Varying coefficient subdistribution regression for left-truncated semi-competing risks data.

Science.gov (United States)

Li, Ruosha; Peng, Limin

2014-10-01

Semi-competing risks data frequently arise in biomedical studies when time to a disease landmark event is subject to dependent censoring by death, the observation of which however is not precluded by the occurrence of the landmark event. In observational studies, the analysis of such data can be further complicated by left truncation. In this work, we study a varying co-efficient subdistribution regression model for left-truncated semi-competing risks data. Our method appropriately accounts for the specifical truncation and censoring features of the data, and moreover has the flexibility to accommodate potentially varying covariate effects. The proposed method can be easily implemented and the resulting estimators are shown to have nice asymptotic properties. We also present inference, such as Kolmogorov-Smirnov type and Cramér Von-Mises type hypothesis testing procedures for the covariate effects. Simulation studies and an application to the Denmark diabetes registry demonstrate good finite-sample performance and practical utility of the proposed method.

Zero and low coefficient of thermal expansion polycrystalline oxides

International Nuclear Information System (INIS)

Skaggs, S.R.

1977-09-01

Polycrystalline oxide systems with zero to low coefficient of thermal expansion (CTE) investigated by the author include hafnia-titania and hafnia. The CTE for 30 to 40 mol% TiO 2 in HfO 2 is less than or equal to 1 x 10 -6 / 0 C, while for other compositions in the range 25 to 60 mol% it is approximately 4 x 10 -6 / 0 C. An investigation of the CTE of 99.999% HfO 2 yielded a value of 4.6 x 10 -6 / 0 C from room temperature to 1000 0 C. Correlation with data on HfO 2 by other investigators shows a definite relationship between the CTE and the amount of ZrO 2 present. Data are listed for comparison of the CTE of several other polycrystalline oxides investigated by Holcombe at Oak Ridge

Zero and low coefficient of thermal expansion polycrystalline oxides

International Nuclear Information System (INIS)

Skaggs, S.R.

1977-01-01

Polycrystalline oxide systems with zero to low coefficient of thermal expansion (CTE) investigated by the author include hafnia-titania and hafnia. The CTE for 30 to 40 mol percent TiO 2 in HfO 2 is less than or equal to 1 x 10 -6 / 0 C, while for other compositions in the range 25 to 60 mol percent approximately 4 x 10 -6 / 0 C. An investigation of the CTE of 99.999 percent HfO 2 yielded a value of 4.6 x 10 -6 / 0 C from room temperature to 1000 0 C. Correlation with data on HfO 2 by other investigators shows a definite relationship between the CTE and the amount of ZrO 2 present. Data are listed for comparison of the CTE of several other polycrystalline oxides investigated by Holcombe at Oak Ridge

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.

The temperature coefficient of the resonance integral for uranium metal and oxide

Energy Technology Data Exchange (ETDEWEB)

Blomberg, P; Hellstrand, E; Homer, S

1960-06-15

The temperature coefficient of the resonance integral in uranium metal and oxide has been measured over a wide temperature range for rods with three different diameters. The results for metal agree with most earlier results from activation measurements but differ as much as a factor of two from results obtained with reactivity methods. For oxide only one measurement has been reported recently. Our value is considerably lower than the result of that measurement. The experiments will continue in order to find the reason for the large discrepancy mentioned above.

The temperature coefficient of the resonance integral for uranium metal and oxide

International Nuclear Information System (INIS)

Blomberg, P.; Hellstrand, E.; Homer, S.

1960-06-01

The temperature coefficient of the resonance integral in uranium metal and oxide has been measured over a wide temperature range for rods with three different diameters. The results for metal agree with most earlier results from activation measurements but differ as much as a factor of two from results obtained with reactivity methods. For oxide only one measurement has been reported recently. Our value is considerably lower than the result of that measurement. The experiments will continue in order to find the reason for the large discrepancy mentioned above

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…

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.

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.

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...

Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression

Science.gov (United States)

Dougherty, Andrew W.

Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide's surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons. In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors' response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays. The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data. The reciprocal kernel is shown to be effective in modeling the sensor

Significance testing in ridge regression for genetic data

Directory of Open Access Journals (Sweden)

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.

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.

Temperature Dependence of the Seebeck Coefficient in Zinc Oxide Thin Films

Science.gov (United States)

Noori, Amirreza; Masoumi, Saeed; Hashemi, Najmeh

2017-12-01

Thermoelectric devices are reliable tools for converting waste heat into electricity as they last long, produce no noise or vibration, have no moving elements, and their light weight makes them suitable for the outer space usage. Materials with high thermoelectric figure of merit (zT) have the most important role in the fabrication of efficient thermoelectric devices. Metal oxide semiconductors, specially zinc oxide has recently received attention as a material suitable for sensor, optoelectronic and thermoelectric device applications because of their wide direct bandgap, chemical stability, high-energy radiation endurance, transparency and acceptable zT. Understanding the thermoelectric properties of the undoped ZnO thin films can help design better ZnO-based devices. Here, we report the results of our experimental work on the thermoelectric properties of the undoped polycrystalline ZnO thin films. These films are deposited on alumina substrates by thermal evaporation of zinc in vacuum followed by a controlled oxidation process in air carried out at the 350-500 °C temperature range. The experimental setup including gradient heaters, thermometry system and Seebeck voltage measurement equipment for high resistance samples is described. Seebeck voltage and electrical resistivity of the samples are measured at different conditions. The observed temperature dependence of the Seebeck coefficient is discussed.

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.

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.

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.

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.

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.

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.

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.

[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.

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,

The number of subjects per variable required in linear regression analyses.

Science.gov (United States)

Austin, Peter C; Steyerberg, Ewout W

2015-06-01

To determine the number of independent variables that can be included in a linear regression model. We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R(2) of the fitted model. A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R(2), although adjusted R(2) estimates behaved well. The bias in estimating the model R(2) statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Experimental determination of the partitioning coefficient of β-pinene oxidation products in SOAs.

Science.gov (United States)

Hohaus, Thorsten; Gensch, Iulia; Kimmel, Joel; Worsnop, Douglas R; Kiendler-Scharr, Astrid

2015-06-14

The composition of secondary organic aerosols (SOAs) formed by β-pinene ozonolysis was experimentally investigated in the Juelich aerosol chamber. Partitioning of oxidation products between gas and particles was measured through concurrent concentration measurements in both phases. Partitioning coefficients (Kp) of 2.23 × 10(-5) ± 3.20 × 10(-6) m(3) μg(-1) for nopinone, 4.86 × 10(-4) ± 1.80 × 10(-4) m(3) μg(-1) for apoverbenone, 6.84 × 10(-4) ± 1.52 × 10(-4) m(3) μg(-1) for oxonopinone and 2.00 × 10(-3) ± 1.13 × 10(-3) m(3) μg(-1) for hydroxynopinone were derived, showing higher values for more oxygenated species. The observed Kp values were compared with values predicted using two different semi-empirical approaches. Both methods led to an underestimation of the partitioning coefficients with systematic differences between the methods. Assuming that the deviation between the experiment and the model is due to non-ideality of the mixed solution in particles, activity coefficients of 4.82 × 10(-2) for nopinone, 2.17 × 10(-3) for apoverbenone, 3.09 × 10(-1) for oxonopinone and 7.74 × 10(-1) for hydroxynopinone would result using the vapour pressure estimation technique that leads to higher Kp. We discuss that such large non-ideality for nopinone could arise due to particle phase processes lowering the effective nopinone vapour pressure such as diol- or dimer formation. The observed high partitioning coefficients compared to modelled results imply an underestimation of SOA mass by applying equilibrium conditions.

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.

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.

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.

The Use of Alternative Regression Methods in Social Sciences and the Comparison of Least Squares and M Estimation Methods in Terms of the Determination of Coefficient

Science.gov (United States)

Coskuntuncel, Orkun

2013-01-01

The purpose of this study is two-fold; the first aim being to show the effect of outliers on the widely used least squares regression estimator in social sciences. The second aim is to compare the classical method of least squares with the robust M-estimator using the "determination of coefficient" (R[superscript 2]). For this purpose,…

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.

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.

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

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...

Determination of the apparent transfer coefficient for CO oxidation on Pt(poly), Pt(111), Pt(665) and Pt(332) using a potential modulation technique.

Science.gov (United States)

Wang, Han-Chun; Ernst, Siegfried; Baltruschat, Helmut

2010-03-07

The apparent transfer coefficient, which gives the magnitude of the potential dependence of the electrochemical reaction rates, is the key quantity for the elucidation of electrochemical reaction mechanisms. We introduce the application of an ac method to determine the apparent transfer coefficient alpha' for the oxidation of pre-adsorbed CO at polycrystalline and single-crystalline Pt electrodes in sulfuric acid. The method allows to record alpha' quasi continuously as a function of potential (and time) in cyclic voltammetry or at a fixed potential, with the reaction rate varying with time. At all surfaces (Pt(poly), Pt(111), Pt(665), and Pt(332)) we clearly observed a transition of the apparent transfer coefficient from values around 1.5 at low potentials to values around 0.5 at higher potentials. Changes of the apparent transfer coefficients for the CO oxidation with potential were observed previously, but only from around 0.7 to values as low as 0.2. In contrast, our experimental findings completely agree with the simulation by Koper et al., J. Chem. Phys., 1998, 109, 6051-6062. They can be understood in the framework of a Langmuir-Hinshelwood mechanism. The transition occurs when the sum of the rate constants for the forward reaction (first step: potential dependent OH adsorption, second step: potential dependent oxidation of CO(ad) with OH(ad)) exceeds the rate constant for the back-reaction of the first step. We expect that the ac method for the determination of the apparent transfer coefficient, which we used here, will be of great help also in many other cases, especially under steady conditions, where the major limitations of the method are avoided.

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

Directory of Open Access Journals (Sweden)

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.

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...

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.

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.

High-resolution Fourier transform measurements of air-induced broadening and shift coefficients in the 0002-0000 main isotopologue band of nitrous oxide

Science.gov (United States)

Werwein, Viktor; Li, Gang; Serdyukov, Anton; Brunzendorf, Jens; Werhahn, Olav; Ebert, Volker

2018-06-01

In the present study, we report highly accurate air-induced broadening and shift coefficients for the nitrous oxide (N2O) 0002-0000 band at 2.26 μm of the main isotopologue retrieved from high-resolution Fourier transform infrared (FTIR) measurements with metrologically determined pressure, temperature, absorption path length and chemical composition. Most of our retrieved air-broadening coefficients agree with previously generated datasets within the expanded (confidence interval of 95%) uncertainties. For the air-shift coefficients our results suggest a different rotational dependence compared to literature. The present study benefits from improved measurement conditions and a detailed metrological uncertainty description. Comparing to literature, the uncertainties of the previous broadening and shift coefficients are improved by a factor of up to 39 and up to 22, respectively.

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.

An R package to compute commonality coefficients in the multiple regression case: an introduction to the package and a practical example.

Science.gov (United States)

Nimon, Kim; Lewis, Mitzi; Kane, Richard; Haynes, R Michael

2008-05-01

Multiple regression is a widely used technique for data analysis in social and behavioral research. The complexity of interpreting such results increases when correlated predictor variables are involved. Commonality analysis provides a method of determining the variance accounted for by respective predictor variables and is especially useful in the presence of correlated predictors. However, computing commonality coefficients is laborious. To make commonality analysis accessible to more researchers, a program was developed to automate the calculation of unique and common elements in commonality analysis, using the statistical package R. The program is described, and a heuristic example using data from the Holzinger and Swineford (1939) study, readily available in the MBESS R package, is presented.

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.

Multivariate regression models for the simultaneous quantitative analysis of calcium and magnesium carbonates and magnesium oxide through drifts data

Directory of Open Access Journals (Sweden)

Marder Luciano

2006-01-01

Full Text Available In the present work multivariate regression models were developed for the quantitative analysis of ternary systems using Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS to determine the concentration in weight of calcium carbonate, magnesium carbonate and magnesium oxide. Nineteen spectra of standard samples previously defined in ternary diagram by mixture design were prepared and mid-infrared diffuse reflectance spectra were recorded. The partial least squares (PLS regression method was applied to the model. The spectra set was preprocessed by either mean-centered and variance-scaled (model 2 or mean-centered only (model 1. The results based on the prediction performance of the external validation set expressed by RMSEP (root mean square error of prediction demonstrated that it is possible to develop good models to simultaneously determine calcium carbonate, magnesium carbonate and magnesium oxide content in powdered samples that can be used in the study of the thermal decomposition of dolomite rocks.

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.

Size of oxide vacancies in fluorite and perovskite structured oxides

DEFF Research Database (Denmark)

Chatzichristodoulou, Christodoulos; Norby, Poul; Hendriksen, Peter Vang

2015-01-01

An analysis of the effective radii of vacancies and the stoichiometric expansion coefficient is performed on metal oxides with fluorite and perovskite structures. Using the hard sphere model with Shannon ion radii we find that the effective radius of the oxide vacancy in fluorites increases...... with increasing ion radius of the host cation and that it is significantly smaller than the radius of the oxide ion in all cases, from 37% smaller for HfO2 to 13 % smaller for ThO2. The perovskite structured LaGaO3 doped with Sr or Mg or both is analyzed in some detail. The results show that the effective radius...... of an oxide vacancy in doped LaGaO3 is only about 6 % smaller than the oxide ion. In spite of this the stoichiometric expansion coefficient (a kind of chemical expansion coefficient) of the similar perovskite, LaCrO3, is significantly smaller than the stoichiometric expansion coefficient of the fluorite...

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

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.

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)

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.

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.

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.

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...

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 ...

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...

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.

Modeling and Predicting the Electrical Conductivity of Composite Cathode for Solid Oxide Fuel Cell by Using Support Vector Regression

Science.gov (United States)

Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.

2012-07-01

The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba0.5Sr0.5Co0.8Fe0.2 O3-δ-xSm0.5Sr0.5CoO3-δ (BSCF-xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF-xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO-SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.

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.

Estimating temperature reactivity coefficients by experimental procedures combined with isothermal temperature coefficient measurements and dynamic identification

International Nuclear Information System (INIS)

Tsuji, Masashi; Aoki, Yukinori; Shimazu, Yoichiro; Yamasaki, Masatoshi; Hanayama, Yasushi

2006-01-01

A method to evaluate the moderator coefficient (MTC) and the Doppler coefficient through experimental procedures performed during reactor physics tests of PWR power plants is proposed. This method combines isothermal temperature coefficient (ITC) measurement experiments and reactor power transient experiments at low power conditions for dynamic identification. In the dynamic identification, either one of temperature coefficients can be determined in such a way that frequency response characteristics of the reactivity change observed by a digital reactivity meter is reproduced from measured data of neutron count rate and the average coolant temperature. The other unknown coefficient can also be determined by subtracting the coefficient obtained from the dynamic identification from ITC. As the proposed method can directly estimate the Doppler coefficient, the applicability of the conventional core design codes to predict the Doppler coefficient can be verified for new types of fuels such as mixed oxide fuels. The digital simulation study was carried out to show the feasibility of the proposed method. The numerical analysis showed that the MTC and the Doppler coefficient can be estimated accurately and even if there are uncertainties in the parameters of the reactor kinetics model, the accuracies of the estimated values are not seriously impaired. (author)

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.)

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

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...

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 ...

Assessment of oxygen diffusion coefficients by studying high-temperature oxidation behaviour of Zr1Nb fuel cladding in the temperature range of 1100–1300 °C

Energy Technology Data Exchange (ETDEWEB)

Négyesi, M., E-mail: negy@seznam.cz [Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Trojanova 13, 120 00 Praha 2 (Czech Republic); UJP PRAHA a.s., Nad Kamínkou 1345, 156 10 Praha – Zbraslav (Czech Republic); Chmela, T. [UJP PRAHA a.s., Nad Kamínkou 1345, 156 10 Praha – Zbraslav (Czech Republic); Veselský, T. [Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Trojanova 13, 120 00 Praha 2 (Czech Republic); Krejčí, J. [Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Trojanova 13, 120 00 Praha 2 (Czech Republic); CHEMCOMEX Praha a.s., Elišky Přemyslovny 379, 156 10 Praha – Zbraslav (Czech Republic); Novotný, L.; Přibyl, A. [UJP PRAHA a.s., Nad Kamínkou 1345, 156 10 Praha – Zbraslav (Czech Republic); Bláhová, O. [New Technologies Research Centre, University of West Bohemia, Univerzitní 8, 306 14 Plzeň (Czech Republic); Burda, J. [NRI Rez plc, Husinec-Řež 130, 250 68 Řež (Czech Republic); Siegl, J. [Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Trojanova 13, 120 00 Praha 2 (Czech Republic); Vrtílková, V. [UJP PRAHA a.s., Nad Kamínkou 1345, 156 10 Praha – Zbraslav (Czech Republic)

2015-01-15

The paper deals with high-temperature steam oxidation behaviour of Zr1Nb fuel cladding. First of all, comprehensive experimental program was conducted to provide sufficient experimental data, such as the thicknesses of evolved phase layers and the overall weight gain kinetics, as well as the oxygen concentration and nanohardness values at phase boundaries. Afterwards, oxygen diffusion coefficients in the oxide, in the α-Zr(O) layer, in the double-phase (α + β)-Zr region, and in the β-phase region have been estimated based on the experimental data employing analytical solution of the multiphase moving boundary problem, assuming the equilibrium conditions being fulfilled at the interface boundaries. Eventually, the determined oxygen diffusion coefficients served as input into the in-house numerical code, which was designed to predict the high-temperature oxidation behaviour of Zr1Nb fuel cladding. Very good agreement has been achieved between the numerical calculations and the experimental data.

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

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

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

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.

Transient simulation of regression rate on thrust regulation process in hybrid rocket motor

Directory of Open Access Journals (Sweden)

Tian Hui

2014-12-01

Full Text Available The main goal of this paper is to study the characteristics of regression rate of solid grain during thrust regulation process. For this purpose, an unsteady numerical model of regression rate is established. Gas–solid coupling is considered between the solid grain surface and combustion gas. Dynamic mesh is used to simulate the regression process of the solid fuel surface. Based on this model, numerical simulations on a H2O2/HTPB (hydroxyl-terminated polybutadiene hybrid motor have been performed in the flow control process. The simulation results show that under the step change of the oxidizer mass flow rate condition, the regression rate cannot reach a stable value instantly because the flow field requires a short time period to adjust. The regression rate increases with the linear gain of oxidizer mass flow rate, and has a higher slope than the relative inlet function of oxidizer flow rate. A shorter regulation time can cause a higher regression rate during regulation process. The results also show that transient calculation can better simulate the instantaneous regression rate in the operation process.

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.

Doppler coefficient measurements in Zebra Core 5

International Nuclear Information System (INIS)

Baker, A.R.; Wheeler, R.C.

1965-11-01

Measurements using a central hot loop in Zebra Core 5 are described. Results are given for the Doppler coefficients found in a number of assemblies with PuO 2 and 16% PuO 2 /84% depleted UO 2 pins, loaded with different combinations of steel, sodium or void pins. The mixed oxide results are in general about 20% more negative than was calculated using the FD2 data set, but agreement is good if the plutonium contributions in the calculations are omitted. The small positive Doppler coefficient calculated for Pu239 was not observed, and two measurements indicated instead a small negative effect. The Doppler effect in the mixed oxide systems was found to vary approximately as 1/T. The results from the empty loop and non-fissile assemblies indicate either a small negative Doppler effect in steel or alternatively the presence of an unexplained expansion effect. (author)

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

Oxidation of zirconium alloys in steam: influence of tetragonal zirconia on oxide growth mechanism

International Nuclear Information System (INIS)

Godlewski, J.

1990-07-01

The oxidation of zirconium alloys in presence of steam, presents after a 'parabolic' growth law, an acceleration of the oxidation velocity. This phenomenon limits the use of zirconium alloys as nuclear fuel cladding element. In order to determine the physico-chemical process leading to this kinetic transition, two approaches have been carried out: the first one has consisted to determine the composition of the oxide layer and its evolution with the oxidation time; and the second one to determine the oxygen diffusion coefficients in the oxide layers of pre- and post-transition as well as their evolution with the oxidation time. The composition of the oxide layers has been determined by two analyses techniques: the X-ray diffraction and the laser Raman spectroscopy. This last method has allowed to confirm the presence of tetragonal zirconium oxide in the oxide layers. Analyses carried out by laser Raman spectroscopy on oxides oblique cuttings have revealed that the tetragonal zirconium oxide is transformed in monoclinic phase during the kinetic transition. A quantitative approach has allowed to corroborate the results obtained by these two techniques. In order to determine the oxygen diffusion coefficients in the oxides layers, two diffusion treatments have been carried out: 1)under low pressure with D 2 18 O 2 ) under high pressure in an autoclave with H 2 18 O. The oxygen 18 concentration profiles have been obtained by two analyses techniques: the nuclear microprobe and the secondary ions emission spectroscopy. The obtained profiles show that the mass transport is made by the volume and particularly by the grain boundaries. The corresponding diffusion coefficients have been calculated with the WHIPPLE and LE CLAIRE solution. The presence of tetragonal zirconium oxide, its relation with the kinetic transition, and the evolution of the diffusion coefficients with the oxidation time, are discussed in terms of internal stresses in the oxide layer and of the oxide layer

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)

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.

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.

Measurement of integrated coefficients of ultracold neutron reflection from solid surfaces

International Nuclear Information System (INIS)

Golikov, V.V.; Kulagin, E.N.; Nikitenko, Yu.V.

1985-01-01

The method of measurement of the integrated coefficients of ultracold neutrons (UCN) reflection from solid surfaces is reported. A simple formula is suggested which expresses the integrated coefficients of UCN reflection from a given sample through the measured counting rate of the detector with and without strong absorber (polyethelene). The parameters are determined describing anisotropic and inhomogeneity properties of UCN reflection from Al, Mg, Pb, Zn, Mo, stainless steel, T and V are measured. The thickness of oxide layers is determined within the 5-10A accuracy limits from the experimental coefficients of UCN reflection from metals having on their surfaces the oxides with boundary velocity larger than that for the metal. It has been determined that the density of 5000 A layer of heavy ice freezed on aluminium is 0.83 +- 0.05 from the crystal ice density

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.

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)

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.

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.

A SOCIOLOGICAL ANALYSIS OF THE CHILDBEARING COEFFICIENT IN THE ALTAI REGION BASED ON METHOD OF FUZZY LINEAR REGRESSION

Directory of Open Access Journals (Sweden)

Sergei Vladimirovich Varaksin

2017-06-01

Full Text Available Purpose. Construction of a mathematical model of the dynamics of childbearing change in the Altai region in 2000–2016, analysis of the dynamics of changes in birth rates for multiple age categories of women of childbearing age. Methodology. A auxiliary analysis element is the construction of linear mathematical models of the dynamics of childbearing by using fuzzy linear regression method based on fuzzy numbers. Fuzzy linear regression is considered as an alternative to standard statistical linear regression for short time series and unknown distribution law. The parameters of fuzzy linear and standard statistical regressions for childbearing time series were defined with using the built in language MatLab algorithm. Method of fuzzy linear regression is not used in sociological researches yet. Results. There are made the conclusions about the socio-demographic changes in society, the high efficiency of the demographic policy of the leadership of the region and the country, and the applicability of the method of fuzzy linear regression for sociological analysis.

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)

A new correlation for two-phase critical discharge coefficient

International Nuclear Information System (INIS)

Park, Jong Woon; Chun, Moon Hyun

1989-01-01

A new simple correlation for subcooled and two-phase critical flow discharge coefficient has been developed by stepwise regression technique. The new discharge coefficient has three independent variables and they are length to hydraulic diameter ratio, degree of subcooling, and stagnation temperature. The new discharge coefficient is applied as a multiplier to homogeneous equilibrium model and Abauf's single phase critical mass flux calculation equation. This method has been tested for its accuracy by comparing with experimental data. Results of the comparison show that the agreement between the predictions with new correlation and the experimental data is good for pipes and nozzles with vertical upward flow for subcooled upstream condition and nozzles with horizontal configuration for two-phase upstream condition

Zirconium metal-water oxidation kinetics. III. Oxygen diffusion in oxide and alpha Zircaloy phases

International Nuclear Information System (INIS)

Pawel, R.E.

1976-10-01

The reaction of Zircaloy in steam at elevated temperature involves the growth of discrete layers of oxide and oxygen-rich alpha Zircaloy from the parent beta phase. The multiphase, moving boundary diffusion problem involved is encountered in a number of important reaction schemes in addition to that of Zircaloy-oxygen and can be completely (albeitly ideally) characterized through an appropriate model in terms of oxygen diffusion coefficients and equilibrium concentrations for the various phases. Conversely, kinetic data for phase growth and total oxygen consumption rates can be used to compute diffusion coefficients. Equations are developed that express the oxygen diffusion coefficients in the oxide and alpha phases in terms of the reaction rate constants and equilibrium solubility values. These equations were applied to recent experimental kinetic data on the steam oxidation of Zircaloy-4 to determine the effective oxygen diffusion coefficients in these phases over the temperature range 1000--1500 0 C

Modeling Group Differences in OLS and Orthogonal Regression: Implications for Differential Validity Studies

Science.gov (United States)

Kane, Michael T.; Mroch, Andrew A.

2010-01-01

In evaluating the relationship between two measures across different groups (i.e., in evaluating "differential validity") it is necessary to examine differences in correlation coefficients and in regression lines. Ordinary least squares (OLS) regression is the standard method for fitting lines to data, but its criterion for optimal fit…

Analysis of quantile regression as alternative to ordinary least squares

OpenAIRE

Ibrahim Abdullahi; Abubakar Yahaya

2015-01-01

In this article, an alternative to ordinary least squares (OLS) regression based on analytical solution in the Statgraphics software is considered, and this alternative is no other than quantile regression (QR) model. We also present goodness of fit statistic as well as approximate distributions of the associated test statistics for the parameters. Furthermore, we suggest a goodness of fit statistic called the least absolute deviation (LAD) coefficient of determination. The procedure is well ...

Research and analyze of physical health using multiple regression analysis

Directory of Open Access Journals (Sweden)

T. S. Kyi

2014-01-01

Full Text Available This paper represents the research which is trying to create a mathematical model of the "healthy people" using the method of regression analysis. The factors are the physical parameters of the person (such as heart rate, lung capacity, blood pressure, breath holding, weight height coefficient, flexibility of the spine, muscles of the shoulder belt, abdominal muscles, squatting, etc.., and the response variable is an indicator of physical working capacity. After performing multiple regression analysis, obtained useful multiple regression models that can predict the physical performance of boys the aged of fourteen to seventeen years. This paper represents the development of regression model for the sixteen year old boys and analyzed results.

Easy methods for extracting individual regression slopes: Comparing SPSS, R, and Excel

Directory of Open Access Journals (Sweden)

Roland Pfister

2013-10-01

Full Text Available Three different methods for extracting coefficientsof linear regression analyses are presented. The focus is on automatic and easy-to-use approaches for common statistical packages: SPSS, R, and MS Excel / LibreOffice Calc. Hands-on examples are included for each analysis, followed by a brief description of how a subsequent regression coefficient analysis is performed.

A Predictive Logistic Regression Model of World Conflict Using Open Source Data

Science.gov (United States)

2015-03-26

No correlation between the error terms and the independent variables 9. Absence of perfect multicollinearity (Menard, 2001) When assumptions are...some of the variables before initial model building. Multicollinearity , or near-linear dependence among the variables will cause problems in the...model. High multicollinearity tends to produce unreasonably high logistic regression coefficients and can result in coefficients that are not

Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

Science.gov (United States)

Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.

2018-03-01

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

Science.gov (United States)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

Varying coefficients model with measurement error.

Science.gov (United States)

Li, Liang; Greene, Tom

2008-06-01

We propose a semiparametric partially varying coefficient model to study the relationship between serum creatinine concentration and the glomerular filtration rate (GFR) among kidney donors and patients with chronic kidney disease. A regression model is used to relate serum creatinine to GFR and demographic factors in which coefficient of GFR is expressed as a function of age to allow its effect to be age dependent. GFR measurements obtained from the clearance of a radioactively labeled isotope are assumed to be a surrogate for the true GFR, with the relationship between measured and true GFR expressed using an additive error model. We use locally corrected score equations to estimate parameters and coefficient functions, and propose an expected generalized cross-validation (EGCV) method to select the kernel bandwidth. The performance of the proposed methods, which avoid distributional assumptions on the true GFR and residuals, is investigated by simulation. Accounting for measurement error using the proposed model reduced apparent inconsistencies in the relationship between serum creatinine and GFR among different clinical data sets derived from kidney donor and chronic kidney disease source populations.

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.

Photon mass attenuation coefficients, effective atomic numbers and ...

Indian Academy of Sciences (India)

of atomic number Z was performed using the logarithmic regression analysis of the data measured by the authors and reported earlier. The best-fit coefficients so obtained in the photon ..... This photon build-up is a function of thickness and atomic number of the sample and also the incident photon energy, which combine to ...

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).

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.

