. Therefore, in this paper we aim at developing a multiple linear regression model to estimate HS on flat agricultural regions using TerraSAR-X satellite data and data from a ground weather station. The results show that the backscatter, the precipitation and the relative humidity are the explanatory variables of HS. The results obtained presented a RMSE of 5.4 and a R2  of about 0.6

Multiple linear stepwise regression of liver lipid levels: proton MR spectroscopy study in vivo at 3.0 T

International Nuclear Information System (INIS)

Xu Li; Liang Changhong; Xiao Yuanqiu; Zhang Zhonglin

2010-01-01

Objective: To analyze the correlations between liver lipid level determined by liver 3.0 T 1 H-MRS in vivo and influencing factors using multiple linear stepwise regression. Methods: The prospective study of liver 1 H-MRS was performed with 3.0 T system and eight-channel torso phased-array coils using PRESS sequence. Forty-four volunteers were enrolled in this study. Liver spectra were collected with a TR of 1500 ms, TE of 30 ms, volume of interest of 2 cm×2 cm×2 cm, NSA of 64 times. The acquired raw proton MRS data were processed by using a software program SAGE. For each MRS measurement, using water as the internal reference, the amplitude of the lipid signal was normalized to the sum of the signal from lipid and water to obtain percentage lipid within the liver. The statistical description of height, weight, age and BMI, Line width and water suppression were recorded, and Pearson analysis was applied to test their relationships. Multiple linear stepwise regression was used to set the statistical model for the prediction of Liver lipid content. Results: Age (39.1±12.6) years, body weight (64.4±10.4) kg, BMI (23.3±3.1) kg/m 2 , linewidth (18.9±4.4) and the water suppression (90.7±6.5)% had significant correlation with liver lipid content (0.00 to 0.96%, median 0.02%), r were 0.11, 0.44, 0.40, 0.52, -0.73 respectively (P<0.05). But only age, BMI, line width, and the water suppression entered into the multiple linear regression equation. Liver lipid content prediction equation was as follows: Y= 1.395 - (0.021×water suppression) + (0.022×BMI) + (0.014×line width) - (0.004×age), and the coefficient of determination was 0. 613, corrected coefficient of determination was 0.59. Conclusion: The regression model fitted well, since the variables of age, BMI, width, and water suppression can explain about 60% of liver lipid content changes. (authors)

Regional oxygen saturation index (rSO2) in brachioradialis and deltoid muscle. Correlation and prognosis in patients with respiratory sepsis.

Science.gov (United States)

Rodríguez, A; Claverias, L; Marín, J; Magret, M; Rosich, S; Bodí, M; Trefler, S; Pascual, S; Gea, J

2015-03-01

To compare oxygen saturation index (rSO2) obtained simultaneously in two different brachial muscles. Prospective and observational study. Intensive care unit. Critically ill patients with community-acquired pneumonia. Two probes of NIRS device (INVOS 5100) were simultaneously placed on the brachioradialis (BR) and deltoid (D) muscles. rSO2 measurements were recorded at baseline (ICU admission) and at 24h. Demographic and clinical variables were registered. Pearson's correlation coefficient was used to assess the association between continuous variables. The consistency of the correlation was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plot. The predictive value of the rSO2 for mortality was calculated by ROC curve. Nineteen patients were included with an ICU mortality of 21.1%. The rSO2 values at baseline and at 24h were significantly higher in D than in BR muscle. Values obtained simultaneously in both limbs showed a strong correlation and adequate consistency: BR (r=0.95; p0.001) but a wide limit of agreement. Non-survivors had rSO2 values significantly lower than survivors at all times of the study. No patient with rSO2 >60% in BR died, and only 17.6% died with an rSO2 value >60% in D. Both muscles showed consistent discriminatory power for mortality. Both BR and D muscles were appropriate for measuring rSO2. Copyright © 2013 Elsevier España, S.L.U. and SEMICYUC. All rights reserved.

Determination of n-octanol/water partition coefficient for DDT-related compounds by RP-HPLC with a novel dual-point retention time correction.

Science.gov (United States)

Han, Shu-ying; Qiao, Jun-qin; Zhang, Yun-yang; Yang, Li-li; Lian, Hong-zhen; Ge, Xin; Chen, Hong-yuan

2011-03-01

n-Octanol/water partition coefficients (P) for DDTs and dicofol were determined by reversed-phase high performance liquid chromatography (RP-HPLC) on a C(18) column using methanol-water mixture as mobile phase. A dual-point retention time correction (DP-RTC) was proposed to rectify chromatographic retention time (t(R)) shift resulted from stationary phase aging. Based on this correction, the relationship between logP and logk(w), the logarithm of the retention factor extrapolated to pure water, was investigated for a set of 12 benzene homologues and DDT-related compounds with reliable experimental P as model compounds. A linear regression logP=(1.10±0.04) logk(w) - (0.60±0.17) was established with correlation coefficient R(2) of 0.988, cross-validated correlation coefficient R(cv)(2) of 0.983 and standard deviation (SD) of 0.156. This model was further validated using four verification compounds, naphthalene, biphenyl, 2,2-bis(4-chlorophenyl)-1,1-dichloroethane (p,p'-DDD) and 2,2-bis(4-chlorophenyl)-1,1-dichloroethene (p,p'-DDE) with similar structure to DDT. The RP-HPLC-determined P values showed good consistency with shake-flask (SFM) or slow-stirring (SSM) results, especially for highly hydrophobic compounds with logP in the range of 4-7. Then, the P values for five DDT-related compounds, 2-(2-chlorophenyl)-2-(4-chlorophenyl)-1,1,1-trichloroethane (o,p'-DDT), 2-(2-chlorophenyl)-2-(4-chlorophenyl)-1,1-dichloroethane (o,p'-DDD), 2-(2-chlorophenyl)-2-(4-chlorophenyl)-1,1-dichloroethene (o,p'-DDE), and 2,2,2-trichloro-1,1-bis(4-chlorophenyl)ethanol (dicofol) and its main degradation product 4,4'-dichlorobenzophenone (p,p'-DBP) were evaluated by the improved RP-HPLC method for the first time. The excellent precision with SD less than 0.03 proved that the novel DP-RTC protocol can significantly increases the determination accuracy and reliability of P by RP-HPLC. Copyright © 2011 Elsevier Ltd. All rights reserved.

R package to estimate intracluster correlation coefficient with confidence interval for binary data.

Science.gov (United States)

Chakraborty, Hrishikesh; Hossain, Akhtar

2018-03-01

The Intracluster Correlation Coefficient (ICC) is a major parameter of interest in cluster randomized trials that measures the degree to which responses within the same cluster are correlated. There are several types of ICC estimators and its confidence intervals (CI) suggested in the literature for binary data. Studies have compared relative weaknesses and advantages of ICC estimators as well as its CI for binary data and suggested situations where one is advantageous in practical research. The commonly used statistical computing systems currently facilitate estimation of only a very few variants of ICC and its CI. To address the limitations of current statistical packages, we developed an R package, ICCbin, to facilitate estimating ICC and its CI for binary responses using different methods. The ICCbin package is designed to provide estimates of ICC in 16 different ways including analysis of variance methods, moments based estimation, direct probabilistic methods, correlation based estimation, and resampling method. CI of ICC is estimated using 5 different methods. It also generates cluster binary data using exchangeable correlation structure. ICCbin package provides two functions for users. The function rcbin() generates cluster binary data and the function iccbin() estimates ICC and it's CI. The users can choose appropriate ICC and its CI estimate from the wide selection of estimates from the outputs. The R package ICCbin presents very flexible and easy to use ways to generate cluster binary data and to estimate ICC and it's CI for binary response using different methods. The package ICCbin is freely available for use with R from the CRAN repository (https://cran.r-project.org/package=ICCbin). We believe that this package can be a very useful tool for researchers to design cluster randomized trials with binary outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Linear Regression between CIE-Lab Color Parameters and Organic Matter in Soils of Tea Plantations

Science.gov (United States)

Chen, Yonggen; Zhang, Min; Fan, Dongmei; Fan, Kai; Wang, Xiaochang

2018-02-01

To quantify the relationship between the soil organic matter and color parameters using the CIE-Lab system, 62 soil samples (0-10 cm, Ferralic Acrisols) from tea plantations were collected from southern China. After air-drying and sieving, numerical color information and reflectance spectra of soil samples were measured under laboratory conditions using an UltraScan VIS (HunterLab) spectrophotometer equipped with CIE-Lab color models. We found that soil total organic carbon (TOC) and nitrogen (TN) contents were negatively correlated with the L* value (lightness) ( r = -0.84 and -0.80, respectively), a* value (correlation coefficient r = -0.51 and -0.46, respectively) and b* value ( r = -0.76 and -0.70, respectively). There were also linear regressions between TOC and TN contents with the L* value and b* value. Results showed that color parameters from a spectrophotometer equipped with CIE-Lab color models can predict TOC contents well for soils in tea plantations. The linear regression model between color values and soil organic carbon contents showed it can be used as a rapid, cost-effective method to evaluate content of soil organic matter in Chinese tea plantations.

Calculations of the magnetic properties of R{sub 2}M{sub 14}B intermetallic compounds (R=rare earth, M=Fe, Co)

Energy Technology Data Exchange (ETDEWEB)

Ito, Masaaki, E-mail: masaaki.ito@neel.cnrs.fr [CNRS, Institut Néel, 25 rue des Martyrs, BP166, 38042 Grenoble (France); University Grenoble Alpes, Institut Néel, 38042 Grenoble (France); Advanced Material Engineering Division, Toyota Motor Corporation, Susono 410-1193 (Japan); Yano, Masao [Advanced Material Engineering Division, Toyota Motor Corporation, Susono 410-1193 (Japan); Dempsey, Nora M. [CNRS, Institut Néel, 25 rue des Martyrs, BP166, 38042 Grenoble (France); University Grenoble Alpes, Institut Néel, 38042 Grenoble (France); Givord, Dominique [CNRS, Institut Néel, 25 rue des Martyrs, BP166, 38042 Grenoble (France); University Grenoble Alpes, Institut Néel, 38042 Grenoble (France); Instituto de Fisica, Universidade Federal do Rio de Janeiro, Rio de Janeiro (Brazil)

2016-02-15

The hard magnetic properties of “R–M–B” (R=rare earth, M=mainly Fe) magnets derive from the specific intrinsic magnetic properties encountered in Fe-rich R{sub 2}M{sub 14}B compounds. Exchange interactions are dominated by the 3d elements, Fe and Co, and may be modeled at the macroscopic scale with good accuracy. Based on classical formulae that relate the anisotropy coefficients to the crystalline electric field parameters and exchange interactions, a simple numerical approach is used to derive the temperature dependence of anisotropy in various R{sub 2}Fe{sub 14}B compounds (R=Pr, Nd, Dy). Remarkably, a unique set of crystal field parameters give fair agreement with the experimentally measured properties of all compounds. This implies reciprocally that the properties of compounds that incorporate a mixture of different rare-earth elements may be predicted accurately. This is of special interest for material optimization that often involves the partial replacement of Nd with another R element and also the substitution of Co for Fe. - Highlights: • Anisotropy constants derived from CEF parameters of R{sub 2}M{sub 14}B compounds (M=Fe, Co). • Anisotropy constants of all R{sub 2}Fe{sub 14}B compounds using unique set of CEF parameters. • Moment non-collinearity in magnetization processes under B{sub app} along hard axis.

Modeling of tropospheric NO2 column over different climatic zones and land use/land cover types in South Asia

Science.gov (United States)

ul-Haq, Zia; Rana, Asim Daud; Tariq, Salman; Mahmood, Khalid; Ali, Muhammad; Bashir, Iqra

2018-03-01

We have applied regression analyses for the modeling of tropospheric NO2 (tropo-NO2) as the function of anthropogenic nitrogen oxides (NOx) emissions, aerosol optical depth (AOD), and some important meteorological parameters such as temperature (Temp), precipitation (Preci), relative humidity (RH), wind speed (WS), cloud fraction (CLF) and outgoing long-wave radiation (OLR) over different climatic zones and land use/land cover types in South Asia during October 2004-December 2015. Simple linear regression shows that, over South Asia, tropo-NO2 variability is significantly linked to AOD, WS, NOx, Preci and CLF. Also zone-5, consisting of tropical monsoon areas of eastern India and Myanmar, is the only study zone over which all the selected parameters show their influence on tropo-NO2 at statistical significance levels. In stepwise multiple linear modeling, tropo-NO2 column over landmass of South Asia, is significantly predicted by the combination of RH (standardized regression coefficient, β = - 49), AOD (β = 0.42) and NOx (β = 0.25). The leading predictors of tropo-NO2 columns over zones 1-5 are OLR, AOD, Temp, OLR, and RH respectively. Overall, as revealed by the higher correlation coefficients (r), the multiple regressions provide reasonable models for tropo-NO2 over South Asia (r = 0.82), zone-4 (r = 0.90) and zone-5 (r = 0.93). The lowest r (of 0.66) has been found for hot semi-arid region in northwestern Indus-Ganges Basin (zone-2). The highest value of β for urban area AOD (of 0.42) is observed for megacity Lahore, located in warm semi-arid zone-2 with large scale crop-residue burning, indicating strong influence of aerosols on the modeled tropo-NO2 column. A statistical significant correlation (r = 0.22) at the 0.05 level is found between tropo-NO2 and AOD over Lahore. Also NOx emissions appear as the highest contributor (β = 0.59) for modeled tropo-NO2 column over megacity Dhaka.

Decreased Renal Function Is Associated with Elevated CHA2DS2VASC and R2CHADS2 Scores in Non-Valvular Atrial Fibrillation Patients Presenting with Stroke.

Science.gov (United States)

Vindhyal, Mohinder; Vindhyal, Shravani R; Haneke, Travis; Ndunda, Paul M; Eid, Freidy; Kallail, K James

2017-12-11

Introduction Atrial fibrillation (AF), the most common cardiac arrhythmia, affects approximately 2.3 million patients in the United States, costing around $26 billion. Atrial fibrillation is associated with a two- to seven-fold increased risk of stroke, one of the most serious complications. Chronic kidney disease affects approximately 13% of the US population and has been associated with higher rates of AF than the general population. In patients with chronic kidney disease (CKD), the risk of stroke increases as the glomerular filtration rate (GFR) decreases, especially in CKD stages three and four. Several risks stratification scores such as CHADS2 (congestive heart failure, hypertension, age, diabetes mellitus, stroke), CHA2DS2VASc (congestive heart failure, hypertension, age, diabetes mellitus, stroke, vascular disease, age, sex), and R2CHADS2 (renal failure, congestive heart failure, age, diabetes, stroke) scores are used for stroke risk assessment in patients with non-valvular atrial fibrillation (NVAF). This study investigates the association between renal functions and risk stratification scoring systems in patients with non-valvular AF presenting with stroke. Methods Using the convenience sampling method, 171 subjects were selected from the eligible population (n = 386). A Pearson product-moment correlation coefficient was calculated to determine the association between the GFR and each of the CHA2DS2VASc and R2CHADS2 scores. In addition, a Pearson product-moment correlation coefficient was calculated to determine the association between the CHA2DS2VASc and R2CHADS2 scores. Results The selected population represented 44.3% of the eligible subjects. Of these, 88% were Caucasian, 60% were female, and the mean age was 78 years. The mean CHA2DS2VASc score was six (range 2-9). The mean eGFR was 69.77 (range 6-108). Both the mode and the median CHA2DS2VASc score was four (range 2-8). A weak, but significant, negative correlation was found between renal

Exact coefficients for higher dimensional operators with sixteen supersymmetries

Energy Technology Data Exchange (ETDEWEB)

Chen, Wei-Ming [Department of Physics and Astronomy, National Taiwan University,Taipei 10617, Taiwan, R.O.C. (China); Huang, Yu-tin [Department of Physics and Astronomy, National Taiwan University,Taipei 10617, Taiwan, R.O.C. (China); School of Natural Sciences, Institute for Advanced Study,Princeton, NJ 08540 (United States); Wen, Congkao [INFN Sezione di Roma “Tor Vergata' ,Via della Ricerca Scientifica, 00133 Roma (Italy)

2015-09-15

We consider constraints on higher-dimensional operators for supersymmetric effective field theories. In four dimensions with maximal supersymmetry and SU(4) R-symmetry, we demonstrate that the coefficients of abelian operators F{sup n} with MHV helicity configurations must satisfy a recursion relation, and are completely determined by that of F{sup 4}. As the F{sup 4} coefficient is known to be one-loop exact, this allows us to derive exact coefficients for all such operators. We also argue that the results are consistent with the SL(2,Z) duality symmetry. Breaking SU(4) to Sp(4), in anticipation for the Coulomb branch effective action, we again find an infinite class of operators whose coefficients are determined exactly. We also consider three-dimensional N=8 as well as six-dimensional N=(2,0),(1,0) and (1,1) theories. In all cases, we demonstrate that the coefficient of dimension-six operator must be proportional to the square of that of dimension-four.

Quantum Non-Markovian Langevin Equations and Transport Coefficients

International Nuclear Information System (INIS)

Sargsyan, V.V.; Antonenko, N.V.; Kanokov, Z.; Adamian, G.G.

2005-01-01

Quantum diffusion equations featuring explicitly time-dependent transport coefficients are derived from generalized non-Markovian Langevin equations. Generalized fluctuation-dissipation relations and analytic expressions for calculating the friction and diffusion coefficients in nuclear processes are obtained. The asymptotic behavior of the transport coefficients and correlation functions for a damped harmonic oscillator that is linearly coupled in momentum to a heat bath is studied. The coupling to a heat bath in momentum is responsible for the appearance of the diffusion coefficient in coordinate. The problem of regression of correlations in quantum dissipative systems is analyzed

2D QSAR studies of the inhibitory activity of a series of substituted purine derivatives against c-Src tyrosine kinase

OpenAIRE

Mukesh C. Sharma

2016-01-01

A series of 34 substituted purine analogues derivatives were subjected to quantitative structure-activity relationship analyses as inhibitors of c-Src tyrosine kinase. Partial least squares regression was applied to derive QSAR models, which were further validated for statistical significance by internal and external validation. The best QSAR model developed had a good predictive correlation coefficient (r2) of 0.8319, a significant cross-validated correlation coefficient (q2) of 0.7550, and ...

On Solving Lq-Penalized Regressions

Directory of Open Access Journals (Sweden)

Tracy Zhou Wu

2007-01-01

Full Text Available Lq-penalized regression arises in multidimensional statistical modelling where all or part of the regression coefficients are penalized to achieve both accuracy and parsimony of statistical models. There is often substantial computational difficulty except for the quadratic penalty case. The difficulty is partly due to the nonsmoothness of the objective function inherited from the use of the absolute value. We propose a new solution method for the general Lq-penalized regression problem based on space transformation and thus efficient optimization algorithms. The new method has immediate applications in statistics, notably in penalized spline smoothing problems. In particular, the LASSO problem is shown to be polynomial time solvable. Numerical studies show promise of our approach.

Repeated Results Analysis for Middleware Regression Benchmarking

Czech Academy of Sciences Publication Activity Database

Bulej, Lubomír; Kalibera, T.; Tůma, P.

2005-01-01

Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005

Ethnomathematics: The use of multiple linier regression Y = b 1 X 1 + b 2 X 2 + e in traditional house construction Saka Roras in Songan Village

Science.gov (United States)

Darmayasa, J. B.; Wahyudin; Mulyana, T.

2018-01-01

Ethnomathematics may be the connecting bridge between culture and technology and arts. Therefore, the exploration of mathematics values that intersects with cultural anthropology should be significantly conducted. One case containing such issue is the construction of Traditional House of Saka Roras in Bali. Thus, this research aimed to explore the mathematic concept adopted in the construction of such traditional Bale (house) located in Songan Village, Kintamani, Bali. Specifically, this research also aimed to investigate the selection of linear regression coefficient for the saka (pillar) in the Bale. This research applied Embedded Mix-Method Design. Meanwhile, the data collection was conducted by interview, observation and measurement of pillars of 32 Bale Saka Roras. The result of this research revealed that the connection between the width and height of pillars was stated in the formula Y = 26,3 + 18,2X, where X acted as stimulus variable. The coefficient value amounted to 18.2 showed that most preceding architects in Songan Village were more likely to use 19 as the coefficient towards the pillar width than the other coefficients such as 17, 20 and 21 as mentioned in book/palm-leaf manuscript entitled Kosala-Kosali. The last but not least, the researchers also figured out that the pillar width depended on the length of the house-owner candidate’s index finger.

Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate

Science.gov (United States)

Guermoui, Mawloud; Gairaa, Kacem; Rabehi, Abdelaziz; Djafer, Djelloul; Benkaciali, Said

2018-06-01

Accurate estimation of solar radiation is the major concern in renewable energy applications. Over the past few years, a lot of machine learning paradigms have been proposed in order to improve the estimation performances, mostly based on artificial neural networks, fuzzy logic, support vector machine and adaptive neuro-fuzzy inference system. The aim of this work is the prediction of the daily global solar radiation, received on a horizontal surface through the Gaussian process regression (GPR) methodology. A case study of Ghardaïa region (Algeria) has been used in order to validate the above methodology. In fact, several combinations have been tested; it was found that, GPR-model based on sunshine duration, minimum air temperature and relative humidity gives the best results in term of mean absolute bias error (MBE), root mean square error (RMSE), relative mean square error (rRMSE), and correlation coefficient ( r) . The obtained values of these indicators are 0.67 MJ/m2, 1.15 MJ/m2, 5.2%, and 98.42%, respectively.

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

Jafri, Y.Z.; Kamal, L.

2007-01-01

Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

A Research on the affinity coefficients of Red Globe grape variety with 140 R, 41 B rootstocks

Directory of Open Access Journals (Sweden)

Gargin Seckin

2014-01-01

Full Text Available This research has been performed in order to evaluate four different affinity coefficients with formulas, with the purpose to determine achievement ratios related to the omega grafting applied onto the 41 B, 140R rootstock of the Red Globe grape variety which is commercially important grape variety in Lakes Region of Turkey. The study was done in Egirdir Fruit Research Station in Isparta city of Turkey. When the affinity values were statistically analyzed according to rootstocks and year, combinations was significantly evaluated and by evaluating 2012 and 2013 year's data consecutively, hopeful combinations were determined. It has been determined that 2013 years affinity coefficients and 140 R rootstock combination were slightly better than 2012 year and 41B combination. It has been determined that evaluating only with the formulations is not sufficient to get an exact result by the determination of a good affinity. Therefore it is needed to continue studies in future for a long time growing. The results which were evaluated in this study and next years' studies results must be evaluated together when a stable affinity occurs, a final result and suggestion can be made about combinations. Rootstock proposals regarding to the varieties and regions can be made paying special attention to these features will be beneficial for successful viticulture in the future.

Background stratified Poisson regression analysis of cohort data.

Science.gov (United States)

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Linear regression metamodeling as a tool to summarize and present simulation model results.

Science.gov (United States)

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

(Vapour + liquid) equilibrium data for the azeotropic {1,1-difluoroethane (R152a) + 1,1,2,2-Tetrafluoroethane (R134)} system at various temperatures from (258.150 to 288.150) K

International Nuclear Information System (INIS)

Guo, Hao; Gong, Maoqiong; Dong, Xueqiang; Wu, Jianfeng

2012-01-01

Highlights: ► VLE data for the {R152a + R134} system were measured. ► The experiment is based on the static–analytic method. ► The VLE data were correlated using the PR–HV–NRTL model. ► A negative azeotropic behaviour was found. - Abstract: (Vapour + liquid) equilibrium (VLE) data for the {1,1-difluoroethane (R152a) + 1,1,2,2-Tetrafluoroethane (R134)} system were measured at T = (258.150 to 288.150) K. The experiment is based on a static–analytic method. Experimental data were correlated with the Peng–Robinson equation of state (PR EoS) and the Huron–Vidal (HV) mixing rule involving the NRTL activity coefficient model. The results show good agreement with experimental results for the binary system at each temperature. It was found that the system has a negative azeotropic behaviour within the temperature range measured here.

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

Science.gov (United States)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

Investigation of Pear Drying Performance by Different Methods and Regression of Convective Heat Transfer Coefficient with Support Vector Machine

Directory of Open Access Journals (Sweden)

Mehmet Das

2018-01-01

Full Text Available In this study, an air heated solar collector (AHSC dryer was designed to determine the drying characteristics of the pear. Flat pear slices of 10 mm thickness were used in the experiments. The pears were dried both in the AHSC dryer and under the sun. Panel glass temperature, panel floor temperature, panel inlet temperature, panel outlet temperature, drying cabinet inlet temperature, drying cabinet outlet temperature, drying cabinet temperature, drying cabinet moisture, solar radiation, pear internal temperature, air velocity and mass loss of pear were measured at 30 min intervals. Experiments were carried out during the periods of June 2017 in Elazig, Turkey. The experiments started at 8:00 a.m. and continued till 18:00. The experiments were continued until the weight changes in the pear slices stopped. Wet basis moisture content (MCw, dry basis moisture content (MCd, adjustable moisture ratio (MR, drying rate (DR, and convective heat transfer coefficient (hc were calculated with both in the AHSC dryer and the open sun drying experiment data. It was found that the values of hc in both drying systems with a range 12.4 and 20.8 W/m2 °C. Three different kernel models were used in the support vector machine (SVM regression to construct the predictive model of the calculated hc values for both systems. The mean absolute error (MAE, root mean squared error (RMSE, relative absolute error (RAE and root relative absolute error (RRAE analysis were performed to indicate the predictive model’s accuracy. As a result, the rate of drying of the pear was examined for both systems and it was observed that the pear had dried earlier in the AHSC drying system. A predictive model was obtained using the SVM regression for the calculated hc values for the pear in the AHSC drying system. The normalized polynomial kernel was determined as the best kernel model in SVM for estimating the hc values.

Sets of Fourier coefficients using numerical quadrature

International Nuclear Information System (INIS)

Lyness, J. N.

2001-01-01

One approach to the calculation of Fourier trigonometric coefficients f(r) of a given function f(x) is to apply the trapezoidal quadrature rule to the integral representation f(r)=(line i ntegral)(sub 0)(sup 1) f(x)e(sup -2(pi)irx)dx. Some of the difficulties in this approach are discussed. A possible way of overcoming many of these is by means of a subtraction function. Thus, one sets f(x)= h(sub p-1)(x)+ g(sub p)(x), where h(sub -1)(x) is an algebraic polynomial of degree p-1, specified in such a way that the Fourier series of g(sub p)(x) converges more rapidly than that of f(x). To obtain the Fourier coefficients of f(x), one uses an analytic expression for those of h(sub p-1)(x) and numerical quadrature to approximately those of g(sub p)(x)

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

DEFF Research Database (Denmark)

Bini, L. M.; Diniz-Filho, J. A. F.; Rangel, T. F. L. V. B.

2009-01-01

A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regress...

Measurements and Application of Partition Coefficients of Compounds Suitable for Tracing Gas Injected Into Oil Reservoirs Mesures et applications des coefficients de partage de composants utilisables comme gaz traceurs injectés dans des réservoirs de pétrole

Directory of Open Access Journals (Sweden)

Dugstad O.

2006-11-01

tritium, est élue le premier; le PLCH, composé lourd, apparaît comme le dernier pic sur le chromatogramme, les pics d'éthane marqué au 14C et de PMCP se situant entre les deux. Dans certaines conditions expérimentales, l'arrivée d'éthane précède celle du PMCP, mais à des pressions supérieures, l'ordre est inversé. Deux séries d'expériences ont été réalisées, l'une dans laquelle le gaz injecté est du méthane, l'autre dans laquelle le gaz injecté est de l'azote. Dans les deux séries, on a fait varier la pression de 100 à 250 bars en la température de 80°C à 120°C. Lorsque la phase de décane est stationnaire et que l'adsorption du traceur sur la surface des grains de sable peut être considérée comme négligeable, la rétention devient une fonction du coefficient de partage et du coefficient de saturation en huile. Dans les essais présentés ici, le coefficient de saturation en huile est obtenu par une mesure pondérale : on enregistre le temps de rétention pour le pic de réponse du traceur. A partir de ces deux paramètres, on calcule le coefficient de partage (équation 8. Pour les trois traceurs considérés, ce dernier (Kc, cf. équation 2 diminue lorsque la pression augmente. Une augmentation de la température tend également à abaisser les valeurs de Kc, mais cette relation est moins marquée. Pour le PMCH utilisé avec du méthane et des décane, la valeur de Kc varie de 4,37 à 80°C et 100 bar à 1,42 à 120°C et 250 bar. Les valeurs correspondantes pour le PMCP sont de 2,45 et de 0,86. Les tendances sont globalement les mêmes avec l'azote. Sous injection d'azote, toutefois, les valeurs de Kc sont légèrement plus élevées à basse pression et légèrement plus faibles à haute pression, comparées avec les résultats des essais au méthane. L'emploi d'huile provenant de réservoirs réels est susceptible d'entraîner des valeurs de Kc différentes. La figure 1 donne le débit relatif des traceurs pour différentes valeurs de

Activity coefficients of CaCl{sub 2} in (maltose + water) and (lactose + water) mixtures at 298.15 K

Energy Technology Data Exchange (ETDEWEB)

Zhuo Kelei [School of Chemistry and Environmental Science, Henan Normal University, Xinxiang, Henan 453007 (China)], E-mail: klzhuo@263.net; Liu Hongxun; Zhang Honghao; Liu Yaohui; Wang Jianji [School of Chemistry and Environmental Science, Henan Normal University, Xinxiang, Henan 453007 (China)

2008-05-15

Activity coefficients of CaCl{sub 2} in disaccharide {l_brace}(maltose, lactose) + water{r_brace} mixtures at 298.15 K were determined by cell potentials. The molalities of CaCl{sub 2} ranged from about 0.01 mol . kg{sup -1} to 0.20 mol . kg{sup -1}, the mass fractions of maltose from 0.05 to 0.25, and those of lactose from 0.025 to 0.125. The cell potentials were analyzed by using the Debye-Hueckel extended equation and the Pitzer equation. The activity coefficients obtained from the two theoretical models are in good agreement with each other. Gibbs free energy interaction parameters (g{sub ES}) and salting constants (k{sub S}) were also obtained. These were discussed in terms of the stereo-chemistry of saccharide molecules and the structural interaction model.

Fourier Series Formalization in ACL2(r

Directory of Open Access Journals (Sweden)

Cuong K. Chau

2015-09-01

Full Text Available We formalize some basic properties of Fourier series in the logic of ACL2(r, which is a variant of ACL2 that supports reasoning about the real and complex numbers by way of non-standard analysis. More specifically, we extend a framework for formally evaluating definite integrals of real-valued, continuous functions using the Second Fundamental Theorem of Calculus. Our extended framework is also applied to functions containing free arguments. Using this framework, we are able to prove the orthogonality relationships between trigonometric functions, which are the essential properties in Fourier series analysis. The sum rule for definite integrals of indexed sums is also formalized by applying the extended framework along with the First Fundamental Theorem of Calculus and the sum rule for differentiation. The Fourier coefficient formulas of periodic functions are then formalized from the orthogonality relations and the sum rule for integration. Consequently, the uniqueness of Fourier sums is a straightforward corollary. We also present our formalization of the sum rule for definite integrals of infinite series in ACL2(r. Part of this task is to prove the Dini Uniform Convergence Theorem and the continuity of a limit function under certain conditions. A key technique in our proofs of these theorems is to apply the overspill principle from non-standard analysis.

On concurvity in nonlinear and nonparametric regression models

Directory of Open Access Journals (Sweden)

Sonia Amodio

2014-12-01

Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.

AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION

OpenAIRE

Krzyśko, Mirosław; Smaga, Łukasz

2017-01-01

In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...

[Evaluation of the Peusner's coefficients matrix for polymeric membrane and ternary non-electrolyte solutions].

Science.gov (United States)

Jasik-Slęzak, Jolanta; Slęzak-Prochazka, Izabella; Slęzak, Andrzej

2014-01-01

A system of network forms of Kedem-Katchalsky (K-K) equations for ternary non-electrolyte solutions is made of eight matrix equations containing Peusner's coefficients R(ij), L(ij), H(ij), W(ij), K(ij), N(ij), S(ij) or P(ij) (i, j ∈ {1, 2, 3}). The equations are the result of symmetric or hybrid transformation of the classic form of K-K equations by the use of methods of Peusner's network thermodynamics (PNT). Calculating concentration dependences of the determinant of Peusner's coefficients matrixes R(ij), L(ij), H(ij), W(ij), S(ij), N(ij), K(ij) and P(ij) (i, j ∈ {1, 2, 3}). The material used in the experiment was a hemodialysis Nephrophan membrane with specified transport properties (L(p), σ, Ω) in aqueous glucose and ethanol solution. The method involved equations for determinants of the matrixes coefficients R(ij), L(ij), H(ij), W(ij), S(ij), N(ij), K(ij) or P(ij) (i, j ∈ {1, 2, 3}). The objective of calculations were dependences of determinants of Peusner's coeffcients matrixes R(ij), L(ij), H(ij), W(ij), S(ij), N(ij), K(ij) or P(ij) (i, j ∈ {1, 2, 3}) within the conditions of solution homogeneity upon an average concentration of one component of solution in the membrane (C1) with a determined value of the second component (C2). The method of calculating the determinants of Peusner's coeffcients matrixes R(ij), L(ij), H(ij), W(ij), S(ij), N(ij), K(ij) or P(ij) (i, j ∈ {1, 2, 3}) is a new tool that may be applicable in studies on membrane transport. Calculations showed that the coefficients are sensitive to concentration and composition of solutions separated by a polymeric membrane.

Correlation between electrical conductivity and apparent diffusion coefficient in breast cancer: effect of necrosis on magnetic resonance imaging.

Science.gov (United States)

Kim, Soo-Yeon; Shin, Jaewook; Kim, Dong-Hyun; Kim, Eun-Kyung; Moon, Hee Jung; Yoon, Jung Hyun; You, Jai Kyung; Kim, Min Jung

2018-03-06

To investigate the correlation between conductivity and ADC in invasive ductal carcinoma according to the presence of necrosis on MRI. Eighty-one women with invasive ductal carcinoma ≥1 cm on T2-weighted fast spin echo sequence of preoperative MRI were included. Phase-based MR electric properties tomography was used to reconstruct conductivity. Mean ADC was measured. Necrosis was defined as an area with very high T2 signal intensity. The relationship between conductivity and ADC was examined using Spearman's correlation coefficient (r). Multiple linear regression analysis was performed to identify factors associated with conductivity or ADC. In the total group, conductivity showed negative correlation with ADC (r = -0.357, p = 0.001). This correlation was maintained in the subgroup without necrosis (n = 53, r = -0.455, p = 0.001), but not in the subgroup with necrosis (n = 28, r = -0.080, p = 0.687). The correlation between the two parameters was different according to necrosis (r = -0.455 vs -0.080, p = 0.047). HER2 enriched subtype was independently associated with conductivity (p = 0.029). Necrosis on MRI was independently associated with ADC (p = 0.027). Conductivity shows negative correlation with ADC that is abolished by the presence of necrosis on MRI. • Electric conductivity showed negative correlation with ADC • However, the correlation was abolished by the presence of necrosis on MRI • HER2-enriched subtype was independently associated with conductivity • Necrosis on MRI was independently associated with ADC.

Coefficient of linear attenuation of beer for γ rays of 662 keV

International Nuclear Information System (INIS)

Ortiz A, M. D.; Cano S, D.; Vega C, H. R.

2017-10-01

The coefficient of linear attenuation of the beer was determined by means of a transmission experiment with a source of Cs 137 and a gamma ray spectrometer with a NaI(Tl) detector of 7.62 cm in diameter and 7.62 cm in height, using narrow geometry. The pulse height spectrum was accumulated for 1 minute of live time, 7 beer thicknesses (0.6 cm) were used. By means of linear regression by weighted squares we determined the linear attenuation coefficient whose value was μ = 0.0843 ± 0.0007 cm -1 . The coefficient of linear attenuation of water is 2.2% times greater than that of beer and to the geometry of the experimental arrangement. (Author)

Multiple Linear Regression Analysis Indicates Association of P-Glycoprotein Substrate or Inhibitor Character with Bitterness Intensity, Measured with a Sensor.

Science.gov (United States)

Yano, Kentaro; Mita, Suzune; Morimoto, Kaori; Haraguchi, Tamami; Arakawa, Hiroshi; Yoshida, Miyako; Yamashita, Fumiyoshi; Uchida, Takahiro; Ogihara, Takuo

2015-09-01

P-glycoprotein (P-gp) regulates absorption of many drugs in the gastrointestinal tract and their accumulation in tumor tissues, but the basis of substrate recognition by P-gp remains unclear. Bitter-tasting phenylthiocarbamide, which stimulates taste receptor 2 member 38 (T2R38), increases P-gp activity and is a substrate of P-gp. This led us to hypothesize that bitterness intensity might be a predictor of P-gp-inhibitor/substrate status. Here, we measured the bitterness intensity of a panel of P-gp substrates and nonsubstrates with various taste sensors, and used multiple linear regression analysis to examine the relationship between P-gp-inhibitor/substrate status and various physical properties, including intensity of bitter taste measured with the taste sensor. We calculated the first principal component analysis score (PC1) as the representative value of bitterness, as all taste sensor's outputs shared significant correlation. The P-gp substrates showed remarkably greater mean bitterness intensity than non-P-gp substrates. We found that Km value of P-gp substrates were correlated with molecular weight, log P, and PC1 value, and the coefficient of determination (R(2) ) of the linear regression equation was 0.63. This relationship might be useful as an aid to predict P-gp substrate status at an early stage of drug discovery. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Estimating Penetration Resistance in Agricultural Soils of Ardabil Plain Using Artificial Neural Network and Regression Methods

Directory of Open Access Journals (Sweden)

Gholam Reza Sheykhzadeh

2017-02-01

replicates. The data were divided into two series as 78 data for training and 27 data for testing. The SPSS 18 with stepwise method and MATLAB software were used to derive the regression and artificial neural network, respectively. A feed forward three-layer (8, 11 and 15 neurons in the hidden layer perceptron network and the tangent sigmoid transfer function were used for the artificial neural network modeling. In estimating penetration resistance, The accuracy of artificial neural network and regression pedotransfer functions were evaluated by coefficient of determination (R2, root mean square error (RMSE, mean error (ME and Akaike information criterion (AIC statistics. Results and discussion: The textural classes of study soils were loamy sand (n= 8, sandy loam (n= 70, loam (n= 6 and silt loam (n= 21. The values of sand (26.26 to 87.43 %, clay (3.99 to 17.34 %, organic carbon (0.3 to 2.41 %, field moisture (4.56 to 33.18 mass percent, Db (1.02 to 1.63 g cm-3 and penetration resistance (1.1 to 6.6 MPa showed a large variations of study soils. There were found significant correlations between penetration resistance and sand (r= - 0.505**, silt (r= 0.447**, clay (r= 0.330**, organic carbon (r= - 0.465**, Db (r= 0.655**, θf (r= -0.63**, CaCO3 (r= 0.290**, total porosity (r= - 0.589** and Dp (r= 0.266*. Generally, 15 regression and artificial neural network pedotransfer functions were constructed to predict penetration resistance from measured readily available soil variables. The results of regression and artificial neural network pedotransfer functions showed that the most suitable variables to estimate penetration resistance were θf, Db and particles size distribution. The input variables were n and θf for the best regression pedotransfer function and also Db, silt, θf and σg for the best artificial neural network pedotransfer function. The values of R2, RMSE, ME and AIC were obtained equal to 0.55, 0.89 MPa, 0.05 MPa and -14.67 and 0.91, 0.37 MPa, - 0.0026 MPa and

NGA-West 2 GMPE average site coefficients for use in earthquake-resistant design

Science.gov (United States)

Borcherdt, Roger D.

2015-01-01

Site coefficients corresponding to those in tables 11.4–1 and 11.4–2 of Minimum Design Loads for Buildings and Other Structures published by the American Society of Civil Engineers (Standard ASCE/SEI 7-10) are derived from four of the Next Generation Attenuation West2 (NGA-W2) Ground-Motion Prediction Equations (GMPEs). The resulting coefficients are compared with those derived by other researchers and those derived from the NGA-West1 database. The derivation of the NGA-W2 average site coefficients provides a simple procedure to update site coefficients with each update in the Maximum Considered Earthquake Response MCER maps. The simple procedure yields average site coefficients consistent with those derived for site-specific design purposes. The NGA-W2 GMPEs provide simple scale factors to reduce conservatism in current simplified design procedures.

Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection

KAUST Repository

Chen, Lisha

2012-12-01

The reduced-rank regression is an effective method in predicting multiple response variables from the same set of predictor variables. It reduces the number of model parameters and takes advantage of interrelations between the response variables and hence improves predictive accuracy. We propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty. We apply a group-lasso type penalty that treats each row of the matrix of the regression coefficients as a group and show that this penalty satisfies certain desirable invariance properties. We develop two numerical algorithms to solve the penalized regression problem and establish the asymptotic consistency of the proposed method. In particular, the manifold structure of the reduced-rank regression coefficient matrix is considered and studied in our theoretical analysis. In our simulation study and real data analysis, the new method is compared with several existing variable selection methods for multivariate regression and exhibits competitive performance in prediction and variable selection. © 2012 American Statistical Association.

Neutrosophic Correlation and Simple Linear Regression

Directory of Open Access Journals (Sweden)

A. A. Salama

2014-09-01

Full Text Available Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache. Recently, Salama et al., introduced the concept of correlation coefficient of neutrosophic data. In this paper, we introduce and study the concepts of correlation and correlation coefficient of neutrosophic data in probability spaces and study some of their properties. Also, we introduce and study the neutrosophic simple linear regression model. Possible applications to data processing are touched upon.

Implicit collinearity effect in linear regression: Application to basal ...

African Journals Online (AJOL)

Collinearity of predictor variables is a severe problem in the least square regression analysis. It contributes to the instability of regression coefficients and leads to a wrong prediction accuracy. Despite these problems, studies are conducted with a large number of observed and derived variables linked with a response ...

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

Science.gov (United States)

Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

2011-08-01

In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

Describing Growth Pattern of Bali Cows Using Non-linear Regression Models

Directory of Open Access Journals (Sweden)

Mohd. Hafiz A.W

2016-12-01

Full Text Available The objective of this study was to evaluate the best fit non-linear regression model to describe the growth pattern of Bali cows. Estimates of asymptotic mature weight, rate of maturing and constant of integration were derived from Brody, von Bertalanffy, Gompertz and Logistic models which were fitted to cross-sectional data of body weight taken from 74 Bali cows raised in MARDI Research Station Muadzam Shah Pahang. Coefficient of determination (R2 and residual mean squares (MSE were used to determine the best fit model in describing the growth pattern of Bali cows. Von Bertalanffy model was the best model among the four growth functions evaluated to determine the mature weight of Bali cattle as shown by the highest R2 and lowest MSE values (0.973 and 601.9, respectively, followed by Gompertz (0.972 and 621.2, respectively, Logistic (0.971 and 648.4, respectively and Brody (0.932 and 660.5, respectively models. The correlation between rate of maturing and mature weight was found to be negative in the range of -0.170 to -0.929 for all models, indicating that animals of heavier mature weight had lower rate of maturing. The use of non-linear model could summarize the weight-age relationship into several biologically interpreted parameters compared to the entire lifespan weight-age data points that are difficult and time consuming to interpret.

Slip safety risk analysis of surface properties using the coefficients of friction of rocks.

Science.gov (United States)

Çoşkun, Gültekin; Sarıışık, Gencay; Sarıışık, Ali

2017-12-19

This study was conducted to determine the most appropriate surface processing techniques (SPT), environmental conditions (EC) and surface roughness (SR) to minimize the risk of slipping when pedestrians walk on a floor covering of rocks barefoot and with shoes. Coefficients of friction (COFs) and values of SR were found using five different types of rocks, four SPT and two (ramp and pendulum) tests. Results indicate that the parameters which affect the COF values of rocks include SR, EC and SPT. Simple linear regression was performed to examine the relationship between the values of the COF and the SR. The value of the COF was identified as R 2  ≥ 0.864. Statistical results, which are based on experimental measurements, show that rocks are classified according to their safe use areas depending on their COF and SR values.

A fast algorithm for computing binomial coefficients modulo powers of two.

Science.gov (United States)

Andreica, Mugurel Ionut

2013-01-01

I present a new algorithm for computing binomial coefficients modulo 2N. The proposed method has an O(N3·Multiplication(N)+N4) preprocessing time, after which a binomial coefficient C(P, Q) with 0≤Q≤P≤2N-1 can be computed modulo 2N in O(N2·log(N)·Multiplication(N)) time. Multiplication(N) denotes the time complexity of multiplying two N-bit numbers, which can range from O(N2) to O(N·log(N)·log(log(N))) or better. Thus, the overall time complexity for evaluating M binomial coefficients C(P, Q) modulo 2N with 0≤Q≤P≤2N-1 is O((N3+M·N2·log(N))·Multiplication(N)+N4). After preprocessing, we can actually compute binomial coefficients modulo any 2R with R≤N. For larger values of P and Q, variations of Lucas' theorem must be used first in order to reduce the computation to the evaluation of multiple (O(log(P))) binomial coefficients C(P', Q') (or restricted types of factorials P'!) modulo 2N with 0≤Q'≤P'≤2N-1.

Background stratified Poisson regression analysis of cohort data

International Nuclear Information System (INIS)

Richardson, David B.; Langholz, Bryan

2012-01-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)

Three Contributions to Robust Regression Diagnostics

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2015-01-01

Roč. 11, č. 2 (2015), s. 69-78 ISSN 1336-9180 Grant - others:GA ČR(CZ) GA13-01930S; Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : robust regression * robust econometrics * hypothesis test ing Subject RIV: BA - General Mathematics http://www.degruyter.com/view/j/jamsi.2015.11.issue-2/jamsi-2015-0013/jamsi-2015-0013.xml?format=INT

Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R

Directory of Open Access Journals (Sweden)

Paulino Pérez

2010-09-01

Full Text Available The availability of dense molecular markers has made possible the use of genomic selection in plant and animal breeding. However, models for genomic selection pose several computational and statistical challenges and require specialized computer programs, not always available to the end user and not implemented in standard statistical software yet. The R-package BLR (Bayesian Linear Regression implements several statistical procedures (e.g., Bayesian Ridge Regression, Bayesian LASSO in a unified framework that allows including marker genotypes and pedigree data jointly. This article describes the classes of models implemented in the BLR package and illustrates their use through examples. Some challenges faced when applying genomic-enabled selection, such as model choice, evaluation of predictive ability through cross-validation, and choice of hyper-parameters, are also addressed.

2D QSAR studies of the inhibitory activity of a series of substituted purine derivatives against c-Src tyrosine kinase

Directory of Open Access Journals (Sweden)

Mukesh C. Sharma

2016-07-01

Full Text Available A series of 34 substituted purine analogues derivatives were subjected to quantitative structure-activity relationship analyses as inhibitors of c-Src tyrosine kinase. Partial least squares regression was applied to derive QSAR models, which were further validated for statistical significance by internal and external validation. The best QSAR model developed had a good predictive correlation coefficient (r2 of 0.8319, a significant cross-validated correlation coefficient (q2 of 0.7550, and an r2 for the external test set (pred_r2 of 0.7983. It was developed from the PLS method with descriptors including the SsCH3E-index, H-Donor Count, T_2_Cl_3, and negative correlation with SsOHcount. The current study provides better insight into the future design of more potent c-Src tyrosine kinase inhibitors prior to synthesis.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

Science.gov (United States)

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Autonomous estimation of Allan variance coefficients of onboard fiber optic gyro

International Nuclear Information System (INIS)

Song Ningfang; Yuan Rui; Jin Jing

2011-01-01

Satellite motion included in gyro output disturbs the estimation of Allan variance coefficients of fiber optic gyro on board. Moreover, as a standard method for noise analysis of fiber optic gyro, Allan variance has too large offline computational effort and data storages to be applied to online estimation. In addition, with the development of deep space exploration, it is urged that satellite requires more autonomy including autonomous fault diagnosis and reconfiguration. To overcome the barriers and meet satellite autonomy, we present a new autonomous method for estimation of Allan variance coefficients including rate ramp, rate random walk, bias instability, angular random walk and quantization noise coefficients. In the method, we calculate differences between angle increments of star sensor and gyro to remove satellite motion from gyro output, and propose a state-space model using nonlinear adaptive filter technique for quantities previously measured from offline data techniques such as the Allan variance method. Simulations show the method correctly estimates Allan variance coefficients, R = 2.7965exp-4 0 /h 2 , K = 1.1714exp-3 0 /h 1.5 , B = 1.3185exp-3 0 /h, N = 5.982exp-4 0 /h 0.5 and Q = 5.197exp-7 0 in real time, and tracks degradation of gyro performance from initail values, R = 0.651 0 /h 2 , K = 0.801 0 /h 1.5 , B = 0.385 0 /h, N = 0.0874 0 /h 0.5 and Q = 8.085exp-5 0 , to final estimations, R = 9.548 0 /h 2 , K = 9.524 0 /h 1.5 , B = 2.234 0 /h, N = 0.5594 0 /h 0.5 and Q = 5.113exp-4 0 , due to gamma radiation in space. The technique proposed here effectively isolates satellite motion, and requires no data storage and any supports from the ground.

Atmospheric extinction coefficients and night sky brightness at the Xuyi Observation Station

International Nuclear Information System (INIS)

Zhang Hui-Hua; Liu Xiao-Wei; Zhang Hua-Wei; Xiang Mao-Sheng; Yuan Hai-Bo; Zhao Hai-Bin; Yao Jin-Sheng

2013-01-01

We present measurements of the optical broadband atmospheric extinction coefficients and the night sky brightness at the Xuyi Observation Station of Purple Mountain Observatory. The measurements are based on CCD imaging data taken in the Sloan Digital Sky Survey's g, r and i bands with the Xuyi 1.04/1.20 m Schmidt Telescope for the Xuyi Schmidt Telescope Photometric Survey of the Galactic Anti-center (XSTPS-GAC), the photometric part of the Digital Sky Survey of the Galactic Anti-center (DSS-GAC). The data were collected during more than 140 winter nights from 2009 to 2011. We find that the atmospheric extinction coefficients for the g, r and i bands are 0.69, 0.55 and 0.38 mag/airmass, respectively, based on observations taken on several photometric nights. The night sky brightness determined from images with good quality has median values of 21.7, 20.8 and 20.0 mag arcsec −2 and reaches 22.1, 21.2 and 20.4 mag arcsec −2 under the best observing conditions for the g, r and i bands, respectively. The relatively large extinction coefficients compared with other good astronomical observing sites are mainly due to the relatively low elevation (i.e. 180 m) and high humidity at the station.

Tracking time-varying coefficient-functions

DEFF Research Database (Denmark)

Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Joensen, Alfred K.

2000-01-01

is a combination of recursive least squares with exponential forgetting and local polynomial regression. It is argued, that it is appropriate to let the forgetting factor vary with the value of the external signal which is the argument of the coefficient functions. Some of the key properties of the modified method...... are studied by simulation...

Evaporating heat transfer characteristics of R22 and R410A in 9.52 mm O.D. smooth and microfin tubes

Energy Technology Data Exchange (ETDEWEB)

Kim, M H; Shin, J S; Lim, B H [Sam Sung Electronics Corporation Limited (Korea, Republic of)

1998-10-01

An experimental investigation of evaporating heat transfer in 9.52 mm horizontal copper tubes was conducted. The refrigerant tested were R22 and near-azeotropic mixture, R410A. The test rig had a straight, horizontal test section with an active length of 0.92 m and was heated by the heat transfer fluid(hot water) circulated in a surrounding annulus. Constant heat flux of 11.0 kW/m{sup 2} was maintained and refrigerant quality varied from 0.2 to 0.8. The results were reported for evaporation at 15 deg. C in a 0.92 m long test section for 30{approx}60 kg/h mass flow rate. The local and average heat transfer coefficients for seven microfin tubes were presented compared to those for a smooth tube. The average evaporation heat transfer coefficients of R22 and R410A for the microfin tubes were 86{approx}227% and 64{approx}199%, respectively, higher than those for the smooth tube. When compared to R22 at the same test conditions, the evaporating heat transfer coefficients for R410A were 97{approx}129% of R22. (author). 23 refs., 9 figs., 4 tabs.

The relation between Pearson’s correlation coefficient r and Salton’s cosine measure

NARCIS (Netherlands)

Egghe, L.; Leydesdorff, L.

2009-01-01

The relation between Pearson's correlation coefficient and Salton's cosine measure is revealed based on the different possible values of the division of the L1-norm and the L2-norm of a vector. These different values yield a sheaf of increasingly straight lines which together form a cloud of points,

riskRegression

DEFF Research Database (Denmark)

Ozenne, Brice; Sørensen, Anne Lyngholm; Scheike, Thomas

2017-01-01

In the presence of competing risks a prediction of the time-dynamic absolute risk of an event can be based on cause-specific Cox regression models for the event and the competing risks (Benichou and Gail, 1990). We present computationally fast and memory optimized C++ functions with an R interface...... for predicting the covariate specific absolute risks, their confidence intervals, and their confidence bands based on right censored time to event data. We provide explicit formulas for our implementation of the estimator of the (stratified) baseline hazard function in the presence of tied event times. As a by...... functionals. The software presented here is implemented in the riskRegression package....

CVTresh: R Package for Level-Dependent Cross-Validation Thresholding

Directory of Open Access Journals (Sweden)

Donghoh Kim

2006-04-01

Full Text Available The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients. This paper reviews a cross-validation method for the selection of the thresholding value in wavelet shrinkage of Oh, Kim, and Lee (2006, and introduces the R package CVThresh implementing details of the calculations for the procedures. This procedure is implemented by coupling a conventional cross-validation with a fast imputation method, so that it overcomes a limitation of data length, a power of 2. It can be easily applied to the classical leave-one-out cross-validation and K-fold cross-validation. Since the procedure is computationally fast, a level-dependent cross-validation can be developed for wavelet shrinkage of data with various sparseness according to levels.

CVTresh: R Package for Level-Dependent Cross-Validation Thresholding

Directory of Open Access Journals (Sweden)

Donghoh Kim

2006-04-01

Full Text Available The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients. This paper reviews a cross-validation method for the selection of the thresholding value in wavelet shrinkage of Oh, Kim, and Lee (2006, and introduces the R package CVThresh implementing details of the calculations for the procedures.This procedure is implemented by coupling a conventional cross-validation with a fast imputation method, so that it overcomes a limitation of data length, a power of 2. It can be easily applied to the classical leave-one-out cross-validation and K-fold cross-validation. Since the procedure is computationally fast, a level-dependent cross-validation can be developed for wavelet shrinkage of data with various sparseness according to levels.

Heat transfer coefficient: Medivance Arctic Sun Temperature Management System vs. water immersion.

Science.gov (United States)

English, M J; Hemmerling, T M

2008-07-01

To improve heat transfer, the Medivance Arctic Sun Temperature Management System (Medivance, Inc., Louisville, CO, USA) features an adhesive, water-conditioned, highly conductive hydrogel pad for intimate skin contact. This study measured and compared the heat transfer coefficient (h), i.e. heat transfer efficiency, of this pad (hPAD), in a heated model and in nine volunteers' thighs; and of 10 degrees C water (hWATER) in 33 head-out immersions by 11 volunteers. Volunteer studies had ethical approval and written informed consent. Calibrated heat flux transducers measured heat flux (W m-2). Temperature gradient (DeltaT) was measured between skin and pad or water temperatures. Temperature gradient was changed through the pad's water temperature controller or by skin cooling on immersion. The heat transfer coefficient is the slope of W m-2/DeltaT: its unit is W m-2 degrees C-1. Average with (95% CI) was: model, hPAD = 110.4 (107.8-113.1), R2 = 0.99, n = 45; volunteers, hPAD = 109.8 (95.5-124.1), R2 = 0.83, n = 51; and water immersion, hWATER = 107.1 (98.1-116), R2 = 0.86, n = 94. The heat transfer coefficient for the pad was the same in the model and volunteers, and equivalent to hWATER. Therefore, for the same DeltaT and heat transfer area, the Arctic Sun's heat transfer rate would equal water immersion. This has important implications for body cooling/rewarming rates.

Flow boiling heat transfer and pressure drop characteristics of R134a, R1234yf and R1234ze in a plate heat exchanger for organic Rankine cycle units

DEFF Research Database (Denmark)

Zhang, Ji; Desideri, Adriano; Kærn, Martin Ryhl

2017-01-01

. This paper is aimed at obtaining flow boiling heat transfer and pressure drop characteristics in a plate heat exchanger under the working conditions prevailing in the evaporator of organic Rankine cycle units. Two hydrofluoroolefins R1234yf and R1234ze, and one hydrofluorocarbon R134a, were selected...... as the working fluids. The heat transfer coefficients and pressure drops of the three working fluids were measured with varying saturation temperatures, mass fluxes, heat fluxes and outlet vapour qualities, which range from 60°C to 80°C, 86 kg/m2 s to 137 kg/m2 s, 9.8 kW/m2 to 36.8 kW/m2 and 0.5 to 1...... developed that are more suitable for evaporation in organic Rankine cycles. The experimental results indicate that heat transfer coefficients are strongly dependent upon the heat flux and saturation temperature. Moreover, the results suggest better thermal-hydraulic performance for R1234yf than the other...

Wind turbine power coefficient estimation by soft computing methodologies: Comparative study

International Nuclear Information System (INIS)

Shamshirband, Shahaboddin; Petković, Dalibor; Saboohi, Hadi; Anuar, Nor Badrul; Inayat, Irum; Akib, Shatirah; Ćojbašić, Žarko; Nikolić, Vlastimir; Mat Kiah, Miss Laiha; Gani, Abdullah

2014-01-01

Highlights: • Variable speed operation of wind turbine to increase power generation. • Changeability and fluctuation of wind has to be accounted. • To build an effective prediction model of wind turbine power coefficient. • The impact of the variation in the blade pitch angle and tip speed ratio. • Support vector regression methodology application as predictive methodology. - Abstract: Wind energy has become a large contender of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, reasonable wind speed is not adequately sustainable everywhere to build an economical wind farm. In wind energy conversion systems, one of the operational problems is the changeability and fluctuation of wind. In most cases, wind speed can vacillate rapidly. Hence, quality of produced energy becomes an important problem in wind energy conversion plants. Several control techniques have been applied to improve the quality of power generated from wind turbines. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of support vector regression (SVR) to estimate optimal power coefficient value of the wind turbines. Instead of minimizing the observed training error, SVR p oly and SVR r bf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach in compare to other soft computing methodologies

Land use regression modeling of oxidative potential of fine particles, NO2, PM2.5 mass and association to type two diabetes mellitus

Science.gov (United States)

Hellack, Bryan; Sugiri, Dorothea; Schins, Roel P. F.; Schikowski, Tamara; Krämer, Ursula; Kuhlbusch, Thomas A. J.; Hoffmann, Barbara

2017-12-01

While land use regression models (LUR) are commonly used, e.g. for the prediction of spatially variable air pollutant mass concentrations, they are scarcely used for predicting the oxidative potential (OP), a suggested unifying predictor of health effects. Therefore a LUR model was developed to examine if long-term OP of fine particulate exposure can be reasonably predicted by LUR modeling and whether it is related to health effects in a study region comprised of urban and rural areas. Four 14-day sampling periods over 1 year at 40 sites in the western Ruhr Area and adjacent northern rural area, Germany, in 2002/2003 were conducted and annual Nitrogen Dioxide (NO2), fine particles (PM2.5), and OP were calculated. LUR models were developed to estimate spatially-resolved annual OP, NO2 and PM2.5 concentrations. The model performance was checked by leave-one-out cross validation (LOOCV) and cox regression was used to analyze the association of modeled residential OP and NO2 with incident type 2 diabetes mellitus (T2DM) in 1784 elderly women during a mean follow-up of 16 years (baseline 1985-1994). The measured OP and NO2 concentrations were moderately correlated (rSpearman 0.57). The LUR models explained 62% and 92% of the OP and NO2 variance (adjusted LOOCV R2 57% and 90%). PM10 emission from combustion in a 5000 m buffer was the most important predictor for OP and NO2. Modeled pollutants were highly correlated (rSpearman 0.87). Model quality for OP was sensitive to the inclusion of a single influential measurement site. For PM2.5 mass only an insufficient model with a low explained variance of 22% (adjusted R2) was developed so no health effects analyses were conducted with estimated PM2.5. Increases in OP and NO2 were associated with an increase in risk of T2DM by a hazard ratio of 1.38 (95% CI 1.06-1.80) and 1.39 (95% CI 1.07-1.81) per interquartile range of OP and NO2, respectively. We conclude that spatially-resolved OP can be predicted by LUR modeling, but

The Crash Intensity Evaluation Using General Centrality Criterions and a Geographically Weighted Regression

Science.gov (United States)

Ghadiriyan Arani, M.; Pahlavani, P.; Effati, M.; Noori Alamooti, F.