Direction of Effects in Multiple Linear Regression Models.

Science.gov (United States)

Wiedermann, Wolfgang; von Eye, Alexander

2015-01-01

Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

Kendall-Theil Robust Line (KTRLine--version 1.0)-A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables

Science.gov (United States)

Granato, Gregory E.

2006-01-01

The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and

A LATENT CLASS POISSON REGRESSION-MODEL FOR HETEROGENEOUS COUNT DATA

NARCIS (Netherlands)

WEDEL, M; DESARBO, WS; BULT, [No Value; RAMASWAMY, [No Value

1993-01-01

In this paper an approach is developed that accommodates heterogeneity in Poisson regression models for count data. The model developed assumes that heterogeneity arises from a distribution of both the intercept and the coefficients of the explanatory variables. We assume that the mixing

FITTING OF THE DATA FOR DIFFUSION COEFFICIENTS IN UNSATURATED POROUS MEDIA

Energy Technology Data Exchange (ETDEWEB)

B. Bullard

1999-05-01

The purpose of this calculation is to evaluate diffusion coefficients in unsaturated porous media for use in the TSPA-VA analyses. Using experimental data, regression techniques were used to curve fit the diffusion coefficient in unsaturated porous media as a function of volumetric water content. This calculation substantiates the model fit used in Total System Performance Assessment-1995 An Evaluation of the Potential Yucca Mountain Repository (TSPA-1995), Section 6.5.4.

FITTING OF THE DATA FOR DIFFUSION COEFFICIENTS IN UNSATURATED POROUS MEDIA

International Nuclear Information System (INIS)

B. Bullard

1999-01-01

The purpose of this calculation is to evaluate diffusion coefficients in unsaturated porous media for use in the TSPA-VA analyses. Using experimental data, regression techniques were used to curve fit the diffusion coefficient in unsaturated porous media as a function of volumetric water content. This calculation substantiates the model fit used in Total System Performance Assessment-1995 An Evaluation of the Potential Yucca Mountain Repository (TSPA-1995), Section 6.5.4

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

Directory of Open Access Journals (Sweden)

Guoqi Qian

2016-01-01

Full Text Available Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.

Application of random regression models to the genetic evaluation ...

African Journals Online (AJOL)

The model included fixed regression on AM (range from 30 to 138 mo) and the effect of herd-measurement date concatenation. Random parts of the model were RRM coefficients for additive and permanent environmental effects, while residual effects were modelled to account for heterogeneity of variance by AY. Estimates ...

Bismuth oxide nanorods based immunosensor for mycotoxin detection

Energy Technology Data Exchange (ETDEWEB)

Solanki, Pratima R., E-mail: pratimarsolanki@gmail.com [DST Centre for Biomolecular Electronics, CSIR-National Physical Laboratory, K.S. Krishnan Marg, New Delhi (India); Special Centre for Nano Sciences, Jawaharlal Nehru University, New Delhi 110067 (India); Singh, Jay [DST Centre for Biomolecular Electronics, CSIR-National Physical Laboratory, K.S. Krishnan Marg, New Delhi (India); Department of Applied Chemistry and Polymer Technology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi 110042 (India); Rupavali, Bharti [DST Centre for Biomolecular Electronics, CSIR-National Physical Laboratory, K.S. Krishnan Marg, New Delhi (India); Tiwari, Sachchidanand [Special Centre for Nano Sciences, Jawaharlal Nehru University, New Delhi 110067 (India); Malhotra, Bansi D., E-mail: bansi.malhotra@gmail.com [DST Centre for Biomolecular Electronics, CSIR-National Physical Laboratory, K.S. Krishnan Marg, New Delhi (India); Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi 110042 (India)

2017-01-01

We report results of the studies relating to fabrication of an efficient immunosensor based on bismuth oxide nanorods (nBi{sub 2}O{sub 3}), electrophoretically deposited onto indium-tin-oxide (ITO) coated glass substrate. This immunosensor was fabricated by immobilization of anti-aflatoxin monoclonal antibodies (Ab-AFB1) and bovine serum albumin (BSA) for aflatoxin B1 detection. The structural and morphological studies of n-Bi{sub 2}O{sub 3} have been carried out by XRD, UV–vis spectrophotometer; SEM, AFM and FTIR. It was found that the nBi{sub 2}O{sub 3} provided improved sensing characteristics to the electrode interface in terms of electroactive surface area, diffusion coefficient, charge transfer rate constant and electron transfer kinetics. The results of electrochemical response studies of this BSA/Ab-AFB1/nBi{sub 2}O{sub 3}/ITO immunosensor revealed good linearity in the range of 1–70 ng dL{sup −1} with low detection limit of 8.715 ng/dL, improved sensitivity of 1.132 μA/(ng/dL cm{sup −2}), regression coefficient R{sup 2} of 0.918 and reproducibility of > 11 times. The association constant for the BSA/Ab-AFB1/nBi{sub 2}O{sub 3}/ITO immunosensor was determined as 7.318 ng/dL. - Highlights: • Use of Bismuth oxide nanorods for aflatoxin B1 detection. • It improved the electrochemical properties. • First report on nBi{sub 2}O{sub 3} for mycotoxin detection.

Correlation, Regression, and Cointegration of Nonstationary Economic Time Series

DEFF Research Database (Denmark)

Johansen, Søren

), and Phillips (1986) found the limit distributions. We propose to distinguish between empirical and population correlation coefficients and show in a bivariate autoregressive model for nonstationary variables that the empirical correlation and regression coefficients do not converge to the relevant population...... values, due to the trending nature of the data. We conclude by giving a simple cointegration analysis of two interests. The analysis illustrates that much more insight can be gained about the dynamic behavior of the nonstationary variables then simply by calculating a correlation coefficient......Yule (1926) introduced the concept of spurious or nonsense correlation, and showed by simulation that for some nonstationary processes, that the empirical correlations seem not to converge in probability even if the processes were independent. This was later discussed by Granger and Newbold (1974...

Oxygen exchange at gas/oxide interfaces: how the apparent activation energy of the surface exchange coefficient depends on the kinetic regime.

Science.gov (United States)

Fielitz, Peter; Borchardt, Günter

2016-08-10

In the dedicated literature the oxygen surface exchange coefficient KO and the equilibrium oxygen exchange rate [Fraktur R] are considered to be directly proportional to each other regardless of the experimental circumstances. Recent experimental observations, however, contradict the consequences of this assumption. Most surprising is the finding that the apparent activation energy of KO depends dramatically on the kinetic regime in which it has been determined, i.e. surface exchange controlled vs. mixed or diffusion controlled. This work demonstrates how the diffusion boundary condition at the gas/solid interface inevitably entails a correlation between the oxygen surface exchange coefficient KO and the oxygen self-diffusion coefficient DO in the bulk ("on top" of the correlation between KO and [Fraktur R] for the pure surface exchange regime). The model can thus quantitatively explain the range of apparent activation energies measured in the different regimes: in the surface exchange regime the apparent activation energy only contains the contribution of the equilibrium exchange rate, whereas in the mixed or in the diffusion controlled regime the contribution of the oxygen self-diffusivity has also to be taken into account, which may yield significantly higher apparent activation energies and simultaneously quantifies the correlation KO ∝ DO(1/2) observed for a large number of oxides in the mixed or diffusion controlled regime, respectively.

Measurement of γ-Ray Attenuation Coefficient for Concrete with Different Aggregate

Energy Technology Data Exchange (ETDEWEB)

Oh, Jeong Hwan [Jeju National University, Jeju (Korea, Republic of); Lee, Jea Hyung; Mun, Young Bum; Choi, Hyun Kook [Sungshin Cement Co, Sejong (Korea, Republic of); Choi, Soo Seok [Jeju National University, Jeju (Korea, Republic of)

2016-05-15

In this work, we used different aggregates in a concrete to examine their effect on gamma-ray shielding. In addition, attenuation coefficient has been evaluated using a gamma-ray measuring system. The attenuation coefficient represents the amount of attenuated radiation by the thickness of a given sample material. Shielding performance improvement is expected to effect on the increasing high-weight aggregate rather than unit weigh and it is consider that additional research is needed for mixing condition of aggregates. In this study, shielding performance of concrete was confirmed to increase, according to the increasing in unit weight and aggregate. However, Iron ore is the density greater than oxidizing slag gravel, but attenuation coefficient is lower than including oxidizing slag gravel. The demand of radiation shielding material for a safe transport and storage of radioactive materials increases rapidly with the commencement of the medium and low-level radioactive waste disposal facility. It is because radioactive materials from a nuclear reactor, spent nuclear fuels, fission products, and many industrial application of radiation influences on environment over a long period by releasing gamma-ray and neutron continuously. Typical radiation shielding materials are lead, boron, iron, water, heavy-weight concrete, etc. In heavy-weight concrete, oxidizing slag from an electric arc furnace, magnetite and barite are used as an aggregate. The radiation shielding rate of the heavy-weight concrete which used oxidizing slag was studied. Both size of coarse aggregate and experiment sample is a few cm thicknesses. Therefore, location of shielding material including metal component in sample is important, according to direction of radiation.

Development and Validation of a Mathematical Model for Olive Oil Oxidation

Science.gov (United States)

Rahmouni, K.; Bouhafa, H.; Hamdi, S.

2009-03-01

A mathematical model describing the stability or the susceptibility to oxidation of extra virgin olive oil has been developed. The model has been resolved by an iterative method using differential finite method. It was validated by experimental data of extra virgin olive oil (EVOO) oxidation. EVOO stability was tested by using a Rancimat at four different temperatures 60, 70, 80 and 90° C until peroxide accumulation reached 20 [meq/kg]. Peroxide formation is speed relatively slow; fits zero order reaction with linear regression coefficients varying from 0, 98 to 0, 99. The mathematical model was used to predict the shelf life of bulk conditioned olive oil. This model described peroxide accumulation inside a container in excess of oxygen as a function of time at various positions from the interface air/oil. Good correlations were obtained between theoretical and experimental values.

The scavenging of silver by manganese and iron oxides in stream sediments collected from two drainage areas of Colorado

Science.gov (United States)

Chao, T.T.; Anderson, B.J.

1974-01-01

Stream sediments of two well-weathered and aerated drainage areas of Colorado containing anomalous amounts of silver were allowed to react by shaking with nitric acid of different concentrations (1-10M). Silver, manganese, and iron simultaneously dissolved were determined by atomic absorption. The relationship between silver dissolution and the dissolution of manganese and/or iron was evaluated by linear and multiple regression analyses. The highly significant correlation coefficient (r = 0.913) between silver and manganese dissolution suggests that manganese oxides are the major control on the scavenging of silver in these stream sediments, whereas iron oxides only play a secondary role in this regard. ?? 1974.

On the misinterpretation of the correlation coefficient in pharmaceutical sciences

DEFF Research Database (Denmark)

Sonnergaard, Jørn

2006-01-01

The correlation coefficient is often used and more often misused as a universal parameter expressing the quality in linear regression analysis. The popularity of this dimensionless quantity is evident as it is easy to communicate and considered to be unproblematic to comprehend. However, illustra...

A note on the use of multiple linear regression in molecular ecology.

Science.gov (United States)

Frasier, Timothy R

2016-03-01

Multiple linear regression analyses (also often referred to as generalized linear models--GLMs, or generalized linear mixed models--GLMMs) are widely used in the analysis of data in molecular ecology, often to assess the relative effects of genetic characteristics on individual fitness or traits, or how environmental characteristics influence patterns of genetic differentiation. However, the coefficients resulting from multiple regression analyses are sometimes misinterpreted, which can lead to incorrect interpretations and conclusions within individual studies, and can propagate to wider-spread errors in the general understanding of a topic. The primary issue revolves around the interpretation of coefficients for independent variables when interaction terms are also included in the analyses. In this scenario, the coefficients associated with each independent variable are often interpreted as the independent effect of each predictor variable on the predicted variable. However, this interpretation is incorrect. The correct interpretation is that these coefficients represent the effect of each predictor variable on the predicted variable when all other predictor variables are zero. This difference may sound subtle, but the ramifications cannot be overstated. Here, my goals are to raise awareness of this issue, to demonstrate and emphasize the problems that can result and to provide alternative approaches for obtaining the desired information. © 2015 John Wiley & Sons Ltd.

Determination and importance of temperature dependence of retention coefficient (RPHPLC) in QSAR model of nitrazepams' partition coefficient in bile acid micelles.

Science.gov (United States)

Posa, Mihalj; Pilipović, Ana; Lalić, Mladena; Popović, Jovan

2011-02-15

Linear dependence between temperature (t) and retention coefficient (k, reversed phase HPLC) of bile acids is obtained. Parameters (a, intercept and b, slope) of the linear function k=f(t) highly correlate with bile acids' structures. Investigated bile acids form linear congeneric groups on a principal component (calculated from k=f(t)) score plot that are in accordance with conformations of the hydroxyl and oxo groups in a bile acid steroid skeleton. Partition coefficient (K(p)) of nitrazepam in bile acids' micelles is investigated. Nitrazepam molecules incorporated in micelles show modified bioavailability (depo effect, higher permeability, etc.). Using multiple linear regression method QSAR models of nitrazepams' partition coefficient, K(p) are derived on the temperatures of 25°C and 37°C. For deriving linear regression models on both temperatures experimentally obtained lipophilicity parameters are included (PC1 from data k=f(t)) and in silico descriptors of the shape of a molecule while on the higher temperature molecular polarisation is introduced. This indicates the fact that the incorporation mechanism of nitrazepam in BA micelles changes on the higher temperatures. QSAR models are derived using partial least squares method as well. Experimental parameters k=f(t) are shown to be significant predictive variables. Both QSAR models are validated using cross validation and internal validation method. PLS models have slightly higher predictive capability than MLR models. Copyright © 2010 Elsevier B.V. All rights reserved.

The intermediate endpoint effect in logistic and probit regression

Science.gov (United States)

MacKinnon, DP; Lockwood, CM; Brown, CH; Wang, W; Hoffman, JM

2010-01-01

Background An intermediate endpoint is hypothesized to be in the middle of the causal sequence relating an independent variable to a dependent variable. The intermediate variable is also called a surrogate or mediating variable and the corresponding effect is called the mediated, surrogate endpoint, or intermediate endpoint effect. Clinical studies are often designed to change an intermediate or surrogate endpoint and through this intermediate change influence the ultimate endpoint. In many intermediate endpoint clinical studies the dependent variable is binary, and logistic or probit regression is used. Purpose The purpose of this study is to describe a limitation of a widely used approach to assessing intermediate endpoint effects and to propose an alternative method, based on products of coefficients, that yields more accurate results. Methods The intermediate endpoint model for a binary outcome is described for a true binary outcome and for a dichotomization of a latent continuous outcome. Plots of true values and a simulation study are used to evaluate the different methods. Results Distorted estimates of the intermediate endpoint effect and incorrect conclusions can result from the application of widely used methods to assess the intermediate endpoint effect. The same problem occurs for the proportion of an effect explained by an intermediate endpoint, which has been suggested as a useful measure for identifying intermediate endpoints. A solution to this problem is given based on the relationship between latent variable modeling and logistic or probit regression. Limitations More complicated intermediate variable models are not addressed in the study, although the methods described in the article can be extended to these more complicated models. Conclusions Researchers are encouraged to use an intermediate endpoint method based on the product of regression coefficients. A common method based on difference in coefficient methods can lead to distorted

Sample size determination for logistic regression on a logit-normal distribution.

Science.gov (United States)

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

Assessing risk factors for periodontitis using regression

Science.gov (United States)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

Correlation of Cadmium Distribution Coefficients to Soil Characteristics

DEFF Research Database (Denmark)

Holm, Peter Engelund; Rootzen, Helle; Borggaard, Ole K.

2003-01-01

on whole soil samples have shown that pH is the main parameter controlling the distribution. To identify further the components that are important for Cd binding in soil we measured Cd distribution coefficients (K-d) at two fixed pH values and at low Cd loadings for 49 soils sampled in Denmark. The Kd...... values for Cd ranged from 5 to 3000 L kg(-1). The soils were described pedologically and characterized in detail (22 parameters) including determination of contents of the various minerals in the clay fraction. Correlating parameters were grouped and step-wise regression analysis revealed...... interlayered clay minerals [HIM], chlorite, quartz, microcline, plagioclase) were significant in explaining the Cd distribution coefficient....

Far infrared extinction coefficients of minerals of interest for astronomical observations

International Nuclear Information System (INIS)

Hasegawa, H.

1984-01-01

Far infrared extinction coefficients of mineral grains of interest for astronomical observations have been measured. The measured mineral species are: amorphous carbon, high temperature magnesium silicates, hydrous silicates, iron oxides, and amorphous silicates. (author)

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

Determining Sample Size for Accurate Estimation of the Squared Multiple Correlation Coefficient.

Science.gov (United States)

Algina, James; Olejnik, Stephen

2000-01-01

Discusses determining sample size for estimation of the squared multiple correlation coefficient and presents regression equations that permit determination of the sample size for estimating this parameter for up to 20 predictor variables. (SLD)

Time-varying coefficient estimation in SURE models. Application to portfolio management

DEFF Research Database (Denmark)

Casas, Isabel; Ferreira, Eva; Orbe, Susan

This paper provides a detailed analysis of the asymptotic properties of a kernel estimator for a Seemingly Unrelated Regression Equations model with time-varying coefficients (tv-SURE) under very general conditions. Theoretical results together with a simulation study differentiates the cases...

Redox Couples with Unequal Diffusion Coefficients: Effect on Redox Cycling

NARCIS (Netherlands)

Mampallil Augustine, Dileep; Mathwig, Klaus; Kang, Shuo; Lemay, Serge Joseph Guy

2013-01-01

Redox cycling between two electrodes separated by a narrow gap allows dramatic amplification of the faradaic current. Unlike conventional electrochemistry at a single electrode, however, the mass-transport-limited current is controlled by the diffusion coefficient of both the reduced and oxidized

SDE based regression for random PDEs

KAUST Repository

Bayer, Christian

2016-01-01

A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.

SDE based regression for random PDEs

KAUST Repository

Bayer, Christian

2016-01-06

A simulation based method for the numerical solution of PDE with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead to an approximation of the global solution of the random PDE. We provide an initial error and complexity analysis of the proposed method along with numerical examples illustrating its behaviour.

Effect of morphology and solvent on two-photon absorption of nano zinc oxide

Energy Technology Data Exchange (ETDEWEB)

Kavitha, M.K. [Department of Chemistry, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram 695547, Kerala (India); Haripadmam, P.C.; Gopinath, Pramod; Krishnan, Bindu [Department of Physics, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram 695547, Kerala (India); John, Honey, E-mail: honey@iist.ac.in [Department of Chemistry, Indian Institute of Space Science and Technology, Valiamala, Thiruvananthapuram 695547, Kerala (India)

2013-05-15

Highlights: ► ZnO nanospheres and triangular structures synthesis by novel precipitation technique. ► The effect of precursor concentration on the size and shape of nano ZnO. ► Open aperture Z-scan measurements of the ZnO nanoparticle dispersions. ► Nanospheres exhibit higher two photon absorption coefficient than triangular nanostructures. ► Nanospheres dispersed in water exhibit higher two photon absorption coefficient than its dispersion in 2-propanol. - Abstract: In this paper, we report the effect of morphology and solvent on the two-photon absorption of nano zinc oxide. Zinc oxide nanoparticles in two different morphologies like nanospheres and triangular nanostructures are synthesized by novel precipitation technique and their two-photon absorption coefficient is measured using open aperture Z-scan technique. Experimental results show that the zinc oxide nanospheres exhibit higher two-photon absorption coefficient than the zinc oxide triangular nanostructures. The zinc oxide nanospheres dispersed in water exhibit higher two-photon absorption coefficient than that of its dispersion in 2-propanol. The zinc oxide nanospheres dispersed in water shows a decrease in two-photon absorption coefficient with an increase in on-axis irradiance. The result confirms the dependence of shape and solvent on the two-photon absorption of nano zinc oxide.

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

Analysis of interactive fixed effects dynamic linear panel regression with measurement error

OpenAIRE

Nayoung Lee; Hyungsik Roger Moon; Martin Weidner

2011-01-01

This paper studies a simple dynamic panel linear regression model with interactive fixed effects in which the variable of interest is measured with error. To estimate the dynamic coefficient, we consider the least-squares minimum distance (LS-MD) estimation method.

Measurement of Activity Coefficients of Solvents in Poly ( ethylene oxide ) Using Gas-Chromatographic Method and Correlation by Polymer-ASOG; Poriechirenokishido chu no yobai katsuryo keisu no gasukuromatogurafu ho ni yoru sokutei to Polymer-ASOG ni yoru sokan

Energy Technology Data Exchange (ETDEWEB)

Tochigi, K.; Kurita, S.; Ohashi, M. [Yuki Gosei Kogyo Co. LTd., (Japan); Kojima, K. [Nihon University, Tokyo (Japan). Department of Industrial Chemistry

1997-09-01

Infinite dilution activity coefficients (353.15-393.15 K) of six solvents (benzene, toluene, p-xylene, cyclohexane, acetone and methylethylketone) and activity coefficient at finite concentrations (353.15 K, 373.15 K) of these solvents in poly (ethylene oxide) are measured using gas-chromatographic method. The experimental data are then correlated by a polymer-ASOG model. 18 refs., 2 figs., 3 tabs.

PENGARUH ADOPSI PSAK NO.24 TERHADAP EARNINGS RESPONSE COEFFICIENT

Directory of Open Access Journals (Sweden)

Ilha Refyal

2012-05-01

Full Text Available This study aims to analyze the influence of PSAK No.24(Revisi 2004 adoption on earningsresponse coefficient (ERC. This study focuses discussion on the differences of ERC between theperiod before to the period after the adoption, the influence of changes in the post-employmentbenefits account (due to revision to the ERC, and the influence of the difference in time ofadoption to the ERC. This study is divided into two tests, which are panel data regression testingand Multiple Cross-section Regression testing. ERC in the period after the adoption of the PSAK24 revision is greater than the period before the adoption of PSAK 24 revision. By usingmanufacturing companies during that adoped PSAK 24 during 2004 or 2005, the research findthat changes in post-employment benefits liability have a significant positive effect on ERC. Thecompanies that adopt the standard earlier (early adopter have a greater ERC compare to thecompanies that adopt at the end of the mandatory time (late adopter The study also supportsprevious research on factors affecting the ERC, which are the capital structure and size. Keywords:Earnings Response Coefficient, Revision PSAK 24, Post-employment Benefits Liability,Adoption Timing.

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.

Accurate reactivity void coefficient calculation for the fast spectrum reactor FBR-IME

Energy Technology Data Exchange (ETDEWEB)

Lima, Fabiano P.C.; Vellozo, Sergio de O.; Velozo, Marta J., E-mail: fabianopetruceli@outlook.com, E-mail: vellozo@cbpf.br, E-mail: martajann@gmail.com [Instituto Militar de Engenharia (IME), Rio de Janeiro, RJ (Brazil). Secao de Engenharia Militar

2017-07-01

This paper aims to present an accurate calculation of the void reactivity coefficient for the FBR-IME, a fast spectrum reactor in development at the Engineering Military Institute (IME). The main design peculiarity lies in using mixed oxide [MOX - PuO{sub 2} + U(natural uranium)O{sub 2}] as fuel core. For this task, SCALE system was used to calculate the reactivity for several voids distributions generated by bubbles in the sodium beyond its boiling point. The results show that although the void reactivity coefficient is positive and location dependent, they are offset by other feedback effects, resulting in a negative overall coefficient. (author)

Variable selection and model choice in geoadditive regression models.

Science.gov (United States)

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Modelling the change in the oxidation coefficient during the aerobic ...

African Journals Online (AJOL)

2013-01-20

Jan 20, 2013 ... activated sludge in batch reactors under different initial phenol concentrations. ... wet air oxidation, ozonation, non-catalytic, catalytic and ... design of aeration devices. ... using an open (flowing gas/static liquid) respirometer.

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.

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

Science.gov (United States)

van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B

2016-11-24

Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

Gamma-ray mass attenuation coefficient and half value layer factor of some oxide glass shielding materials

International Nuclear Information System (INIS)

Waly, El-Sayed A.; Fusco, Michael A.; Bourham, Mohamed A.

2016-01-01

The variation in dosimetric parameters such as mass attenuation coefficient, half value layer factor, exposure buildup factor, and the photon mean free path for different oxide glasses for the incident gamma energy range 0.015–15 MeV has been studied using MicroShield code. It has been inferred that the addition of PbO and Bi 2 O 3 improves the gamma ray shielding properties. Thus, the effect of chemical composition on these parameters is investigated in the form of six different glass compositions, which are compared with specialty concrete for nuclear radiation shielding. The composition termed ‘Glass 6’ in this paper has the highest mass attenuation and the smallest half value layer and may have potential applications in radiation shielding. An example dry storage cask utilizing an additional layer of Glass 6 as an intermediate shielding layer, simulated in MicroShield, is capable of reducing the exposure rate at the cask surface by over 20 orders of magnitude compared to the case without a glass layer. Based on this study, Glass 6 shows promise as a gamma-ray shielding material, particularly for dry cask storage.

Analysis of Satellite Drag Coefficient Based on Wavelet Transform

Science.gov (United States)

Liu, Wei; Wang, Ronglan; Liu, Siqing

Abstract: Drag coefficient sequence was obtained by solving Tiangong1 continuous 55days GPS orbit data with different arc length. The same period solar flux f10.7 and geomagnetic index Ap ap series were high and low frequency multi-wavelet decomposition. Statistical analysis results of the layers sliding correlation between space environmental parameters and decomposition of Cd, showed that the satellite drag coefficient sequence after wavelet decomposition and the corresponding level of f10.7 Ap sequence with good lag correlation. It also verified that the Cd prediction is feasible. Prediction residuals of Cd with different regression models and different sample length were analysed. The results showed that the case was best when setting sample length 20 days and f10.7 regression model were used. It also showed that NRLMSIS-00 model's response in the region of 350km (Tiangong's altitude) and low-middle latitude (Tiangong's inclination) is excessive in ascent stage of geomagnetic activity Ap and is inadequate during fall off segment. Additionally, the low-frequency decomposition components NRLMSIS-00 model's response is appropriate in f10.7 rising segment. High frequency decomposition section, Showed NRLMSIS-00 model's response is small-scale inadequate during f10.7 ascent segment and is reverse in decline of f10.7. Finally, the potential use of a summary and outlook were listed; This method has an important reference value to improve the spacecraft orbit prediction accuracy. Key words: wavelet transform; drag coefficient; lag correlation; Tiangong1;space environment

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.

Giant piezoelectric voltage coefficient in grain-oriented modified PbTiO3 material.

Science.gov (United States)

Yan, Yongke; Zhou, Jie E; Maurya, Deepam; Wang, Yu U; Priya, Shashank

2016-10-11

A rapid surge in the research on piezoelectric sensors is occurring with the arrival of the Internet of Things. Single-phase oxide piezoelectric materials with giant piezoelectric voltage coefficient (g, induced voltage under applied stress) and high Curie temperature (T c ) are crucial towards providing desired performance for sensing, especially under harsh environmental conditions. Here, we report a grain-oriented (with 95% texture) modified PbTiO 3 ceramic that has a high T c (364 °C) and an extremely large g 33 (115 × 10 -3  Vm N -1 ) in comparison with other known single-phase oxide materials. Our results reveal that self-polarization due to grain orientation along the spontaneous polarization direction plays an important role in achieving large piezoelectric response in a domain motion-confined material. The phase field simulations confirm that the large piezoelectric voltage coefficient g 33 originates from maximized piezoelectric strain coefficient d 33 and minimized dielectric permittivity ɛ 33 in [001]-textured PbTiO 3 ceramics where domain wall motions are absent.

Assessment of deforestation using regression; Hodnotenie odlesnenia s vyuzitim regresie

Energy Technology Data Exchange (ETDEWEB)

Juristova, J. [Univerzita Komenskeho, Prirodovedecka fakulta, Katedra kartografie, geoinformatiky a DPZ, 84215 Bratislava (Slovakia)

2013-04-16

This work is devoted to the evaluation of deforestation using regression methods through software Idrisi Taiga. Deforestation is evaluated by the method of logistic regression. The dependent variable has discrete values '0' and '1', indicating that the deforestation occurred or not. Independent variables have continuous values, expressing the distance from the edge of the deforested areas of forests from urban areas, the river and the road network. The results were also used in predicting the probability of deforestation in subsequent periods. The result is a map showing the output probability of deforestation for the periods 1990/2000 and 200/2006 in accordance with predetermined coefficients (values of independent variables). (authors)

Fixed kernel regression for voltammogram feature extraction

International Nuclear Information System (INIS)

Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N

2009-01-01

Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals

Bayesian Inference of a Multivariate Regression Model

Directory of Open Access Journals (Sweden)

Marick S. Sinay

2014-01-01

Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.

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.

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.

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.

Determination of extinction coefficients of human hemoglobin in various redox states.

Science.gov (United States)

Meng, Fantao; Alayash, Abdu I

2017-03-15

The role of hemoglobin (Hb) redox forms in tissue and organ toxicities remain ambiguous despite the well-documented contribution of Hb redox reactivity to cellular and subcellular oxidative changes. Moreover, several recent studies, in which Hb toxicity were investigated, have shown conflicting outcomes. Uncertainties over the potential role of these species may in part be due to the protein preparation method of choice, the use of published extinction coefficients and the lack of suitable controls for Hb oxidation and heme loss. Highly purified and well characterized redox forms of human Hb were used in this study and the extinction coefficients of each Hb species (ferrous/oxy, ferric/met and ferryl) were determined. A new set of equations were established to improve accuracy in determining the transient ferryl Hb species. Additionally, heme concentrations in solutions and in human plasma were determined using a novel reversed phase HPLC method in conjugation with our photometric measurements. The use of more accurate redox-specific extinction coefficients and method calculations will be an invaluable tool for both in vitro and in vivo experiments aimed at determining the role of Hb-mediated vascular pathology in hemolytic anemias and when Hb is used as oxygen therapeutics. Published by Elsevier Inc.

Regression analysis of sparse asynchronous longitudinal data.

Science.gov (United States)

Cao, Hongyuan; Zeng, Donglin; Fine, Jason P

2015-09-01

We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.

A Formula for the Coefficient of Thermal Expansion of Crude Oils ...

African Journals Online (AJOL)

A new formula for the calculation of the coefficient of world crude oils has been developed. The formula is semi theoretical. The empirical part was obtained by regression calculation of the Formation Volume Factor of the gas free crude oil at reservoir temperature. Comparison of the calculated values of the Formation ...

Analysis of the kinetics of methanol oxidation in a porous Pt-Ru anode

Energy Technology Data Exchange (ETDEWEB)

Sun, Yan-Ping; Xing, Lei [Chemical Engineering Department, Taiyuan University of Technology, Shanxi 030024 (China); Scott, Keith [School of Chemical Engineering and Advanced Materials, Merz Court, University of Newcastle, Newcastle upon Tyne NE1 7RU (United Kingdom)

2010-01-01

A kinetic model of a porous Pt-Ru anode for methanol oxidation is presented. It was based on the dual-site mechanism for methanol oxidation and used to predict anode performance and the influence of species adsorption on the overall oxidation (macro-) kinetics. The performance of the porous Pt-Ru anode depended on the parameters of the intrinsic chemical kinetics of methanol oxidation and physical parameters such as electrode thickness, surface area, effective diffusion and charge transfer coefficients and concentration of methanol and temperature. The model was solved by using the finite difference method with a subroutine for solving a set of nonlinear algebraic equations in each step. Surface coverage ratio distributions of adsorbed species, effectiveness of the porous electrode and macro-polarisation curves were obtained. The simulated polarisation curves were compared to experimental polarisation data for methanol oxidation on Pt-Ru porous anodes at different temperatures and methanol concentrations. The intrinsic kinetic parameters were regressed from the corresponding experimental data. The predicted polarisation curves calculated by the model, were consistent with experimental polarisation data at lower current densities. The departure of experimental data from the predicted polarisation curves at high concentration and high apparent current densities was believed to be due to two-phase flow in the electrode. (author)

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.

Determination of benzo(apyrene content in PM10 using regression methods

Directory of Open Access Journals (Sweden)

Jacek Gębicki

2015-12-01

Full Text Available The paper presents an attempt of application of multidimensional linear regression to estimation of an empirical model describing the factors influencing on B(aP content in suspended dust PM10 in Olsztyn and Elbląg city regions between 2010 and 2013. During this period annual average concentration of B(aP in PM10 exceeded the admissible level 1.5-3 times. Conducted investigations confirm that the reasons of B(aP concentration increase are low-efficiency individual home heat stations or low-temperature heat sources, which are responsible for so-called low emission during heating period. Dependences between the following quantities were analysed: concentration of PM10 dust in air, air temperature, wind velocity, air humidity. A measure of model fitting to actual B(aP concentration in PM10 was the coefficient of determination of the model. Application of multidimensional linear regression yielded the equations characterized by high values of the coefficient of determination of the model, especially during heating season. This parameter ranged from 0.54 to 0.80 during the analyzed period.

Physical properties of beryllium oxide - Irradiation effects

International Nuclear Information System (INIS)

Elston, J.; Caillat, R.

1958-01-01

This work has been carried out in view of determining several physical properties of hot-pressed beryllium oxide under various conditions and the change of these properties after irradiation. Special attention has been paid on to the measurement of the thermal conductivity coefficient and thermal diffusivity coefficient. Several designs for the measurement of the thermal conductivity coefficient have been achieved. They permit its determination between 50 and 300 deg. C, between 400 and 800 deg. C. Some measurements have been made above 1000 deg. C. In order to measure the thermal diffusivity coefficient, we heat a perfectly flat surface of a sample in such a way that the heat flux is modulated (amplitude and frequency being adjustable). The thermal diffusivity coefficient is deduced from the variations of temperature observed on several spots. Tensile strength; compressive strength; expansion coefficient; sound velocity and crystal parameters have been also measured. Some of the measurements have been carried out after neutron irradiation. Some data have been obtained on the change of the properties of beryllium oxide depending on the integrated neutron flux. (author) [fr

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.

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)

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.

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

Directory of Open Access Journals (Sweden)

Maarten van Smeden

2016-11-01

Full Text Available Abstract Background Ten events per variable (EPV is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. Methods The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared. Results The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’. We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. Conclusions The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

The error analysis of the determination of the activity coefficients via the isopiestic method

International Nuclear Information System (INIS)

Zhou Jun; Chen Qiyuan; Fang Zheng; Liang Yizeng; Liu Shijun; Zhou Yong

2005-01-01

Error analysis is very important to experimental designs. The error analysis of the determination of activity coefficients for a binary system via the isopiestic method shows that the error sources include not only the experimental errors of the analyzed molalities and the measured osmotic coefficients, but also the deviation of the regressed values from the experimental data when the regression function is used. It also shows that the accurate chemical analysis of the molality of the test solution is important, and it is preferable to keep the error of the measured osmotic coefficients changeless in all isopiestic experiments including those experiments on the very dilute solutions. The isopiestic experiments on the dilute solutions are very important, and the lowest molality should be low enough so that a theoretical method can be used below the lowest molality. And it is necessary that the isopiestic experiment should be done on the test solutions of lower than 0.1 mol . kg -1 . For most electrolytes solutions, it is usually preferable to require the lowest molality to be less than 0.05 mol . kg -1 . Moreover, the experimental molalities of the test solutions should be firstly arranged by keeping the interval of the logarithms of the molalities nearly constant, and secondly more number of high molalities should be arranged, and we propose to arrange the experimental molalities greater than 1 mol . kg -1 according to some kind of the arithmetical progression of the intervals of the molalities. After experiments, the error of the calculated activity coefficients of the solutes could be calculated from the actually values of the errors of the measured isopiestic molalities and the deviations of the regressed values from the experimental values with our obtained equations

Electron scattering on N2O-from cross sections to diffusion coefficients

International Nuclear Information System (INIS)

Mechlinska-Drewko, J.; Wroblewski, T.; Petrovic, Z.L.; Novakovic, V.; Karwasz, G.P.

2003-01-01

Results of measurements of the ratio of transverse (D T /μ) and longitudinal (D L /μ) diffusion coefficients to mobility and drift velocity (W) as function of reduced electrical field (E/N) for electrons in nitrous oxide are presented. The coefficients D T /μ and D L /μ have been determined by applying the Townsend-Huxley method. The drift velocities were obtained by using the Bradbury-Nielsen technique. Also the deduced set of total and partial cross sections has been used to calculate the D T /μ and W

Gamma-ray attenuation coefficients in some heavy metal oxide borate glasses at 662 keV

International Nuclear Information System (INIS)

Khanna, A.; Bhatti, S.S.; Singh, K.J.; Thind, K.S.

1996-01-01

The linear attenuation coefficient (μ) and mass attenuation coefficients (μ/ρ) of glasses in three systems: xPbO(1-x)B 2 O 3 , 0.25PbO.xCdO(0.75-x)B 2 O 3 and xBi 2 O 3 (1-x)B 2 O 3 were measured at 662 keV. Appreciable variations were noted in the attenuation coefficients due to changes in the chemical composition of glasses. In addition to this, absorption cross-sections per atom were also calculated. A comparison of shielding properties of these glasses with standar d shielding materials like lead, lead glass and concrete has proven that these glasses have a potential application as transparent radiation shielding. (orig.)

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.

Regression Modeling of EDM Process for AISI D2 Tool Steel with RSM

Directory of Open Access Journals (Sweden)

Shakir M. Mousa

2018-01-01

Full Text Available In this paper, Response Surface Method (RSM is utilized to carry out an investigation of the impact of input parameters: electrode type (E.T. [Gr, Cu and CuW], pulse duration of current (Ip, pulse duration on time (Ton, and pulse duration off time (Toff on the surface finish in EDM operation. To approximate and concentrate the suggested second- order regression model is generally accepted for Surface Roughness Ra, a Central Composite Design (CCD is utilized for evaluating the model constant coefficients of the input parameters on Surface Roughness (Ra. Examinations were performed on AISI D2 tool steel. The important coefficients are gotten by achieving successfully an Analysis of Variance (ANOVA at the 5 % confidence interval. The outcomes discover that Surface Roughness (Ra is much more impacted by E.T., Ton, Toff, Ip and little of their interactions action or influence. To predict the average Surface Roughness (Ra, a mathematical regression model was developed. Furthermore, for saving in time, the created model could be utilized for the choice of the high levels in the EDM procedure. The model adequacy was extremely agreeable as the constant Coefficient of Determination (R2 is observed to be 99.72% and adjusted R2-measurement (R2adj 99.60%.

A multi-scale relevance vector regression approach for daily urban water demand forecasting

Science.gov (United States)

Bai, Yun; Wang, Pu; Li, Chuan; Xie, Jingjing; Wang, Yin

2014-09-01

Water is one of the most important resources for economic and social developments. Daily water demand forecasting is an effective measure for scheduling urban water facilities. This work proposes a multi-scale relevance vector regression (MSRVR) approach to forecast daily urban water demand. The approach uses the stationary wavelet transform to decompose historical time series of daily water supplies into different scales. At each scale, the wavelet coefficients are used to train a machine-learning model using the relevance vector regression (RVR) method. The estimated coefficients of the RVR outputs for all of the scales are employed to reconstruct the forecasting result through the inverse wavelet transform. To better facilitate the MSRVR forecasting, the chaos features of the daily water supply series are analyzed to determine the input variables of the RVR model. In addition, an adaptive chaos particle swarm optimization algorithm is used to find the optimal combination of the RVR model parameters. The MSRVR approach is evaluated using real data collected from two waterworks and is compared with recently reported methods. The results show that the proposed MSRVR method can forecast daily urban water demand much more precisely in terms of the normalized root-mean-square error, correlation coefficient, and mean absolute percentage error criteria.

Genetic variability, partial regression, Co-heritability studies and their implication in selection of high yielding potato gen

International Nuclear Information System (INIS)

Iqbal, Z.M.; Khan, S.A.

2003-01-01

Partial regression coefficient, genotypic and phenotypic variabilities, heritability co-heritability and genetic advance were studied in 15 Potato varieties of exotic and local origin. Both genotypic and phenotypic coefficients of variations were high for scab and rhizoctonia incidence percentage. Significant partial regression coefficient for emergence percentage indicated its relative importance in tuber yield. High heritability (broadsense) estimates coupled with high genetic advance for plant height, number of stems per plant and scab percentage revealed substantial contribution of additive genetic variance in the expression of these traits. Hence, the selection based on these characters could play a significant role in their improvement the dominance and epistatic variance was more important for character expression of yield ha/sup -1/, emergence and rhizoctonia percentage. This phenomenon is mainly due to the accumulative effects of low heritability and low to moderate genetic advance. The high co-heritability coupled with negative genotypic and phenotypic covariance revealed that selection of varieties having low scab and rhizoctonia percentage resulted in more potato yield. (author)

Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

Science.gov (United States)

Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

2006-11-01

We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

Regularized multivariate regression models with skew-t error distributions

KAUST Repository

Chen, Lianfu

2014-06-01

We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.

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.

Heat-induced redistribution of surface oxide in uranium

International Nuclear Information System (INIS)

Swissa, E.; Shamir, N.; Bloch, J.; Mintz, M.H.; Israel Atomic Energy Commission, Beersheba. Nuclear Research Center-Negev)

1990-01-01

The redistribution of oxygen and uranium metal at the vicinity of the metal-oxide interface of native and grown oxides due to vacuum thermal annealing was studied for uranium and uranium-chromium alloy using Auger depth profiling and metallographic techniques. It was found that uranium metal is segregating out through the uranium oxide layer for annealing temperatures above 450deg C. At the same time the oxide is redistributed in the metal below the oxide-metal interface in a diffusion like process. By applying a diffusion equation of a finite source, the diffusion coefficients for the process were obtained from the oxygen depth profiles measured for different annealing times. An Arrhenius like behavior was found for the diffusion coefficient between 400 and 800deg C. The activation energy obtained was E a =15.4±1.9 kcal/mole and the pre-exponential factor, D 0 =1.1x10 -8 cm 2 /s. An internal oxidation mechanism is proposed to explain the results. (orig.)

Heat-induced redistribution of surface oxide in uranium

Science.gov (United States)

Swissa, Eli; Shamir, Noah; Mintz, Moshe H.; Bloch, Joseph

1990-09-01

The redistribution of oxygen and uranium metal at the vicinity of the metal-oxide interface of native and grown oxides due to vacuum thermal annealing was studied for uranium and uranium-chromium alloy using Auger depth profiling and metallographic techniques. It was found that uranium metal is segregating out through the uranium oxide layer for annealing temperatures above 450°C. At the same time the oxide is redistributed in the metal below the oxide-metal interface in a diffusion like process. By applying a diffusion equation of a finite source, the diffusion coefficients for the process were obtained from the oxygen depth profiles measured for different annealing times. An Arrhenius like behavior was found for the diffusion coefficient between 400 and 800°C. The activation energy obtained was Ea = 15.4 ± 1.9 kcal/mole and the pre-exponential factor, D0 = 1.1 × 10 -8cm2/ s. An internal oxidation mechanism is proposed to explain the results.

Experimental study of overall heat transfer coefficient in the application of dilute nanofluids in the car radiator

International Nuclear Information System (INIS)

Peyghambarzadeh, S.M.; Hashemabadi, S.H.; Naraki, M.; Vermahmoudi, Y.

2013-01-01

Heat transfer of coolant flow through the automobile radiators is of great importance for the optimization of fuel consumption. In this study, the heat transfer performance of the automobile radiator is evaluated experimentally by calculating the overall heat transfer coefficient (U) according to the conventional ε-NTU technique. Copper oxide (CuO) and Iron oxide (Fe 2 O 3 ) nanoparticles are added to the water at three concentrations 0.15, 0.4, and 0.65 vol.% with considering the best pH for longer stability. In these experiments, the liquid side Reynolds number is varied in the range of 50–1000 and the inlet liquid to the radiator has a constant temperature which is changed at 50, 65 and 80 °C. The ambient air for cooling of the hot liquid is used at constant temperature and the air Reynolds number is varied between 500 and 700. However, the effects of these variables on the overall heat transfer coefficient are deeply investigated. Results demonstrate that both nanofluids show greater overall heat transfer coefficient in comparison with water up to 9%. Furthermore, increasing the nanoparticle concentration, air velocity, and nanofluid velocity enhances the overall heat transfer coefficient. In contrast, increasing the nanofluid inlet temperature, lower overall heat transfer coefficient was recorded. -- Highlights: ► Overall heat transfer coefficient in the car radiator measured experimentally. ► Nanofluids showed greater heat transfer performance comparing with water. ► Increasing liquid and air Re increases the overall heat transfer coefficient. ► Increasing the inlet liquid temperature decreases the overall heat transfer coefficient

The kinetics for ammonium and nitrite oxidation under the effect of hydroxylamine.

Science.gov (United States)

Wan, Xinyu; Xiao, Pengying; Zhang, Daijun; Lu, Peili; Yao, Zongbao; He, Qiang

2016-01-01

The kinetics for ammonium (NH4(+)) oxidation and nitrite (NO2(-)) oxidation under the effect of hydroxylamine (NH2OH) were studied by respirometry using the nitrifying sludge from a laboratory-scale sequencing batch reactor. Modified models were used to estimate kinetics parameters of ammonia and nitrite oxidation under the effect of hydroxylamine. An inhibition effect of hydroxylamine on the ammonia oxidation was observed under different hydroxylamine concentration levels. The self-inhibition coefficient of hydroxylamine oxidation and noncompetitive inhibition coefficient of hydroxylamine for nitrite oxidation was estimated by simulating exogenous oxygen-uptake rate profiles, respectively. The inhibitive effect of NH2OH on nitrite-oxidizing bacteria was stronger than on ammonia-oxidizing bacteria. This work could provide fundamental data for the kinetic investigation of the nitrification process.

Two SPSS programs for interpreting multiple regression results.

Science.gov (United States)

Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo

2010-02-01

When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.

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...

[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.

The Use of Structure Coefficients to Address Multicollinearity in Sport and Exercise Science

Science.gov (United States)

Yeatts, Paul E.; Barton, Mitch; Henson, Robin K.; Martin, Scott B.

2017-01-01

A common practice in general linear model (GLM) analyses is to interpret regression coefficients (e.g., standardized ß weights) as indicators of variable importance. However, focusing solely on standardized beta weights may provide limited or erroneous information. For example, ß weights become increasingly unreliable when predictor variables are…

Application of support vector regression (SVR) for stream flow prediction on the Amazon basin

CSIR Research Space (South Africa)

Du Toit, Melise

2016-10-01

Full Text Available regression technique is used in this study to analyse historical stream flow occurrences and predict stream flow values for the Amazon basin. Up to twelve month predictions are made and the coefficient of determination and root-mean-square error are used...

Surplus thermal energy model of greenhouses and coefficient analysis for effective utilization

Directory of Open Access Journals (Sweden)

Seung-Hwan Yang

2016-03-01

Full Text Available If a greenhouse in the temperate and subtropical regions is maintained in a closed condition, the indoor temperature commonly exceeds that required for optimal plant growth, even in the cold season. This study considered this excess energy as surplus thermal energy (STE, which can be recovered, stored and used when heating is necessary. To use the STE economically and effectively, the amount of STE must be estimated before designing a utilization system. Therefore, this study proposed an STE model using energy balance equations for the three steps of the STE generation process. The coefficients in the model were determined by the results of previous research and experiments using the test greenhouse. The proposed STE model produced monthly errors of 17.9%, 10.4% and 7.4% for December, January and February, respectively. Furthermore, the effects of the coefficients on the model accuracy were revealed by the estimation error assessment and linear regression analysis through fixing dynamic coefficients. A sensitivity analysis of the model coefficients indicated that the coefficients have to be determined carefully. This study also provides effective ways to increase the amount of STE.

Surplus thermal energy model of greenhouses and coefficient analysis for effective utilization

Energy Technology Data Exchange (ETDEWEB)

Yang, S.H.; Son, J.E.; Lee, S.D.; Cho, S.I.; Ashtiani-Araghi, A.; Rhee, J.Y.

2016-11-01

If a greenhouse in the temperate and subtropical regions is maintained in a closed condition, the indoor temperature commonly exceeds that required for optimal plant growth, even in the cold season. This study considered this excess energy as surplus thermal energy (STE), which can be recovered, stored and used when heating is necessary. To use the STE economically and effectively, the amount of STE must be estimated before designing a utilization system. Therefore, this study proposed an STE model using energy balance equations for the three steps of the STE generation process. The coefficients in the model were determined by the results of previous research and experiments using the test greenhouse. The proposed STE model produced monthly errors of 17.9%, 10.4% and 7.4% for December, January and February, respectively. Furthermore, the effects of the coefficients on the model accuracy were revealed by the estimation error assessment and linear regression analysis through fixing dynamic coefficients. A sensitivity analysis of the model coefficients indicated that the coefficients have to be determined carefully. This study also provides effective ways to increase the amount of STE. (Author)

Method for producing ceramic composition having low friction coefficient at high operating temperatures

Science.gov (United States)

Lankford, Jr., James

1988-01-01

A method for producing a stable ceramic composition having a surface with a low friction coefficient and high wear resistance at high operating temperatures. A first deposition of a thin film of a metal ion is made upon the surface of the ceramic composition and then a first ion implantation of at least a portion of the metal ion is made into the near surface region of the composition. The implantation mixes the metal ion and the ceramic composition to form a near surface composite. The near surface composite is then oxidized sufficiently at high oxidizing temperatures to form an oxide gradient layer in the surface of the ceramic composition.

Nickel-base alloys having a low coefficient of thermal expansion

International Nuclear Information System (INIS)

Baldwin, J.F.; Maxwell, D.H.

1975-01-01

Alloy compositions consisting predominantly of nickel, chromium, molybdenum, carbon, and boron are disclosed. The alloys possess a duplex structure consisting of a nickel--chromium--molybdenum matrix and a semi-continuous network of refractory carbides and borides. A combination of desirable properties is provided by these alloys, including elevated temperature strength, resistance to oxidation and hot corrosion, and a very low coefficient of thermal expansion

Estimation of the soil-water partition coefficient normalized to organic carbon for ionizable organic chemicals

DEFF Research Database (Denmark)

Franco, Antonio; Trapp, Stefan

2008-01-01

The sorption of organic electrolytes to soil was investigated. A dataset consisting of 164 electrolytes, composed of 93 acids, 65 bases, and six amphoters, was collected from literature and databases. The partition coefficient log KOW of the neutral molecule and the dissociation constant pKa were...... calculated by the software ACD/Labs®. The Henderson-Hasselbalch equation was applied to calculate dissociation. Regressions were developed to predict separately for the neutral and the ionic molecule species the distribution coefficient (Kd) normalized to organic carbon (KOC) from log KOW and pKa. The log...... KOC of strong acids (pKa correlated to these parameters. The regressions derived for weak acids and bases (undissociated at environmental pH) were similar. The highest sorption was found for strong bases (pKa > 7.5), probably due to electrical interactions. Nonetheless, their log KOC...

Oxidation kinetics of zircaloy-4 in the temperature range correspondent to alpha phase

International Nuclear Information System (INIS)

Medeiros, L.F.

1975-12-01

Oxidation kinetics of Zry-4 in the alpha phase is isothermally studied in the temperature range from 600 0 C to 800 0 C, by continuous and discontinuous gravimetric methods. The total mass gain during the oxidation takes place by two distinct ways: oxide formation and solid solution formation. The first one has been studied by microscopy: the latter by microhardness. The oxygen diffusion coefficients in the zirconium are experimentally determined by microhardness measurements and are compared with those obtained by the oxide layer thickness and by oxygen mass in the oxide. The oxygen diffusion coefficients in the oxide are obtained too by oxide layer thickness and by oxygen diffusivities in the alpha phase and compared with literature. (author)

Thermal expansion of beryllium oxide

International Nuclear Information System (INIS)

Solodukhin, A.V.; Kruzhalov, A.V.; Mazurenko, V.G.; Maslov, V.A.; Medvedev, V.A.; Polupanova, T.I.

1987-01-01

Precise measurements of temperature dependence of the coefficient of linear expansion in the 22-320 K temperature range on beryllium oxide monocrystals are conducted. A model of thermal expansion is suggested; the range of temperature dependence minimum of the coefficient of thermal expansion is well described within the frames of this model. The results of the experiment may be used for investigation of thermal stresses in crystals

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...

Estimation of instantaneous heat transfer coefficients for a direct-injection stratified-charge rotary engine

Science.gov (United States)

Lee, C. M.; Addy, H. E.; Bond, T. H.; Chun, K. S.; Lu, C. Y.

1987-01-01

The main objective of this report was to derive equations to estimate heat transfer coefficients in both the combustion chamber and coolant pasage of a rotary engine. This was accomplished by making detailed temperature and pressure measurements in a direct-injection stratified-charge rotary engine under a range of conditions. For each sppecific measurement point, the local physical properties of the fluids were calculated. Then an empirical correlation of the coefficients was derived by using a multiple regression program. This correlation expresses the Nusselt number as a function of the Prandtl number and Reynolds number.

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.

Analysis of Differences in Void Coefficient Predictions for Mixed-Oxide-Fueled Tight-Pitch Light Water Reactor Cells

International Nuclear Information System (INIS)

Unesaki, Hironobu; Shiroya, Seiji; Kanda, Keiji; Cathalau, Stephane; Carre, Franck-Olivier; Aizawa, Otohiko; Takeda, Toshikazu

2000-01-01

Analysis of the benchmark problems on the void coefficient of mixed-oxide (MOX)-fueled tight-pitch cells has been performed using the Japanese SRAC code system with the JENDL-3.2 library and the French APOLLO-2 code with the CEA93 library based on JEF-2.2. The benchmark problems have been specified to investigate the physical phenomena occurring during the progressive voidage of MOX-fueled tight-pitch lattices, such as high conversion light water reactor lattices, and to evaluate the impact of nuclear data and calculational methods. Despite the most recently compiled nuclear data libraries and the sophisticated calculation schemes employed in both code systems, the k ∞ and void reactivity values obtained by the two code systems show considerable discrepancy especially in the highly voided state. The discrepancy of k ∞ values shows an obvious dependence on void fraction and also has been shown to be sensitive to the isotopic composition of plutonium. The observed discrepancies are analyzed by being decomposed into contributing isotopes and reactions and have been shown to be caused by a complicated balance of both negative and positive components, which are mainly attributable to differences in a limited number of isotopes including 239 Pu, 241 Pu, 16 O, and stainless steel

On Weighted Support Vector Regression

DEFF Research Database (Denmark)

Han, Xixuan; Clemmensen, Line Katrine Harder

2014-01-01

We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...

A Linear Regression Model for Global Solar Radiation on Horizontal Surfaces at Warri, Nigeria

Directory of Open Access Journals (Sweden)

Michael S. Okundamiya

2013-10-01

Full Text Available The growing anxiety on the negative effects of fossil fuels on the environment and the global emission reduction targets call for a more extensive use of renewable energy alternatives. Efficient solar energy utilization is an essential solution to the high atmospheric pollution caused by fossil fuel combustion. Global solar radiation (GSR data, which are useful for the design and evaluation of solar energy conversion system, are not measured at the forty-five meteorological stations in Nigeria. The dearth of the measured solar radiation data calls for accurate estimation. This study proposed a temperature-based linear regression, for predicting the monthly average daily GSR on horizontal surfaces, at Warri (latitude 5.020N and longitude 7.880E an oil city located in the south-south geopolitical zone, in Nigeria. The proposed model is analyzed based on five statistical indicators (coefficient of correlation, coefficient of determination, mean bias error, root mean square error, and t-statistic, and compared with the existing sunshine-based model for the same study. The results indicate that the proposed temperature-based linear regression model could replace the existing sunshine-based model for generating global solar radiation data. Keywords: air temperature; empirical model; global solar radiation; regression analysis; renewable energy; Warri

The Impact of One Heat Treated Contact Element on the Coefficient of Static Friction

Directory of Open Access Journals (Sweden)

P. Todorović, , , , , ,

2013-12-01

Full Text Available The subject of the paper includes theoretical considerations, the conducting of experimental tests, and the analysis of exposed test results related to determination of the coefficient of static friction of previously heat-treated contact pairs. One contact element is previously, before the procedure of determining the coefficient of static friction, heated at temperatures in the range of ambient temperature to 280°C and then cooled down to ambient temperature. The results of experimental tests of five different materials show that depending on the heat treatment of one contact element, there is a significant decrease in the coefficient of static friction. The authors of the paper consider that the reasons for the decreasing coefficient of static friction are related to oxide formation and changes in the surface layer of the contact element which is previously heat-treated.

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.

Diffusion of insoluble carbon in zirconium oxides

CERN Document Server

Vykhodets, V B; Koester, U; Kondrat'ev, V V; Kesarev, A G; Hulsen, C; Kurennykh, T E

2011-01-01

The diffusion coefficient of insoluble carbon in zirconium oxides has been obtained for the temperature range of 900-1000A degrees C. There are no published data on the diffusion of insoluble impurities; these data are of current interest for the diffusion theory and nuclear technologies. Tracer atoms 13C have been introduced into oxides by means of ion implantation and the kinetics of their emission from the samples in the process of annealing in air has been analyzed. The measurements have been performed using the methods of nuclear microanalysis and X-ray photoelectron spectroscopy. The diffusion activation energy is 2.7 eV and the carbon diffusion coefficient is about six orders of magnitude smaller than that for oxygen self-diffusion in the same systems. This result indicates the strong anomaly of the diffusion properties of carbon in oxides. As a result, zirconium oxides cannot be used in some nuclear technologies, in particular, as a material of sources for accelerators of short-lived carbon isotopes.