2017-09-01

Today, one of the social problems influencing on the lives of many people is the road traffic crashes especially the highway ones. In this regard, this paper focuses on highway of capital and the most populous city in the U.S. state of Georgia and the ninth largest metropolitan area in the United States namely Atlanta. Geographically weighted regression and general centrality criteria are the aspects of traffic used for this article. In the first step, in order to estimate of crash intensity, it is needed to extract the dual graph from the status of streets and highways to use general centrality criteria. With the help of the graph produced, the criteria are: Degree, Pageranks, Random walk, Eccentricity, Closeness, Betweenness, Clustering coefficient, Eigenvector, and Straightness. The intensity of crash point is counted for every highway by dividing the number of crashes in that highway to the total number of crashes. Intensity of crash point is calculated for each highway. Then, criteria and crash point were normalized and the correlation between them was calculated to determine the criteria that are not dependent on each other. The proposed hybrid approach is a good way to regression issues because these effective measures result to a more desirable output. R2 values for geographically weighted regression using the Gaussian kernel was 0.539 and also 0.684 was obtained using a triple-core cube. The results showed that the triple-core cube kernel is better for modeling the crash intensity.

Experimental determination of the Onsager coefficients of transport for Ce0.8Pr0.2O2−δ

DEFF Research Database (Denmark)

Chatzichristodoulou, Christodoulos; Park, Woo-Seok; Kim, Hong-Seok

2010-01-01

versa for the flux of electrons (Je). It is common practice to assume that electrons and mobile ions migrate independently, despite the lack of experimental evidence in support of this. Here, all the Onsager coefficients, including the cross coefficients, have been measured for Ce0.8Pr0.2O2−δ within...... the aO2 range 10−21–1 at 800 °C, using local ionic and electronic probes in a four-probe configuration. The cross coefficients of transport were found to be negligible in comparison to the direct coefficients in the aO2 range 10−21–10−4, but of the same order of magnitude as the direct coefficients...

Experimental measurement of vapor pressures and (vapor + liquid) equilibrium for {l_brace}1,1,1,2-tetrafluoroethane (R134a) + propane (R290){r_brace} by a recirculation apparatus with view windows

Energy Technology Data Exchange (ETDEWEB)

Dong Xueqiang [Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, P.O. Box 2711, Beijing 100190 (China); Graduate University of Chinese Academy of Sciences, Beijing 100039 (China); Gong Maoqiong, E-mail: gongmq@mail.ipc.ac.c [Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, P.O. Box 2711, Beijing 100190 (China); Liu Junsheng [Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, P.O. Box 2711, Beijing 100190 (China); Graduate University of Chinese Academy of Sciences, Beijing 100039 (China); Wu Jianfeng, E-mail: jfwu@mail.ipc.ac.c [Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, P.O. Box 2711, Beijing 100190 (China)

2011-03-15

The saturated vapor pressures of 1,1,1,2-tetrafluoroethane (R134a) and propane (R290), and the (vapor + liquid) equilibrium (VLE) data at (255.000, 265.000, 275.000, and 285.000) K for the (R134a + R290) system were measured by a recirculation apparatus with view windows. The uncertainty of the temperatures, pressures, and compositions are less than {+-}5 mK, {+-}0.0005 MPa, and {+-}0.005, respectively. The saturated vapor pressures data were correlated by a Wagner type equation and compared with the reference data. The binary VLE data were correlated with the Peng-Robinson equation of state (PR EoS) incorporating the Huron-Vidal (HV) mixing rule utilizing the nonrandom two-liquid (NRTL) activity coefficient model. For mixtures, the maximum average absolute relative deviation of pressure is 0.15%, while the maximum average absolute deviation of vapor phase mole fraction is 0.0045. Azeotropic behavior can be found for the (R134a + R290) system at measured temperatures.

Using measured octanol-air partition coefficients to explain environmental partitioning of organochlorine pesticides.

Science.gov (United States)

Shoeib, Mahiba; Harner, Tom

2002-05-01

Octanol-air partition coefficients (Koa) were measured directly for 19 organochlorine (OC) pesticides over the temperature range of 5 to 35 degrees C. Values of log Koa at 25 degrees C ranged over three orders of magnitude, from 7.4 for hexachlorobenzene to 10.1 for 1,1-dichloro-2,2-bis(p-chlorophenyl) ethane. Measured values were compared to values calculated as KowRT/H (where R is the ideal gas constant [8.314 J mol(-1) K(-1)], T is absolute temperature, and H is Henry's law constant) were, in general, larger. Discrepancies of up to three orders of magnitude were observed, highlighting the need for direct measurements of Koa. Plots of Koa versus inverse absolute temperature exhibited a log-linear correlation. Enthalpies of phase transition between octanol and air (deltaHoa) were determined from the temperature slopes and were in the range of 56 to 105 kJ mol(-1) K(-1). Activity coefficients in octanol (gamma(o)) were determined from Koa and reported supercooled liquid vapor pressures (pL(o)), and these were in the range of 0.3 to 12, indicating near-ideal solution behavior. Differences in Koa values for structural isomers of hexachlorocyclohexane were also explored. A Koa-based model was described for predicting the partitioning of OC pesticides to aerosols and used to calculate particulate fractions at 25 and -10 degrees C. The model also agreed well with experimental results for several OC pesticides that were equilibrated with urban aerosols in the laboratory. A log-log regression of the particle-gas partition coefficient versus Koa had a slope near unity, indicating that octanol is a good surrogate for the aerosol organic matter.

Correlation between apparent diffusion coefficients and HER2 status in gastric cancers: pilot study

International Nuclear Information System (INIS)

He, Jian; Shi, Hua; Zhou, Zhuping; Chen, Jun; Guan, Wenxian; Wang, Hao; Yu, Haiping; Liu, Song; Zhou, Zhengyang; Yang, Xiaofeng; Liu, Tian

2015-01-01

To evaluate whether apparent diffusion coefficient (ADC) value of gastric cancer obtained from diffusion weighted imaging (DWI) correlates with the HER2 status. Forty-five patients, who had been diagnosed with gastric cancer through biopsy, were enrolled in this IRB-approved study. Each patient underwent a DWI (b values: 0 and 1,000 sec/mm 2 ) prior to surgery (curative gastrectomy or palliative resection). Postoperative microscopic findings, HER2 status by immunohistochemical analysis and fluorescence in situ hybridization (FISH) were obtained. HER2 status was compared among gastric cancers with various histopathological features using the chi square test. The ADC values of gastric cancers with positive and negative HER2 were compared using the student t test. A weak yet significant correlation was observed between the mean ADC values and HER2 status (r = 0.312, P = 0.037) and scores (r = 0.419, P = 0.004). The mean ADC value of HER2-positive gastric cancers was significantly higher than those of HER2-negative tumors (1.211 vs. 0.984 mm 2 /s, P = 0.020). The minimal ADC value of HER2-positive gastric cancers was significantly higher than those of HER2-negative tumors (1.105 vs. 0.905 × 10 −3 mm 2 /s, P = 0.036). In this pilot study, we have demonstrated that the ADC values of gastric cancer correlate with the HER2 status. Future research is warranted to see if DWI can predict HER2 status and help in tailoring therapy for gastric cancer

Câbles électriques - Calcul du courant admissible - Partie 2: Résistance thermique - Section 2: Méthode de calcul des coefficients de réduction de l'intensité de courant admissible pour des groupes de câbles posés à l'air libre et protégés du rayonnement solaire direct

CERN Document Server

1995-01-01

Câbles électriques - Calcul du courant admissible - Partie 2: Résistance thermique - Section 2: Méthode de calcul des coefficients de réduction de l'intensité de courant admissible pour des groupes de câbles posés à l'air libre et protégés du rayonnement solaire direct

Effects of temperature and anion species on CO2 permeability and CO2/N2 separation coefficient through ionic liquid membranes

International Nuclear Information System (INIS)

Jindaratsamee, Pinyarat; Shimoyama, Yusuke; Morizaki, Hironobu; Ito, Akira

2011-01-01

The permeability of carbon dioxide (CO 2 ) through imidazolium-based ionic liquid membranes was measured by a sweep gas method. Six species of ionic liquids were studied in this work as follows: [emim][BF 4 ], [bmim][BF 4 ], [bmim][PF 6 ], [bmim][Tf 2 N], [bmim][OTf], and [bmim][dca]. The ionic liquids were supported with a polyvinylidene fluoride porous membrane. The measurements were performed at T = (303.15 to 343.15) K. The partial pressure difference between feed and permeate sides was 0.121 MPa. The permeability of the CO 2 increases with temperature for the all ionic liquid species. Base on solution diffusion theory, it can be explained that the diffusion coefficient of CO 2 in an ionic liquid affects the temperature dependence more strongly than the solubility coefficient. The greatest permeability was obtained with the [bmim][Tf 2 N] membrane. The membrane of [bmim][PF 6 ] presents the lowest permeability. The separation coefficient between CO 2 and N 2 through the ionic liquid membranes was also investigated at the volume fraction of CO 2 at feed side 0.10. The separation coefficient decreases with the increase of temperature for the all ionic liquid species. The membrane of [emim][BF 4 ] and [bmim][BF 4 ] gives the highest separation coefficient at constant temperature. The lowest separation coefficient was obtained from [bmim][Tf 2 N] membrane which presents the highest permeability of CO 2 .

Using wave intensity analysis to determine local reflection coefficient in flexible tubes.

Science.gov (United States)

Li, Ye; Parker, Kim H; Khir, Ashraf W

2016-09-06

It has been shown that reflected waves affect the shape and magnitude of the arterial pressure waveform, and that reflected waves have physiological and clinical prognostic values. In general the reflection coefficient is defined as the ratio of the energy of the reflected to the incident wave. Since pressure has the units of energy per unit volume, arterial reflection coefficient are traditionally defined as the ratio of reflected to the incident pressure. We demonstrate that this approach maybe prone to inaccuracies when applied locally. One of the main objectives of this work is to examine the possibility of using wave intensity, which has units of energy flux per unit area, to determine the reflection coefficient. We used an in vitro experimental setting with a single inlet tube joined to a second tube with different properties to form a single reflection site. The second tube was long enough to ensure that reflections from its outlet did not obscure the interactions of the initial wave. We generated an approximately half sinusoidal wave at the inlet of the tube and took measurements of pressure and flow along the tube. We calculated the reflection coefficient using wave intensity (R dI and R dI 0.5 ) and wave energy (R I and R I 0.5 ) as well as the measured pressure (R dP ) and compared these results with the reflection coefficient calculated theoretically based on the mechanical properties of the tubes. The experimental results show that the reflection coefficients determined by all the techniques we studied increased or decreased with distance from the reflection site, depending on the type of reflection. In our experiments, R dP , R dI 0.5 and R I 0.5 are the most reliable parameters to measure the mean reflection coefficient, whilst R dI and R I provide the best measure of the local reflection coefficient, closest to the reflection site. Additional work with bifurcations, tapered tubes and in vivo experiments are needed to further understand, validate the

Limiting partition coefficients of solutes in biphasic trihexyltetradecylphosphonium chloride ionic liquid-supercritical CO2 system: measurement and LSER-based correlation

Czech Academy of Sciences Publication Activity Database

Planeta, Josef; Karásek, Pavel; Roth, Michal

2007-01-01

Roč. 111, č. 26 (2007), s. 7620-7625 ISSN 1520-6106 R&D Projects: GA AV ČR KJB400310504; GA ČR GA203/05/2106; GA ČR GA203/07/0886 Institutional research plan: CEZ:AV0Z40310501 Keywords : phosphonium ionic liquid * supercritical carbon dioxide * solute partition coefficient Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 4.086, year: 2007

Power coefficient anomaly in Joyo, (2)

International Nuclear Information System (INIS)

Ishikawa, Makoto; Yamashita, Yoshioki; Sasaki, Makoto; Nara, Yoshihiko.

1981-12-01

In this report, the presumption about the mechanism having caused the power coefficient anomaly in Joyo during the 75 MW power-raising test in 1979 is described. After the previous report, the new information about the results of the post-irradiation examination and the analysis of the power coefficient of Joyo were able to be obtained. From these information, the mechanism of causing the anomaly was presumed as follows. In 50 MW operation, the fuel burnup reached about 10,000 MWD/ton at the end of second cycle, and produced fission gas was almost retained in fuel pellets. When the power was raised from 50 MW to 75 MW for the first time, the fission gas began to be released when 50 MW was somewhat exceeded. The fission gas release caused the temperature rise and cracking of fuel pellets, and elongated fuel stack length abruptly. These phenomena induced to enlarge the fuel expansion reactivity effect and Doppler reactivity effect, and caused the anomalous behavior of power coefficient. After reaching 75 MW, the fuel stack length did not respond normally to reactor power change, and the magnitude of power coefficient became smaller. The reactivity was lost considerably from the core after the anomaly. (Kako, I.)

Prediction of Thermal Properties of Sweet Sorghum Bagasse as a Function of Moisture Content Using Artificial Neural Networks and Regression Models

Directory of Open Access Journals (Sweden)

Gosukonda Ramana

2017-06-01

Full Text Available Artificial neural networks (ANN and traditional regression models were developed for prediction of thermal properties of sweet sorghum bagasse as a function of moisture content and room temperature. Predictions were made for three thermal properties: 1 thermal conductivity, 2 volumetric specific heat, and 3 thermal diffusivity. Each thermal property had five levels of moisture content (8.52%, 12.93%, 18.94%, 24.63%, and 28.62%, w. b. and room temperature as inputs. Data were sub-partitioned for training, testing, and validation of models. Backpropagation (BP and Kalman Filter (KF learning algorithms were employed to develop nonparametric models between input and output data sets. Statistical indices including correlation coefficient (R between actual and predicted outputs were produced for selecting the suitable models. Prediction plots for thermal properties indicated that the ANN models had better accuracy from unseen patterns as compared to regression models. In general, ANN models were able to strongly generalize and interpolate unseen patterns within the domain of training.

Critical heat flux of R134A and R245FA in a 2.2 mm circular tube

Energy Technology Data Exchange (ETDEWEB)

Tibirica, Cristiano Bigonha; Ribatski, Gherhardt [Universidade de Sao Paulo (EESC/USP), Sao Carlos, SP (Brazil). Escola de Engenharia. Dept. de Engenharia Mecanica], E-mails: bigonha@sc.usp.br, ribatski@sc.usp.br; Szczukiewicz, Sylwia; Thome, John Richard [Ecole Polytechnique Federale de Lausanne (LTCM/EPFL) (Switzerland). Lab. of Heat and Mass Transfer], Emails: sylwia.szczukiewicz@epfl.ch, john.thome@epfl.ch

2010-07-01

Critical heat flux (CHF) during flow boiling is generally related to a drastic decrease in the heat transfer coefficient and it is the maximum operational heat flux that can be achieved under safe operation. Due to such a fact, this topic has attracted great attention of the academic society dealing with boiling heat transfer and also in the industrial sector involved with the dissipation of high heat flux densities. In the specific case of high heat flux densities, micro-channel flow boiling is a promising technique for pursuing this objective. The boundary where microscale effects start in flow boiling is still an open issue in the literature and a 3 mm internal diameter (ID) threshold value, as suggested by Kandlikar and Grande (2003) is frequently adopted to characterize this point. Considering the needs for a better understanding of the micro/macro transition, this paper presents new experimental critical heat flux results in saturated flow boiling conditions for a macro/micro-scale tube. The data were obtained in a horizontal 2.20 mm ID stainless steel tube with heating lengths of 361 and 154 mm, R134a and R245fa as working fluids, mass velocities ranging from 100 to 1500 kg/m{sup 2s}, critical heat fluxes from 25 to 300 kW/m2, exit saturation temperatures of 25, 31 and 35 degree C, and critical vapor qualities ranging from 0.55 to 1. The experimental results show that critical heat flux increases with increasing mass velocity and inlet subcooling but decreases with increasing saturation temperature and heated length. The data also indicated a higher CHF for R245fa when compared with R134a at similar conditions. The experimental data were compared against the following CHF predictive methods: Katto and Ohno (1984), Shah (1987), Zhang et al. (2006) and Ong and Thome(2010). Katto and Ohno (1984) and Ong and Thome (2010) best predicted the database with a mean average error smaller than 15%. Both correlations include low and high pressure fluids in their

Testing Heteroscedasticity in Robust Regression

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2011-01-01

Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf

Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection

KAUST Repository

Chen, Lisha; Huang, Jianhua Z.

2012-01-01

and hence improves predictive accuracy. We propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty. We apply a group-lasso type penalty that treats each row of the matrix of the regression coefficients as a group

NO2 and Cancer Incidence in Saudi Arabia

Directory of Open Access Journals (Sweden)

Khalid Al-Ahmadi

2013-11-01

Full Text Available Air pollution exposure has been shown to be associated with an increased risk of specific cancers. This study investigated whether the number and incidence of the most common cancers in Saudi Arabia were associated with urban air pollution exposure, specifically NO2. Overall, high model goodness of fit (GOF was observed in the Eastern, Riyadh and Makkah regions. The significant coefficients of determination (r2 were higher at the regional level (r2 = 0.32–0.71, weaker at the governorate level (r2 = 0.03–0.43, and declined slightly at the city level (r2 = 0.17–0.33, suggesting that an increased aggregated spatial level increased the explained variability and the model GOF. However, the low GOF at the lowest spatial level suggests that additional variation remains unexplained. At different spatial levels, associations between NO2 concentration and the most common cancers were marginally improved in geographically weighted regression (GWR analysis, which explained both global and local heterogeneity and variations in cancer incidence. High coefficients of determination were observed between NO2 concentration and lung and breast cancer incidences, followed by prostate, bladder, cervical and ovarian cancers, confirming results from other studies. These results could be improved using individual explanatory variables such as environmental, demographic, behavioral, socio-economic, and genetic risk factors.

Measurements and modeling of transport and impurity radial profiles in the EXTRAP T2R reversed field pinch

Science.gov (United States)

Kuldkepp, M.; Brunsell, P. R.; Cecconello, M.; Dux, R.; Menmuir, S.; Rachlew, E.

2006-09-01

Radial impurity profiles of oxygen in the rebuilt reversed field pinch EXTRAP T2R [P. R. Brunsell et al., Plasma Phys. Control. Fusion 43, 1457 (2001)] have been measured with a multichannel spectrometer. Absolute ion densities for oxygen peak between 1-4×1010cm-3 for a central electron density of 1×1013cm-3. Transport simulations with the one-dimensional transport code STRAHL with a diffusion coefficient of 20m2 s-1 yield density profiles similar to those measured. Direct measurement of the ion profile evolution during pulsed poloidal current drive suggests that the diffusion coefficient is reduced by a factor ˜2 in the core but remains unaffected toward the edge. Core transport is not significantly affected by the radial magnetic field growth seen at the edge in discharges without feedback control. This indicates that the mode core amplitude remains the same while the mode eigenfunction increases at the edge.

Measurements and modeling of transport and impurity radial profiles in the EXTRAP T2R reversed field pinch

International Nuclear Information System (INIS)

Kuldkepp, M.; Brunsell, P. R.; Cecconello, M.; Dux, R.; Menmuir, S.; Rachlew, E.

2006-01-01

Radial impurity profiles of oxygen in the rebuilt reversed field pinch EXTRAP T2R [P. R. Brunsell et al., Plasma Phys. Control. Fusion 43, 1457 (2001)] have been measured with a multichannel spectrometer. Absolute ion densities for oxygen peak between 1-4x10 10 cm -3 for a central electron density of 1x10 13 cm -3 . Transport simulations with the one-dimensional transport code STRAHL with a diffusion coefficient of 20 m 2 s -1 yield density profiles similar to those measured. Direct measurement of the ion profile evolution during pulsed poloidal current drive suggests that the diffusion coefficient is reduced by a factor ∼2 in the core but remains unaffected toward the edge. Core transport is not significantly affected by the radial magnetic field growth seen at the edge in discharges without feedback control. This indicates that the mode core amplitude remains the same while the mode eigenfunction increases at the edge

An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

Science.gov (United States)

Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

2018-05-01

Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

Elliptical multiple-output quantile regression and convex optimization

Czech Academy of Sciences Publication Activity Database

Hallin, M.; Šiman, Miroslav

2016-01-01

Roč. 109, č. 1 (2016), s. 232-237 ISSN 0167-7152 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : quantile regression * elliptical quantile * multivariate quantile * multiple-output regression Subject RIV: BA - General Mathematics Impact factor: 0.540, year: 2016 http://library.utia.cas.cz/separaty/2016/SI/siman-0458243.pdf

Cationic agent contrast-enhanced computed tomography imaging of cartilage correlates with the compressive modulus and coefficient of friction.