Prediction of the temperature of the atmosphere of the primary containment: comparison between neural networks and polynomial regression

International Nuclear Information System (INIS)

Alvarez Huerta, A.; Gonzalez Miguelez, R.; Garcia Metola, D.; Noriega Gonzalez, A.

2011-01-01

The modelization is carried out through two different techniques, a conventional polynomial regression and other based on an approach by neural networks artificial. He is a comparison between the quality of the forecast would make different models based on the polynomial regression and neural network with generalization by Bayesian regulation, using the indicators of the root of the mean square error and the coefficient of determination, in view of the results, the neural network generates a prediction more accurate and reliable than the polynomial regression.

Determination of trace aluminum by fluorescence quenching method based on catalysis of potassium chlorate oxidizing alizarin red

Science.gov (United States)

Shao-Qin, Lin; Xuan, Lin; Shi-Rong, Hu; Li-Qing, Zeng; Yan, Wang; Li, Chen; Jia-Ming, Liu; Long-Di, Li

2005-11-01

A new method for the determination of trace aluminum has been proposed. It is based on the fact that alizarin red can emit strong and stable fluorescence at 80 °C for 30 min and Al 3+ can effectively catalyze potassium chlorate oxidizing alizarin red to form non-fluorescence complex which cause the fluorescence quenching. The linear dynamic range of this method is 0.040-4.00 ng l -1 with a detection limit of 5.3 pg l -1. The regression equation can be expressed as Δ If = 8.731 + 21.73 c (ng l -1), with the correlation coefficient r = 0.9992 ( n = 6). This sensitive, rapid and accurate method has been applied to the determination of trace aluminum(III) in human hair and tea samples successfully. What is more, the mechanism of catalyzing potassium chlorate oxidizing alizarin red by the fluorescence quenching method is also discussed.

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.

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions.

Science.gov (United States)

Rativa, Diego; Fernandes, Bruno J T; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care.

Dual Regression

OpenAIRE

Spady, Richard; Stouli, Sami

2012-01-01

We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...

Correlation and prediction of osmotic coefficient and water activity of aqueous electrolyte solutions by a two-ionic parameter model

International Nuclear Information System (INIS)

Pazuki, G.R.

2005-01-01

In this study, osmotic coefficients and water activities in aqueous solutions have been modeled using a new approach based on the Pitzer model. This model contains two physically significant ionic parameters regarding ionic solvation and the closest distance of approach between ions in a solution. The proposed model was evaluated by estimating the osmotic coefficients of nine electrolytes in aqueous solutions. The obtained results showed that the model is suitable for predicting the osmotic coefficients in aqueous electrolyte solutions. Using adjustable parameters, which have been calculated from regression between the experimental osmotic coefficient and the results of this model, the water activity coefficients of aqueous solutions were calculated. The average absolute relative deviations of the osmotic coefficients between the experimental data and the calculated results were in agreement

Estimation of Oxidation Kinetics and Oxide Scale Void Position of Ferritic-Martensitic Steels in Supercritical Water

Directory of Open Access Journals (Sweden)

Li Sun

2017-01-01

Full Text Available Exfoliation of oxide scales from high-temperature heating surfaces of power boilers threatened the safety of supercritical power generating units. According to available space model, the oxidation kinetics of two ferritic-martensitic steels are developed to predict in supercritical water at 400°C, 500°C, and 600°C. The iron diffusion coefficients in magnetite and Fe-Cr spinel are extrapolated from studies of Backhaus and Töpfer. According to Fe-Cr-O ternary phase diagram, oxygen partial pressure at the steel/Fe-Cr spinel oxide interface is determined. The oxygen partial pressure at the magnetite/supercritical water interface meets the equivalent oxygen partial pressure when system equilibrium has been attained. The relative error between calculated values and experimental values is analyzed and the reasons of error are suggested. The research results show that the results of simulation at 600°C are approximately close to experimental results. The iron diffusion coefficient is discontinuous in the duplex scale of two ferritic-martensitic steels. The simulation results of thicknesses of the oxide scale on tubes (T91 of final superheater of a 600 MW supercritical boiler are compared with field measurement data and calculation results by Adrian’s method. The calculated void positions of oxide scales are in good agreement with a cross-sectional SEM image of the oxide layers.

PENGARUH PENGUNGKAPAN CORPORATE SOCIAL RESPONSIBILITY TERHADAP EARNING RESPONSE COEFFICIENT

Directory of Open Access Journals (Sweden)

MI Mitha Dwi Restuti

2012-03-01

Full Text Available Tujuan penelitian ini adalah untuk mengetahui pengaruh negatif pengungkapan Corporate Sosial Responsibility (CSR disclosure terhadap Earning Response Coefficient (ERC. Alat analisis yang digunakan dalam penelitian ini menggunakan metode analisis regresi berganda.Sampel yang digunakan adalah sebanyak 150 perusahaan yang terdaftar pada Bursa Efek Indonesia pada tahun 2010. Berdasarkan hasil penelitian ditemukan bahwa pengungkapan Corporate Social Responsibility tidak berpengaruh terhadap Earning Response Coefficient (ERC. Hal ini dapat dikatakan bahwa investor belum memperhatikan informasi-informasi sosial yang diungkapkan dalam laporan tahunan perusahaan sebagai informasi yang dapat mempengaruhi investor dalam melakukan keputusan investasi. Investor masih mengganggap informasi laba lebih bermanfaat dalam menilai perusahaan dan dianggap lebih mampu memberikan informasi untuk mendapatkan return saham yang diharapkan oleh investor dibandingkan dengan informasi sosial yang diungkapkan oleh perusahaan.The purpose of this study is to determine the negative effect of Corporate Social Responsibility disclosure (CSR disclosure of Earnings Response Coefficient (ERC. Multiple regressions were used to analyze the data. The samples were 150 companies listed on the Indonesia Stock Exchange in 2010. Based on the research, the result was the disclosures of Corporate Social Responsibility did not influence Earning Response Coefficient (ECR. It can be said that investors did not pay attention to social information that was disclosed in the company’s annual report as information that could affect investors in making investment decisions. Investor did not consider sosial information; they only consider profit information to assess the company value and their investment return

Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure.

Science.gov (United States)

Li, Yanming; Nan, Bin; Zhu, Ji

2015-06-01

We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. © 2015, The International Biometric Society.

Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression

International Nuclear Information System (INIS)

Guo, Yin; Nazarian, Ehsan; Ko, Jeonghan; Rajurkar, Kamlakar

2014-01-01

Highlights: • Developed hourly-indexed ARX models for robust cooling-load forecasting. • Proposed a two-stage weighted least-squares regression approach. • Considered the effect of outliers as well as trend of cooling load and weather patterns. • Included higher order terms and day type patterns in the forecasting models. • Demonstrated better accuracy compared with some ARX and ANN models. - Abstract: This paper presents a robust hourly cooling-load forecasting method based on time-indexed autoregressive with exogenous inputs (ARX) models, in which the coefficients are estimated through a two-stage weighted least squares regression. The prediction method includes a combination of two separate time-indexed ARX models to improve prediction accuracy of the cooling load over different forecasting periods. The two-stage weighted least-squares regression approach in this study is robust to outliers and suitable for fast and adaptive coefficient estimation. The proposed method is tested on a large-scale central cooling system in an academic institution. The numerical case studies show the proposed prediction method performs better than some ANN and ARX forecasting models for the given test data set

Multiple Response Regression for Gaussian Mixture Models with Known Labels.

Science.gov (United States)

Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng

2012-12-01

Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.

Marginalized zero-inflated negative binomial regression with application to dental caries.

Science.gov (United States)

Preisser, John S; Das, Kalyan; Long, D Leann; Divaris, Kimon

2016-05-10

The zero-inflated negative binomial regression model (ZINB) is often employed in diverse fields such as dentistry, health care utilization, highway safety, and medicine to examine relationships between exposures of interest and overdispersed count outcomes exhibiting many zeros. The regression coefficients of ZINB have latent class interpretations for a susceptible subpopulation at risk for the disease/condition under study with counts generated from a negative binomial distribution and for a non-susceptible subpopulation that provides only zero counts. The ZINB parameters, however, are not well-suited for estimating overall exposure effects, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. In this paper, a marginalized zero-inflated negative binomial regression (MZINB) model for independent responses is proposed to model the population marginal mean count directly, providing straightforward inference for overall exposure effects based on maximum likelihood estimation. Through simulation studies, the finite sample performance of MZINB is compared with marginalized zero-inflated Poisson, Poisson, and negative binomial regression. The MZINB model is applied in the evaluation of a school-based fluoride mouthrinse program on dental caries in 677 children. Copyright © 2015 John Wiley & Sons, Ltd.

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%.

Correlation of Benzene, 1,1,1-Trichloroethane, and Naphthalene Distribution Coefficients to the Characteristics of Aquifer Materials With Low Organic Carbon Content

DEFF Research Database (Denmark)

Larsen, Thomas; Kjeldsen, Peter; Christensen, Thomas Højlund

1992-01-01

area of the aquifer materials as a second regression parameter did not significantly improve the correlation. Estimated Koc values were up to 3 times higher than those predicted from regression equations based on the octanol-water partition coefficient. The reason for this is not known, but may...

Conservatism Accountancy, Profit Persistence and Systematic Risk Towards The Earnings Responses Coefficient

Directory of Open Access Journals (Sweden)

Sri Agustina Basuki

2017-09-01

Full Text Available The purpose of this research is to understand the influence of investor reaction towards profit that measured by the earning response coefficient with the variable of conservatism accountancy, persistence of profit and the systematic risk at the company, which have high market capitalization and listed in the LQ 45 index.  Population in the research are companies, which are listed in the LQ 45 index from the period of 2011 to 2015 that have complete financial information, and have financial notation in the form of Rupiah and excluded from the banking sector. The analysis method that being used is multiple linier regressions analysis and the result shows that conservatism accountancy partially significant affecting the Earning Response Coefficient. It shows that there is an investor reaction towards companies in the Index LQ 45, which applies conservatism accountancy in gaining profit.  Profit persistence and the systematic risk is not significantly affecting earnings response coefficient.

Data mining-based coefficient of influence factors optimization of test paper reliability

Science.gov (United States)

Xu, Peiyao; Jiang, Huiping; Wei, Jieyao

2018-05-01

Test is a significant part of the teaching process. It demonstrates the final outcome of school teaching through teachers' teaching level and students' scores. The analysis of test paper is a complex operation that has the characteristics of non-linear relation in the length of the paper, time duration and the degree of difficulty. It is therefore difficult to optimize the coefficient of influence factors under different conditions in order to get text papers with clearly higher reliability with general methods [1]. With data mining techniques like Support Vector Regression (SVR) and Genetic Algorithm (GA), we can model the test paper analysis and optimize the coefficient of impact factors for higher reliability. It's easy to find that the combination of SVR and GA can get an effective advance in reliability from the test results. The optimal coefficient of influence factors optimization has a practicability in actual application, and the whole optimizing operation can offer model basis for test paper analysis.

BANK FAILURE PREDICTION WITH LOGISTIC REGRESSION

Directory of Open Access Journals (Sweden)

Taha Zaghdoudi

2013-04-01

Full Text Available In recent years the economic and financial world is shaken by a wave of financial crisis and resulted in violent bank fairly huge losses. Several authors have focused on the study of the crises in order to develop an early warning model. It is in the same path that our work takes its inspiration. Indeed, we have tried to develop a predictive model of Tunisian bank failures with the contribution of the binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures. The results obtained using our provisional model show that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach.

Science.gov (United States)

Furmanchuk, Al'ona; Saal, James E; Doak, Jeff W; Olson, Gregory B; Choudhary, Alok; Agrawal, Ankit

2018-02-05

The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off-stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application (http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Marginal regression analysis of recurrent events with coarsened censoring times.

Science.gov (United States)

Hu, X Joan; Rosychuk, Rhonda J

2016-12-01

Motivated by an ongoing pediatric mental health care (PMHC) study, this article presents weakly structured methods for analyzing doubly censored recurrent event data where only coarsened information on censoring is available. The study extracted administrative records of emergency department visits from provincial health administrative databases. The available information of each individual subject is limited to a subject-specific time window determined up to concealed data. To evaluate time-dependent effect of exposures, we adapt the local linear estimation with right censored survival times under the Cox regression model with time-varying coefficients (cf. Cai and Sun, Scandinavian Journal of Statistics 2003, 30, 93-111). We establish the pointwise consistency and asymptotic normality of the regression parameter estimator, and examine its performance by simulation. The PMHC study illustrates the proposed approach throughout the article. © 2016, The International Biometric Society.

[Correlation of molecular weight and nanofiltration mass transfer coefficient of phenolic acid composition from Salvia miltiorrhiza].

Science.gov (United States)

Li, Cun-Yu; Wu, Xin; Gu, Jia-Mei; Li, Hong-Yang; Peng, Guo-Ping

2018-04-01

Based on the molecular sieving and solution-diffusion effect in nanofiltration separation, the correlation between initial concentration and mass transfer coefficient of three typical phenolic acids from Salvia miltiorrhiza was fitted to analyze the relationship among mass transfer coefficient, molecular weight and concentration. The experiment showed a linear relationship between operation pressure and membrane flux. Meanwhile, the membrane flux was gradually decayed with the increase of solute concentration. On the basis of the molecular sieving and solution-diffusion effect, the mass transfer coefficient and initial concentration of three phenolic acids showed a power function relationship, and the regression coefficients were all greater than 0.9. The mass transfer coefficient and molecular weight of three phenolic acids were negatively correlated with each other, and the order from high to low is protocatechualdehyde >rosmarinic acid> salvianolic acid B. The separation mechanism of nanofiltration for phenolic acids was further clarified through the analysis of the correlation of molecular weight and nanofiltration mass transfer coefficient. The findings provide references for nanofiltration separation, especially for traditional Chinese medicine with phenolic acids. Copyright© by the Chinese Pharmaceutical Association.

Regression models to predict the behavior of the coefficient of friction of AISI 316L on UHMWPE under ISO 14243-3 conditions.

Science.gov (United States)

Garcia-Garcia, A L; Alvarez-Vera, M; Montoya-Santiyanes, L A; Dominguez-Lopez, I; Montes-Seguedo, J L; Sosa-Savedra, J C; Barceinas-Sanchez, J D O

2018-06-01

Friction is the natural response of all tribosystems. In a total knee replacement (TKR) prosthetic device, its measurement is hindered by the complex geometry of its integrating parts and that of the testing simulation rig operating under the ISO 14243-3:2014 standard. To develop prediction models of the coefficient of friction (COF) between AISI 316L steel and ultra-high molecular weight polyethylene (UHMWPE) lubricated with fetal bovine serum dilutions, the arthrokinematics and loading conditions prescribed by the ISO 142433: 2014 standard were translated to a simpler geometrical setup, via Hertz contact theory. Tribological testing proceeded by loading a stainless steel AISI 316L ball against the surface of a UHMWPE disk, with the test fluid at 37 °C. The method has been applied to study the behavior of the COF during a whole walking cycle. On the other hand, the role of protein aggregation phenomena as a lubrication mechanism has been extensively studied in hip joint replacements but little explored for the operating conditions of a TKR. Lubricant testing fluids were prepared with fetal bovine serum (FBS) dilutions having protein mass concentrations of 5, 10, 20 and 36 g/L. The results were contrasted against deionized, sterilized water. The results indicate that even at protein concentration as low as 5 g/L, protein aggregation phenomena play an important role in the lubrication of the metal-on-polymer tribopair. The regression models of the COF developed herein are available for numerical simulations of the tribological behavior of the aforementioned tribosystem. In this case, surface stress rather than film thickness should be considered. Copyright © 2018 Elsevier Ltd. All rights reserved.

Some characteristics of uranium oxides in China

International Nuclear Information System (INIS)

Xu, Guoqing; Wang, Aizhen; Gu, Qifang; Zhang, Jingyi; Zhang, Zhaoming; Huang, Yuzhu

1981-01-01

According to the analytical data of seventy-seven samples from several tens of uranium ore deposits and occurrences in China, chemical properties, cell dimensions and reflectance of uranium oxides are studied. Chemical properties of uranium oxides from different types of uranium ore deposits and the influence of various mineralization ages and hosts on the compositions of uranium oxides are presented. The influence of these factor such as mineralization temperatures, the compositions of hosts and geochemical background on the compositions of uranium oxides are evident. Lead in proterozoic uranium oxides is relatively enriched by the decay of radio-active elements. Cell dimensions have positive correlation with mineralization ages, formation temperatures and concentration of rare earths and Pb, and negative correlation with the oxidation coefficient. The cell size is an exponential function of the content in CaO. It is suggested that among the factors of influence the most important is the mineralization temperature. The size of ionic radius of elements substituted U 4 + and autooxidation of U 4 + during the process of the decay of radioactive elements are of secondary importance. The reflectance is independent of the content of CaO and SiO 2 . The reflectance is positively correlative with the cell size and negatively correlative with oxidation coefficient. The relation between the reflectance and the content of PbO is logarithmic

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.

A hierarchical estimator development for estimation of tire-road friction coefficient.

Directory of Open Access Journals (Sweden)

Xudong Zhang

Full Text Available The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified "magic formula" tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method.

A hierarchical estimator development for estimation of tire-road friction coefficient.

Science.gov (United States)

Zhang, Xudong; Göhlich, Dietmar

2017-01-01

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified "magic formula" tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method.

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

Science.gov (United States)

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

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.

Regression: A Bibliography.

Science.gov (United States)

Pedrini, D. T.; Pedrini, Bonnie C.

Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

Mid-infrared response of reduced graphene oxide and its high-temperature coefficient of resistance

Directory of Open Access Journals (Sweden)

Haifeng Liang

2014-10-01

Full Text Available Much effort has been made to study the formation mechanisms of photocurrents in graphene and reduced graphene oxide films under visible and near-infrared light irradiation. A built-in field and photo-thermal electrons have been applied to explain the experiments. However, much less attention has been paid to clarifying the mid-infrared response of reduced graphene oxide films at room temperature. Thus, mid-infrared photoresponse and annealing temperature-dependent resistance experiments were carried out on reduced graphene oxide films. A maximum photocurrent of 75 μA was observed at room temperature, which was dominated by the bolometer effect, where the resistance of the films decreased as the temperature increased after they had absorbed light. The electrons localized in the defect states and the residual oxygen groups were thermally excited into the conduction band, forming a photocurrent. In addition, a temperature increase of 2 °C for the films after light irradiation for 2 minutes was observed using absorption power calculations. This work details a way to use reduced graphene oxide films that contain appropriate defects and residual oxygen groups as bolometer-sensitive materials in the mid-infrared range.

Geographically Weighted Logistic Regression Applied to Credit Scoring Models

Directory of Open Access Journals (Sweden)

Pedro Henrique Melo Albuquerque

Full Text Available Abstract This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC, granted to clients residing in the Distrito Federal (DF, to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower’s geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters, with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.

Modeling and data analysis of the NASA-WSTF frictional heating apparatus - Effects of test parameters on friction coefficient

Science.gov (United States)

Zhu, Sheng-Hu; Stoltzfus, Joel M.; Benz, Frank J.; Yuen, Walter W.

1988-01-01

A theoretical model is being developed jointly by the NASA White Sands Test Facility (WSTF) and the University of California at Santa Barbara (UCSB) to analyze data generated from the WSTF frictional heating test facility. Analyses of the data generated in the first seconds of the frictional heating test are shown to be effective in determining the friction coefficient between the rubbing interfaces. Different friction coefficients for carobn steel and Monel K-500 are observed. The initial condition of the surface is shown to affect only the initial value of the friction coefficient but to have no significant influence on the average steady-state friction coefficient. Rotational speed and the formation of oxide film on the rotating surfaces are shown to have a significant effect on the friction coefficient.

Association among retinol-binding protein 4, small dense LDL cholesterol and oxidized LDL levels in dyslipidemia subjects.

Science.gov (United States)

Wu, Jia; Shi, Yong-hui; Niu, Dong-mei; Li, Han-qing; Zhang, Chun-ni; Wang, Jun-jun

2012-06-01

To investigate retinol-binding protein 4 (RBP4), small dense low-density lipoprotein cholesterol (sdLDL-C) and oxidized low-density lipoprotein (ox-LDL) levels and their associations in dyslipidemia subjects. We determined RBP4, sdLDL-C, ox-LDL levels in 150 various dyslipidemia subjects and 50 controls. The correlation analysis and multiple linear regression analysis were performed. The RBP4, sdLDL-C and ox-LDL levels were found increased in various dyslipidemia subjects. The sdLDL-C levels were positively correlated with RBP4 (r=0.273, P=0.001) and ox-LDL (r=0.273, P=0.001). RBP4 levels were also correlated with ox-LDL (r=0.167, P=0.043). The multiple regression analysis showed that only sdLDL-C was a significant independent predictor for RBP4 (β coefficient=0.219, P=0.009; adjusted R(2)=0.041) and ox-LDL (β coefficient=0.253, P=0.003; adjusted R(2)=0.057) levels, respectively. The independent associations of sdLDL-C with RBP4 and ox-LDL were observed in dyslipidemia subjects. RBP4 may play an important role in lipid metabolism of atherosclerosis, particularly in formation of sdLDL. Copyright © 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

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.

On macroeconomic values investigation using fuzzy linear regression analysis

Directory of Open Access Journals (Sweden)

Richard Pospíšil

2017-06-01

Full Text Available The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets - fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used. The linear approximation of the vague function together with its possibility area is analytically and graphically expressed. A suitable application is performed in the tasks of the time series fuzzy regression analysis. The time-trend and seasonal cycles including their possibility areas are calculated and expressed. The examples are presented from the economy field, namely the time-development of unemployment, agricultural production and construction respectively between 2009 and 2011 in the Czech Republic. The results are shown in the form of the fuzzy regression models of variables of time series. For the period 2009-2011, the analysis assumptions about seasonal behaviour of variables and the relationship between them were confirmed; in 2010, the system behaved fuzzier and the relationships between the variables were vaguer, that has a lot of causes, from the different elasticity of demand, through state interventions to globalization and transnational impacts.

Advanced statistics: linear regression, part I: simple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

Science.gov (United States)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

An improved partial least-squares regression method for Raman spectroscopy

Science.gov (United States)

Momenpour Tehran Monfared, Ali; Anis, Hanan

2017-10-01

It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

Science.gov (United States)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Boosting structured additive quantile regression for longitudinal childhood obesity data.

Science.gov (United States)

Fenske, Nora; Fahrmeir, Ludwig; Hothorn, Torsten; Rzehak, Peter; Höhle, Michael

2013-07-25

Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.

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

Identification of cotton properties to improve yarn count quality by using regression analysis

International Nuclear Information System (INIS)

Amin, M.; Ullah, M.; Akbar, A.

2014-01-01

Identification of raw material characteristics towards yarn count variation was studied by using statistical techniques. Regression analysis is used to meet the objective. Stepwise regression is used for mode) selection, and coefficient of determination and mean squared error (MSE) criteria are used to identify the contributing factors of cotton properties for yam count. Statistical assumptions of normality, autocorrelation and multicollinearity are evaluated by using probability plot, Durbin Watson test, variance inflation factor (VIF), and then model fitting is carried out. It is found that, invisible (INV), nepness (Nep), grayness (RD), cotton trash (TR) and uniformity index (VI) are the main contributing cotton properties for yarn count variation. The results are also verified by Pareto chart. (author)

Linear regression based on Minimum Covariance Determinant (MCD) and TELBS methods on the productivity of phytoplankton

Science.gov (United States)

Gusriani, N.; Firdaniza

2018-03-01

The existence of outliers on multiple linear regression analysis causes the Gaussian assumption to be unfulfilled. If the Least Square method is forcedly used on these data, it will produce a model that cannot represent most data. For that, we need a robust regression method against outliers. This paper will compare the Minimum Covariance Determinant (MCD) method and the TELBS method on secondary data on the productivity of phytoplankton, which contains outliers. Based on the robust determinant coefficient value, MCD method produces a better model compared to TELBS method.

Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

Science.gov (United States)

Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi

2017-09-20

Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Kinetics and oxidation mechanisms of polycrystaline niobium

International Nuclear Information System (INIS)

Paschoal, J.O.A.

1979-01-01

The oxidation kinetics of annealed niobium was determined by thermogravimetric analysis between 450 and 800 0 C and for oxygen pressures varying from 20 to 700 mmHg. The oxidation kinetics of cold worked and/or irradiated niobium for temperatures between 500 and 700 0 C, with oxygen pressures varying from 100 to 300 mmHg. Was also determined. Using X-ray diffraction it was found that the oxide formed in the range of temperature and oxygen pressure considered in this research is γ-Nb 2 O 5 . Optical and scanning eletronic microscopy showed that for annealed niobium oxidized under 600 0 C there was formation of non-uniform oxide layers, containing cracks and pores, presenting very irregular metal/pentoxide interface. The presence of sub-oxide NbOsub(z) platelets was observed in this interface. This sub-oxide platelets where not observed in annealed oxidized niobium samples over 600 0 C; the oxide layers formed were compact. At 800 0 C and the beginning at 700 0 C the interfaces were quite regular. Through microhardness measurements for the metal near the metal/pentoxide interface, the formation of oxygen solid solution was found and the oxygen diffusion coefficient was calculated. The results showed that at 600 0 C the oxygen diffusion coefficient in cold worked niobium is three times larger than the value obtained for annealed niobium. The results suggest that the reaction between annealed niobium and oxygen undaer 600 0 C is controlled by reaction in interface where the oxide layers are not compacted, parcially due to Nb sub(z) platelets formation.(Author) [pt

Study of oxide/metal/oxide thin films for transparent electronics and solar cells applications by spectroscopic ellipsometry

Directory of Open Access Journals (Sweden)

Mihaela Girtan

2017-05-01

Full Text Available A comprehensive study of a class of Oxide/Metal/Oxide (Oxide = ITO, AZO, TiO2 and Bi2O3, Metal = Au thin films was done by correlating the spectrophotometric studies with the ellispometric models. Films were deposited by successive sputtering from metallic targets In:Sn, Zn:Al, Ti and Bi in reactive atmosphere (for the oxide films and respective inert atmosphere (for the metallic Au interlayer films on glass substrates. The measurements of optical constants n—the refractive index and k—the extinction coefficient, at different incident photon energies for single oxide films and also for the three layers films oxide/metal/oxide samples were made using the spectroscopic ellipsometry (SE technique. The ellipsometry modelling process was coupled with the recorded transmission spectra data of a double beam spectrophotometer and the best fitting parameters were obtained not only by fitting the n and k experimental data with the dispersion fitting curves as usual is practiced in the most reported data in literature, but also by comparing the calculated the transmission coefficient from ellipsometry with the experimental values obtained from direct spectrophotometry measurements. In this way the best dispersion model was deduced for each sample. Very good correlations were obtained for the other different thin films characteristics such as the films thickness, optical band gap and electrical resistivity obtained by other measurements and calculation techniques. The ellipsometric modelling, can hence give the possibility in the future to predict, by ellipsometric simulations, the proper device architecture in function of the preferred optical and electrical properties.

A special covariance structure for random coefficient models with both between and within covariates

International Nuclear Information System (INIS)

Riedel, K.S.

1990-07-01

We review random coefficient (RC) models in linear regression and propose a bias correction to the maximum likelihood (ML) estimator. Asymmptotic expansion of the ML equations are given when the between individual variance is much larger or smaller than the variance from within individual fluctuations. The standard model assumes all but one covariate varies within each individual, (we denote the within covariates by vector χ 1 ). We consider random coefficient models where some of the covariates do not vary in any single individual (we denote the between covariates by vector χ 0 ). The regression coefficients, vector β k , can only be estimated in the subspace X k of X. Thus the number of individuals necessary to estimate vector β and the covariance matrix Δ of vector β increases significantly in the presence of more than one between covariate. When the number of individuals is sufficient to estimate vector β but not the entire matrix Δ , additional assumptions must be imposed on the structure of Δ. A simple reduced model is that the between component of vector β is fixed and only the within component varies randomly. This model fails because it is not invariant under linear coordinate transformations and it can significantly overestimate the variance of new observations. We propose a covariance structure for Δ without these difficulties by first projecting the within covariates onto the space perpendicular to be between covariates. (orig.)

Mapping Surface Water DOC in the Northern Gulf of Mexico Using CDOM Absorption Coefficients and Remote Sensing Imagery

Science.gov (United States)

Kelly, B.; Chelsky, A.; Bulygina, E.; Roberts, B. J.