Science.gov (United States)

Lakin, B A; Grasso, D J; Shah, S S; Stewart, R C; Bansal, P N; Freedman, J D; Grinstaff, M W; Snyder, B D

2013-01-01

The aim of this study is to evaluate whether contrast-enhanced computed tomography (CECT) attenuation, using a cationic contrast agent (CA4+), correlates with the equilibrium compressive modulus (E) and coefficient of friction (μ) of ex vivo bovine articular cartilage. Correlations between CECT attenuation and E (Group 1, n = 12) and μ (Group 2, n = 10) were determined using 7 mm diameter bovine osteochondral plugs from the stifle joints of six freshly slaughtered, skeletally mature cows. The equilibrium compressive modulus was measured using a four-step, unconfined, compressive stress-relaxation test, and the coefficients of friction were determined from a torsional friction test. Following mechanical testing, samples were immersed in CA4+, imaged using μCT, rinsed, and analyzed for glycosaminoglycan (GAG) content using the 1,9-dimethylmethylene blue (DMMB) assay. The CECT attenuation was positively correlated with the GAG content of bovine cartilage (R(2) = 0.87, P coefficients of friction: CECT vs μ(static) (R(2) = 0.71, P = 0.002), CECT vs μ(static_equilibrium) (R(2) = 0.79, P coefficient of friction. Copyright © 2012 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

The Odorant ( R)-Citronellal Attenuates Caffeine Bitterness by Inhibiting the Bitter Receptors TAS2R43 and TAS2R46.

Science.gov (United States)

Suess, Barbara; Brockhoff, Anne; Meyerhof, Wolfgang; Hofmann, Thomas

2018-03-14

Sensory studies showed the volatile fraction of lemon grass and its main constituent, the odor-active citronellal, to significantly decrease the perceived bitterness of a black tea infusion as well as caffeine solutions. Seven citronellal-related derivatives were synthesized and shown to inhibit the perceived bitterness of caffeine in a structure-dependent manner. The aldehyde function at carbon 1, the ( R)-configuration of the methyl-branched carbon 3, and a hydrophobic carbon chain were found to favor the bitter inhibitory activity of citronellal; for example, even low concentrations of 25 ppm were observed to reduce bitterness perception of caffeine solution (6 mmol/L) by 32%, whereas ( R)-citronellic acid (100 pm) showed a reduction of only 21% and ( R)-citronellol (100 pm) was completely inactive. Cell-based functional experiments, conducted with the human bitter taste receptors TAS2R7, TAS2R10, TAS2R14, TAS2R43, and TAS2R46 reported to be sensitive to caffeine, revealed ( R)-citronellal to completely block caffeine-induced calcium signals in TAS2R43-expressing cells, and, to a lesser extent, in TAS2R46-expressing cells. Stimulation of TAS2R43-expressing cells with structurally different bitter agonists identified ( R)-citronellal as a general allosteric inhibitor of TAS2R43. Further structure/activity studies indicated 3-methyl-branched aliphatic aldehydes with a carbon chain of ≥4 C atoms as best TAS2R43 antagonists. Whereas odor-taste interactions have been mainly interpreted in the literature to be caused by a central neuronal integration of odors and tastes, rather than by peripheral events at the level of reception, the findings of this study open up a new dimension regarding the interaction of the two chemical senses.

Continuous validation of ASTEC containment models and regression testing

International Nuclear Information System (INIS)

Nowack, Holger; Reinke, Nils; Sonnenkalb, Martin

2014-01-01

The focus of the ASTEC (Accident Source Term Evaluation Code) development at GRS is primarily on the containment module CPA (Containment Part of ASTEC), whose modelling is to a large extent based on the GRS containment code COCOSYS (COntainment COde SYStem). Validation is usually understood as the approval of the modelling capabilities by calculations of appropriate experiments done by external users different from the code developers. During the development process of ASTEC CPA, bugs and unintended side effects may occur, which leads to changes in the results of the initially conducted validation. Due to the involvement of a considerable number of developers in the coding of ASTEC modules, validation of the code alone, even if executed repeatedly, is not sufficient. Therefore, a regression testing procedure has been implemented in order to ensure that the initially obtained validation results are still valid with succeeding code versions. Within the regression testing procedure, calculations of experiments and plant sequences are performed with the same input deck but applying two different code versions. For every test-case the up-to-date code version is compared to the preceding one on the basis of physical parameters deemed to be characteristic for the test-case under consideration. In the case of post-calculations of experiments also a comparison to experimental data is carried out. Three validation cases from the regression testing procedure are presented within this paper. The very good post-calculation of the HDR E11.1 experiment shows the high quality modelling of thermal-hydraulics in ASTEC CPA. Aerosol behaviour is validated on the BMC VANAM M3 experiment, and the results show also a very good agreement with experimental data. Finally, iodine behaviour is checked in the validation test-case of the THAI IOD-11 experiment. Within this test-case, the comparison of the ASTEC versions V2.0r1 and V2.0r2 shows how an error was detected by the regression testing

Condensation heat transfer of R22 and R410A in horizontal smooth and microfin tubes

Energy Technology Data Exchange (ETDEWEB)

Kim, Man-Hoe; Shin, Joeng-Seob [Korea Advanced Institute of Science and Technology, Daejeon (Korea). Department of Mechanical Engineering

2005-09-01

An experimental investigation of condensation heat transfer in 9.52 mm O.D. horizontal copper tubes was conducted using R22 and R410A. The test rig had a straight, horizontal test section with an active length of 0.92 m and was cooled by the heat transfer fluid (cold water) circulated in a surrounding annulus. Constant heat flux of 11.0 kW/m{sup 2} was maintained throughout the experiment and refrigerant quality varied from 0.9 to 0.1. The condensation test results at 45 {sup o}C were reported for 40-80 kg/h mass flow rate. The local and average condensation coefficients for seven microfin tubes were presented compared to those for a smooth tube. The average condensation coefficients of R22 and R410A for the microfin tubes were 1.7-3.19 and 1.7-2.94 times larger than those in smooth tube, respectively. (author)

Bayesian regression of piecewise homogeneous Poisson processes

Directory of Open Access Journals (Sweden)

Diego Sevilla

2015-12-01

Full Text Available In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes. Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018 Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015

Circulating levels of miR-133a predict the regression potential of left ventricular hypertrophy after valve replacement surgery in patients with aortic stenosis.

Science.gov (United States)

García, Raquel; Villar, Ana V; Cobo, Manuel; Llano, Miguel; Martín-Durán, Rafael; Hurlé, María A; Nistal, J Francisco

2013-08-15

Myocardial microRNA-133a (miR-133a) is directly related to reverse remodeling after pressure overload release in aortic stenosis patients. Herein, we assessed the significance of plasma miR-133a as an accessible biomarker with prognostic value in predicting the reversibility potential of LV hypertrophy after aortic valve replacement (AVR) in these patients. The expressions of miR-133a and its targets were measured in LV biopsies from 74 aortic stenosis patients. Circulating miR-133a was measured in peripheral and coronary sinus blood. LV mass reduction was determined echocardiographically. Myocardial and plasma levels of miR-133a correlated directly (r=0.46, Pregression analysis identified plasma miR-133a as a positive predictor of the hypertrophy reversibility after surgery. The discrimination of the model yielded an area under the receiver operator characteristic curve of 0.89 (Pregression analysis revealed plasma miR-133a and its myocardial target Wolf-Hirschhorn syndrome candidate 2/Negative elongation factor A as opposite predictors of the LV mass loss (g) after AVR. Preoperative plasma levels of miR-133a reflect their myocardial expression and predict the regression potential of LV hypertrophy after AVR. The value of this bedside information for the surgical timing, particularly in asymptomatic aortic stenosis patients, deserves confirmation in further clinical studies.

An analytical approach to the positive reactivity void coefficient of TRIGA Mark-II reactor

International Nuclear Information System (INIS)

Edgue, Erdinc; Yarman, Tolga

1988-01-01

Previous calculations of reactivity void coefficient of I.T.U. TRIGA Mark-II Reactor was done by the second author et al. The theoretical predictions were afterwards, checked in this reactor experimentally. In this work an analytical approach is developed to evaluate rather quickly the reactivity void coefficient of I.T.U. TRIGA Mark-II, versus the size of the void inserted into the reactor. It is thus assumed that the reactor is a cylindrical, bare nuclear system. Next a belt of water of 2πrΔrH is introduced axially at a distance r from the center line of the system. r here, is the thickness of the belt, and H is the height of the reactor. The void is described by decreasing the water density in the belt region. A two group diffusion theory is adopted to determine the criticality of our configuration. The space dependency of the group fluxes are, thereby, assumed to be J 0 (2.405 r / R) cos (π Z / H), the same as that associated with the original bare reactor uniformly loaded prior to the change. A perturbation type of approach, thence, furnishes the effect of introducing a void in the belt region. The reactivity void coefficient can, rather surprisingly, be indeed positive. To our knowledge, this fact had not been established, by the supplier. The agreement of our predictions with the experimental results is good. (author)

Flow patterns and heat transfer coefficients in flow-boiling and convective condensation of R22 inside a micro fin of new design

International Nuclear Information System (INIS)

Muzzio, A.; Niro, A.; Garaviglia, M.

1998-01-01

Saturated flow boiling and convective condensation experiments for oil-free refrigerant R22 been carried out with a micro fin tube of new design and with a smooth tube. Both tube have the same outer diameter of 9.52 mm and are horizontally operated. Two-phase flow pattern data have been obtained in addition of heat transfer coefficient and pressure drops; more-over, adiabatic tests have been also performed in order for flow pattern map to cover even adiabatic flows. Data are for mass fluxes ranging from about 90 to 400 Kg/s m 2 . In boiling tests, the nominal saturation temperature is 5 degree C, with inlet quality varying from 0.2 to 0.6 and the quality change ranging from 0.1 to 0.5. In condensation, results are for saturation temperature equal to 35 degree C, with inlet quality between 0.8 and 0.4, and quality change within 0.6 and 0.2. The comparison shows a large heat transfer augmentation with a moderate increment of pressure drops, especially in evaporation were the enhancement factor comes up to 4 while the penalty factor is about 1.4 at the most. Heat transfer coefficients both in evaporation and condensation are compared to the predictions of some recent correlations specifically proposed or modified for micro fin tube

Mapping the spatial pattern of temperate forest above ground biomass by integrating airborne lidar with Radarsat-2 imagery via geostatistical models

Science.gov (United States)

Li, Wang; Niu, Zheng; Gao, Shuai; Wang, Cheng

2014-11-01

Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are two competitive active remote sensing techniques in forest above ground biomass estimation, which is important for forest management and global climate change study. This study aims to further explore their capabilities in temperate forest above ground biomass (AGB) estimation by emphasizing the spatial auto-correlation of variables obtained from these two remote sensing tools, which is a usually overlooked aspect in remote sensing applications to vegetation studies. Remote sensing variables including airborne LiDAR metrics, backscattering coefficient for different SAR polarizations and their ratio variables for Radarsat-2 imagery were calculated. First, simple linear regression models (SLR) was established between the field-estimated above ground biomass and the remote sensing variables. Pearson's correlation coefficient (R2) was used to find which LiDAR metric showed the most significant correlation with the regression residuals and could be selected as co-variable in regression co-kriging (RCoKrig). Second, regression co-kriging was conducted by choosing the regression residuals as dependent variable and the LiDAR metric (Hmean) with highest R2 as co-variable. Third, above ground biomass over the study area was estimated using SLR model and RCoKrig model, respectively. The results for these two models were validated using the same ground points. Results showed that both of these two methods achieved satisfactory prediction accuracy, while regression co-kriging showed the lower estimation error. It is proved that regression co-kriging model is feasible and effective in mapping the spatial pattern of AGB in the temperate forest using Radarsat-2 data calibrated by airborne LiDAR metrics.

Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

KAUST Repository

Abdul Jameel, Abdul Gani

2016-09-14

An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH–CH2 groups, naphthenic CH–CH2 groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using 1H NMR spectroscopy. Multiple linear regression (MLR) modeling was used to develop an empirical relationship between D/CN and the eight structural parameters. This was then used to predict the DCN of many hydrocarbon fuels. The developed model has a high correlation coefficient (R2 = 0.97) and was validated with experimentally measured DCN of twenty-two real fuel mixtures (e.g., gasolines and diesels) and fifty-nine blends of known composition, and the predicted values matched well with the experimental data.

On generalized elliptical quantiles in the nonlinear quantile regression setup

Czech Academy of Sciences Publication Activity Database

Hlubinka, D.; Šiman, Miroslav

2015-01-01

Roč. 24, č. 2 (2015), s. 249-264 ISSN 1133-0686 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : multivariate quantile * elliptical quantile * quantile regression * multivariate statistical inference * portfolio optimization Subject RIV: BA - General Mathematics Impact factor: 1.207, year: 2015 http://library.utia.cas.cz/separaty/2014/SI/siman-0434510.pdf

Relationships between the structure of wheat gluten and ACE inhibitory activity of hydrolysate: stepwise multiple linear regression analysis.

Science.gov (United States)

Zhang, Yanyan; Ma, Haile; Wang, Bei; Qu, Wenjuan; Wali, Asif; Zhou, Cunshan

2016-08-01

Ultrasound pretreatment of wheat gluten (WG) before enzymolysis can improve the angiotensin converting enzyme (ACE) inhibitory activity of the hydrolysates by alerting the structure of substrate proteins. Establishment of a relationship between the structure of WG and ACE inhibitory activity of the hydrolysates to judge the end point of the ultrasonic pretreatment is vital. The results of stepwise multiple linear regression (MLR) showed that the contents of free sulfhydryl, α-helix, disulfide bond, surface hydrophobicity and random coil were significantly correlated to ACE Inhibitory activity of the hydrolysate, with the standard partial regression coefficients were 3.729, -0.676, -0.252, 0.022 and 0.156, respectively. The R(2) of this model was 0.970. External validation showed that the stepwise MLR model could well predict the ACE inhibitory activity of hydrolysate based on the content of free sulfhydryl, α-helix, disulfide bond, surface hydrophobicity and random coil of WG before hydrolysis. A stepwise multiple linear regression model describing the quantitative relationships between the structure of WG and the ACE Inhibitory activity of the hydrolysates was established. This model can be used to predict the endpoint of the ultrasonic pretreatment. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Application of Soft Computing Techniques and Multiple Regression Models for CBR prediction of Soils

Directory of Open Access Journals (Sweden)

Fatimah Khaleel Ibrahim

2017-08-01

Full Text Available The techniques of soft computing technique such as Artificial Neutral Network (ANN have improved the predicting capability and have actually discovered application in Geotechnical engineering. The aim of this research is to utilize the soft computing technique and Multiple Regression Models (MLR for forecasting the California bearing ratio CBR( of soil from its index properties. The indicator of CBR for soil could be predicted from various soils characterizing parameters with the assist of MLR and ANN methods. The data base that collected from the laboratory by conducting tests on 86 soil samples that gathered from different projects in Basrah districts. Data gained from the experimental result were used in the regression models and soft computing techniques by using artificial neural network. The liquid limit, plastic index , modified compaction test and the CBR test have been determined. In this work, different ANN and MLR models were formulated with the different collection of inputs to be able to recognize their significance in the prediction of CBR. The strengths of the models that were developed been examined in terms of regression coefficient (R2, relative error (RE% and mean square error (MSE values. From the results of this paper, it absolutely was noticed that all the proposed ANN models perform better than that of MLR model. In a specific ANN model with all input parameters reveals better outcomes than other ANN models.

Downscaling soil moisture over East Asia through multi-sensor data fusion and optimization of regression trees

Science.gov (United States)

Park, Seonyoung; Im, Jungho; Park, Sumin; Rhee, Jinyoung

2017-04-01

optimization based on pruning of rules derived from the modified regression trees was conducted. Root Mean Square Error (RMSE) and Correlation coefficients (r) were used to optimize the rules, and finally 59 rules from modified regression trees were produced. The results show high validation r (0.79) and low validation RMSE (0.0556m3/m3). The 1 km downscaled soil moisture was evaluated using ground soil moisture data at 14 stations, and both soil moisture data showed similar temporal patterns (average r=0.51 and average RMSE=0.041). The spatial distribution of the 1 km downscaled soil moisture well corresponded with GLDAS soil moisture that caught both extremely dry and wet regions. Correlation between GLDAS and the 1 km downscaled soil moisture during growing season was positive (mean r=0.35) in most regions.

Application of regression model on stream water quality parameters

International Nuclear Information System (INIS)

Suleman, M.; Maqbool, F.; Malik, A.H.; Bhatti, Z.A.

2012-01-01

Statistical analysis was conducted to evaluate the effect of solid waste leachate from the open solid waste dumping site of Salhad on the stream water quality. Five sites were selected along the stream. Two sites were selected prior to mixing of leachate with the surface water. One was of leachate and other two sites were affected with leachate. Samples were analyzed for pH, water temperature, electrical conductivity (EC), total dissolved solids (TDS), Biological oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO) and total bacterial load (TBL). In this study correlation coefficient r among different water quality parameters of various sites were calculated by using Pearson model and then average of each correlation between two parameters were also calculated, which shows TDS and EC and pH and BOD have significantly increasing r value, while temperature and TDS, temp and EC, DO and BL, DO and COD have decreasing r value. Single factor ANOVA at 5% level of significance was used which shows EC, TDS, TCL and COD were significantly differ among various sites. By the application of these two statistical approaches TDS and EC shows strongly positive correlation because the ions from the dissolved solids in water influence the ability of that water to conduct an electrical current. These two parameters significantly vary among 5 sites which are further confirmed by using linear regression. (author)

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data.

Science.gov (United States)

Alexeeff, Stacey E; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A

2015-01-01

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R(2) yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R(2) yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.

Supremum Norm Posterior Contraction and Credible Sets for Nonparametric Multivariate Regression

NARCIS (Netherlands)

Yoo, W.W.; Ghosal, S

2016-01-01

In the setting of nonparametric multivariate regression with unknown error variance, we study asymptotic properties of a Bayesian method for estimating a regression function f and its mixed partial derivatives. We use a random series of tensor product of B-splines with normal basis coefficients as a

Development of an empirical model of turbine efficiency using the Taylor expansion and regression analysis

International Nuclear Information System (INIS)

Fang, Xiande; Xu, Yu

2011-01-01

The empirical model of turbine efficiency is necessary for the control- and/or diagnosis-oriented simulation and useful for the simulation and analysis of dynamic performances of the turbine equipment and systems, such as air cycle refrigeration systems, power plants, turbine engines, and turbochargers. Existing empirical models of turbine efficiency are insufficient because there is no suitable form available for air cycle refrigeration turbines. This work performs a critical review of empirical models (called mean value models in some literature) of turbine efficiency and develops an empirical model in the desired form for air cycle refrigeration, the dominant cooling approach in aircraft environmental control systems. The Taylor series and regression analysis are used to build the model, with the Taylor series being used to expand functions with the polytropic exponent and the regression analysis to finalize the model. The measured data of a turbocharger turbine and two air cycle refrigeration turbines are used for the regression analysis. The proposed model is compact and able to present the turbine efficiency map. Its predictions agree with the measured data very well, with the corrected coefficient of determination R c 2 ≥ 0.96 and the mean absolute percentage deviation = 1.19% for the three turbines. -- Highlights: → Performed a critical review of empirical models of turbine efficiency. → Developed an empirical model in the desired form for air cycle refrigeration, using the Taylor expansion and regression analysis. → Verified the method for developing the empirical model. → Verified the model.

R2R-printed inverted OPV modules - towards arbitrary patterned designs

Science.gov (United States)

Välimäki, M.; Apilo, P.; Po, R.; Jansson, E.; Bernardi, A.; Ylikunnari, M.; Vilkman, M.; Corso, G.; Puustinen, J.; Tuominen, J.; Hast, J.