2017-12-01

Remote sensing techniques have become valuable tools to researchers, providing the capability to measure and visualize important parameters without the need for time or resource intensive sampling trips. Relationships between dissolved organic carbon (DOC), colored dissolved organic matter (CDOM) and spectral data have been used to remotely sense DOC concentrations in riverine systems, however, this approach has not been applied to the northern Gulf of Mexico (GoM) and needs to be tested to determine how accurate these relationships are in riverine-dominated shelf systems. In April, July, and October 2017 we sampled surface water from 80+ sites over an area of 100,000 km2 along the Louisiana-Texas shelf in the northern GoM. DOC concentrations were measured on filtered water samples using a Shimadzu TOC-VCSH analyzer using standard techniques. Additionally, DOC concentrations were estimated from CDOM absorption coefficients of filtered water samples on a UV-Vis spectrophotometer using a modification of the methods of Fichot and Benner (2011). These values were regressed against Landsat visible band spectral data for those same locations to establish a relationship between the spectral data, CDOM absorption coefficients. This allowed us to spatially map CDOM absorption coefficients in the Gulf of Mexico using the Landsat spectral data in GIS. We then used a multiple linear regressions model to derive DOC concentrations from the CDOM absorption coefficients and applied those to our map. This study provides an evaluation of the viability of scaling up CDOM absorption coefficient and remote-sensing derived estimates of DOC concentrations to the scale of the LA-TX shelf ecosystem.

Oxidation kinetics of polycyclic aromatic hydrocarbons by permanganate

Energy Technology Data Exchange (ETDEWEB)

Forsey, S.P.; Thomson, N.R.; Barker, J.F. [University of Waterloo, Waterloo, ON (Canada). Dept. of Civil & Environmental Engineering

2010-04-15

The reactivity of permanganate towards polycyclic aromatics hydrocarbons (PAHs) is well known but little kinetic information is available. This study investigated the oxidation kinetics of a selected group of coal tar creosote compounds and alkylbenzenes in water using permanganate, and the correlation between compound reactivity and physical/chemical properties. The oxidation of naphthalene, phenanthrene, chrysene, 1-methylnaphthalene, 2-methylnaphthalene, acenaphthene, fluorene, carbazole isopropylbenzene, ethylbenzene and methylbenzene closely followed pseudo first-order reaction kinetics. The oxidation of pyrene was initially very rapid and did not follow pseudo first-order kinetics at early times. Fluoranthene was only partially oxidized and the oxidation of anthracene was too fast to be captured. Biphenyl, dibenzofuran, benzene and tert-butylbenzene were non-reactive under the study conditions. The oxidation rate was shown to increase with increasing number of polycyclic rings because less energy is required to overcome the aromatic character of a polycyclic ring than is required for benzene. Thus the rate of oxidation increased in the series naphthalene < phenanthrene < pyrene. The rate of side chain reactivity is controlled by the C-H bond strength. For the alkyl substituted benzenes an excellent correlation was observed between the reaction rate coefficients and bond dissociation energies, but for the substituted PAHs the relationship was poor. A trend was found between the reaction rate coefficients and the calculated heats of complexation indicating that significant ring oxidation occurred in addition to side chain oxidation. Clar's aromatic sextet theory was used to predict the relative stability of arenes towards ring oxidation by permanganate.

Mass Transfer Coefficients and Bubble Sizes in Oxidative Ladle Refining of Silicon

OpenAIRE

Bjørnstad, Erlend Lunnan

2016-01-01

The mass transfer of [Al] and [Ca] between three synthetic SiO_{2}-CaO-Al_{2}O_{3} slags, and 8N silicon, has been investigated to find the overall mass transfer coefficient k_{i,t} for the individual species. Samples were kept at 1873K for 5, 10, 20, 30 and 180min before quenching. The metal phase was later analyzed by ICP-MS to view how the concentrations of impurities change with respect to time. This work then compares these results to industrial data gathered from ladles used for oxidati...

Multivariate linear regression of high-dimensional fMRI data with multiple target variables.

Science.gov (United States)

Valente, Giancarlo; Castellanos, Agustin Lage; Vanacore, Gianluca; Formisano, Elia

2014-05-01

Multivariate regression is increasingly used to study the relation between fMRI spatial activation patterns and experimental stimuli or behavioral ratings. With linear models, informative brain locations are identified by mapping the model coefficients. This is a central aspect in neuroimaging, as it provides the sought-after link between the activity of neuronal populations and subject's perception, cognition or behavior. Here, we show that mapping of informative brain locations using multivariate linear regression (MLR) may lead to incorrect conclusions and interpretations. MLR algorithms for high dimensional data are designed to deal with targets (stimuli or behavioral ratings, in fMRI) separately, and the predictive map of a model integrates information deriving from both neural activity patterns and experimental design. Not accounting explicitly for the presence of other targets whose associated activity spatially overlaps with the one of interest may lead to predictive maps of troublesome interpretation. We propose a new model that can correctly identify the spatial patterns associated with a target while achieving good generalization. For each target, the training is based on an augmented dataset, which includes all remaining targets. The estimation on such datasets produces both maps and interaction coefficients, which are then used to generalize. The proposed formulation is independent of the regression algorithm employed. We validate this model on simulated fMRI data and on a publicly available dataset. Results indicate that our method achieves high spatial sensitivity and good generalization and that it helps disentangle specific neural effects from interaction with predictive maps associated with other targets. Copyright © 2013 Wiley Periodicals, Inc.

Molar extinction coefficients and other properties of an improved reaction center preparation from Rhodopseudomonas viridis

Energy Technology Data Exchange (ETDEWEB)

Clayton, R.K.; Clayton, B.J.

1978-01-01

Reaction centers have been purified from chromatophores of Rhodopseudomonas viridis by treatment with lauryl dimethyl amine oxide followed by hydroxyapatite chromatography and precipitation with ammonium sulfate. The absorption spectrum at low temperature shows bands at 531 and 543 nm, assigned to two molecules of bacteriopheophytin b. The 600 nm band of bacteriochlorophyll b is resolved at low temperature into components at 601 and 606.5 nm. At room temperature the light-induced difference spectrum shows a negative band centered at 615 nm, where the absorption spectrum shows only a week shoulder adjacent to the 600 nm band. The fluorescence spectrum shows a band at 1000 nm and no fluorescence corresponding to the 830 nm absorption band. Two molecules of cytochrome 558 and three of cytochrome 552 accompany each reaction center. The differential extinction coefficient (reduced minus oxidized) of cytochrome 558 nm was estimated as 20 +- 2 mM/sup -1/.cm/sup -1/ through a coupled reaction with equine cytochrome c. The extinction coefficient of reaction centers at 960 nm was determined to be 123 +- 25 mM/sup -1/.cm/sup -1/ by measuring the light-induced bleaching of P-960 and the coupled oxidation of cytochrome 558. The corresponding extinction coefficient at 830 nm is 300 +- 65 mM/sup -1/.cm/sup -1/. The absorbance ratio ..cap alpha../sub 280nm/..cap alpha../sub 830nm/ in our preparations was 2.1, and there was 190 kg protein per mol of reaction centers. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis showed three major components of apparent molecular weights 31,000, 37,000, and 41,000.

Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

International Nuclear Information System (INIS)

Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

2007-01-01

Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

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.

TRANSPARENT CONDUCTING OXIDE SYNTHESIS OF ALUMINIUM DOPED ZINC OXIDES BY CHEMICAL COPRECIPITATION

Directory of Open Access Journals (Sweden)

Silvia Maioco

2013-03-01

Full Text Available Aluminium doped zinc oxides (AZO are promising replacements for tin doped indium oxides (ITO but thin films show a wide range of physical properties strongly dependent on deposition process conditions. Submicrometric 1% aluminum doped zinc oxide ceramics (AZO are examined, prepared by coprecipitation, from Zn(NO32 and Al(NO33 aqueous solutions, sintered at 1200°C and subsequently annealed in 10-16 atm controlled oxygen fugacity atmospheres, at 1000°C. Electrical resistivity diminishes by two orders of magnitude after two hours of annealing and the Seebeck coefficient gradually changes from -140 to -50 µV/K within 8 h. It is concluded that increased mobility is dominant over the increased carrier density, induced by changes in metal-oxygen stoichiometry

Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

Science.gov (United States)

Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

2013-09-01

Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p regression analysis showed a greater likelihood for minimum frontal breadth (p regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

Calculation of U, Ra, Th and K contents in uranium ore by multiple linear regression method

International Nuclear Information System (INIS)

Lin Chao; Chen Yingqiang; Zhang Qingwen; Tan Fuwen; Peng Guanghui

1991-01-01

A multiple linear regression method was used to compute γ spectra of uranium ore samples and to calculate contents of U, Ra, Th, and K. In comparison with the inverse matrix method, its advantage is that no standard samples of pure U, Ra, Th and K are needed for obtaining response coefficients

Polynomial regression analysis and significance test of the regression function

International Nuclear Information System (INIS)

Gao Zhengming; Zhao Juan; He Shengping

2012-01-01

In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

Significance tests to determine the direction of effects in linear regression models.

Science.gov (United States)

Wiedermann, Wolfgang; Hagmann, Michael; von Eye, Alexander

2015-02-01

Previous studies have discussed asymmetric interpretations of the Pearson correlation coefficient and have shown that higher moments can be used to decide on the direction of dependence in the bivariate linear regression setting. The current study extends this approach by illustrating that the third moment of regression residuals may also be used to derive conclusions concerning the direction of effects. Assuming non-normally distributed variables, it is shown that the distribution of residuals of the correctly specified regression model (e.g., Y is regressed on X) is more symmetric than the distribution of residuals of the competing model (i.e., X is regressed on Y). Based on this result, 4 one-sample tests are discussed which can be used to decide which variable is more likely to be the response and which one is more likely to be the explanatory variable. A fifth significance test is proposed based on the differences of skewness estimates, which leads to a more direct test of a hypothesis that is compatible with direction of dependence. A Monte Carlo simulation study was performed to examine the behaviour of the procedures under various degrees of associations, sample sizes, and distributional properties of the underlying population. An empirical example is given which illustrates the application of the tests in practice. © 2014 The British Psychological Society.

Magnitude conversion to unified moment magnitude using orthogonal regression relation

Science.gov (United States)

Das, Ranjit; Wason, H. R.; Sharma, M. L.

2012-05-01

Homogenization of earthquake catalog being a pre-requisite for seismic hazard assessment requires region based magnitude conversion relationships. Linear Standard Regression (SR) relations fail when both the magnitudes have measurement errors. To accomplish homogenization, techniques like Orthogonal Standard Regression (OSR) are thus used. In this paper a technique is proposed for using such OSR for preparation of homogenized earthquake catalog in moment magnitude Mw. For derivation of orthogonal regression relation between mb and Mw, a data set consisting of 171 events with observed body wave magnitudes (mb,obs) and moment magnitude (Mw,obs) values has been taken from ISC and GCMT databases for Northeast India and adjoining region for the period 1978-2006. Firstly, an OSR relation given below has been developed using mb,obs and Mw,obs values corresponding to 150 events from this data set. M=1.3(±0.004)m-1.4(±0.130), where mb,proxy are body wave magnitude values of the points on the OSR line given by the orthogonality criterion, for observed (mb,obs, Mw,obs) points. A linear relation is then developed between these 150 mb,obs values and corresponding mb,proxy values given by the OSR line using orthogonality criterion. The relation obtained is m=0.878(±0.03)m+0.653(±0.15). The accuracy of the above procedure has been checked with the rest of the data i.e., 21 events values. The improvement in the correlation coefficient value between mb,obs and Mw estimated using the proposed procedure compared to the correlation coefficient value between mb,obs and Mw,obs shows the advantage of OSR relationship for homogenization. The OSR procedure developed in this study can be used to homogenize any catalog containing various magnitudes (e.g., ML, mb, MS) with measurement errors, by their conversion to unified moment magnitude Mw. The proposed procedure also remains valid in case the magnitudes have measurement errors of different orders, i.e. the error variance ratio is

Thermal-hydraulics and neutronics studies on the FP7 CP-ESFR oxide and carbide cores

Energy Technology Data Exchange (ETDEWEB)

Ammirabile, L.; Tsige-Tamirat, H. [European Commission, JRC, Inst. for Energy, Petten (Netherlands)

2011-07-01

In the framework of the the Collaborative Project on European Sodium Fast Reactor (CP-ESFR) two core designs that are currently being proposed for the 3600 MWth sodium-cooled reactor concept: one is based on oxide fuel and the other on carbide fuel. Using the European Safety Assessment Platform (ESAP), JRC-IE has conducted static calculation on neutronics (incl. reactivity coefficients) and thermal-hydraulic characteristics for both oxide and carbide reference cores. The quantities evaluated include: keff, coolant heat-up, void, and Doppler reactivity coefficients, axial and radial expansion reactivity coefficients, pin-by-pin calculated power profiles, average and peak channel temperatures. This paper presents the ESAP models applied in the study together with the relevant results for the oxide and carbide core. (author)

Thermal-hydraulics and neutronics studies on the FP7 CP-ESFR oxide and carbide cores

International Nuclear Information System (INIS)

Ammirabile, L.; Tsige-Tamirat, H.

2011-01-01

In the framework of the the Collaborative Project on European Sodium Fast Reactor (CP-ESFR) two core designs that are currently being proposed for the 3600 MWth sodium-cooled reactor concept: one is based on oxide fuel and the other on carbide fuel. Using the European Safety Assessment Platform (ESAP), JRC-IE has conducted static calculation on neutronics (incl. reactivity coefficients) and thermal-hydraulic characteristics for both oxide and carbide reference cores. The quantities evaluated include: keff, coolant heat-up, void, and Doppler reactivity coefficients, axial and radial expansion reactivity coefficients, pin-by-pin calculated power profiles, average and peak channel temperatures. This paper presents the ESAP models applied in the study together with the relevant results for the oxide and carbide core. (author)

Bayesian semiparametric regression models to characterize molecular evolution

Directory of Open Access Journals (Sweden)

Datta Saheli

2012-10-01

Full Text Available Abstract Background Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. Results The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. Conclusions The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.

UV absorption coefficients of Y2(1-x-y)Gd2xEu2yO3 phosphors

International Nuclear Information System (INIS)

Ling, M.; Yocom, P.W.; Soules, T.F.

1990-01-01

The ability of a phosphor to absorb 254 nm excitation is important in the development of phosphors for fluorescent lamps. Recently the optical properties of phosphor coating were modeled using ray tracing Monte-Carlo techniques. These calculations provided a relationship between absorptance measured on a semi-infinite plaque at a given wavelength and the product of the absorption coefficient of the phosphor and its particle diameter. The purpose of this work is to provide experimental data for comparison with the calculated data, to demonstrate a technique for obtaining absorption coefficients and to provide UV absorption coefficients obtained in this way for important yttrium oxide europium red-emitting phosphors

Coefficient estimates of negative powers and inverse coefficients for ...

Indian Academy of Sciences (India)

and the inequality is sharp for the inverse of the Koebe function k(z) = z/(1 − z)2. An alternative approach to the inverse coefficient problem for functions in the class S has been investigated by Schaeffer and Spencer [27] and FitzGerald [6]. Although, the inverse coefficient problem for the class S has been completely solved ...

Exposure to polycyclic aromatic hydrocarbons, arsenic and environmental tobacco smoke, nutrient intake, and oxidative stress in Japanese preschool children.

Science.gov (United States)

Mori, Takuya; Yoshinaga, Jun; Suzuki, Kei; Mizoi, Miho; Adachi, Shu-Ichi; Tao, Hiroaki; Nakazato, Tetsuya; Li, Yun-Shan; Kawai, Kazuaki; Kasai, Hiroshi

2011-07-01

The association between oxidative stress and exposure to environmental chemicals was assessed in a group of Japanese preschool children. The concentrations of 8-hydroxy-2'-deoxyguanosine (8-OHdG), 1-hydroxypyrene (1-OHP), inorganic arsenic (iAs) and monomethylarsonic acid (MMA), and cotinine in spot urine samples, collected from 134 children (3-6 yrs) from a kindergarten in Kanagawa, Japan, were measured as biomarkers of oxidative stress or exposure to environmental chemicals. For 76 subjects of the 134, intakes of anti-oxidant nutrients (vitamins A, C, and E, manganese, copper, zinc and selenium (Se)) were estimated from a food consumption survey carried out 2-4 weeks after urine sampling and by urine analysis (Se). The median (min-max) creatinine-corrected concentrations of urinary biomarkers were 4.45 (1.98-12.3), 0.127 (0.04-2.41), 4.78 (1.18-12.7), and 0.62 (iAs+MMA, and cotinine, respectively. Multiple regression analysis was carried out using 8-OHdG concentration as a dependent variable and urinary biomarkers of exposure and Se intake, intakes of vitamins and biological attributes of the subjects as independent variables. To explain 8-OHdG concentrations, intake of vitamin A and age were significant variables with negative coefficients, while 1-OHP concentration had a positive coefficient. These results indicated that oxidative stress of children is affected by chemical exposure at environmental levels, by nutrient intake and by physiological factors in a complex manner. On the other hand, unstable statistical results due to sub-grouping of subject, based on the availability of food consumption data, were found: the present results should further be validated by future studies with suitable research design. Copyright © 2011 Elsevier B.V. All rights reserved.

Quantile Regression Methods

DEFF Research Database (Denmark)

Fitzenberger, Bernd; Wilke, Ralf Andreas

2015-01-01

if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

Converting Sabine absorption coefficients to random incidence absorption coefficients

DEFF Research Database (Denmark)

Jeong, Cheol-Ho

2013-01-01

are suggested: An optimization method for the surface impedances for locally reacting absorbers, the flow resistivity for extendedly reacting absorbers, and the flow resistance for fabrics. With four porous type absorbers, the conversion methods are validated. For absorbers backed by a rigid wall, the surface...... coefficients to random incidence absorption coefficients are proposed. The overestimations of the Sabine absorption coefficient are investigated theoretically based on Miki's model for porous absorbers backed by a rigid wall or an air cavity, resulting in conversion factors. Additionally, three optimizations...... impedance optimization produces the best results, while the flow resistivity optimization also yields reasonable results. The flow resistivity and flow resistance optimization for extendedly reacting absorbers are also found to be successful. However, the theoretical conversion factors based on Miki's model...

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

Optimization of Fenton's oxidation of herbicide dicamba in water using response surface methodology

Science.gov (United States)

Sangami, Sanjeev; Manu, Basavaraju

2017-12-01

In this study Fenton's oxidation of dicamba in aqueous medium was investigated by using the response surface methodology. The influence of H2O2/COD ( A), H2O2/Fe2+ ( B), pH ( C) and reaction time ( D) as independent variables were studied on two responses (COD and dicamba removal efficiency). The dosage of H2O2 (5.35-17.4 mM) and Fe2+ (0.09-2.13 mM) were varied and optimum percentage removal of dicamba of 84.01% with H2O2 and Fe2+ dosage of 11.38 and 0.33 mM respectively. The whole oxidation process was monitored by high performance liquid chromatography (HPLC) along with liquid chromatography/mass spectrometry (LC/MS). It was found that 82% of dicamba was mineralized to oxalic acid, chloride ion, CO2 and H2O, which was confirmed with COD removal of 81.53%. The regression analysis was performed, in which standard deviation (2.74), coefficient of correlation ( R 2 = R_{adj}2) and adequate precision (>12) were in good agreement with model values. Finally, the treatment process was validated by performing the additional experiments.

Empirical formulae for mass attenuation and energy absorption coefficients from 1 keV to 20 MeV

International Nuclear Information System (INIS)

Manjunatha, H.C.; Sowmya, N.; Seenappa, L.; Sridhar, K.N.; Hanumantharayappa, C.

2017-01-01

Mass attenuation and energy absorption coefficients represents attenuation and absorption of X-rays and gamma rays in the material medium. A new empirical formula is proposed for mass attenuation and energy absorption coefficients in the region 1 < Z < 92 and from 1 keV to 20 MeV. The mass attenuation and energy absorption coefficients do not varies linearly with energy. We have performed the nonlinear regressions/nonlinear least square fittings and proposed the simple empirical relations between mass attenuation coefficients (μ/ρ) and mass energy absorption coefficients (μ en /ρ) and energy. We have compared the values produced by this formula with that of experiments. A good agreement of present formula with the experiments/previous models suggests that the present formulae could be used to evaluate mass attenuation and energy absorption coefficients in the region 1 < Z < 92. This formula is a model-independent formula and is the first of its kind that produces a mass attenuation and energy absorption coefficient values with the only simple input of energy for wide energy range 1 keV - 20 MeV in the atomic number region 1 < Z < 92. This formula is very much useful in the fields of radiation physics and dosimetry

Random effects coefficient of determination for mixed and meta-analysis models.

Science.gov (United States)

Demidenko, Eugene; Sargent, James; Onega, Tracy

2012-01-01

The key feature of a mixed model is the presence of random effects. We have developed a coefficient, called the random effects coefficient of determination, [Formula: see text], that estimates the proportion of the conditional variance of the dependent variable explained by random effects. This coefficient takes values from 0 to 1 and indicates how strong the random effects are. The difference from the earlier suggested fixed effects coefficient of determination is emphasized. If [Formula: see text] is close to 0, there is weak support for random effects in the model because the reduction of the variance of the dependent variable due to random effects is small; consequently, random effects may be ignored and the model simplifies to standard linear regression. The value of [Formula: see text] apart from 0 indicates the evidence of the variance reduction in support of the mixed model. If random effects coefficient of determination is close to 1 the variance of random effects is very large and random effects turn into free fixed effects-the model can be estimated using the dummy variable approach. We derive explicit formulas for [Formula: see text] in three special cases: the random intercept model, the growth curve model, and meta-analysis model. Theoretical results are illustrated with three mixed model examples: (1) travel time to the nearest cancer center for women with breast cancer in the U.S., (2) cumulative time watching alcohol related scenes in movies among young U.S. teens, as a risk factor for early drinking onset, and (3) the classic example of the meta-analysis model for combination of 13 studies on tuberculosis vaccine.

Coefficient Alpha: A Reliability Coefficient for the 21st Century?

Science.gov (United States)

Yang, Yanyun; Green, Samuel B.

2011-01-01

Coefficient alpha is almost universally applied to assess reliability of scales in psychology. We argue that researchers should consider alternatives to coefficient alpha. Our preference is for structural equation modeling (SEM) estimates of reliability because they are informative and allow for an empirical evaluation of the assumptions…

A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

Science.gov (United States)

Meaney, Christopher; Moineddin, Rahim

2014-01-24

In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

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.

Regression Phalanxes

OpenAIRE

Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.

2017-01-01

Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Red River of the North at Fargo and Grand Forks, North Dakota, 2003-12

Science.gov (United States)

Galloway, Joel M.

2014-01-01

The Red River of the North (hereafter referred to as “Red River”) Basin is an important hydrologic region where water is a valuable resource for the region’s economy. Continuous water-quality monitors have been operated by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, Minnesota Pollution Control Agency, City of Fargo, City of Moorhead, City of Grand Forks, and City of East Grand Forks at the Red River at Fargo, North Dakota, from 2003 through 2012 and at Grand Forks, N.Dak., from 2007 through 2012. The purpose of the monitoring was to provide a better understanding of the water-quality dynamics of the Red River and provide a way to track changes in water quality. Regression equations were developed that can be used to estimate concentrations and loads for dissolved solids, sulfate, chloride, nitrate plus nitrite, total phosphorus, and suspended sediment using explanatory variables such as streamflow, specific conductance, and turbidity. Specific conductance was determined to be a significant explanatory variable for estimating dissolved solids concentrations at the Red River at Fargo and Grand Forks. The regression equations provided good relations between dissolved solid concentrations and specific conductance for the Red River at Fargo and at Grand Forks, with adjusted coefficients of determination of 0.99 and 0.98, respectively. Specific conductance, log-transformed streamflow, and a seasonal component were statistically significant explanatory variables for estimating sulfate in the Red River at Fargo and Grand Forks. Regression equations provided good relations between sulfate concentrations and the explanatory variables, with adjusted coefficients of determination of 0.94 and 0.89, respectively. For the Red River at Fargo and Grand Forks, specific conductance, streamflow, and a seasonal component were statistically significant explanatory variables for estimating chloride. For the Red River at Grand Forks, a time

Nitrous Oxide/Paraffin Hybrid Rocket Engines

Science.gov (United States)

Zubrin, Robert; Snyder, Gary

2010-01-01

Nitrous oxide/paraffin (N2OP) hybrid rocket engines have been invented as alternatives to other rocket engines especially those that burn granular, rubbery solid fuels consisting largely of hydroxyl- terminated polybutadiene (HTPB). Originally intended for use in launching spacecraft, these engines would also be suitable for terrestrial use in rocket-assisted takeoff of small airplanes. The main novel features of these engines are (1) the use of reinforced paraffin as the fuel and (2) the use of nitrous oxide as the oxidizer. Hybrid (solid-fuel/fluid-oxidizer) rocket engines offer advantages of safety and simplicity over fluid-bipropellant (fluid-fuel/fluid-oxidizer) rocket en - gines, but the thrusts of HTPB-based hybrid rocket engines are limited by the low regression rates of the fuel grains. Paraffin used as a solid fuel has a regression rate about 4 times that of HTPB, but pure paraffin fuel grains soften when heated; hence, paraffin fuel grains can, potentially, slump during firing. In a hybrid engine of the present type, the paraffin is molded into a 3-volume-percent graphite sponge or similar carbon matrix, which supports the paraffin against slumping during firing. In addition, because the carbon matrix material burns along with the paraffin, engine performance is not appreciably degraded by use of the matrix.

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).

Optimizing Oily Wastewater Treatment Via Wet Peroxide Oxidation Using Response Surface Methodology

International Nuclear Information System (INIS)

Shi, Jianzhong; Wang, Xiuqing; Wang, Xiaoyin

2014-01-01

The process of petroleum involves in a large amount of oily wastewater that contains high levels of chemical oxygen demand (COD) and toxic compounds. So they must be treated before their discharge into the receptor medium. In this paper, wet peroxide oxidation (WPO) was adopted to treat the oily wastewater. Central composite design, an experimental design for response surface methodology (RSM), was used to create a set of 31 experimental runs needed for optimizing of the operating conditions. Quadratic regression models with estimated coefficients were developed to describe the COD removals. The experimental results show that WPO could effectively reduce COD by 96.8% at the optimum conditions of temperature 290 .deg. C, H 2 O 2 excess (HE) 0.8, the initial concentration of oily wastewater 3855 mg/L and reaction time 9 min. RSM could be effectively adopted to optimize the operating multifactors in complex WPO process

Recurrence relations between transformation coefficients of hyperspherical harmonics and their application to Moshinsky coefficients

International Nuclear Information System (INIS)

Raynal, J.

1976-01-01

Closed formulae and recurrence relations for the transformation of a two-body harmonic oscillator wave function to the hyperspherical formalism are given. With them Moshinsky or Smirnov coefficients are obtained from the transformation coefficients of hyperspheric harmonics. For these coefficients the diagonalization method of Talman and Lande reduces to simple recurrence relations which can be used directly to compute them. New closed formulae for these coefficients are also derived: they are needed to compute the two simplest coefficients which determine the sign for the recurrence relation. (Auth.)

Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions.

Science.gov (United States)

Xia, Yin; Cai, Tianxi; Cai, T Tony

2018-01-01

Motivated by applications in genomics, we consider in this paper global and multiple testing for the comparisons of two high-dimensional linear regression models. A procedure for testing the equality of the two regression vectors globally is proposed and shown to be particularly powerful against sparse alternatives. We then introduce a multiple testing procedure for identifying unequal coordinates while controlling the false discovery rate and false discovery proportion. Theoretical justifications are provided to guarantee the validity of the proposed tests and optimality results are established under sparsity assumptions on the regression coefficients. The proposed testing procedures are easy to implement. Numerical properties of the procedures are investigated through simulation and data analysis. The results show that the proposed tests maintain the desired error rates under the null and have good power under the alternative at moderate sample sizes. The procedures are applied to the Framingham Offspring study to investigate the interactions between smoking and cardiovascular related genetic mutations important for an inflammation marker.

Auto-associative Kernel Regression Model with Weighted Distance Metric for Instrument Drift Monitoring

International Nuclear Information System (INIS)

Shin, Ho Cheol; Park, Moon Ghu; You, Skin

2006-01-01

Recently, many on-line approaches to instrument channel surveillance (drift monitoring and fault detection) have been reported worldwide. On-line monitoring (OLM) method evaluates instrument channel performance by assessing its consistency with other plant indications through parametric or non-parametric models. The heart of an OLM system is the model giving an estimate of the true process parameter value against individual measurements. This model gives process parameter estimate calculated as a function of other plant measurements which can be used to identify small sensor drifts that would require the sensor to be manually calibrated or replaced. This paper describes an improvement of auto associative kernel regression (AAKR) by introducing a correlation coefficient weighting on kernel distances. The prediction performance of the developed method is compared with conventional auto-associative kernel regression

Conjugated polymer/graphene oxide nanocomposite as thermistor

Energy Technology Data Exchange (ETDEWEB)

Joshi, Girish M., E-mail: varadgm@gmail.com; Deshmukh, Kalim [Polymer Nanocomposite Laboratory, Material Physics Division, School of Advanced Sciences, VIT University, Vellore - 632014, TN (India)

2015-06-24

We demonstrated the synthesis and measurement of temperature dependent electrical resistivity of graphene oxide (GO) reinforced poly (3, 4 - ethylenedioxythiophene) - tetramethacrylate (PEDOTTMA)/Polymethylmethacrylate (PMMA) based nanocomposites. Negative temperature coefficient (NTC) was observed for 0.5, 1 % GO loading and the positive temperature coefficient (PTC) was observed for 1.5 and 2 % Go loading in the temperature (40 to 120 °C). The GO inducted nanocomposite perform as an excellent thermistor and suitable for electronic and sensor domain.

Conjugated polymer/graphene oxide nanocomposite as thermistor

International Nuclear Information System (INIS)

Joshi, Girish M.; Deshmukh, Kalim

2015-01-01

We demonstrated the synthesis and measurement of temperature dependent electrical resistivity of graphene oxide (GO) reinforced poly (3, 4 - ethylenedioxythiophene) - tetramethacrylate (PEDOTTMA)/Polymethylmethacrylate (PMMA) based nanocomposites. Negative temperature coefficient (NTC) was observed for 0.5, 1 % GO loading and the positive temperature coefficient (PTC) was observed for 1.5 and 2 % Go loading in the temperature (40 to 120 °C). The GO inducted nanocomposite perform as an excellent thermistor and suitable for electronic and sensor domain

Comparison of field-measured radon diffusion coefficients with laboratory-measured coefficients

International Nuclear Information System (INIS)

Lepel, E.A.; Silker, W.B.; Thomas, V.W.; Kalkwarf, D.R.

1983-04-01

Experiments were conducted to compare radon diffusion coefficients determined for 0.1-m depths of soils by a steady-state method in the laboratory and diffusion coefficients evaluated from radon fluxes through several-fold greater depths of the same soils covering uranium-mill tailings. The coefficients referred to diffusion in the total pore volume of the soils and are equivalent to values for the quantity, D/P, in the Generic Environmental Impact Statement on Uranium Milling prepared by the US Nuclear Regulatory Commission. Two soils were tested: a well-graded sand and an inorganic clay of low plasticity. For the flux evaluations, radon was collected by adsorption on charcoal following passive diffusion from the soil surface and also from air recirculating through an aluminum tent over the soil surface. An analysis of variance in the flux evaluations showed no significant difference between these two collection methods. Radon diffusion coefficients evaluated from field data were statistically indistinguishable, at the 95% confidence level, from those measured in the laboratory; however, the low precision of the field data prevented a sensitive validation of the laboratory measurements. From the field data, the coefficients were calculated to be 0.03 +- 0.03 cm 2 /s for the sand cover and 0.0036 +- 0.0004 cm 2 /s for the clay cover. The low precision in the coefficients evaluated from field data was attributed to high variation in radon flux with time and surface location at the field site

Improved model of the retardance in citric acid coated ferrofluids using stepwise regression

Science.gov (United States)

Lin, J. F.; Qiu, X. R.

2017-06-01

Citric acid (CA) coated Fe3O4 ferrofluids (FFs) have been conducted for biomedical application. The magneto-optical retardance of CA coated FFs was measured by a Stokes polarimeter. Optimization and multiple regression of retardance in FFs were executed by Taguchi method and Microsoft Excel previously, and the F value of regression model was large enough. However, the model executed by Excel was not systematic. Instead we adopted the stepwise regression to model the retardance of CA coated FFs. From the results of stepwise regression by MATLAB, the developed model had highly predictable ability owing to F of 2.55897e+7 and correlation coefficient of one. The average absolute error of predicted retardances to measured retardances was just 0.0044%. Using the genetic algorithm (GA) in MATLAB, the optimized parametric combination was determined as [4.709 0.12 39.998 70.006] corresponding to the pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The maximum retardance was found as 31.712°, close to that obtained by evolutionary solver in Excel and a relative error of -0.013%. Above all, the stepwise regression method was successfully used to model the retardance of CA coated FFs, and the maximum global retardance was determined by the use of GA.

A parameterization scheme for the x-ray linear attenuation coefficient and energy absorption coefficient.

Science.gov (United States)

Midgley, S M

2004-01-21

A novel parameterization of x-ray interaction cross-sections is developed, and employed to describe the x-ray linear attenuation coefficient and mass energy absorption coefficient for both elements and mixtures. The new parameterization scheme addresses the Z-dependence of elemental cross-sections (per electron) using a simple function of atomic number, Z. This obviates the need for a complicated mathematical formalism. Energy dependent coefficients describe the Z-direction curvature of the cross-sections. The composition dependent quantities are the electron density and statistical moments describing the elemental distribution. We show that it is possible to describe elemental cross-sections for the entire periodic table and at energies above the K-edge (from 6 keV to 125 MeV), with an accuracy of better than 2% using a parameterization containing not more than five coefficients. For the biologically important elements 1 coefficients. At higher energies, the parameterization uses fewer coefficients with only two coefficients needed at megavoltage energies.

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.

Boosted beta regression.

Directory of Open Access Journals (Sweden)

Matthias Schmid

Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

Regression to Causality : Regression-style presentation influences causal attribution

DEFF Research Database (Denmark)

Bordacconi, Mats Joe; Larsen, Martin Vinæs

2014-01-01

of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

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.

Measurement of gas/water uptake coefficients for trace gases active in the marine environment

Energy Technology Data Exchange (ETDEWEB)

Davidovits, P. (Boston Coll., Chestnut Hill, MA (United States). Dept. of Chemistry); Worsnop, D.W.; Zahniser, M.S.; Kolb, C.E. (Aerodyne Research, Inc., Billerica, MA (United States). Center for Chemical and Environmental Physics)

1992-02-01

Ocean produced reduced sulfur compounds including dimethylsulfide (DMS), hydrogen sulfide (H{sub 2}S), carbon disulfide (CS{sub 2}), methyl mercaptan (CH{sub 3}CH) and carbonyl sulfide (OCS) deliver a sulfur burden to the atmosphere which is roughly equal to sulfur oxides produced by fossil fuel combustion. These species and their oxidation products dimethyl sulfoxide (DMSO), dimethyl sulfone (DMSO{sub 2}) and methane sulfonic acid (MSA) dominate aerosol and CCN production in clean marine air. Furthermore, oxidation of reduced sulfur species will be strongly influenced by NO{sub x}/O{sub 3} chemistry in marine atmospheres. The multiphase chemical processes for these species must be understood in order to study the evolving role of combustion produced sulfur oxides over the oceans. We have measured the chemical and physical parameters affecting the uptake of reduced sulfur compounds, their oxidation products, ozone, and nitrogen oxides by the ocean's surface, and marine clouds, fogs, and aerosols. These parameters include: gas/surface mass accommodation coefficients; physical and chemically modified (effective) Henry's law constants; and surface and liquid phase reaction constants. These parameters are critical to understanding both the interaction of gaseous trace species with cloud and fog droplets and the deposition of trace gaseous species to dew covered, fresh water and marine surfaces.

Non-stationary hydrologic frequency analysis using B-spline quantile regression

Science.gov (United States)

Nasri, B.; Bouezmarni, T.; St-Hilaire, A.; Ouarda, T. B. M. J.

2017-11-01

Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic and water resources systems under the assumption of stationarity. However, with increasing evidence of climate change, it is possible that the assumption of stationarity, which is prerequisite for traditional frequency analysis and hence, the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extremes based on B-Spline quantile regression which allows to model data in the presence of non-stationarity and/or dependence on covariates with linear and non-linear dependence. A Markov Chain Monte Carlo (MCMC) algorithm was used to estimate quantiles and their posterior distributions. A coefficient of determination and Bayesian information criterion (BIC) for quantile regression are used in order to select the best model, i.e. for each quantile, we choose the degree and number of knots of the adequate B-spline quantile regression model. The method is applied to annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for an annual maximum and minimum discharge with high annual non-exceedance probabilities.

Robust best linear estimation for regression analysis using surrogate and instrumental variables.

Science.gov (United States)

Wang, C Y

2012-04-01

We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the calibration sample, we assume that there is an instrumental variable (IV) that is available for all study subjects. An IV is correlated with the unobserved true exposure variable and hence can be useful in the estimation of the regression coefficients. We propose a robust best linear estimator that uses all the available data, which is the most efficient among a class of consistent estimators. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. For Poisson or linear regression, the proposed estimator is consistent even if the measurement error from the surrogate or IV is heteroscedastic. Finite-sample performance of the proposed estimator is examined and compared with other estimators via intensive simulation studies. The proposed method and other methods are applied to a bladder cancer case-control study.

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

Sensitivity analysis of an experimental methodology to determine radionuclide diffusion coefficients in granite

International Nuclear Information System (INIS)

Alonso, U.; Missana, T.; Garcia-Gutierrez, M.; Patelli, A.; Rigato, V.

2005-01-01

Full text of publication follows: The long-term quantitative analysis of the migration behaviour of the relevant radionuclides (RN) within the geological barrier of a radioactive waste repository requires, amongst other data, the introduction of reliable transport parameters, as diffusion coefficients. Since the determination of diffusion coefficients within crystalline rocks is complex and requires long experimental times even for non-sorbing radionuclides, the data available in the literature are very scarce. The nuclear ion beam technique RBS (Rutherford Backscattering Spectrometry) that is successfully used to determine diffusion profiles in thin film science is here examined as possible suitable technique to determine the diffusion coefficients of different RN within granite. As first step, the technique sensitivity and limitations to analyse diffusion coefficients in granite samples is evaluated, considering that the technique is especially sensitive to heavy elements. The required experimental conditions in terms of experimental times, concentration and methodology of analysis are discussed. The diffusants were selected accounting the RBS sensitivity but also trying to cover different behaviours of critical RN and a wide range of possible oxidation states. In particular, Cs(I) was chosen as representative fission product, while as relevant actinides or homologues, the diffusion of Th(IV), U(IV) and Eu (III) was studied. The diffusion of these above-mentioned cations is compared to the diffusion of Re, and I as representative of anionic species. The methodology allowed evaluating diffusion coefficients in the granite samples and, for most of the elements, the values obtained are in agreement with the values found in the literature. The diffusion coefficients calculated ranged from 10 -13 to 10 -16 m 2 /s. It is remarkable that the RBS technique is especially promising to determine diffusion coefficients of high-sorbing RN and it is applicable to a wide range

Capacitance Regression Modelling Analysis on Latex from Selected Rubber Tree Clones

International Nuclear Information System (INIS)

Rosli, A D; Baharudin, R; Hashim, H; Khairuzzaman, N A; Mohd Sampian, A F; Abdullah, N E; Kamaru'zzaman, M; Sulaiman, M S

2015-01-01

This paper investigates the capacitance regression modelling performance of latex for various rubber tree clones, namely clone 2002, 2008, 2014 and 3001. Conventionally, the rubber tree clones identification are based on observation towards tree features such as shape of leaf, trunk, branching habit and pattern of seeds texture. The former method requires expert persons and very time-consuming. Currently, there is no sensing device based on electrical properties that can be employed to measure different clones from latex samples. Hence, with a hypothesis that the dielectric constant of each clone varies, this paper discusses the development of a capacitance sensor via Capacitance Comparison Bridge (known as capacitance sensor) to measure an output voltage of different latex samples. The proposed sensor is initially tested with 30ml of latex sample prior to gradually addition of dilution water. The output voltage and capacitance obtained from the test are recorded and analyzed using Simple Linear Regression (SLR) model. This work outcome infers that latex clone of 2002 has produced the highest and reliable linear regression line with determination coefficient of 91.24%. In addition, the study also found that the capacitive elements in latex samples deteriorate if it is diluted with higher volume of water. (paper)

Distributed Monitoring of the R(sup 2) Statistic for Linear Regression

Science.gov (United States)

Bhaduri, Kanishka; Das, Kamalika; Giannella, Chris R.

2011-01-01

The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more dependent target variables. This problem becomes challenging for large scale data in a distributed computing environment when only a subset of instances is available at individual nodes and the local data changes frequently. Data centralization and periodic model recomputation can add high overhead to tasks like anomaly detection in such dynamic settings. Therefore, the goal is to develop techniques for monitoring and updating the model over the union of all nodes data in a communication-efficient fashion. Correctness guarantees on such techniques are also often highly desirable, especially in safety-critical application scenarios. In this paper we develop DReMo a distributed algorithm with very low resource overhead, for monitoring the quality of a regression model in terms of its coefficient of determination (R2 statistic). When the nodes collectively determine that R2 has dropped below a fixed threshold, the linear regression model is recomputed via a network-wide convergecast and the updated model is broadcast back to all nodes. We show empirically, using both synthetic and real data, that our proposed method is highly communication-efficient and scalable, and also provide theoretical guarantees on correctness.

Iridescent cellulose nanocrystal/polyethylene oxide composite films with low coefficient of thermal expansion

Science.gov (United States)

Jairo A. Diaz; Julia L. Braun; Robert J. Moon; Jeffrey P. Youngblood

2015-01-01

Simultaneous control over optical and thermal properties is particularly challenging and highly desired in fields like organic electronics. Here we incorporated cellulose nanocrystals (CNCs) into polyethylene oxide (PEO) in an attempt to preserve the iridescent CNC optical reflection given by their chiral nematic organisation, while reducing the composite thermal...

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

Full Text Available Introduction: Due to sensitiveness of flow to roughness coefficient (RC, selection of this coefficient is important in earth canals designing purposes. Precision selection of this coefficient is necessary for design and operation of earthen canals purposes. Overestimation of the actual amount of this coefficient will cause an underestimation for flow velocity. Accordingly, sedimentation in the earth canals will reduce canals’ capacitances. Adversely, underestimation of this coefficient will cause an overestimation for flow velocity and water flux in the earth canals. It will also increase the risk of soil erosion in the channels. This coefficient is expressed by Manning, Chezy and Darcy Weisbach equations. While, hydraulic engineers have selected Manning equation to estimate the flow rate in open channels due to ease of use and acceptable precision in the application of this equation. Water for crop production in Moghan, as one of the most important agricultural centers in Iran, is supplied from Moghan-Meel diversion dam via main canal of irrigation and drainage network with a capacity of 80 m3 s-1 with a length of 116 km. All of the branched 63-channel from the main channel are earthen. Continual sedimentation in the earth canals reduced the capacity of them and re-estimation the capacity of this canals needs to the precise quantities of variables such as roughness coefficient. Because the overestimation of the actual value of the coefficient would reduce the canals’ capacity and underestimation of the coefficient increase the risk of erosion in earth canals. The analysis of the correlation among variables, regression, analysis of statistical distribution of variables, analysis of variance of variables and the analysis of the events probabilities for stochastic variables can be made by statistical methods. Therefore, these methods were applied to analysis of roughness coefficient in the earth canals. Also, due to the importance of roughness

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

Lutetium oxide-based transparent ceramic scintillators

Science.gov (United States)

Seeley, Zachary; Cherepy, Nerine; Kuntz, Joshua; Payne, Stephen A.

2016-01-19

In one embodiment, a transparent ceramic of sintered nanoparticles includes gadolinium lutetium oxide doped with europium having a chemical composition (Lu.sub.1-xGd.sub.x).sub.2-YEu.sub.YO.sub.3, where X is any value within a range from about 0.05 to about 0.45 and Y is any value within a range from about 0.01 to about 0.2, and where the transparent ceramic exhibits a transparency characterized by a scatter coefficient of less than about 10%/cm. In another embodiment, a transparent ceramic scintillator of sintered nanoparticles, includes a body of sintered nanoparticles including gadolinium lutetium oxide doped with a rare earth activator (RE) having a chemical composition (Lu.sub.1-xGd.sub.x).sub.2-YRE.sub.YO.sub.3, where RE is selected from the group consisting of: Sm, Eu, Tb, and Dy, where the transparent ceramic exhibits a transparency characterized by a scatter coefficient of less than about 10%/cm.

The Truth About Ballistic Coefficients

OpenAIRE

Courtney, Michael; Courtney, Amy

2007-01-01

The ballistic coefficient of a bullet describes how it slows in flight due to air resistance. This article presents experimental determinations of ballistic coefficients showing that the majority of bullets tested have their previously published ballistic coefficients exaggerated from 5-25% by the bullet manufacturers. These exaggerated ballistic coefficients lead to inaccurate predictions of long range bullet drop, retained energy and wind drift.

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.

A primer for biomedical scientists on how to execute model II linear regression analysis.

Science.gov (United States)

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Dental age assessment of young Iranian adults using third molars: A multivariate regression study.

Science.gov (United States)

Bagherpour, Ali; Anbiaee, Najmeh; Partovi, Parnia; Golestani, Shayan; Afzalinasab, Shakiba

2012-10-01

In recent years, a noticeable increase in forensic age estimations of living individuals has been observed. Radiologic assessment of the mineralisation stage of third molars is of particular importance, with regard to the relevant age group. To attain a referral database and regression equations for dental age estimation of unaccompanied minors in an Iranian population was the goal of this study. Moreover, determination was made concerning the probability of an individual being over the age of 18 in case of full third molar(s) development. Using the scoring system of Gleiser and Hunt, modified by Köhler, an investigation of a cross-sectional sample of 1274 orthopantomograms of 885 females and 389 males aged between 15 and 22 years was carried out. Using kappa statistics, intra-observer reliability was tested. With Spearman correlation coefficient, correlation between the scores of all four wisdom teeth, was evaluated. We also carried out the Wilcoxon signed-rank test on asymmetry and calculated the regression formulae. A strong intra-observer agreement was displayed by the kappa value. No significant difference (p-value for upper and lower jaws were 0.07 and 0.59, respectively) was discovered by Wilcoxon signed-rank test for left and right asymmetry. The developmental stage of upper right and upper left third molars yielded the greatest correlation coefficient. The probability of an individual being over the age of 18 is 95.6% for males and 100.0% for females in case four fully developed third molars are present. Taking into consideration gender, location and number of wisdom teeth, regression formulae were arrived at. Use of population-specific standards is recommended as a means of improving the accuracy of forensic age estimates based on third molars mineralisation. To obtain more exact regression formulae, wider age range studies are recommended. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Lorenz curve and Gini coefficient reveal hot spots and hot moments for nitrous oxide emissions

Science.gov (United States)

Identifying hot spots and hot moments of N2O emissions in the landscape is critical for monitoring and mitigating the emission of this powerful greenhouse gas. We propose a novel use of the Lorenz curve and Gini coefficient (G) to quantify the heterogeneous distribution of N2O emissions from a lands...

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.

Experimental Investigation of Discharge Coefficient in Mesh Panel Bottom Intakes

Directory of Open Access Journals (Sweden)

keivan bina

2012-04-01

Full Text Available Bottom racks is a hydraulic structure which is placed in the bed of stream through which, part of flow in the main channel is diverted. These structures have very wide application in industry, irrigation, drainage and etc. Of course much attention had been paid to the study of such structures, but characteristics of flow through bottom racks are complex. The present study was directed to estimate the discharge coefficient of a new kind of bottom racks including both transverse and longitudinal bars that named "mesh panel racks" without considering any solids in the fluid. This kind of bottom intake has advantages from structural point of view and has less deformation under static and dynamic loads. Laboratory setup with three mesh panel intakes was built and the effects of various parameters such as racks slope, porosity and geometry were explored. A dimensional analysis using Buckingham theory showed the effective hydraulic and geometric factors that affect the discharge coefficient (Cd of bottom racks. Then, a statistical approach to determine the discharge coefficient of a rack structure was developed with linear and nonlinear regression using SPSS software. The efficiency of the proposed technique is high enough that the associated error is limited to 10%. Finally, hydraulic performance of mesh panel intakes was compared with regular type of bottom intakes, which consist of longitudinal bars. For this purpose, diverted discharge through both type of intakes calculated in same situation

Development of database on the distribution coefficient. 1. Collection of the distribution coefficient data

Energy Technology Data Exchange (ETDEWEB)

Takebe, Shinichi; Abe, Masayoshi [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2001-03-01

The distribution coefficient is very important parameter for environmental impact assessment on the disposal of radioactive waste arising from research institutes. The literature survey in the country was mainly carried out for the purpose of selecting the reasonable distribution coefficient value on the utilization of this value in the safety evaluation. This report was arranged much informations on the distribution coefficient for inputting to the database for each literature, and was summarized as a literature information data on the distribution coefficient. (author)

Tuning the nonlinear optical absorption of reduced graphene oxide by chemical reduction.

Science.gov (United States)

Shi, Hongfei; Wang, Can; Sun, Zhipei; Zhou, Yueliang; Jin, Kuijuan; Redfern, Simon A T; Yang, Guozhen

2014-08-11

Reduced graphene oxides with varying degrees of reduction have been produced by hydrazine reduction of graphene oxide. The linear and nonlinear optical properties of both graphene oxide as well as the reduced graphene oxides have been measured by single beam Z-scan measurement in the picosecond region. The results reveal both saturable absorption and two-photon absorption, strongly dependent on the intensity of the pump pulse: saturable absorption occurs at lower pump pulse intensity (~1.5 GW/cm2 saturation intensity) whereas two-photon absorption dominates at higher intensities (≥5.7 GW/cm2). Intriguingly, we find that the two-photon absorption coefficient (from 1.5 cm/GW to 4.5cm/GW) and the saturation intensity (from 1 GW/cm2 to 2 GW/cm2) vary with chemical reduction, which is ascribed to the varying concentrations of sp2 domains and sp2 clusters in the reduced graphene oxides. Our results not only provide an insight into the evolution of the nonlinear optical coefficient in reduced graphene oxide, but also suggest that chemical engineering techniques may usefully be applied to tune the nonlinear optical properties of various nano-materials, including atomically thick graphene sheets.

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.

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.

Evaluating the Applicability of Phi Coefficient in Indicating Habitat Preferences of Forest Soil Fauna Based on a Single Field Study in Subtropical China.

Science.gov (United States)

Cui, Yang; Wang, Silong; Yan, Shaokui

2016-01-01

Phi coefficient directly depends on the frequencies of occurrence of organisms and has been widely used in vegetation ecology to analyse the associations of organisms with site groups, providing a characterization of ecological preference, but its application in soil ecology remains rare. Based on a single field experiment, this study assessed the applicability of phi coefficient in indicating the habitat preferences of soil fauna, through comparing phi coefficient-induced results with those of ordination methods in charactering soil fauna-habitat(factors) relationships. Eight different habitats of soil fauna were implemented by reciprocal transfer of defaunated soil cores between two types of subtropical forests. Canonical correlation analysis (CCorA) showed that ecological patterns of fauna-habitat relationships and inter-fauna taxa relationships expressed, respectively, by phi coefficients and predicted abundances calculated from partial redundancy analysis (RDA), were extremely similar, and a highly significant relationship between the two datasets was observed (Pillai's trace statistic = 1.998, P = 0.007). In addition, highly positive correlations between phi coefficients and predicted abundances for Acari, Collembola, Nematode and Hemiptera were observed using linear regression analysis. Quantitative relationships between habitat preferences and soil chemical variables were also obtained by linear regression, which were analogous to the results displayed in a partial RDA biplot. Our results suggest that phi coefficient could be applicable on a local scale in evaluating habitat preferences of soil fauna at coarse taxonomic levels, and that the phi coefficient-induced information, such as ecological preferences and the associated quantitative relationships with habitat factors, will be largely complementary to the results of ordination methods. The application of phi coefficient in soil ecology may extend our knowledge about habitat preferences and distribution

High-Temperature Oxidation-Resistant and Low Coefficient of Thermal Expansion NiAl-Base Bond Coat Developed for a Turbine Blade Application

Science.gov (United States)

2003-01-01

Many critical gas turbine engine components are currently made from Ni-base superalloys that are coated with a thermal barrier coating (TBC). The TBC consists of a ZrO2-based top coat and a bond coat that is used to enhance the bonding between the superalloy substrate and the top coat. MCrAlY alloys (CoCrAlY and NiCrAlY) are currently used as bond coats and are chosen for their very good oxidation resistance. TBC life is frequently limited by the oxidation resistance of the bond coat, along with a thermal expansion mismatch between the metallic bond coat and the ceramic top coat. The aim of this investigation at the NASA Glenn Research Center was to develop a new longer life, higher temperature bond coat by improving both the oxidation resistance and the thermal expansion characteristics of the bond coat. Nickel aluminide (NiAl) has excellent high-temperature oxidation resistance and can sustain a protective Al2O3 scale to longer times and higher temperatures in comparison to MCrAlY alloys. Cryomilling of NiAl results in aluminum nitride (AlN) formation that reduces the coefficient of thermal expansion (CTE) of the alloy and enhances creep strength. Thus, additions of cryomilled NiAl-AlN to CoCrAlY were examined as a potential bond coat. In this work, the composite alloy was investigated as a stand-alone substrate to demonstrate its feasibility prior to actual use as a coating. About 85 percent of prealloyed NiAl and 15 percent of standard commercial CoCrAlY alloys were mixed and cryomilled in an attritor with stainless steel balls used as grinding media. The milling was carried out in the presence of liquid nitrogen. The milled powder was consolidated by hot extrusion or by hot isostatic pressing. From the consolidated material, oxidation coupons, four-point bend, CTE, and tensile specimens were machined. The CTE measurements were made between room temperature and 1000 C in an argon atmosphere. It is shown that the CTE of the NiAl-AlN-CoCrAlY composite bond coat

Indirect spectrophotometric determination of arbutin, whitening agent through oxidation by periodate and complexation with ferric chloride

Science.gov (United States)

Barsoom, B. N.; Abdelsamad, A. M. E.; Adib, N. M.

2006-07-01

A simple and accurate spectrophotometric method for the determination of arbutin (glycosylated hydroquinone) is described. It is based on the oxidation of arbutin by periodate in presence of iodate. Excess periodate causes liberation of iodine at pH 8.0. The unreacted periodate is determined by measurement of the liberated iodine spectrophotometrically in the wavelength range (300-500 nm). A calibration curve was constructed for more accurate results and the correlation coefficient of linear regression analysis was -0.9778. The precision of this method was better than 6.17% R.S.D. ( n = 3). Regression analysis of Bear-Lambert plot shows good correlation in the concentration range 25-125 ug/ml. The identification limit was determined to be 25 ug/ml a detailed study of the reaction conditions was carried out, including effect of changing pH, time, temperature and volume of periodate. Analyzing pure and authentic samples containing arbutin tested the validity of the proposed method which has an average percent recovery of 100.86%. An alternative method is also proposed which involves a complexation reaction between arbutin and ferric chloride solution. The produced complex which is yellowish-green in color was determined spectophotometrically.

Extending the Constant Coefficient Solution Technique to Variable Coefficient Ordinary Differential Equations

Science.gov (United States)

Mohammed, Ahmed; Zeleke, Aklilu

2015-01-01

We introduce a class of second-order ordinary differential equations (ODEs) with variable coefficients whose closed-form solutions can be obtained by the same method used to solve ODEs with constant coefficients. General solutions for the homogeneous case are discussed.

Contribution of gait parameters and available coefficient of friction to perceptions of slipperiness.

Science.gov (United States)

Chang, Wen-Ruey; Lesch, Mary F; Chang, Chien-Chi; Matz, Simon

2015-01-01

Perceived slipperiness rating (PSR) has been widely used to assess walkway safety. In this experiment, 29 participants were exposed to 5 floor types under dry, wet and glycerol conditions. The relationship between their PSR and objective measurements, including utilized coefficient of friction (UCOF), gait kinematics and available coefficient of friction (ACOF), was explored with a regression analysis using step-wise backward elimination. The results showed that UCOF and ACOF, as well as their difference, were the major predictors of the PSR under wet and glycerol conditions. Under wet conditions, the participants appeared to rely on the potential for foot slip to form their PSR. Under glycerol conditions, some kinematic variables also became major predictors of PSR. The results show how different proprioceptive responses and ACOF contributed to the prediction of PSR under different surface conditions. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Deuterium permeation behavior of HTUPS4 steel with thermal oxidation layer

International Nuclear Information System (INIS)

Xu, Yu-Ping; Liu, Feng; Zhao, Si-Xiang; Li, Xiao-Chun; Wang, Jing; An, Zhong-Qing; Lu, Tao; Liu, Hao-Dong; Ding, Fang; Zhou, Hai-Shan; Luo, Guang-Nan

2016-01-01

The permeation behavior of creep-resistant, Al 2 O 3 -forming HTUPS austenitic stainless steels was studied using a gas driven permeation (GDP) device. The steel samples were first thermal oxidized at air condition, followed by GDP experiments. The permeability and diffusion coefficients of oxidized samples and bare 316L steels were derived and compared. In order to characterize the oxide layer, X-ray photoelectron spectroscopy was performed. An oxide layer with a thickness of 200 nm which mainly consists of Al 2 O 3 was detected.

Household water treatment in developing countries: comparing different intervention types using meta-regression.