2015-05-01

We describe the fabrication of roll-to-roll (R2R) printed organic photovoltaic (OPV) modules using gravure printing and rotary screen-printing processes. These two-dimensional printing techniques are differentiating factors from coated OPVs enabling the direct patterning of arbitrarily shaped and sized features into visual shapes and, increasing the freedom to connect the cells in modules. The inverted OPV structures comprise five layers that are either printed or patterned in an R2R printing process. We examined the rheological properties of the inks used and their relationship with the printability, the compatibility between the processed inks, and the morphology of the R2R-printed layers. We also evaluate the dimensional accuracy of the printed pattern, which is an important consideration in designing arbitrarily-shaped OPV structures. The photoactive layer and top electrode exhibited excellent cross-dimensional accuracy corresponding to the designed width. The transparent electron transport layer extended 300 µm beyond the designed values, whereas the hole transport layer shrank 100 µm. We also examined the repeatability of the R2R fabrication process when the active area of the module varied from 32.2 cm2 to 96.5 cm2. A thorough layer-by-layer optimization of the R2R printing processes resulted in realization of R2R-printed 96.5 cm2 sized modules with a maximum power conversion efficiency of 2.1% (mean 1.8%) processed with high functionality.

Autonomous estimation of Allan variance coefficients of onboard fiber optic gyro

Energy Technology Data Exchange (ETDEWEB)

Song Ningfang; Yuan Rui; Jin Jing, E-mail: rayleing@139.com [School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191 (China)

2011-09-15

Satellite motion included in gyro output disturbs the estimation of Allan variance coefficients of fiber optic gyro on board. Moreover, as a standard method for noise analysis of fiber optic gyro, Allan variance has too large offline computational effort and data storages to be applied to online estimation. In addition, with the development of deep space exploration, it is urged that satellite requires more autonomy including autonomous fault diagnosis and reconfiguration. To overcome the barriers and meet satellite autonomy, we present a new autonomous method for estimation of Allan variance coefficients including rate ramp, rate random walk, bias instability, angular random walk and quantization noise coefficients. In the method, we calculate differences between angle increments of star sensor and gyro to remove satellite motion from gyro output, and propose a state-space model using nonlinear adaptive filter technique for quantities previously measured from offline data techniques such as the Allan variance method. Simulations show the method correctly estimates Allan variance coefficients, R = 2.7965exp-4 {sup 0}/h{sup 2}, K = 1.1714exp-3 {sup 0}/h{sup 1.5}, B = 1.3185exp-3 {sup 0}/h, N = 5.982exp-4 {sup 0}/h{sup 0.5} and Q = 5.197exp-7 {sup 0} in real time, and tracks degradation of gyro performance from initail values, R = 0.651 {sup 0}/h{sup 2}, K = 0.801 {sup 0}/h{sup 1.5}, B = 0.385 {sup 0}/h, N = 0.0874 {sup 0}/h{sup 0.5} and Q = 8.085exp-5 {sup 0}, to final estimations, R = 9.548 {sup 0}/h{sup 2}, K = 9.524 {sup 0}/h{sup 1.5}, B = 2.234 {sup 0}/h, N = 0.5594 {sup 0}/h{sup 0.5} and Q = 5.113exp-4 {sup 0}, due to gamma radiation in space. The technique proposed here effectively isolates satellite motion, and requires no data storage and any supports from the ground.

R245fa Flow Boiling inside a 4.2 mm ID Microfin Tube

Science.gov (United States)

Longo, G. A.; Mancin, S.; Righetti, G.; Zilio, C.

2017-11-01

This paper presents the R245fa flow boiling heat transfer and pressure drop measurements inside a mini microfin tube with internal diameter at the fin tip of 4.2 mm, having 40 fins, 0.15 mm high with a helix angle of 18°. The tube was brazed inside a copper plate and electrically heated from the bottom. Sixteen T-type thermocouples are located in the copper plate to monitor the wall temperature. The experimental measurements were carried out at constant mean saturation temperature of 30 °C, by varying the refrigerant mass velocity between 100 kg m-2 s-1 and 300 kg m-2 s-1, the vapour quality from 0.15 to 0.95, at two different heat fluxes: 30 and 60 kW m-2. The experimental results are presented in terms of two-phase heat transfer coefficient, onset dryout vapour quality, and frictional pressure drop. Moreover, the experimental measurements are compared against the most updated models for boiling heat transfer coefficient and frictional pressure drop estimations available in the open literature for microfin tubes.

Asymptotic Distribution of Eigenvalues for a Class of Second-Order Elliptic Operators with Irregular Coefficients in R{sup d}

Energy Technology Data Exchange (ETDEWEB)

Zielinski, Lech [Universite du Littoral, LMPA (France)], E-mail: lech.zielinski@lmpa.univ-littoral.fr

2002-06-15

Let A=A{sub 0}+v(x) where A{sub 0} is a second-order uniformly elliptic self-adjoint operator in R{sup d} and v is a real valued polynomially growing potential. Assuming that v and the coefficients of A{sub 0} are Hoelder continuous, we study the asymptotic behaviour of the counting function N(A,{lambda}) ({lambda}{sup {yields}}{infinity}) with the remainder estimates depending on the regularity hypotheses. Our strongest regularity hypotheses involve Lipschitz continuity and give the remainder estimate N(A,{lambda})O({l_brace}{lambda}{r_brace}{sup -{mu}}), where {mu} may take an arbitrary value strictly smaller than the best possible value known in the smooth case. In particular, our results are obtained without any hypothesis on critical points of the potential.

SSR marker analysis on genetic variation of M3 from maize inbred lines 48-2 and R08 after irradiation inducement

International Nuclear Information System (INIS)

Li Qi; Shi Haichun; Ke Yongpei; Yuan Jichao; Yu Xuejie

2011-01-01

Analyzing the biological effects and the genetic variations of maize mutagenic progenies is important to facilitate effective selections and utilization of the mutants. In this study, the genetic variation of 103 mutagenic progenies of M 3 lines of inbred lines 48-2 and R08 with 60 Co γ-rays inducement were evaluated with SSR molecular markers. The results indicated that, the amplitude of polymorphism information content (PIC) of the 48-2 and R08 M 3 lines ranged 0.307 ∼ 0.948 and 0.108 ∼ 0.955, with an average of 0.762 and 0.701, respectively. The amplitude of genetic diversity indexes (H') ranged 0.552 ∼ 2.830 and 0.254 ∼ 3.309, with an average of 1.830 and 1.777, respectively. The average value of genetic similarity coefficient of the 49 M 3 lines of 48-2 with its check (M673) was 0.8194. However, the average value of genetic similarity coefficient of the M 3 lines of R08 with its check (M487) was 0.8373. Based on the genetic similarity coefficient, inbred lines 48-2, R08 and their 101 M 3 lines were clustered in 7 and 5 populations respectively. This phenomenon indicated that massive genetic variation could appear in progenies due to irradiation. The strengthen of selection and utilization of mutants based on the breeding objectives and in accordance with the feature and regularity on genetic variations of main characteristics of mutant lines in various populations could be enhanced in breeding program, to some extent, which can increase the breeding efficiency of irradiation induced mutation in maize. (authors)

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

Science.gov (United States)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross

miR-122 targets pyruvate kinase M2 and affects metabolism of hepatocellular carcinoma.

Directory of Open Access Journals (Sweden)

Angela M Liu

Full Text Available In contrast to normal differentiated cells that depend on mitochondrial oxidative phosphorylation for energy production, cancer cells have evolved to utilize aerobic glycolysis (Warburg's effect, with benefit of providing intermediates for biomass production. MicroRNA-122 (miR-122 is highly expressed in normal liver tissue regulating a wide variety of biological processes including cellular metabolism, but is reduced in hepatocellular carcinoma (HCC. Overexpression of miR-122 was shown to inhibit cancer cell proliferation, metastasis, and increase chemosensitivity, but its functions in cancer metabolism remains unknown. The present study aims to identify the miR-122 targeted genes and to investigate the associated regulatory mechanisms in HCC metabolism. We found the ectopic overexpression of miR-122 affected metabolic activities of HCC cells, evidenced by the reduced lactate production and increased oxygen consumption. Integrated gene expression analysis in a cohort of 94 HCC tissues revealed miR-122 level tightly associated with a battery of glycolytic genes, in which pyruvate kinase (PK gene showed the strongest anti-correlation coefficient (Pearson r = -0.6938, p = <0.0001. In addition, reduced PK level was significantly associated with poor clinical outcomes of HCC patients. We found isoform M2 (PKM2 is the dominant form highly expressed in HCC and is a direct target of miR-122, as overexpression of miR-122 reduced both the mRNA and protein levels of PKM2, whereas PKM2 re-expression abrogated the miR-122-mediated glycolytic activities. The present study demonstrated the regulatory role of miR-122 on PKM2 in HCC, having an implication of therapeutic intervention targeting cancer metabolic pathways.

Advanced colorectal neoplasia risk stratification by penalized logistic regression.

Science.gov (United States)

Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F

2016-08-01

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.

Linear regression and the normality assumption.

Science.gov (United States)

Schmidt, Amand F; Finan, Chris

2017-12-16

Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.

Environmental influences on the species diversity, biomass and population density of soft bottom macrofauna in the estuarine system of Goa, west coast of India

Digital Repository Service at National Institute of Oceanography (India)

Harkantra, S.N.; Rodrigues, N.R.

biomass and total population density and to construct the predictive models. The regression explaining the greatest amount of variation (R2) with all the significant parameter coefficients (r) were presented as the best fit based on adjusted R2... and population density at all the sites for the significant (P < .001-0.0001) best multiple linear regression model except for temperature (Table 2). This explained 32-72% of the total variation (Table 2). No significant best regression fit could...

Building a SuAVE browse interface to R2R's Linked Data

Science.gov (United States)

Clark, D.; Stocks, K. I.; Arko, R. A.; Zaslavsky, I.; Whitenack, T.

2017-12-01

The Rolling Deck to Repository program (R2R) is creating and evaluating a new browse portal based on the SuAVE platform and the R2R linked data graph. R2R manages the underway sensor data collected by the fleet of US academic research vessels, and provides a discovery and access point to those data at its website, www.rvdata.us. R2R has a database-driven search interface, but seeks a more capable and extensible browse interface that could be built off of the substantial R2R linked data resources. R2R's Linked Data graph organizes its data holdings around key concepts (e.g. cruise, vessel, device type, operator, award, organization, publication), anchored by persistent identifiers where feasible. The "Survey Analysis via Visual Exploration" or SuAVE platform (suave.sdsc.edu) is a system for online publication, sharing, and analysis of images and metadata. It has been implemented as an interface to diverse data collections, but has not been driven off of linked data in the past. SuAVE supports several features of interest to R2R, including faceted searching, collaborative annotations, efficient subsetting, Google maps-like navigation over an image gallery, and several types of data analysis. Our initial SuAVE-based implementation was through a CSV export from the R2R PostGIS-enabled PostgreSQL database. This served to demonstrate the utility of SuAVE but was static and required reloading as R2R data holdings grew. We are now working to implement a SPARQL-based ("RDF Query Language") service that directly leverages the R2R Linked Data graph and offers the ability to subset and/or customize output.We will show examples of SuAVE faceted searches on R2R linked data concepts, and discuss our experience to date with this work in progress.

Análise dos coeficientes de endogamia e de parentesco para qualquer nível de ploidia usando o pacote estatístico R Analysis of the inbreeding and coancestry coefficients for any ploidy level using the statistical package R

Directory of Open Access Journals (Sweden)

Luiz Alexandre Peternelli

2009-01-01

Full Text Available São poucos os softwares disponíveis para a análise de parentesco e, até a presente data, nenhum pode realizar a análise de parentesco para organismos poliplóides, com número par (2k de cromossomos. Implementou-se junto ao programa R uma rotina capaz de executar a análise de parentesco para qualquer nível 2k de ploidia, número de indivíduos (ou populações e número de gerações envolvidas na árvore genealógica. A função principal, calc.rxy( calcula o coeficiente de endogamia (F X de cada indivíduo; coeficientes de parentesco (rXY entre dois indivíduos quaisquer; e coeficiente de parentesco médio, variância, mínimo e máximo, para cada indivíduo. Ela pode mostrar a matriz completa de parentesco ou uma submatriz pré-definida de indivíduos selecionados. Adicionalmente, foram incluidas funções que servem para identificar erros de redundância dos dados originais (checar.nomes( e para identificar os pares de indivíduos com rXY superior a certo limite especificado pelo pesquisador (corte.rxy(. Essas funções podem ser usadas na análise de pedigrees com um número elevado de indivíduos e poderão ser utilizadas pela comunidade científica livremente, sem restrições à plataforma operacional utilizada. Destaca-se a vantagem em se poder trabalhar com qualquer nível 2k de ploidia, mesmo quando existe a possibilidade de certo indivíduo ser oriundo de autofecundação, aspecto comum em várias espécies vegetais.There are few softwares available to analyze relatedness among individuals and, to date, none can perform this analysis for polyploid organisms, with even number (2k of chromosomes. This work implements within package able to execute an analysis of relatedness for any 2k-ploidy level, number of individuals (or populations and number of generations in the pedigree. The main function, calc.rxy(, calculates the inbreeding coefficient (F X of each individual; coancestry coefficients (rXY between any two individuals

Electrical resistivity, Hall coefficient and electronic mobility in indium antimonide at different magnetic fields and temperatures

International Nuclear Information System (INIS)

Jee, Madan; Prasad, Vijay; Singh, Amita

1995-01-01

The electrical resistivity, Hall coefficient and electronic mobility of n-type and p-type crystals of indium antimonide have been measured from 25 degC-100 degC temperature range. It has been found by this measurement that indium antimonide is a compound semiconductor with a high mobility 10 6 cm 2 /V.S. The Hall coefficient R H was measured as a function of magnetic field strength H for a number of samples of both p and n-type using fields up to 12 kilo gauss. The Hall coefficient R h decreases with increasing magnetic fields as well as with increase in temperature of the sample. The electric field is more effective on samples with high mobilities and consequently the deviations from linearity are manifested at comparatively low values of the electric field. The measurement of R H in weak and strong magnetic fields makes it possible to determine the separate concentration of heavy and light holes. Measured values of Hall coefficient and electrical resistivity show that there is a little variation of ρ and R h with temperatures as well as with magnetic fields. (author). 12 refs., 5 tabs

THE CRASH INTENSITY EVALUATION USING GENERAL CENTRALITY CRITERIONS AND A GEOGRAPHICALLY WEIGHTED REGRESSION

Directory of Open Access Journals (Sweden)

M. Ghadiriyan Arani

2017-09-01

Full Text Available Today, one of the social problems influencing on the lives of many people is the road traffic crashes especially the highway ones. In this regard, this paper focuses on highway of capital and the most populous city in the U.S. state of Georgia and the ninth largest metropolitan area in the United States namely Atlanta. Geographically weighted regression and general centrality criteria are the aspects of traffic used for this article. In the first step, in order to estimate of crash intensity, it is needed to extract the dual graph from the status of streets and highways to use general centrality criteria. With the help of the graph produced, the criteria are: Degree, Pageranks, Random walk, Eccentricity, Closeness, Betweenness, Clustering coefficient, Eigenvector, and Straightness. The intensity of crash point is counted for every highway by dividing the number of crashes in that highway to the total number of crashes. Intensity of crash point is calculated for each highway. Then, criteria and crash point were normalized and the correlation between them was calculated to determine the criteria that are not dependent on each other. The proposed hybrid approach is a good way to regression issues because these effective measures result to a more desirable output. R2 values for geographically weighted regression using the Gaussian kernel was 0.539 and also 0.684 was obtained using a triple-core cube. The results showed that the triple-core cube kernel is better for modeling the crash intensity.

REGRES: A FORTRAN-77 program to calculate nonparametric and ``structural'' parametric solutions to bivariate regression equations

Science.gov (United States)

Rock, N. M. S.; Duffy, T. R.

REGRES allows a range of regression equations to be calculated for paired sets of data values in which both variables are subject to error (i.e. neither is the "independent" variable). Nonparametric regressions, based on medians of all possible pairwise slopes and intercepts, are treated in detail. Estimated slopes and intercepts are output, along with confidence limits, Spearman and Kendall rank correlation coefficients. Outliers can be rejected with user-determined stringency. Parametric regressions can be calculated for any value of λ (the ratio of the variances of the random errors for y and x)—including: (1) major axis ( λ = 1); (2) reduced major axis ( λ = variance of y/variance of x); (3) Y on Xλ = infinity; or (4) X on Y ( λ = 0) solutions. Pearson linear correlation coefficients also are output. REGRES provides an alternative to conventional isochron assessment techniques where bivariate normal errors cannot be assumed, or weighting methods are inappropriate.

Computation of Clebsch-Gordan and Gaunt coefficients using binomial coefficients

International Nuclear Information System (INIS)

Guseinov, I.I.; Oezmen, A.; Atav, Ue

1995-01-01

Using binomial coefficients the Clebsch-Gordan and Gaunt coefficients were calculated for extremely large quantum numbers. The main advantage of this approach is directly calculating these coefficients, instead of using recursion relations. Accuracy of the results is quite high for quantum numbers l 1 , and l 2 up to 100. Despite direct calculation, the CPU times are found comparable with those given in the related literature. 11 refs., 1 fig., 2 tabs

Phase stability and oxygen diffusion in RBa2Cu3O6+x (R=Y, Nd)

International Nuclear Information System (INIS)

Mozhaev, A.P.; Mazo, G.N.; Galkin, A.A.; Khromova, N.V.

1996-01-01

Phase stability boundaries of RBa 2 Cu 3 O 6 + x (R=Y, Nd) compounds for oxygen partial pressure wide range were determined by means of Coulomb titration. Phase decomposition is shown to occur without formation of liquid phase. Principial differences in the chemical composition of decomposition product of Y- and Nd-containing phases were detected. Dependences of oxygen non-stoichiometry of the compounds on temperature were determined. Fragments of P o 2 -T-x-diagrams were plotted. Oxygen diffusion coefficients within wide range of temperatures and partial pressures of oxygen were determined. Dependence of diffusion parameters on oxygen non-stoichiometry and P o 2 was determined. Oxygen diffusion was determined to occur more rapidly in orthorhombic phase than in tetragonal one. Diffusion coefficients were shown to increase at transition from Y-to Nd-containing phase. 13 refs., 6 figs., 2 tabs

Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression.

Science.gov (United States)

Chen, Qiang; Mei, Kun; Dahlgren, Randy A; Wang, Ting; Gong, Jian; Zhang, Minghua

2016-12-01

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to

Systematic Risk on Istanbul Stock Exchange: Traditional Beta Coefficient Versus Downside Beta Coefficient

Directory of Open Access Journals (Sweden)

Gülfen TUNA

2013-03-01

Full Text Available The aim of this study is to test the validity of Downside Capital Asset Pricing Model (D-CAPM on the ISE. At the same time, the explanatory power of CAPM's traditional beta and D-CAPM's downside beta on the changes in the average return values are examined comparatively. In this context, the monthly data for seventy three stocks that are continuously traded on the ISE for the period 1991-2009 is used. Regression analysis is applied in this study. The research results have shown that D-CAPM is valid on the ISE. In addition, it is obtained that the power of downside beta coefficient is higher than traditional beta coefficient on explaining the return changes. Therefore, it can be said that the downside beta is superior to traditional beta in the ISE for chosen period.

The use of regression analysis in determining reference intervals for low hematocrit and thrombocyte count in multiple electrode aggregometry and platelet function analyzer 100 testing of platelet function.

Science.gov (United States)

Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D

2017-11-01

Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.

R{sup 2} supergravity

Energy Technology Data Exchange (ETDEWEB)

Ferrara, Sergio [Physics Department, Theory Unit, CERN,CH 1211, Geneva 23 (Switzerland); INFN - Laboratori Nazionali di Frascati,Via Enrico Fermi 40, I-00044 Frascati (Italy); Department of Physics and Astronomy, University of California,Los Angeles, CA 90095-1547 (United States); Kehagias, Alex [Physics Division, National Technical University of Athens,15780 Zografou, Athens (Greece); Porrati, Massimo [Physics Department, Theory Unit, CERN,CH 1211, Geneva 23 (Switzerland); CCPP, Department of Physics,NYU 4 Washington Pl. New York NY 10003 (United States)

2015-08-03

We formulate R{sup 2} pure supergravity as a scale invariant theory built only in terms of superfields describing the geometry of curved superspace. The standard supergravity duals are obtained in both “old' and “new' minimal formulations of auxiliary fields. These theories have massless fields in de Sitter space as they do in their non supersymmetric counterpart. Remarkably, the dual theory of R{sup 2} supergravity in the new minimal formulation is an extension of the Freedman model, describing a massless gauge field and a massless chiral multiplet in de Sitter space, with inverse radius proportional to the Fayet-Iliopoulos term. This model can be interpreted as the “de-Higgsed' phase of the dual companion theory of R+R{sup 2} supergravity.