Science.gov (United States)

Hunter, Paul R

2009-12-01

Household water treatment (HWT) is being widely promoted as an appropriate intervention for reducing the burden of waterborne disease in poor communities in developing countries. A recent study has raised concerns about the effectiveness of HWT, in part because of concerns over the lack of blinding and in part because of considerable heterogeneity in the reported effectiveness of randomized controlled trials. This study set out to attempt to investigate the causes of this heterogeneity and so identify factors associated with good health gains. Studies identified in an earlier systematic review and meta-analysis were supplemented with more recently published randomized controlled trials. A total of 28 separate studies of randomized controlled trials of HWT with 39 intervention arms were included in the analysis. Heterogeneity was studied using the "metareg" command in Stata. Initial analyses with single candidate predictors were undertaken and all variables significant at the P Risk and the parameter estimates from the final regression model. The overall effect size of all unblinded studies was relative risk = 0.56 (95% confidence intervals 0.51-0.63), but after adjusting for bias due to lack of blinding the effect size was much lower (RR = 0.85, 95% CI = 0.76-0.97). Four main variables were significant predictors of effectiveness of intervention in a multipredictor meta regression model: Log duration of study follow-up (regression coefficient of log effect size = 0.186, standard error (SE) = 0.072), whether or not the study was blinded (coefficient 0.251, SE 0.066) and being conducted in an emergency setting (coefficient -0.351, SE 0.076) were all significant predictors of effect size in the final model. Compared to the ceramic filter all other interventions were much less effective (Biosand 0.247, 0.073; chlorine and safe waste storage 0.295, 0.061; combined coagulant-chlorine 0.2349, 0.067; SODIS 0.302, 0.068). A Monte Carlo model predicted that over 12 months

Modified Regression Rate Formula of PMMA Combustion by a Single Plane Impinging Jet

Directory of Open Access Journals (Sweden)

Tsuneyoshi Matsuoka

2017-01-01

Full Text Available A modified regression rate formula for the uppermost stage of CAMUI-type hybrid rocket motor is proposed in this study. Assuming a quasi-steady, one-dimensional, an energy balance against a control volume near the fuel surface is considered. Accordingly, the regression rate formula which can calculate the local regression rate by the quenching distance between the flame and the regression surface is derived. An experimental setup which simulates the combustion phenomenon involved in the uppermost stage of a CAMUI-type hybrid rocket motor was constructed and the burning tests with various flow velocities and impinging distances were performed. A PMMA slab of 20 mm height, 60 mm width, and 20 mm thickness was chosen as a sample specimen and pure oxygen and O2/N2 mixture (50/50 vol.% were employed as the oxidizers. The time-averaged regression rate along the fuel surface was measured by a laser displacement sensor. The quenching distance during the combustion event was also identified from the observation. The comparison between the purely experimental and calculated values showed good agreement, although a large systematic error was expected due to the difficulty in accurately identifying the quenching distance.

Combined treatment of retting flax wastewater using Fenton oxidation and granular activated carbon

Directory of Open Access Journals (Sweden)

Sohair I. Abou-Elela

2016-07-01

Full Text Available The process of retting flax produces a huge amount of wastewater which is characterized with bad unpleasant smell and high concentration of organic materials. Treatment of such waste had always been difficult because of the presence of refractory organic pollutants such as lignin. In this study, treatment of retting wastewater was carried out using combined system of Fenton oxidation process followed by adsorption on granular activated carbon (GAC. The effects of operating condition on Fenton oxidation process such as hydrogen peroxide and iron concentration were investigated. In addition, kinetic study of the adsorption process was elaborated. The obtained results indicated that degradation of organic matters follows a pseudo-first order reaction with regression coefficient of 0.98. The kinetic model suggested that the rate of reaction was highly affected by the concentration of hydrogen peroxide. Moreover, the results indicated that the treatment module was very efficient in removing the organic and inorganic pollutants. The average percentage removal of chemical oxygen demand (COD, total suspended solid (TSS, oil, and grease was 98.60%, 86.60%, and 94.22% with residual values of 44, 20, and 5 mg/L, respectively. The treated effluent was complying with the National Regulatory Standards for wastewater discharge into surface water or reuse in the retting process.

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

Science.gov (United States)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

Regression analysis with categorized regression calibrated exposure: some interesting findings

Directory of Open Access Journals (Sweden)

Hjartåker Anette

2006-07-01

Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

Define of internal recirculation coefficient for biological wastewater treatment in anoxic and aerobic bioreactors

Science.gov (United States)

Rossinskyi, Volodymyr

2018-02-01

The biological wastewater treatment technologies in anoxic and aerobic bioreactors with recycle of sludge mixture are used for the effective removal of organic compounds from wastewater. The change rate of sludge mixture recirculation between bioreactors leads to a change and redistribution of concentrations of organic compounds in sludge mixture in bioreactors and change hydrodynamic regimes in bioreactors. Determination of the coefficient of internal recirculation of sludge mixture between bioreactors is important for the choice of technological parameters of biological treatment (wastewater treatment duration in anoxic and aerobic bioreactors, flow capacity of recirculation pumps). Determination of the coefficient of internal recirculation of sludge mixture requires integrated consideration of hydrodynamic parameter (flow rate), kinetic parameter (rate of oxidation of organic compounds) and physical-chemical parameter of wastewater (concentration of organic compounds). The conducted numerical experiment from the proposed mathematical equations allowed to obtain analytical dependences of the coefficient of internal recirculation sludge mixture between bioreactors on the concentration of organic compounds in wastewater, the duration of wastewater treatment in bioreactors.

Enhanced piezoelectricity of monolayer phosphorene oxides: a theoretical study.

Science.gov (United States)

Yin, Huabing; Zheng, Guang-Ping; Gao, Jingwei; Wang, Yuanxu; Ma, Yuchen

2017-10-18

Two-dimensional (2D) piezoelectric materials have potential applications in miniaturized sensors and energy conversion devices. In this work, using first-principles simulations at different scales, we systematically study the electronic structures and piezoelectricity of a series of 2D monolayer phosphorene oxides (POs). Our calculations show that the monolayer POs have tunable band gaps along with remarkable piezoelectric properties. The calculated piezoelectric coefficient d 11 of 54 pm V -1 in POs is much larger than those of 2D transition metal dichalcogenide monolayers and the widely used bulk α-quartz and AlN, and almost reaches the level of the piezoelectric effect in recently discovered 2D GeS. Furthermore, two other considerable piezoelectric coefficients, i.e., d 31 and d 26 with values of -10 pm V -1 and 21 pm V -1 , respectively, are predicted in some monolayer POs. We also examine the correlation between the piezoelectric coefficients and energy stability. The enhancement of piezoelectricity for monolayer phosphorene by oxidation will broaden the applications of phosphorene and phosphorene derivatives in nano-sized electronic and piezotronic devices.

On the Kendall Correlation Coefficient

OpenAIRE

Stepanov, Alexei

2015-01-01

In the present paper, we first discuss the Kendall rank correlation coefficient. In continuous case, we define the Kendall rank correlation coefficient in terms of the concomitants of order statistics, find the expected value of the Kendall rank correlation coefficient and show that the later is free of n. We also prove that in continuous case the Kendall correlation coefficient converges in probability to its expected value. We then propose to consider the expected value of the Kendall rank ...

Correlation coefficients in neutron β-decay

International Nuclear Information System (INIS)

Byrne, J.

1978-01-01

The various angular and polarisation coefficients in neutron decay are the principal sources of information on the β-interaction. Measurements of the electron-neutrino angular correlation coefficient (a), the neutron-spin-electron-momentum correlation coefficient (A), the neutron-spin-neutrino-momentum correlation coefficient (B), and the triple correlation coefficient D and time-reversal invariance are reviewed and the results discussed. (U.K.)

Improved profile fitting and quantification of uncertainty in experimental measurements of impurity transport coefficients using Gaussian process regression

International Nuclear Information System (INIS)

Chilenski, M.A.; Greenwald, M.; Howard, N.T.; White, A.E.; Rice, J.E.; Walk, J.R.; Marzouk, Y.

2015-01-01

The need to fit smooth temperature and density profiles to discrete observations is ubiquitous in plasma physics, but the prevailing techniques for this have many shortcomings that cast doubt on the statistical validity of the results. This issue is amplified in the context of validation of gyrokinetic transport models (Holland et al 2009 Phys. Plasmas 16 052301), where the strong sensitivity of the code outputs to input gradients means that inadequacies in the profile fitting technique can easily lead to an incorrect assessment of the degree of agreement with experimental measurements. In order to rectify the shortcomings of standard approaches to profile fitting, we have applied Gaussian process regression (GPR), a powerful non-parametric regression technique, to analyse an Alcator C-Mod L-mode discharge used for past gyrokinetic validation work (Howard et al 2012 Nucl. Fusion 52 063002). We show that the GPR techniques can reproduce the previous results while delivering more statistically rigorous fits and uncertainty estimates for both the value and the gradient of plasma profiles with an improved level of automation. We also discuss how the use of GPR can allow for dramatic increases in the rate of convergence of uncertainty propagation for any code that takes experimental profiles as inputs. The new GPR techniques for profile fitting and uncertainty propagation are quite useful and general, and we describe the steps to implementation in detail in this paper. These techniques have the potential to substantially improve the quality of uncertainty estimates on profile fits and the rate of convergence of uncertainty propagation, making them of great interest for wider use in fusion experiments and modelling efforts. (paper)

Evaluation Standard for Safety Coefficient of Roller Compacted Concrete Dam Based on Finite Element Method

Directory of Open Access Journals (Sweden)

Bo Li

2014-01-01

Full Text Available The lack of evaluation standard for safety coefficient based on finite element method (FEM limits the wide application of FEM in roller compacted concrete dam (RCCD. In this paper, the strength reserve factor (SRF method is adopted to simulate gradual failure and possible unstable modes of RCCD system. The entropy theory and catastrophe theory are used to obtain the ultimate bearing resistance and failure criterion of the RCCD. The most dangerous sliding plane for RCCD failure is found using the Latin hypercube sampling (LHS and auxiliary analysis of partial least squares regression (PLSR. Finally a method for determining the evaluation standard of RCCD safety coefficient based on FEM is put forward using least squares support vector machines (LSSVM and particle swarm optimization (PSO. The proposed method is applied to safety coefficient analysis of the Longtan RCCD in China. The calculation shows that RCCD failure is closely related to RCCD interface strength, and the Longtan RCCD is safe in the design condition. Considering RCCD failure characteristic and combining the advantages of several excellent algorithms, the proposed method determines the evaluation standard for safety coefficient of RCCD based on FEM for the first time and can be popularized to any RCCD.

A sensitive and selective chemiluminescence sensor for the determination of dopamine based on silanized magnetic graphene oxide-molecularly imprinted polymer.

Science.gov (United States)

Duan, Huimin; Li, Leilei; Wang, Xiaojiao; Wang, Yanhui; Li, Jianbo; Luo, Chuannan

2015-03-15

Based on silanized magnetic graphene oxide-molecularly imprinted polymer (Si-MG-MIP), a sensitive and selective chemiluminescence sensor for dopamine measurement was developed. Si-MG-MIP, in which silanes was introduced to improve the mass transfer, graphene oxide was employed to improve absorption capacity, Fe3O4 nanoparticles were applied for separation easily and molecularly imprinted polymer was used to improve selectivity, demonstrated the advantages of the sensor. All the composites were confirmed by SEM, TEM, XRD and FTIR. Under the optimal conditions of chemiluminescence, dopamine could be assayed in the range of 8.0-200.0 ng/mL with a correlation coefficient of linear regression of 0.9970. The detection limit was 1.5 ng/mL (3δ) and the precision for 11 replicate detections of 80.0 ng/mL dopamine was 3.4% (RSD). When the sensor was applied in determining dopamine in actual samples, recovery ranged from 94% to 110%, which revealed that the results were satisfactory. Copyright © 2014 Elsevier B.V. All rights reserved.

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

Measurement of particle transport coefficients on Alcator C-Mod

Energy Technology Data Exchange (ETDEWEB)

Luke, T.C.T.

1994-10-01

The goal of this thesis was to study the behavior of the plasma transport during the divertor detachment in order to explain the central electron density rise. The measurement of particle transport coefficients requires sophisticated diagnostic tools. A two color interferometer system was developed and installed on Alcator C-Mod to measure the electron density with high spatial ({approx} 2 cm) and high temporal ({le} 1.0 ms) resolution. The system consists of 10 CO{sub 2} (10.6 {mu}m) and 4 HeNe (.6328 {mu}m) chords that are used to measure the line integrated density to within 0.08 CO{sub 2} degrees or 2.3 {times} 10{sup 16}m{sup {minus}2} theoretically. Using the two color interferometer, a series of gas puffing experiments were conducted. The density was varied above and below the threshold density for detachment at a constant magnetic field and plasma current. Using a gas modulation technique, the particle diffusion, D, and the convective velocity, V, were determined. Profiles were inverted using a SVD inversion and the transport coefficients were extracted with a time regression analysis and a transport simulation analysis. Results from each analysis were in good agreement. Measured profiles of the coefficients increased with the radius and the values were consistent with measurements from other experiments. The values exceeded neoclassical predictions by a factor of 10. The profiles also exhibited an inverse dependence with plasma density. The scaling of both attached and detached plasmas agreed well with this inverse scaling. This result and the lack of change in the energy and impurity transport indicate that there was no change in the underlying transport processes after detachment.

Measurement of particle transport coefficients on Alcator C-Mod

International Nuclear Information System (INIS)

Luke, T.C.T.

1994-10-01

The goal of this thesis was to study the behavior of the plasma transport during the divertor detachment in order to explain the central electron density rise. The measurement of particle transport coefficients requires sophisticated diagnostic tools. A two color interferometer system was developed and installed on Alcator C-Mod to measure the electron density with high spatial (∼ 2 cm) and high temporal (≤ 1.0 ms) resolution. The system consists of 10 CO 2 (10.6 μm) and 4 HeNe (.6328 μm) chords that are used to measure the line integrated density to within 0.08 CO 2 degrees or 2.3 x 10 16 m -2 theoretically. Using the two color interferometer, a series of gas puffing experiments were conducted. The density was varied above and below the threshold density for detachment at a constant magnetic field and plasma current. Using a gas modulation technique, the particle diffusion, D, and the convective velocity, V, were determined. Profiles were inverted using a SVD inversion and the transport coefficients were extracted with a time regression analysis and a transport simulation analysis. Results from each analysis were in good agreement. Measured profiles of the coefficients increased with the radius and the values were consistent with measurements from other experiments. The values exceeded neoclassical predictions by a factor of 10. The profiles also exhibited an inverse dependence with plasma density. The scaling of both attached and detached plasmas agreed well with this inverse scaling. This result and the lack of change in the energy and impurity transport indicate that there was no change in the underlying transport processes after detachment

Experimental determination of the partitioning coefficient and volatility of important BVOC oxidation products using the Aerosol Collection Module (ACM) coupled to a PTR-ToF-MS

Science.gov (United States)

Gkatzelis, G.; Hohaus, T.; Tillmann, R.; Schmitt, S. H.; Yu, Z.; Schlag, P.; Wegener, R.; Kaminski, M.; Kiendler-Scharr, A.

2015-12-01

Atmospheric aerosol can alter the Earth's radiative budget and global climate but can also affect human health. A dominant contributor to the submicrometer particulate matter (PM) is organic aerosol (OA). OA can be either directly emitted through e.g. combustion processes (primary OA) or formed through the oxidation of organic gases (secondary organic aerosol, SOA). A detailed understanding of SOA formation is of importance as it constitutes a major contribution to the total OA. The partitioning between the gas and particle phase as well as the volatility of individual components of SOA is yet poorly understood adding uncertainties and thus complicating climate modelling. In this work, a new experimental methodology was used for compound-specific analysis of organic aerosol. The Aerosol Collection Module (ACM) is a newly developed instrument that deploys an aerodynamic lens to separate the gas and particle phase of an aerosol. The particle phase is directed to a cooled sampling surface. After collection particles are thermally desorbed and transferred to a detector for further analysis. In the present work, the ACM was coupled to a Proton Transfer Reaction-Time of Flight-Mass Spectrometer (PTR-ToF-MS) to detect and quantify organic compounds partitioning between the gas and particle phase. This experimental approach was used in a set of experiments at the atmosphere simulation chamber SAPHIR to investigate SOA formation. Ozone oxidation with subsequent photochemical aging of β-pinene, limonene and real plant emissions from Pinus sylvestris (Scots pine) were studied. Simultaneous measurement of the gas and particle phase using the ACM-PTR-ToF-MS allows to report partitioning coefficients of important BVOC oxidation products. Additionally, volatility trends and changes of the SOA with photochemical aging are investigated and compared for all systems studied.

Investigation on leaching of actinide oxides into supercritical fluids

International Nuclear Information System (INIS)

Shafikov, D.N.; Kamachev, V.A.; Babain, V.A.; Murzin, A.A.; Shadrin, A.Yu.; Podojnitsin, S.V.

2006-01-01

The extraction of actinide oxides into solutions of the TBP-HNO 3 complex in supercritical (SC) CO 2 was investigated. Experiments on the extraction of the TBP-HNO 3 complex into SC CO 2 were first conducted. It was found that a constant concentration of TBP in SC CO 2 of 13.5-14.8 % vol. can be attained using a constant molar ratio of [HNO 3 ]:[TBP] about 2.5 : 1. Joint leaching of uranium, plutonium and neptunium from mixtures of actinide oxides with solutions of TBP-HNO 3 in SC CO 2 was found feasible. If the leaching of uranium is about 95 %, its purification coefficients from major gamma-emitting radionuclides (Cs and Sr) exceed 100, while the purification coefficients of uranium from rare earth elements are 10-20

Urinary F2-Isoprostanes and Metabolic Markers of Fat Oxidation

Directory of Open Access Journals (Sweden)

Dora Il’yasova

2015-01-01

Full Text Available Metabolomic studies of increased fat oxidation showed increase in circulating acylcarnitines C2, C8, C10, and C12 and decrease in C3, C4, and C5. We hypothesize that urinary F2-isoprostanes reflect intensity of fatty acid oxidation and are associated with circulating C2, C8, C10, and C12 directly and with C3, C4, and C5 inversely. Four urinary F2-isoprostane isomers and serum acylcarnitines are quantified using LC-MS/MS within the Insulin Resistance Atherosclerosis Study nondiabetic cohort (n = 682. Cross-sectional associations between fasting urinary F2-isoprostanes (summarized as a composite index and the selected acylcarnitines are examined using generalized linear models. F2-isoprostane index is associated with C2 and C12 directly and with C5 inversely: the adjusted beta coefficients are 0.109, 0.072, and −0.094, respectively (P < 0.05. For these acylcarnitines and for F2-isoprostanes, the adjusted odds ratios (ORs of incident diabetes are calculated from logistic regression models: the ORs (95% CI are 0.77 (0.60–0.97, 0.79 (0.62–1.01, 1.18 (0.92–1.53, and 0.51 (0.35–0.76 for C2, C12, C5, and F2-isoprostanes, respectively. The direction of the associations between urinary F2-isoprostanes and three acylcarnitines (C2, C5, and C12 supports our hypothesis. The inverse associations of C2 and C12 and with incident diabetes are consistent with the suggested protective role of efficient fat oxidation.

Regression Model to Predict Global Solar Irradiance in Malaysia

Directory of Open Access Journals (Sweden)

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.

Hemoglobin binding of aromatic amines: molecular dosimetry and quantitative structure-activity relationships for N-oxidation.

Science.gov (United States)

Sabbioni, G

1993-01-01

Aromatic amines are important intermediates in industrial manufacturing. N-Oxidation to N-hydroxyarylamines is a key step in determining the genotoxic properties of aromatic amines. N-Hydroxyarylamines can form adducts with DNA, with tissue proteins, and with the blood proteins albumin and hemoglobin in a dose-dependent manner. The determination of hemoglobin adducts is a useful tool for biomonitoring exposed populations. We have established the hemoglobin binding index (HBI) [(mmole compound/mole hemoglobin)/(mmole compound/kg body weight)] of several aromatic amines in female Wistar rats. Including the values from other researchers obtained in the same rat strain, the logarithm of hemoglobin binding (logHBI) was plotted against the following parameters: the sum of the Hammett constants(sigma sigma = sigma p + sigma m), pKa, logP (octanol/water), the half-wave oxidation potential (E1/2), and the electronic descriptors of the amines and their corresponding nitrenium ions obtained by semi-empirical calculations (MNDO, AMI, and PM3), such as atomic charge densities, energies of the highest occupied molecular orbit and lowest occupied molecular orbit and their coefficients, the bond order of C-N, the dipole moments, and the reaction enthalpy [MNDOHF, AM1HF or PM3HF = Hf(nitrenium) - Hf(amine)]. The correlation coefficients were determined from the plots of all parameters against log HBI for all amines by means of linear regression analysis. The amines were classified in three groups: group 1, all parasubstituted amines (maximum, n = 9); group 2, all amines with halogens (maximun, n = 11); and group 3, all amines with alkyl groups (maximum, n = 13).(ABSTRACT TRUNCATED AT 250 WORDS) PMID:8319626

Effects of micro-sized and nano-sized WO_3 on mass attenauation coefficients of concrete by using MCNPX code

International Nuclear Information System (INIS)

Tekin, H.O.; Singh, V.P.; Manici, T.

2017-01-01

In the present work the effect of tungsten oxide (WO_3) nanoparticles on mass attenauation coefficients of concrete has been investigated by using MCNPX (version 2.4.0). The validation of generated MCNPX simulation geometry has been provided by comparing the results with standard XCOM data for mass attenuation coefficients of concrete. A very good agreement between XCOM and MCNPX have been obtained. The validated geometry has been used for definition of nano-WO_3 and micro-WO_3 into concrete sample. The mass attenuation coefficients of pure concrete and WO_3 added concrete with micro-sized and nano-sized have been compared. It was observed that shielding properties of concrete doped with WO_3 increased. The results of mass attenauation coefficients also showed that the concrete doped with nano-WO_3 significanlty improve shielding properties than micro-WO_3. It can be concluded that addition of nano-sized particles can be considered as another mechanism to reduce radiation dose. - Highlights: • It was found that size of the WO_3 affected the mass attenuation coefficients of concrete in all photon energies.

Testing of a Fiber Optic Wear, Erosion and Regression Sensor

Science.gov (United States)

Korman, Valentin; Polzin, Kurt A.

2011-01-01

The nature of the physical processes and harsh environments associated with erosion and wear in propulsion environments makes their measurement and real-time rate quantification difficult. A fiber optic sensor capable of determining the wear (regression, erosion, ablation) associated with these environments has been developed and tested in a number of different applications to validate the technique. The sensor consists of two fiber optics that have differing attenuation coefficients and transmit light to detectors. The ratio of the two measured intensities can be correlated to the lengths of the fiber optic lines, and if the fibers and the host parent material in which they are embedded wear at the same rate the remaining length of fiber provides a real-time measure of the wear process. Testing in several disparate situations has been performed, with the data exhibiting excellent qualitative agreement with the theoretical description of the process and when a separate calibrated regression measurement is available good quantitative agreement is obtained as well. The light collected by the fibers can also be used to optically obtain the spectra and measure the internal temperature of the wear layer.

Development of planning level transportation safety tools using Geographically Weighted Poisson Regression.

Science.gov (United States)

Hadayeghi, Alireza; Shalaby, Amer S; Persaud, Bhagwant N

2010-03-01

A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models. Copyright 2009 Elsevier Ltd. All rights reserved.

Research on friction coefficient of nuclear Reactor Vessel Internals Hold Down Spring: Stress coefficient test analysis method

International Nuclear Information System (INIS)

Linjun, Xie; Guohong, Xue; Ming, Zhang

2016-01-01

Graphical abstract: HDS stress coefficient test apparatus. - Highlights: • This paper performs mathematic deduction to the physical model of Hold Down Spring (HDS), establishes a mathematic model of axial load P and stress, stress coefficient and friction coefficient and designs a set of test apparatuses for simulating the pretightening process of the HDS for the first time according to a model similarity criterion. • The mathematical relation between the load and the strain is obtained about the HDS, and the mathematical model of the stress coefficient and the friction coefficient is established. So, a set of test apparatuses for obtaining the stress coefficient is designed according to the model scaling criterion and the friction coefficient of the K1000 HDS is calculated to be 0.336 through the obtained stress coefficient. • The relation curve between the theoretical load and the friction coefficient is obtained through analysis and indicates that the change of the friction coefficient f would influence the pretightening load under the condition of designed stress. The necessary pretightening load in the design process is calculated to be 5469 kN according to the obtained friction coefficient. Therefore, the friction coefficient and the pretightening load under the design conditions can provide accurate pretightening data for the analysis and design of the reactor HDS according to the operations. - Abstract: This paper performs mathematic deduction to the physical model of Hold Down Spring (HDS), establishes a mathematic model of axial load P and stress, stress coefficient and friction coefficient and designs a set of test apparatuses for simulating the pretightening process of the HDS for the first time according to a model similarity criterion. By carrying out tests and researches through a stress testing technique, P–σ curves in loading and unloading processes of the HDS are obtained and the stress coefficient k f of the HDS is obtained. So, the

Research on friction coefficient of nuclear Reactor Vessel Internals Hold Down Spring: Stress coefficient test analysis method

Energy Technology Data Exchange (ETDEWEB)

Linjun, Xie, E-mail: linjunx@zjut.edu.cn [College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014 (China); Guohong, Xue; Ming, Zhang [Shanghai Nuclear Engineering Research & Design Institute, Shanghai 200233 (China)

2016-08-01

Graphical abstract: HDS stress coefficient test apparatus. - Highlights: • This paper performs mathematic deduction to the physical model of Hold Down Spring (HDS), establishes a mathematic model of axial load P and stress, stress coefficient and friction coefficient and designs a set of test apparatuses for simulating the pretightening process of the HDS for the first time according to a model similarity criterion. • The mathematical relation between the load and the strain is obtained about the HDS, and the mathematical model of the stress coefficient and the friction coefficient is established. So, a set of test apparatuses for obtaining the stress coefficient is designed according to the model scaling criterion and the friction coefficient of the K1000 HDS is calculated to be 0.336 through the obtained stress coefficient. • The relation curve between the theoretical load and the friction coefficient is obtained through analysis and indicates that the change of the friction coefficient f would influence the pretightening load under the condition of designed stress. The necessary pretightening load in the design process is calculated to be 5469 kN according to the obtained friction coefficient. Therefore, the friction coefficient and the pretightening load under the design conditions can provide accurate pretightening data for the analysis and design of the reactor HDS according to the operations. - Abstract: This paper performs mathematic deduction to the physical model of Hold Down Spring (HDS), establishes a mathematic model of axial load P and stress, stress coefficient and friction coefficient and designs a set of test apparatuses for simulating the pretightening process of the HDS for the first time according to a model similarity criterion. By carrying out tests and researches through a stress testing technique, P–σ curves in loading and unloading processes of the HDS are obtained and the stress coefficient k{sub f} of the HDS is obtained. So, the

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

Science.gov (United States)

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

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.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

Science.gov (United States)

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Time-adaptive quantile regression

DEFF Research Database (Denmark)

Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

2008-01-01

and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

Tissue/blood partition coefficients for xenon in various adipose tissue depots in man

DEFF Research Database (Denmark)

Bülow, J; Jelnes, Rolf; Astrup, A

1987-01-01

Tissue/blood partition coefficients (lambda) for xenon were calculated for subcutaneous adipose tissue from the abdominal wall and the thigh, and for the perirenal adipose tissue after chemical analysis of the tissues for lipid, water and protein content. The lambda in the perirenal tissue...... was found to correlate linearly to the relative body weight (RBW) in per cent with the regression equation lambda = 0.045 . RBW + 0.99. The subcutaneous lambda on the abdomen correlated linearly to the local skinfold thickness (SFT) with the equation lambda = 0.22 SFT + 2.99. Similarly lambda on the thigh...... correlated to SFT with the equation lambda = 0.20 . SFT + 4.63. It is concluded that the previously accepted lambda value of 10 is generally too high in perirenal as well as in subcutaneous tissue. Thus, by application of the present regression equations, it is possible to obtain more exact estimates...