Differentiating regressed melanoma from regressed lichenoid keratosis.

Science.gov (United States)

Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

2017-04-01

Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Locoregional control of non-small cell lung cancer in relation to automated early assessment of tumor regression on cone beam computed tomography

DEFF Research Database (Denmark)

Brink, Carsten; Bernchou, Uffe; Bertelsen, Anders

2014-01-01

was estimated on the basis of the first one third and two thirds of the scans. The concordance between estimated and actual relative volume at the end of radiation therapy was quantified by Pearson's correlation coefficient. On the basis of the estimated relative volume, the patients were stratified into 2...... for other clinical characteristics. RESULTS: Automatic measurement of the tumor regression from standard CBCT images was feasible. Pearson's correlation coefficient between manual and automatic measurement was 0.86 in a sample of 9 patients. Most patients experienced tumor volume regression, and this could...

Influence diagnostics in meta-regression model.

Science.gov (United States)

Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

2017-09-01

This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

Science.gov (United States)

Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors.

Science.gov (United States)

Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman

2017-02-01

The soil sorption partition coefficient logK oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year. Copyright © 2016 Elsevier Ltd. All rights reserved.

Local bilinear multiple-output quantile/depth regression

Czech Academy of Sciences Publication Activity Database

Hallin, M.; Lu, Z.; Paindaveine, D.; Šiman, Miroslav

2015-01-01

Roč. 21, č. 3 (2015), s. 1435-1466 ISSN 1350-7265 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : conditional depth * growth chart * halfspace depth * local bilinear regression * multivariate quantile * quantile regression * regression depth Subject RIV: BA - General Mathematics Impact factor: 1.372, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/siman-0446857.pdf

Detrended fluctuation analysis as a regression framework: Estimating dependence at different scales

Czech Academy of Sciences Publication Activity Database

Krištoufek, Ladislav

2015-01-01

Roč. 91, č. 1 (2015), 022802-1-022802-5 ISSN 1539-3755 R&D Projects: GA ČR(CZ) GP14-11402P Grant - others:GA ČR(CZ) GAP402/11/0948 Program:GA Institutional support: RVO:67985556 Keywords : Detrended cross-correlation analysis * Regression * Scales Subject RIV: AH - Economics Impact factor: 2.288, year: 2014 http://library.utia.cas.cz/separaty/2015/E/kristoufek-0452315.pdf

Efficient detection of differentially methylated regions using DiMmeR

DEFF Research Database (Denmark)

Almeida, Diogo Marinho; Uhrenfeldt Skov, Ida; Silva, Artur

2017-01-01

in the programming language R, have no user interface, and do not offer all necessary steps to guide users from raw data all the way down to statistically significant differentially methylated regions (DMRs) and the associated genes. RESULTS: Here, we present DiMmeR (Discovery of Multiple Differentially Methylated...... Regions), the first free standalone software that interactively guides with a user-friendly graphical user interface (GUI) scientists the whole way through EWAS data analysis. It offers parallelized statistical methods for efficiently identifying DMRs in both Illumina 450K and 850K EPIC chip data. Di......MmeR computes empirical p-values through randomization tests, even for big data sets of hundreds of patients and thousands of permutations within a few minutes on a standard desktop PC. It is independent of any third-party libraries, computes regression coefficients, p-values and empirical p...

Electron Transport Coefficients and Effective Ionization Coefficients in SF6-O2 and SF6-Air Mixtures Using Boltzmann Analysis

Science.gov (United States)

Wei, Linsheng; Xu, Min; Yuan, Dingkun; Zhang, Yafang; Hu, Zhaoji; Tan, Zhihong

2014-10-01

The electron drift velocity, electron energy distribution function (EEDF), density-normalized effective ionization coefficient and density-normalized longitudinal diffusion velocity are calculated in SF6-O2 and SF6-Air mixtures. The experimental results from a pulsed Townsend discharge are plotted for comparison with the numerical results. The reduced field strength varies from 40 Td to 500 Td (1 Townsend=10-17 V·cm2) and the SF6 concentration ranges from 10% to 100%. A Boltzmann equation associated with the two-term spherical harmonic expansion approximation is utilized to gain the swarm parameters in steady-state Townsend. Results show that the accuracy of the Boltzmann solution with a two-term expansion in calculating the electron drift velocity, electron energy distribution function, and density-normalized effective ionization coefficient is acceptable. The effective ionization coefficient presents a distinct relationship with the SF6 content in the mixtures. Moreover, the E/Ncr values in SF6-Air mixtures are higher than those in SF6-O2 mixtures and the calculated value E/Ncr in SF6-O2 and SF6-Air mixtures is lower than the measured value in SF6-N2. Parametric studies conducted on these parameters using the Boltzmann analysis offer substantial insight into the plasma physics, as well as a basis to explore the ozone generation process.

Pedigrees or markers: Which are better in estimating relatedness and inbreeding coefficient?

Science.gov (United States)

Wang, Jinliang

2016-02-01

Individual inbreeding coefficient (F) and pairwise relatedness (r) are fundamental parameters in population genetics and have important applications in diverse fields such as human medicine, forensics, plant and animal breeding, conservation and evolutionary biology. Traditionally, both parameters are calculated from pedigrees, but are now increasingly estimated from genetic marker data. Conceptually, a pedigree gives the expected F and r values, FP and rP, with the expectations being taken (hypothetically) over an infinite number of individuals with the same pedigree. In contrast, markers give the realised (actual) F and r values at the particular marker loci of the particular individuals, FM and rM. Both pedigree (FP, rP) and marker (FM, rM) estimates can be used as inferences of genomic inbreeding coefficients FG and genomic relatedness rG, which are the underlying quantities relevant to most applications (such as estimating inbreeding depression and heritability) of F and r. In the pre-genomic era, it was widely accepted that pedigrees are much better than markers in delineating FG and rG, and markers should better be used to validate, amend and construct pedigrees rather than to replace them. Is this still true in the genomic era when genome-wide dense SNPs are available? In this simulation study, I showed that genomic markers can yield much better estimates of FG and rG than pedigrees when they are numerous (say, 10(4) SNPs) under realistic situations (e.g. genome and population sizes). Pedigree estimates are especially poor for species with a small genome, where FG and rG are determined to a large extent by Mendelian segregations and may thus deviate substantially from their expectations (FP and rP). Simulations also confirmed that FM, when estimated from many SNPs, can be much more powerful than FP for detecting inbreeding depression in viability. However, I argue that pedigrees cannot be replaced completely by genomic SNPs, because the former allows for

Prediction of the thermal expansion coefficients of bio diesels from several sources through the application of linear regression; Predicao dos coeficientes de expansao termica de biodieseis de diversas origens atraves da aplicacao da regressa linear

Energy Technology Data Exchange (ETDEWEB)

Canciam, Cesar Augusto [Universidade Tecnologica Federal do Parana (UTFPR), Campus Ponta Grossa, PR (Brazil)], e-mail: canciam@utfpr.edu.br

2012-07-01

When evaluating the consumption of bio fuels, the knowledge of the density is of great importance for rectify the effect of temperature. The thermal expansion coefficient is a thermodynamic property that provides a measure of the density variation in response to temperature variation, keeping the pressure constant. This study aimed to predict the thermal expansion coefficients of ethyl bio diesels from castor beans, soybeans, sunflower seeds and Mabea fistulifera Mart. oils and of methyl bio diesels from soybeans, sunflower seeds, souari nut, cotton, coconut, castor beans and palm oils, from beef tallow, chicken fat and hydrogenated vegetable fat residual. For this purpose, there was a linear regression analysis of the density of each bio diesel a function of temperature. These data were obtained from other works. The thermal expansion coefficients for bio diesels are between 6.3729x{sup 10-4} and 1.0410x10{sup -3} degree C-1. In all the cases, the correlation coefficients were over 0.99. (author)

High performance Li2MnO3/rGO composite cathode for lithium ion batteries

Science.gov (United States)

Zhao, Wei; Xiong, Lilong; Xu, Youlong; Li, Houli; Ren, Zaihuang

2017-05-01

The novel composite Li2MnO3 (LMO)/reduced graphene oxide (rGO) has been synthesized successfully. Based on the scanning electron microscopy and transmission electron microscopy, LMO is found to distribute separately on the rGO sheets by forming a laminated structure, which is in favor of good electrical contact between the cathode active materials and the rGO matrix, and also facilitates the separation of LMO secondary particles with reduced size. Cyclic voltammetry and electrochemical impedance spectroscopy tests show that the charge transfer resistance decreases from 81.2 Ω for LMO to 29.6 Ω for LMO/rGO composite. The Li-ion diffusion coefficient of LMO/rGO composite is almost triple that of LMO. As a result, the LMO/rGO composite delivers an initial discharge capacity of 284.9 mAh g-1 with a capacity retention of 86.6% after 45 cycles at 0.1 C between 2.0 and 4.6 V. Cycle performance is even better at a higher current density 0.2 C while the retention ratio is up to 97.1% after 45 cycles. The rate capability is also significantly enhanced, and the LMO/rGO composite could exhibit a large discharge capacity of 123.7 mAh g-1 which is more than three times larger than that of LMO (40.8 mAh g-1) at a high rate of 8 C.

Corrosion Inhibition of Q235A Steel in Acid Medium Using Isatin Derivatives: A Qsar Study

International Nuclear Information System (INIS)

Abdo M Al-Fakih; Madzlan Aziz; Abdo M Al-Fakih; Abdallah, H.H.; Hasmerya Maarof; Rosmahaida Jamaludin; Bishir Usman

2016-01-01

Quantitative Structure-Activity Relationship (QSAR) study was performed on 10 isatin derivatives which were reportedly used as corrosion inhibitors. Dragon software was used to calculate the molecular descriptors. Partial least square (PLS) method was used to run the regression analysis between the descriptors and the corrosion inhibition efficiencies (IE) of the inhibitors. A predictive QSAR model was developed with a correlation coefficient (r 2 cal ) of 0.9676. The model validity was assessed through internal and external validation. The results show that cross-validation regression coefficient (r 2 cv ) and prediction regression coefficient (r 2 pred ) are 0.8163 and 0.9189, respectively. The model was used to predict the IE for ten isatin derivatives. The results confirm a good stability and predictive ability of the model. Dragon-based descriptors provide a very good description of the corrosion inhibition properties of the inhibitors. The results of the QSAR study were found to be consistent with the experimental data. (author)

Anomalous field dependence of the Hall coefficient in disordered metals

International Nuclear Information System (INIS)

Tousson, E.; Ovadyahu, Z.

1988-01-01

We report on a comprehensive study of the Hall coefficient, R/sub H/, in disordered three-dimensional In 2 O/sub 3-//sub x/ films as a function of the magnetic field strength, temperature, and degree of spatial disorder. Our main result is that, at sufficiently small fields, R/sub H/ is virtually temperature, field, and disorder independent, even at the metal-insulator transition itself. On the other hand, at the limit of strong magnetic fields, R/sub H/ has an explicit temperature dependence, in apparent agreement with the prediction of Al'tshuler, Aronov, and Lee. For intermediate values of fields, R/sub H/ is field and temperature dependent. It is also shown that the behavior of the conductivity as a function of temperature, σ(T), at small fields, is qualitatively different than that measured at the limit of strong magnetic fields. The low- and high-field regimes seem to correlate with the respective regimes in terms of the Hall-coefficient behavior. It is suggested that the magnetotransport in the high-field limit is considerably influenced by Coulomb-correlation effects. However, in the low-field regime, where both correlations and weak-localization effects are, presumably, equally important (and where both theories are the more likely to be valid), is problematic; neither R/sub H/ nor σ(T) gives any unambiguous evidence to the existence of interaction effects. This problem is discussed in light of the experimental results pertaining to the behavior of R/sub H/(T) in two-dimensional In 2 O/sub 3-//sub x/ films as well as in other disordered systems

Rate Coefficients for the OH + (CHO)2 (Glyoxal) Reaction Between 240 and 400 K

Science.gov (United States)

Feierabend, K. J.; Talukdar, R. K.; Zhu, L.; Ravishankara, A. R.; Burkholder, J. B.

2006-12-01

Glyoxal (CHO)2, the simplest dialdehyde, is an end product formed in the atmospheric oxidation of biogenic hydrocarbons, for example, isoprene. As such, glyoxal plays a role in regional air quality and ozone production in certain locations. Glyoxal is lost in the atmosphere via UV photolysis and reaction with OH. However, the currently available rate coefficient data for the OH + glyoxal reaction is limited to a single room- temperature measurement made using the relative rate method. A determination of the rate coefficient temperature dependence is therefore needed for a more complete interpretation of the atmospheric processing of glyoxal. This study reports the rate coefficient for the OH + (CHO)2 reaction measured under pseudo- first-order conditions in OH ([(CHO)2] > 1000 [OH]0). OH radicals were produced using 248 nm pulsed laser photolysis of H2O2 or HNO3 and detected by pulsed laser induced fluorescence. The concentration of glyoxal in the reactor was determined using three independent techniques; gas flow rates as well as in situ UV and IR absorption. The total pressure in the reactor was varied from 40 to 300 Torr (He), and the rate coefficient was found to be independent of pressure over the temperature range studied. The rate coefficient exhibits a negative temperature dependence between 240 and 400 K consistent with the dependence previously observed for many other aldehydes. Our room-temperature rate coefficient is smaller than the relative rate value that is currently recommended for use in atmospheric model calculations. Our measured rate coefficients are discussed with respect to those for other aldehydes. The atmospheric implications of our work will also be discussed.

Dose-Dependent Effects of Statins for Patients with Aneurysmal Subarachnoid Hemorrhage: Meta-Regression Analysis.

Science.gov (United States)

To, Minh-Son; Prakash, Shivesh; Poonnoose, Santosh I; Bihari, Shailesh

2018-05-01

The study uses meta-regression analysis to quantify the dose-dependent effects of statin pharmacotherapy on vasospasm, delayed ischemic neurologic deficits (DIND), and mortality in aneurysmal subarachnoid hemorrhage. Prospective, retrospective observational studies, and randomized controlled trials (RCTs) were retrieved by a systematic database search. Summary estimates were expressed as absolute risk (AR) for a given statin dose or control (placebo). Meta-regression using inverse variance weighting and robust variance estimation was performed to assess the effect of statin dose on transformed AR in a random effects model. Dose-dependence of predicted AR with 95% confidence interval (CI) was recovered by using Miller's Freeman-Tukey inverse. The database search and study selection criteria yielded 18 studies (2594 patients) for analysis. These included 12 RCTs, 4 retrospective observational studies, and 2 prospective observational studies. Twelve studies investigated simvastatin, whereas the remaining studies investigated atorvastatin, pravastatin, or pitavastatin, with simvastatin-equivalent doses ranging from 20 to 80 mg. Meta-regression revealed dose-dependent reductions in Freeman-Tukey-transformed AR of vasospasm (slope coefficient -0.00404, 95% CI -0.00720 to -0.00087; P = 0.0321), DIND (slope coefficient -0.00316, 95% CI -0.00586 to -0.00047; P = 0.0392), and mortality (slope coefficient -0.00345, 95% CI -0.00623 to -0.00067; P = 0.0352). The present meta-regression provides weak evidence for dose-dependent reductions in vasospasm, DIND and mortality associated with acute statin use after aneurysmal subarachnoid hemorrhage. However, the analysis was limited by substantial heterogeneity among individual studies. Greater dosing strategies are a potential consideration for future RCTs. Copyright © 2018 Elsevier Inc. All rights reserved.

Mapping Thermal Expansion Coefficients in Freestanding 2D Materials at the Nanometer Scale

Science.gov (United States)

Hu, Xuan; Yasaei, Poya; Jokisaari, Jacob; Öǧüt, Serdar; Salehi-Khojin, Amin; Klie, Robert F.

2018-02-01

Two-dimensional materials, including graphene, transition metal dichalcogenides and their heterostructures, exhibit great potential for a variety of applications, such as transistors, spintronics, and photovoltaics. While the miniaturization offers remarkable improvements in electrical performance, heat dissipation and thermal mismatch can be a problem in designing electronic devices based on two-dimensional materials. Quantifying the thermal expansion coefficient of 2D materials requires temperature measurements at nanometer scale. Here, we introduce a novel nanometer-scale thermometry approach to measure temperature and quantify the thermal expansion coefficients in 2D materials based on scanning transmission electron microscopy combined with electron energy-loss spectroscopy to determine the energy shift of the plasmon resonance peak of 2D materials as a function of sample temperature. By combining these measurements with first-principles modeling, the thermal expansion coefficients (TECs) of single-layer and freestanding graphene and bulk, as well as monolayer MoS2 , MoSe2 , WS2 , or WSe2 , are directly determined and mapped.

A study on direct determination of uranium in ore by analyzing γ-ray spectrum with dual linear regression

International Nuclear Information System (INIS)

Liu Chunkui

1996-01-01

The method introduced is based on different energy of γ-ray emitted from radionuclide in the uranium-radium decay series in ore. The pulse counting rates of two spectra bands, i.e. N 1 (55∼193 keV) and N 2 (260∼1500 keV), are measured by portable type HYX-3 400-channel γ-ray spectrometer. On the other side, the uranium content (Q U ) is obtained by chemical analysis of channel sampling. Then the regression coefficients (b 0 , b 1 ,b 2 ) can be determined through dual linear regression by using Q U and N 1 , N 2 . The direct determination of uranium can be made with the regression equation Q U = b 0 + b 1 N 1 + b 2 N 2

Weak contrast PP wave displacement R/T coefficients in weakly anisotropic elastic media

Czech Academy of Sciences Publication Activity Database

Pšenčík, Ivan; Vavryčuk, Václav

1998-01-01

Roč. 151, 2/4 (1998), s. 699-718 ISSN 0033-4553. [International workshop on geodynamics of the lithosphere and the Earth's mantle, 08.07.1996-13.07.1996] R&D Projects: GA ČR GA205/96/0968 Grant - others:INCO-Copernicus(XE) IC15 CT96 200 Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 0.612, year: 1998

Pengaruh Bauran Pemasaran Terhadap Keputusan Pembelian Produk Mobil Mazda 2R Pada PT Nusantara Batavia Motor Jakarta Pusat

OpenAIRE

Ratnasari, Desy; Sunardi, HP

2015-01-01

The purpose of this study was to determine the effect of the product, price, place and promotion on purchase decisions. The population is people who buy cars mazda 2R At PT Nusantara Batavia Motor Jakarta. The samples used were 125 people who buy cars mazda 2R using questionnaire techniques. The collection of data through questionnaires. The results of multiple linear regression analysis, using SPSS show that all variable products, price, place and promotion has a significant positive effect ...

The Peltier and Zeebeck coefficients of the Cd-CdI2 melt

International Nuclear Information System (INIS)

Kuzyakin, E.B.; Kuz'minskij, E.V.

1979-01-01

For the CdI 2 -Cd melt with the usage of molybdenum ''inert'' electrodes in the temperature range of 670-850 K and metal cadmium concentration of 0-5 mol % experimentally determined are the Peltier (PI = 0.67+0.07 V at T = 722 K and 0.23 mol %) and Zeebeck (epsilonsub(in) 1.175+-0.107 mV/deg -1 at 0.20 mol % Cd and T = 700-780 K) coefficients. Calculated is heat transfer coefficient from the electrode to the melt (a = 65+-10 W/m 2 K), reaffirmed is applicability of the second Thomson ratio (PI = Txepsilonsub(in)). It is shown that the method of non-stationary temperature waves, suggested for the Peltier coefficient determination can be applied for evaluation of metal solubility values in their molten salts

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

Science.gov (United States)

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

Computing multiple-output regression quantile regions

Czech Academy of Sciences Publication Activity Database

Paindaveine, D.; Šiman, Miroslav

2012-01-01

Roč. 56, č. 4 (2012), s. 840-853 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M06047 Institutional research plan: CEZ:AV0Z10750506 Keywords : halfspace depth * multiple-output regression * parametric linear programming * quantile regression Subject RIV: BA - General Mathematics Impact factor: 1.304, year: 2012 http://library.utia.cas.cz/separaty/2012/SI/siman-0376413.pdf

Heat transfer coefficient for boiling carbon dioxide

DEFF Research Database (Denmark)

Knudsen, Hans Jørgen Høgaard; Jensen, Per Henrik

1998-01-01

Heat transfer coefficient and pressure drop for boiling carbon dioxide (R744) flowing in a horizontal pipe has been measured. The calculated heat transfer coeeficient has been compared with the Chart correlation of Shah. The Chart Correlation predits too low heat transfer coefficient but the ratio...... between the measured and the calculated heat transfer coefficient is nearly constant and equal 1.9. With this factor the correlation predicts the measured data within 14% (RMS). The pressure drop is of the same order as the measuring uncertainty and the pressure drop has not been compared with correlation's....

Effective diffusion coefficients of 3H2O in several porous materials

International Nuclear Information System (INIS)

Terashima, Yutaka; Kumaki, Toru.

1976-01-01

Diffusion coefficients of radionuclides in some porous structural materials and porous components of earth stratum are important as the basis for the safety evaluation of the storage and disposal of radioactive wastes. In our previous works, the method of analysis and experiment using a permeative type diffusion cell for measurement of effective diffusion coefficient was established, and experimental results were reported. In this paper, effective diffusion coefficients of 3 H 2 O in mortar, concrete, brick, clay layer, and sand layer were measured, and characteristics of these pore structure were discussed on the basis of tourtusity factor. (auth.)

Evaluation of a Panel of Single-Nucleotide Polymorphisms in miR-146a and miR-196a2 Genomic Regions in Patients with Chronic Periodontitis.

Science.gov (United States)

Venugopal, Priyanka; Lavu, Vamsi; RangaRao, Suresh; Venkatesan, Vettriselvi

2017-04-01

Periodontitis is an inflammatory disease caused by bacterial triggering of the host immune-inflammatory response, which in turn is regulated by microRNAs (miRNA). Polymorphisms in the miRNA pathways affect the expression of several target genes such as tumor necrosis factor-α and interleukins, which are associated with progression of disease. The objective of this study was to identify the association between the MiR-146a single nucleotide polymorphisms (SNPs) (rs2910164, rs57095329, and rs73318382), the MiR-196a2 (rs11614913) SNP and chronic periodontitis. Genotyping was performed for the MiR-146a (rs2910164, rs57095329, and rs73318382) and the MiR-196a2 (rs11614913) polymorphisms in 180 healthy controls and 190 cases of chronic periodontitis by the direct Sanger sequencing technique. The strength of the association between the polymorphisms and chronic periodontitis was evaluated using logistic regression analysis. Haplotype and linkage analyses among the polymorphisms was performed. Multifactorial dimensionality reduction was performed to determine epistatic interaction among the polymorphisms. The MiR-196a2 polymorphism revealed a significant inverse association with chronic periodontitis. Haplotype analysis of MiR-146a and MiR-196a2 polymorphisms revealed 13 different combinations, of which 5 were found to have an inverse association with chronic periodontitis. The present study has demonstrated a significant inverse association of MiR-196a2 polymorphism with chronic periodontitis.

Crystallization and preliminary X-ray study of a (2R,3R)-2,3-butanediol dehydrogenase from Bacillus coagulans 2-6.

Science.gov (United States)

Miao, Xiangzhi; Huang, Xianhui; Zhang, Guofang; Zhao, Xiufang; Zhu, Xianming; Dong, Hui

2013-10-01

(2R,3R)-2,3-Butanediol dehydrogenase (R,R-BDH) from Bacillus coagulans 2-6 is a zinc-dependent medium-chain alcohol dehydrogenase. Recombinant R,R-BDH with a His6 tag at the C-terminus was expressed in Escherichia coli BL21 (DE3) cells and purified by Ni2+-chelating affinity and size-exclusion chromatography. Crystals were grown by the hanging-drop vapour-diffusion method at 289 K. The crystallization condition consisted of 8%(v/v) Tacsimate pH 4.6, 18%(w/v) polyethylene glycol 3350. The crystal diffracted to 2.8 Å resolution in the orthorhombic space group P2₁2₁2₁, with unit-cell parameters a=88.35, b=128.73, c=131.03 Å.

Multivariate nonparametric regression and visualization with R and applications to finance

CERN Document Server

Klemelä, Jussi

2014-01-01

A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generatingmechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functio

(2S,4R-2-[(1R-1-(4-Bromophenyl-2-nitroethyl]-4-ethylcyclohexanone

Directory of Open Access Journals (Sweden)

Chi-Xiao Zhang

2013-02-01

Full Text Available The crystal structure of the title compound, C16H20BrNO3, contains three chiral centers in the configuration 1R,2S,6R. The cyclohexane ring is in a chair conformation. In the crystal, molecules are linked by weak C—H...O interactions, forming chains along the a-axis direction.

Synthesis of carbasugars from aldonolactones, part III - A study on the allylic substitution of (1R,5R,8R)- and (1R,5R,8S)-8-hydroxy-2-oxabicyclo[3.3.0]oct-6-en-3-one derivatives - Preparation of (1S,2R,3R)-9-[2-hydroxy-3-(2-hydroxyethyl)cyclopent-4-en-1-yl]-9H-adenine

DEFF Research Database (Denmark)

Johansen, Steen Karsk; Lundt, Inge

2001-01-01

The palladium-catalyzed substitution of acylated (1R,5R,8R)- and (1R,SR,8S)-8-hydroxy-2-oxabicyclo[3.3.0] ones has been studied using a number of C- and N-nucleophiles, In all cases, the exo derivatives (8R) were found to be more reactive than the corresponding endo derivatives (8S). The reaction...... with these nucleophiles. Additionally, Mitsunobu substitution of (1R,5R,8R)-8-hydroxy-2-oxabicyclo[3.3.0]oct-B-en-3-one (3) with 6-chloropurine, followed by reduction of the lactone moiety and treatment with Liquid ammonia, gave the carbocyclic nucleoside (1S,2R,3R)-9-[2-hydroxy-3-(2-hydroxyethyl)cyclopent-4-en-1-yl]-9H...

Non-Markovian dynamics of quantum systems: formalism, transport coefficients

International Nuclear Information System (INIS)

Kanokov, Z.; Palchikov, Yu.V.; Antonenko, N.V.; Adamian, G.G.; Kanokov, Z.; Adamian, G.G.; Scheid, W.

2004-01-01

Full text: The generalized Linbland equations with non-stationary transport coefficients are derived from the Langevin equations for the case of nonlinear non-Markovian noise [1]. The equations of motion for the collective coordinates are consistent with the generalized quantum fluctuation dissipation relations. The microscopic justification of the Linbland axiomatic approach is performed. Explicit expressions for the time-dependent transport coefficients are presented for the case of FC- and RWA-oscillators and a general linear coupling in coordinate and in momentum between the collective subsystem and heat bath. The explicit equations for the correlation functions show that the Onsanger's regression hypothesis does not hold exactly for the non-Markovian equations of motion. However, under some conditions the regression of fluctuations goes to zero in the same manner as the average values. In the low and high temperature regimes we found that the dissipation leads to long-time tails in correlation functions in the RWA-oscillator. In the case of the FC-oscillator a non-exponential power-like decay of the correlation function in coordinate is only obtained only at the low temperature limit. The calculated results depend rather weakly on the memory time in many applications. The found transient times for diffusion coefficients D pp (t), D qp (t) and D qq (t) are quite short. The value of classical diffusion coefficients in momentum underestimates the asymptotic value of quantum one D pp (t), but the asymptotic values of classical σ qq c and quantum σ qq second moments are close due to the negativity of quantum mixed diffusion coefficient D qp (t)

1,2-Dichlorohexafluoro-Cyclobutane (1,2-c-C4F6Cl2, R-316c) a Potent Ozone Depleting Substance and Greenhouse Gas: Atmospheric Loss Processes, Lifetimes, and Ozone Depletion and Global Warming Potentials for the (E) and (Z) stereoisomers

Science.gov (United States)

Papadimitriou, Vassileios C.; McGillen, Max R.; Smith, Shona C.; Jubb, Aaron M.; Portmann, Robert W.; Hall, Bradley D.; Fleming, Eric L.; Jackman, Charles H.; Burkholder, James B.

2013-01-01

The atmospheric processing of (E)- and (Z)-1,2-dichlorohexafluorocyclobutane (1,2-c-C4F6Cl2, R-316c) was examined in this work as the ozone depleting (ODP) and global warming (GWP) potentials of this proposed replacement compound are presently unknown. The predominant atmospheric loss processes and infrared absorption spectra of the R-316c isomers were measured to provide a basis to evaluate their atmospheric lifetimes and, thus, ODPs and GWPs. UV absorption spectra were measured between 184.95 to 230 nm at temperatures between 214 and 296 K and a parametrization for use in atmospheric modeling is presented. The Cl atom quantum yield in the 193 nm photolysis of R- 316c was measured to be 1.90 +/- 0.27. Hexafluorocyclobutene (c-C4F6) was determined to be a photolysis co-product with molar yields of 0.7 and 1.0 (+/-10%) for (E)- and (Z)-R-316c, respectively. The 296 K total rate coefficient for the O(1D) + R-316c reaction, i.e., O(1D) loss, was measured to be (1.56 +/- 0.11) × 10(exp -10)cu cm/ molecule/s and the reactive rate coefficient, i.e., R-316c loss, was measured to be (1.36 +/- 0.20) × 10(exp -10)cu cm/molecule/s corresponding to a approx. 88% reactive yield. Rate coefficient upper-limits for the OH and O3 reaction with R-316c were determined to be model to be 74.6 +/- 3 and 114.1 +/-10 years, respectively, where the estimated uncertainties are due solely to the uncertainty in the UV absorption spectra. Stratospheric photolysis is the predominant atmospheric loss process for both isomers with the O(1D) reaction making a minor, approx. 2% for the (E) isomer and 7% for the (Z) isomer, contribution to the total atmospheric loss. Ozone depletion potentials for (E)- and (Z)-R-316c were calculated using the 2-D model to be 0.46 and 0.54, respectively. Infrared absorption spectra for (E)- and (Z)-R-316c were measured at 296 K and used to estimate their radiative efficiencies (REs) and GWPs; 100-year time-horizon GWPs of 4160 and 5400 were obtained for (E)- and (Z)-R

Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

Directory of Open Access Journals (Sweden)

Hailun Wang

2017-01-01

Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

cp-R, an interface the R programming language for clinical laboratory method comparisons.

Science.gov (United States)

Holmes, Daniel T

2015-02-01

Clinical scientists frequently need to compare two different bioanalytical methods as part of assay validation/monitoring. As a matter necessity, regression methods for quantitative comparison in clinical chemistry, hematology and other clinical laboratory disciplines must allow for error in both the x and y variables. Traditionally the methods popularized by 1) Deming and 2) Passing and Bablok have been recommended. While commercial tools exist, no simple open source tool is available. The purpose of this work was to develop and entirely open-source GUI-driven program for bioanalytical method comparisons capable of performing these regression methods and able to produce highly customized graphical output. The GUI is written in python and PyQt4 with R scripts performing regression and graphical functions. The program can be run from source code or as a pre-compiled binary executable. The software performs three forms of regression and offers weighting where applicable. Confidence bands of the regression are calculated using bootstrapping for Deming and Passing Bablok methods. Users can customize regression plots according to the tools available in R and can produced output in any of: jpg, png, tiff, bmp at any desired resolution or ps and pdf vector formats. Bland Altman plots and some regression diagnostic plots are also generated. Correctness of regression parameter estimates was confirmed against existing R packages. The program allows for rapid and highly customizable graphical output capable of conforming to the publication requirements of any clinical chemistry journal. Quick method comparisons can also be performed and cut and paste into spreadsheet or word processing applications. We present a simple and intuitive open source tool for quantitative method comparison in a clinical laboratory environment. Copyright © 2014 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

Science.gov (United States)

Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

2018-03-01

Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error model predicts the exposure of

Lineshape test on overlapped transitions (R9F1, R9F2) of the 2v3 band of 12CH4 by frequency-stabilized cavity ring-down spectroscopy

Science.gov (United States)

Yang, L.; Lin, H.; Plimmer, M. D.; Feng, X. J.; Zhang, J. T.

2018-05-01

The performances of a multi-spectral fit for the spectra of pressure-broadened overlapping lines (R9F1, R9F2) of 12CH4 in binary mixtures with N2 were studied by applying different lineshape models, from the simplest Voigt profile (VP) to the Harmann-Tran profile (HTP). Line-mixing was approximated in the first order in the spectral fits. Data were acquired using a high-resolution cavity ring-down spectrometer of minimum detectable absorption coefficient of 2.8 × 10-12 cm-1. The lines were observed with a signal-to-noise ratio of 19 365 for pressures from 5 to 40 kPa. The study reveals that the multi-spectral fits using the HTP and the speed-dependent Nelkin-Ghatak profile (SDNGP) yield the best among all tested. The two models gave the maximum relative residuals of less than 0.065 %. All things considered, the HTP and the SDNGP appear to be the most reliable models for treating the present case of multi-spectral fitting of unresolved dual-component spectra.

Association of Human Development Index with global bladder, kidney, prostate and testis cancer incidence and mortality.

Science.gov (United States)

Greiman, Alyssa K; Rosoff, James S; Prasad, Sandip M

2017-12-01

To describe contemporary worldwide age-standardized incidence and mortality rates for bladder, kidney, prostate and testis cancer and their association with development. We obtained gender-specific, age-standardized incidence and mortality rates for 184 countries and 16 major world regions from the GLOBOCAN 2012 database. We compared the mortality-to-incidence ratios (MIRs) at national and regional levels in males and females, and assessed the association with socio-economic development using the 2014 United Nations Human Development Index (HDI). Age-standardized incidence rates were 2.9 (bladder) to 7.4 (testis) times higher for genitourinary malignancies in more developed countries compared with less developed countries. Age-standardized mortality rates were 1.5-2.2 times higher in more vs less developed countries for prostate, bladder and kidney cancer, with no variation in mortality rates observed in testis cancer. There was a strong inverse relationship between HDI and MIR in testis (regression coefficient 1.65, R 2 = 0.78), prostate (regression coefficient -1.56, R 2 = 0.85), kidney (regression coefficient -1.34, R 2 = 0.74), and bladder cancer (regression coefficient -1.01, R 2 = 0.80). While incidence and mortality rates for genitourinary cancers vary widely throughout the world, the MIR is highest in less developed countries for all four major genitourinary malignancies. Further research is needed to understand whether differences in comorbidities, exposures, time to diagnosis, access to healthcare, diagnostic techniques or treatment options explain the observed inequalities in genitourinary cancer outcomes. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.

Bayesian median regression for temporal gene expression data

Science.gov (United States)

Yu, Keming; Vinciotti, Veronica; Liu, Xiaohui; 't Hoen, Peter A. C.

2007-09-01

Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression. In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC-based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance. The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T2-test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data.

An eight-legged tactile sensor to estimate coefficient of static friction.

Science.gov (United States)

Wei Chen; Rodpongpun, Sura; Luo, William; Isaacson, Nathan; Kark, Lauren; Khamis, Heba; Redmond, Stephen J

2015-08-01

It is well known that a tangential force larger than the maximum static friction force is required to initiate the sliding motion between two objects, which is governed by a material constant called the coefficient of static friction. Therefore, knowing the coefficient of static friction is of great importance for robot grippers which wish to maintain a stable and precise grip on an object during various manipulation tasks. Importantly, it is most useful if grippers can estimate the coefficient of static friction without having to explicitly explore the object first, such as lifting the object and reducing the grip force until it slips. A novel eight-legged sensor, based on simplified theoretical principles of friction is presented here to estimate the coefficient of static friction between a planar surface and the prototype sensor. Each of the sensor's eight legs are straight and rigid, and oriented at a specified angle with respect to the vertical, allowing it to estimate one of five ranges (5 = 8/2 + 1) that the coefficient of static friction can occupy. The coefficient of friction can be estimated by determining whether the legs have slipped or not when pressed against a surface. The coefficients of static friction between the sensor and five different materials were estimated and compared to a measurement from traditional methods. A least-squares linear fit of the sensor estimated coefficient showed good correlation with the reference coefficient with a gradient close to one and an r(2) value greater than 0.9.

The Bland-Altman Method Should Not Be Used in Regression Cross-Validation Studies

Science.gov (United States)

O'Connor, Daniel P.; Mahar, Matthew T.; Laughlin, Mitzi S.; Jackson, Andrew S.

2011-01-01

The purpose of this study was to demonstrate the bias in the Bland-Altman (BA) limits of agreement method when it is used to validate regression models. Data from 1,158 men were used to develop three regression equations to estimate maximum oxygen uptake (R[superscript 2] = 0.40, 0.61, and 0.82, respectively). The equations were evaluated in a…

determination of reaeration coefficient k2 for polluted stream as a ...

African Journals Online (AJOL)

include velocity of flow, average depth of flow, DO, self purification ratio (f) and reaeration constant (K2). ... Determination of Reaeration Reaeration Coefficient K2 for Polluted Stream. 175. Table 1: ... and theory on which it is based. The result ...

Linear regression

CERN Document Server

Olive, David J

2017-01-01

This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...

Space-dependent perfusion coefficient estimation in a 2D bioheat transfer problem

Science.gov (United States)

Bazán, Fermín S. V.; Bedin, Luciano; Borges, Leonardo S.

2017-05-01

In this work, a method for estimating the space-dependent perfusion coefficient parameter in a 2D bioheat transfer model is presented. In the method, the bioheat transfer model is transformed into a time-dependent semidiscrete system of ordinary differential equations involving perfusion coefficient values as parameters, and the estimation problem is solved through a nonlinear least squares technique. In particular, the bioheat problem is solved by the method of lines based on a highly accurate pseudospectral approach, and perfusion coefficient values are estimated by the regularized Gauss-Newton method coupled with a proper regularization parameter. The performance of the method on several test problems is illustrated numerically.

Evaluation of role 2 (R2) medical resources in the Afghanistan combat theater: Initial review of the joint trauma system R2 registry.

Science.gov (United States)

Mann-Salinas, Elizabeth A; Le, Tuan D; Shackelford, Stacy A; Bailey, Jeffrey A; Stockinger, Zsolt T; Spott, Mary Ann; Wirt, Michael D; Rickard, Rory; Lane, Ian B; Hodgetts, Timothy; Cardin, Sylvain; Remick, Kyle N; Gross, Kirby R

2016-11-01

A Role 2 registry (R2R) was developed in 2008 by the US Joint Trauma System (JTS). The purpose of this project was to undertake a preliminary review of the R2R to understand combat trauma epidemiology and related interventions at these facilities to guide training and optimal use of forward surgical capability in the future. A retrospective review of available JTS R2R records; the registry is a convenience sample entered voluntarily by members of the R2 units. Patients were classified according to basic demographics, affiliation, region where treatment was provided, mechanism of injury, type of injury, time and method of transport from point of injury (POI) to R2 facility, interventions at R2, and survival. Analysis included trauma patients aged ≥18 years or older wounded in year 2008 to 2014, and treated in Afghanistan. A total of 15,404 patients wounded and treated in R2 were included in the R2R from February 2008 to September 2014; 12,849 patients met inclusion criteria. The predominant patient affiliations included US Forces, 4,676 (36.4%); Afghan Forces, 4,549 (35.4%); and Afghan civilians, 2,178 (17.0%). Overall, battle injuries predominated (9,792 [76.2%]). Type of injury included penetrating, 7,665 (59.7%); blunt, 4,026 (31.3%); and other, 633 (4.9%). Primary mechanism of injury included explosion, 5,320 (41.4%); gunshot wounds, 3,082 (24.0%); and crash, 1,209 (9.4%). Of 12,849 patients who arrived at R2, 167 (1.3%) were dead; of 12,682 patients who were alive upon arrival, 342 (2.7%) died at R2. This evaluation of the R2R describes the patient profiles of and common injuries treated in a sample of R2 facilities in Afghanistan. Ongoing and detailed analysis of R2R information may provide evidence-based guidance to military planners and medical leaders to best prepare teams and allocate R2 resources in future operations. Given the limitations of the data set, conclusions must be interpreted in context of other available data and analyses, not in isolation

Decreased placental and maternal serum TRAIL-R2 levels are associated with placenta accreta.