Agreement of central site measurements and land use regression modeled oxidative potential of PM{sub 2.5} with personal exposure

Energy Technology Data Exchange (ETDEWEB)

Yang, Aileen, E-mail: Yang@uu.nl [National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720BA Bilthoven (Netherlands); Institute for Risk Assessment Sciences, Division Environmental Epidemiology, Utrecht University, P.O. Box 80.178, 3508TD Utrecht (Netherlands); Hoek, Gerard; Montagne, Denise [Institute for Risk Assessment Sciences, Division Environmental Epidemiology, Utrecht University, P.O. Box 80.178, 3508TD Utrecht (Netherlands); Leseman, Daan L.A.C. [National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720BA Bilthoven (Netherlands); Hellack, Bryan [Air Quality & Sustainable Nanotechnology, Institute of Energy and Environmental Technology (IUTA), e.V., Blierheimer Str. 58-60, 47229 Duisburg (Germany); Kuhlbusch, Thomas A.J. [Air Quality & Sustainable Nanotechnology, Institute of Energy and Environmental Technology (IUTA), e.V., Blierheimer Str. 58-60, 47229 Duisburg (Germany); Center for Nanointegration Duisburg-Essen (CENIDE), University Duisburg-Essen, Carl-Benz-Straße 199, 47057 Duisburg (Germany); Cassee, Flemming R. [National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720BA Bilthoven (Netherlands); Institute for Risk Assessment Sciences, Division Environmental Epidemiology, Utrecht University, P.O. Box 80.178, 3508TD Utrecht (Netherlands); Brunekreef, Bert [Institute for Risk Assessment Sciences, Division Environmental Epidemiology, Utrecht University, P.O. Box 80.178, 3508TD Utrecht (Netherlands); Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht (Netherlands); Janssen, Nicole A.H. [National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720BA Bilthoven (Netherlands)

2015-07-15

Oxidative potential (OP) of ambient particulate matter (PM) has been suggested as a health-relevant exposure metric. In order to use OP for exposure assessment, information is needed about how well central site OP measurements and modeled average OP at the home address reflect temporal and spatial variation of personal OP. We collected 96-hour personal, home outdoor and indoor PM{sub 2.5} samples from 15 volunteers living either at traffic, urban or regional background locations in Utrecht, the Netherlands. OP was also measured at one central reference site to account for temporal variations. OP was assessed using electron spin resonance (OP{sup ESR}) and dithiothreitol (OP{sup DTT}). Spatial variation of average OP at the home address was modeled using land use regression (LUR) models. For both OP{sup ESR} and OP{sup DTT}, temporal correlations of central site measurements with home outdoor measurements were high (R>0.75), and moderate to high (R=0.49–0.70) with personal measurements. The LUR model predictions for OP correlated significantly with the home outdoor concentrations for OP{sup DTT} and OP{sup ESR} (R=0.65 and 0.62, respectively). LUR model predictions were moderately correlated with personal OP{sup DTT} measurements (R=0.50). Adjustment for indoor sources, such as vacuum cleaning and absence of fume-hood, improved the temporal and spatial agreement with measured personal exposure for OP{sup ESR}. OP{sup DTT} was not associated with any indoor sources. Our study results support the use of central site OP for exposure assessment of epidemiological studies focusing on short-term health effects. - Highlights: • Oxidative potential (OP) of PM was proposed as a health-relevant exposure metric. • We evaluated the relationship between measured and modeled outdoor and personal OP. • Temporal correlations of central site with personal OP are moderate to high. • Adjusting for indoor sources improved the agreement with personal OP. • Our results

Measuring of heat transfer coefficient

DEFF Research Database (Denmark)

Henningsen, Poul; Lindegren, Maria

Subtask 3.4 Measuring of heat transfer coefficient Subtask 3.4.1 Design and setting up of tests to measure heat transfer coefficient Objective: Complementary testing methods together with the relevant experimental equipment are to be designed by the two partners involved in order to measure...... the heat transfer coefficient for a wide range of interface conditions in hot and warm forging processes. Subtask 3.4.2 Measurement of heat transfer coefficient The objective of subtask 3.4.2 is to determine heat transfer values for different interface conditions reflecting those typically operating in hot...

Diffusivities and Viscosities of Poly(ethylene oxide) Oligomers †

KAUST Repository

Hong, Bingbing; Escobedo, Fernando; Panagiotopoulos, Athanassios Z.

2010-01-01

Diffusivities and viscosities of poly(ethylene oxide) (PEO) oligomer melts with 1 to 12 repeat units have been obtained from equilibrium molecular dynamics simulations using the TraPPE-UA force field. The simulations generated diffusion coefficients

Clustering Coefficients for Correlation Networks

Directory of Open Access Journals (Sweden)

Naoki Masuda

2018-03-01

Full Text Available Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients

Clustering Coefficients for Correlation Networks.

Science.gov (United States)

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly

Clustering Coefficients for Correlation Networks

Science.gov (United States)

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly

The association between low-grade inflammation, iron status and nucleic acid oxidation in the elderly

DEFF Research Database (Denmark)

Broedbaek, Kasper; Siersma, Volkert Dirk; Andersen, Jon T

2011-01-01

This study applied a case-control approach to investigate the association between low-grade inflammation, defined by high values within the normal range of C-reactive protein (CRP) and interleukin-6 (IL-6), and urinary markers of nucleic acid oxidation. No differences in excretion of urinary...... markers of nucleic acid oxidation between cases and controls were found and multivariable linear regression analysis showed no association between urinary markers of nucleic acid oxidation and inflammatory markers. Post-hoc multivariable linear regression analysis showed significant associations between...... suggest that low-grade inflammation only has a negligible impact on whole body nucleic acid oxidation, whereas iron status seems to be of great importance....

Fine-Tuning Nonhomogeneous Regression for Probabilistic Precipitation Forecasts: Unanimous Predictions, Heavy Tails, and Link Functions

DEFF Research Database (Denmark)

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

2017-01-01

functions for the optimization of regression coefficients for the scale parameter. These three refinements are tested for 10 stations in a small area of the European Alps for lead times from +24 to +144 h and accumulation periods of 24 and 6 h. Together, they improve probabilistic forecasts...... to obtain automatically corrected weather forecasts. This study applies the nonhomogenous regression framework as a state-of-the-art ensemble postprocessing technique to predict a full forecast distribution and improves its forecast performance with three statistical refinements. First of all, a novel split...... for precipitation amounts as well as the probability of precipitation events over the default postprocessing method. The improvements are largest for the shorter accumulation periods and shorter lead times, where the information of unanimous ensemble predictions is more important....

An Aurivillius Oxide Based Cathode with Excellent CO2 Tolerance for Intermediate-Temperature Solid Oxide Fuel Cells.

Science.gov (United States)

Zhu, Yinlong; Zhou, Wei; Chen, Yubo; Shao, Zongping

2016-07-25

The Aurivillius oxide Bi2 Sr2 Nb2 MnO12-δ (BSNM) was used as a cobalt-free cathode for intermediate-temperature solid oxide fuel cells (IT-SOFCs). To the best of our knowledge, the BSNM oxide is the only alkaline-earth-containing cathode material with complete CO2 tolerance that has been reported thus far. BSNM not only shows favorable activity in the oxygen reduction reaction (ORR) at intermediate temperatures but also exhibits a low thermal expansion coefficient, excellent structural stability, and good chemical compatibility with the electrolyte. These features highlight the potential of the new BSNM material as a highly promising cathode material for IT-SOFCs. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Form of multicomponent Fickian diffusion coefficients matrix

International Nuclear Information System (INIS)

Wambui Mutoru, J.; Firoozabadi, Abbas

2011-01-01

Highlights: → Irreversible thermodynamics establishes form of multicomponent diffusion coefficients. → Phenomenological coefficients and thermodynamic factors affect sign of diffusion coefficients. → Negative diagonal elements of diffusion coefficients matrix can occur in non-ideal mixtures. → Eigenvalues of the matrix of Fickian diffusion coefficients may not be all real. - Abstract: The form of multicomponent Fickian diffusion coefficients matrix in thermodynamically stable mixtures is established based on the form of phenomenological coefficients and thermodynamic factors. While phenomenological coefficients form a symmetric positive definite matrix, the determinant of thermodynamic factors matrix is positive. As a result, the Fickian diffusion coefficients matrix has a positive determinant, but its elements - including diagonal elements - can be negative. Comprehensive survey of reported diffusion coefficients data for ternary and quaternary mixtures, confirms that invariably the determinant of the Fickian diffusion coefficients matrix is positive.

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

Discrimination of paediatric brain tumours using apparent diffusion coefficient histograms

International Nuclear Information System (INIS)

Bull, Jonathan G.; Clark, Christopher A.; Saunders, Dawn E.

2012-01-01

To determine if histograms of apparent diffusion coefficients (ADC) can be used to differentiate paediatric brain tumours. Imaging of histologically confirmed tumours with pre-operative ADC maps were reviewed (54 cases, 32 male, mean age 6.1 years; range 0.1-15.8 years) comprising 6 groups. Whole tumour ADC histograms were calculated; normalised for volume. Stepwise logistic regression analysis was used to differentiate tumour types using histogram metrics, initially for all groups and then for specific subsets. All 6 groups (5 dysembryoplastic neuroectodermal tumours, 22 primitive neuroectodermal tumours (PNET), 5 ependymomas, 7 choroid plexus papillomas, 4 atypical teratoid rhabdoid tumours (ATRT) and 9 juvenile pilocytic astrocytomas (JPA)) were compared. 74% (40/54) were correctly classified using logistic regression of ADC histogram parameters. In the analysis of posterior fossa tumours, 80% of ependymomas, 100% of astrocytomas and 94% of PNET-medulloblastoma were classified correctly. All PNETs were discriminated from ATRTs (22 PNET and 4 supratentorial ATRTs) (100%). ADC histograms are useful in differentiating paediatric brain tumours, in particular, the common posterior fossa tumours of childhood. PNETs were differentiated from supratentorial ATRTs, in all cases, which has important implications in terms of clinical management. (orig.)

A varying-coefficient method for analyzing longitudinal clinical trials data with nonignorable dropout

Science.gov (United States)

Forster, Jeri E.; MaWhinney, Samantha; Ball, Erika L.; Fairclough, Diane

2011-01-01

Dropout is common in longitudinal clinical trials and when the probability of dropout depends on unobserved outcomes even after conditioning on available data, it is considered missing not at random and therefore nonignorable. To address this problem, mixture models can be used to account for the relationship between a longitudinal outcome and dropout. We propose a Natural Spline Varying-coefficient mixture model (NSV), which is a straightforward extension of the parametric Conditional Linear Model (CLM). We assume that the outcome follows a varying-coefficient model conditional on a continuous dropout distribution. Natural cubic B-splines are used to allow the regression coefficients to semiparametrically depend on dropout and inference is therefore more robust. Additionally, this method is computationally stable and relatively simple to implement. We conduct simulation studies to evaluate performance and compare methodologies in settings where the longitudinal trajectories are linear and dropout time is observed for all individuals. Performance is assessed under conditions where model assumptions are both met and violated. In addition, we compare the NSV to the CLM and a standard random-effects model using an HIV/AIDS clinical trial with probable nonignorable dropout. The simulation studies suggest that the NSV is an improvement over the CLM when dropout has a nonlinear dependence on the outcome. PMID:22101223

Study on transfer coefficients of 90Sr, 137Cs, natural U, 226Ra and 239Pu in terrestrial food chains

International Nuclear Information System (INIS)

Qin Suyun; Qi Yong; Li Shuqin; Zhou Caiyun; Zhang Jingjuan; Li Jikai; Li Xuequn

1995-01-01

The aim of study was to provide values of transfer parameter of 90 Sr, 137 Cs, Natural U, 226 Ra and 239 Pu in terrestrial food chains, more applicable for Chinese socio-natural conditions. Data of radionuclides contents in agricultural crops and in associated soils, in sheep tissues and in associated grasses were collected in couples. The transfer coefficients in terrestrial food chains (soil-crops, grasses-sheep tissues) were calculated. On basis of statistical analysis, the representative values and 95% ranges of transfer coefficient for 5 radionuclides in 7 kind of agricultural products for southern moist areas and north dry areas were given. Regression analysis showed that relation between the transfer coefficients and the radionuclide contents in their associated soils present a negative correlation, it could be described with a equation: Y = aX -b

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

Partitioning of the diffuse attenuation coefficient for photosynthetically available irradiance in a deep dendritic tropical lake

Directory of Open Access Journals (Sweden)

LUCIANA P.M. BRANDÃO

Full Text Available ABSTRACT We studied the effects of particulate and dissolved optically active components on the attenuation of photosynthetic active radiation (PAR in a tropical lake. The temporal and spatial distribution of tripton, Chl-a and aCDOM(440 and their relative contribution to the diffuse PAR attenuation coefficient (Kd was investigated at 21 sites (dry and wet seasons and two intermediate periods and at monthly interval at 1 pelagic site. Higher values of Kd were observed during the mixing period, characterized by a higher concentration of tripton and Chl-a compared to the stratified rainy season. In the spatial sampling PAR attenuation was dominated by tripton absorption/scattering (average relative contribution of 79%, followed by Chl-a (average 11.6%. In the monthly sampling tripton and Chl-a accounted for most of the Kd with relative contributions of 47.8% and 35.6%, respectively. Multiple linear regression analysis showed that Chl-a and tripton in combination explained 97% of the monthly variation in Kd (p<0.001, but Chl-a had more influence (higher regression coefficient. Thus, although most of light attenuation was due to tripton, seasonal variations in phytoplankton abundance were responsible for most of the temporal fluctuations in Kd.

RETROSPECTIVE RESEARCH ON REGRESSION PATTERN IN MALE SKELETAL EPIPHYSEAL FUSION

Directory of Open Access Journals (Sweden)

Ranga Rao

2016-02-01

Full Text Available In developing countries like India, because of illiteracy, ignorance regarding the importance of official records like birth and death, vast majority of population fail to give information of such vital events to the concerning authorities. This causes paucity in such information when needed in a medico-legal case or for research purpose. There is a wide confusion and controversy regarding the standard method to be used for estimating age in the Indian Sub-continent. The aim of the current study is to find the correlation among the various parameters of commonly examined ossification centres, through which a regression formula with positive correlation and maximum coefficient of determination can be, derived which can be attempted to be standardised for the estimation of age.

Study of the oxidation mechanisms between impurities and surfaces applied to the future gas-cooled nuclear reactors

International Nuclear Information System (INIS)

Duval, A.

2010-01-01

Inconel 617, main candidate for the heat exchangers of the gas-cooled next generation of nuclear reactors has been investigated. Two different problems occurring in the cooling system splits the study into two parts. Oxidizing impurities contained in the coolant can cause severe corrosion at 850 C. Radioactive impurities, coming from the fission reaction of the core can, in another hand contaminate the cooling loop and cause radioprotection problem for the maintenance and dismantling operations. Firstly, oxidizing gas partial pressure influence on oxidation of IN 617 at 850 C was investigated varying oxygen and water vapour partial pressure between 1.10 -5 mbar and 200 mbar. Oxide layers were characterized using XPS, SEM, EDX, GD-OES, XRD. Influence of partial pressure on layers structure and composition was determined. Effect of water vapour and partial pressure on growth mechanisms were also investigated. The second part of this study is focused on diffusion of Ag, stable isotope of Ag-110m in IN617 alloy and in the oxide layer forming at its surface at 850 C. Concentration profiles were obtained by GD-OES calibrated analysis. Diffusion coefficient could be obtained from these diffusion profiles: volume diffusion and grain boundary diffusion coefficients for the diffusion in the alloy, and an apparent diffusion coefficient for the diffusion in the oxide, due to the porosity of the structure. (author) [fr

Measurement of gas/water uptake coefficients for trace gases active in the marine environment. [Annual report

Energy Technology Data Exchange (ETDEWEB)

Davidovits, P. [Boston Coll., Chestnut Hill, MA (United States). Dept. of Chemistry; Worsnop, D.W.; Zahniser, M.S.; Kolb, C.E. [Aerodyne Research, Inc., Billerica, MA (United States). Center for Chemical and Environmental Physics

1992-02-01

Ocean produced reduced sulfur compounds including dimethylsulfide (DMS), hydrogen sulfide (H{sub 2}S), carbon disulfide (CS{sub 2}), methyl mercaptan (CH{sub 3}CH) and carbonyl sulfide (OCS) deliver a sulfur burden to the atmosphere which is roughly equal to sulfur oxides produced by fossil fuel combustion. These species and their oxidation products dimethyl sulfoxide (DMSO), dimethyl sulfone (DMSO{sub 2}) and methane sulfonic acid (MSA) dominate aerosol and CCN production in clean marine air. Furthermore, oxidation of reduced sulfur species will be strongly influenced by NO{sub x}/O{sub 3} chemistry in marine atmospheres. The multiphase chemical processes for these species must be understood in order to study the evolving role of combustion produced sulfur oxides over the oceans. We have measured the chemical and physical parameters affecting the uptake of reduced sulfur compounds, their oxidation products, ozone, and nitrogen oxides by the ocean`s surface, and marine clouds, fogs, and aerosols. These parameters include: gas/surface mass accommodation coefficients; physical and chemically modified (effective) Henry`s law constants; and surface and liquid phase reaction constants. These parameters are critical to understanding both the interaction of gaseous trace species with cloud and fog droplets and the deposition of trace gaseous species to dew covered, fresh water and marine surfaces.

Scavenging ratios, wet deposition, and in-cloud oxidation: An application to the oxides of sulphur and nitrogen

International Nuclear Information System (INIS)

Barrie, L.A.

1985-01-01

With special regard to the class of substances in precipitation that potentially originate from both gaseous and particulate precursors, the process of precipitation scavenging is discussed. A model relating daily average ground level scavenging ratios (W) to nucleation scavenging and in-cloud chemical transformation is introduced and is used as guidance in the regression analysis of 3 years of SO 4 = and NO 3 - scavenging-ratio observations made at six locations in eastern Canada. It was found that W of SO 4 = -, NO 3 = -, and SO 4 = -bearing particles is inversely proportional to the one-third power of the precipitation amount in the event. The best regression model explained 41% of the variance in log W for SO 4 = . It included the effects of location, precipitation amount, precipitation type, and in-cloud SO 2 oxidation. The last effect accounted for 50% of the variance explained. The analysis predicts that, on average, in-cloud SO 2 oxidation accounts for 42--79% of the SO 4 = observed in rain and with the exception of one site, less than 20% of the SO 4 = observed in snow. These results are consistent with a mechanism of SO 2 oxidation involving photochemically produced H 2 O 2 . A similar analysis for NO 3 - supports the hypothesis that throughout the year much of the NO 3 - in precipitation originates from in-cloud NO 2 oxidation. It suggests that, depending on location, oxidation of 0.5--1.2 ppbv of NO 2 is sufficient to explain observations. One possible mechanism of oxidation is the reaction with O 3 to form NO 3 and hence soluble N 2 O 5

Inbreeding coefficients and degree of consanguineous marriages in Spain: a review.

Science.gov (United States)

Fuster, Vicente; Colantonio, Sonia Edith

2003-01-01

The contribution of consanguineous marriages corresponding to uncle-niece or aunt-nephew (C12), first cousin (C22), first cousin once removed (C23), and second cousin (C33) to the inbreeding coefficient (alpha) was analyzed from a sample of Spanish areas and periods. Multiple regressions were performed taking as independent variables the different degrees of consanguinity previously selected (C12, C22, C23, and C33) and as dependent variable the inbreeding coefficient (alpha). According to the results obtained for any degree and period, rural frequencies always surpass urban. However, the pattern is similar in both areas. In the period where consanguinity was more elevated (1890-1929) the C22/C33 ratio increased. Its variation is not due to C22 and C33 changes in the same way. In rural areas, this ratio surpasses the expected value by a factor of 2-3, but in urban areas it was 7-10 times larger, in some cases due to migration. While in rural Spain the C33 frequency was approximately 1.5 times C22, in cities C22 was 1.5 times C33. The best fit among the various types of consanguineous matings and alpha involves a lineal relationship. Regardless of the number of variables contributing significantly to alpha, C22 matings are always present. Moreover, their standardized (beta) coefficients are the highest. The above indicates that this consanguineous relationship conditions the inbreeding coefficient the most. In the period of greater consanguinity, close relationships, uncle-niece C12, and first cousin once removed (C23) make a significant contribution to alpha. In rural Spain second cousins (C33) always significantly determined alpha; however, in cities the inbreeding variation was mainly due to C12 and C23. Copyright 2003 Wiley-Liss, Inc.

Regression analysis by example

CERN Document Server

Chatterjee, Samprit

2012-01-01

Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

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

Directory of Open Access Journals (Sweden)

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.

Applied logistic regression

CERN Document Server

Hosmer, David W; Sturdivant, Rodney X

2013-01-01

 A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

Science.gov (United States)

Bulcock, J. W.

The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

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

Sabine absorption coefficients to random incidence absorption coefficients

DEFF Research Database (Denmark)

Jeong, Cheol-Ho

2014-01-01

into random incidence absorption coefficients for porous absorbers are investigated. Two optimization-based conversion methods are suggested: the surface impedance estimation for locally reacting absorbers and the flow resistivity estimation for extendedly reacting absorbers. The suggested conversion methods...

Electrochemical oxidation of niclosamide at a glassy carbon ...

African Journals Online (AJOL)

Cyclic voltammetry, square-wave voltammetry and controlled potential electrolysis have been used to study the electrochemical oxidation behaviour of niclosamide at a glassy carbon electrode. The number of electrons transferred, the wave characteristics, the diffusion coefficient and reversibility of the reactions have been ...

Applied linear regression

CERN Document Server

Weisberg, Sanford

2013-01-01

Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus

Transport Coefficients of Fluids

CERN Document Server

Eu, Byung Chan

2006-01-01

Until recently the formal statistical mechanical approach offered no practicable method for computing the transport coefficients of liquids, and so most practitioners had to resort to empirical fitting formulas. This has now changed, as demonstrated in this innovative monograph. The author presents and applies new methods based on statistical mechanics for calculating the transport coefficients of simple and complex liquids over wide ranges of density and temperature. These molecular theories enable the transport coefficients to be calculated in terms of equilibrium thermodynamic properties, and the results are shown to account satisfactorily for experimental observations, including even the non-Newtonian behavior of fluids far from equilibrium.

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

Directory of Open Access Journals (Sweden)

Leonard eVanbrabant

2015-01-01

Full Text Available 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 beta1 is larger than beta2 and beta3. The corresponding hypothesis is H: beta1 > {beta2, beta3} and this is known as an (order constrained hypothesis. A major advantage of testing such a hypothesis is that power can be gained and inherently a smaller sample size is needed. This article discusses this gain in sample size reduction, when an increasing number of constraints is included into the hypothesis. The main goal is to present sample-size tables for constrained hypotheses. A sample-size table contains the necessary sample-size at a prespecified power (say, 0.80 for an increasing number of constraints. To obtain sample-size tables, two Monte Carlo simulations were performed, one for ANOVA and one for multiple regression. Three results are salient. First, in an ANOVA the needed sample-size decreases with 30% to 50% when complete ordering of the parameters is taken into account. Second, small deviations from the imposed order have only a minor impact on the power. Third, at the maximum number of constraints, the linear regression results are comparable with the ANOVA results. However, in the case of fewer constraints, ordering the parameters (e.g., beta1 > beta2 results in a higher power than assigning a positive or a negative sign to the parameters (e.g., beta1 > 0.

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

Science.gov (United States)

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

Using ab initio 'data' to accurately determine the fourth density virial coefficient of helium

International Nuclear Information System (INIS)

Moldover, Michael R.; McLinden, Mark O.

2010-01-01

We combine accurate ab initio calculations of the second and third density virial coefficients, B(T) and C(T), of 4 He with measurements of its (p-ρ-T) behavior to determine the fourth density virial coefficient D(T). The measurements were made with a two-sinker, magnetic-suspension densimeter at pressures up to 38 MPa. The measurements on isotherms from T = 223 K to T = 323 K were previously published; new measurements from T = 323 K to T = 500 K are presented here. On each isotherm, a regression of the virial expansion was constrained to the ab initio values of B(T) and C(T); the regression determined D(T) as well as two apparatus-dependent parameters that compensated for systematic errors in the measurements. The percentage uncertainties of D(T) ranged from 2.6% at T = 223 K to 9.5% at T = 400 K to 24.7% at T = 500 K, where these uncertainties are expanded uncertainties with coverage factor of k = 2 corresponding to a 95% confidence interval. These uncertainties are 1/6th of the uncertainty obtained without the ab initio values of B(T) and C(T). The apparatus-dependent parameters can be used to calibrate the densimeter, and this will reduce the uncertainty of other measurements made with this two-sinker densimeter. The new values of D(T) will find applications in accurate gas metrology, such as a primary pressure standard based on the refractive index of helium.

THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE.

Science.gov (United States)

Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan

2015-10-01

The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran's universities. This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran's public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran's libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries.

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.

Model-based bootstrapping when correcting for measurement error with application to logistic regression.

Science.gov (United States)

Buonaccorsi, John P; Romeo, Giovanni; Thoresen, Magne

2018-03-01

When fitting regression models, measurement error in any of the predictors typically leads to biased coefficients and incorrect inferences. A plethora of methods have been proposed to correct for this. Obtaining standard errors and confidence intervals using the corrected estimators can be challenging and, in addition, there is concern about remaining bias in the corrected estimators. The bootstrap, which is one option to address these problems, has received limited attention in this context. It has usually been employed by simply resampling observations, which, while suitable in some situations, is not always formally justified. In addition, the simple bootstrap does not allow for estimating bias in non-linear models, including logistic regression. Model-based bootstrapping, which can potentially estimate bias in addition to being robust to the original sampling or whether the measurement error variance is constant or not, has received limited attention. However, it faces challenges that are not present in handling regression models with no measurement error. This article develops new methods for model-based bootstrapping when correcting for measurement error in logistic regression with replicate measures. The methodology is illustrated using two examples, and a series of simulations are carried out to assess and compare the simple and model-based bootstrap methods, as well as other standard methods. While not always perfect, the model-based approaches offer some distinct improvements over the other methods. © 2017, The International Biometric Society.

Thin-film method-XRF determination of the composition of rare earth oxides

International Nuclear Information System (INIS)

Xiao Deming

1992-01-01

The author describes the thin-film sample preparation by precipitation-pumping filtering method and the composition of rare earth oxide materials by XRF determination. The determination limits are 0.01% to 0.17%. The coefficients of variation are in the range of 0.85% to 14.9%. The analytical results of several kinds of rare earth oxide materials show that this method can be applied to the determination of the composition of rare earth oxide mixtures

Understanding poisson regression.

Science.gov (United States)

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Science.gov (United States)

Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

2018-01-01

Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

Energy coefficients for a propeller series

DEFF Research Database (Denmark)

Olsen, Anders Smærup

2004-01-01

The efficiency for a propeller is calculated by energy coefficients. These coefficients are related to four types of losses, i.e. the axial, the rotational, the frictional, and the finite blade number loss, and one gain, i.e. the axial gain. The energy coefficients are derived by use...... of the potential theory with the propeller modelled as an actuator disk. The efficiency based on the energy coefficients is calculated for a propeller series. The results show a good agreement between the efficiency based on the energy coefficients and the efficiency obtained by a vortex-lattice method....

Alternative Methods of Regression

CERN Document Server

Birkes, David

2011-01-01

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

A drying coefficient for building materials

DEFF Research Database (Denmark)

Scheffler, Gregor Albrecht; Plagge, Rudolf

2009-01-01

coefficient is defined which can be determined based on measured drying data. The correlation of this coefficient with the water absorption and the vapour diffusion coefficient is analyzed and its additional information content is critically challenged. As result, a drying coefficient has been derived......The drying experiment is an important element of the hygrothermal characterisation of building materials. Contrary to other moisture transport experiments as the vapour diffusion and the water absorption test, it is until now not possible to derive a simple coefficient for the drying. However......, in many cases such a coefficient would be highly appreciated, e.g. in interaction of industry and research or for the distinction and selection of suitable building materials throughout design and practise. This article first highlights the importance of drying experiments for hygrothermal...

Intermolecular potential energy surface and thermophysical properties of ethylene oxide.

Science.gov (United States)

Crusius, Johann-Philipp; Hellmann, Robert; Hassel, Egon; Bich, Eckard

2014-10-28

A six-dimensional potential energy hypersurface (PES) for two interacting rigid ethylene oxide (C2H4O) molecules was determined from high-level quantum-chemical ab initio calculations. The counterpoise-corrected supermolecular approach at the MP2 and CCSD(T) levels of theory was utilized to determine interaction energies for 10178 configurations of two molecules. An analytical site-site potential function with 19 sites per ethylene oxide molecule was fitted to the interaction energies and fine tuned to agree with data for the second acoustic virial coefficient from accurate speed of sound measurements. The PES was validated by computing the second virial coefficient, shear viscosity, and thermal conductivity. The values of these properties are substantiated by the best experimental data as they tend to fall within the uncertainty intervals and also obey the experimental temperature functions, except for viscosity, where experimental data are insufficient. Due to the lack of reliable data, especially for the transport properties, our calculated values are currently the most accurate estimates for these properties of ethylene oxide.

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.

Determination of the external mass transfer coefficient and influence of mixing intensity in moving bed biofilm reactors for wastewater treatment.

Science.gov (United States)

Nogueira, Bruno L; Pérez, Julio; van Loosdrecht, Mark C M; Secchi, Argimiro R; Dezotti, Márcia; Biscaia, Evaristo C

2015-09-01

In moving bed biofilm reactors (MBBR), the removal of pollutants from wastewater is due to the substrate consumption by bacteria attached on suspended carriers. As a biofilm process, the substrates are transported from the bulk phase to the biofilm passing through a mass transfer resistance layer. This study proposes a methodology to determine the external mass transfer coefficient and identify the influence of the mixing intensity on the conversion process in-situ in MBBR systems. The method allows the determination of the external mass transfer coefficient in the reactor, which is a major advantage when compared to the previous methods that require mimicking hydrodynamics of the reactor in a flow chamber or in a separate vessel. The proposed methodology was evaluated in an aerobic lab-scale system operating with COD removal and nitrification. The impact of the mixing intensity on the conversion rates for ammonium and COD was tested individually. When comparing the effect of mixing intensity on the removal rates of COD and ammonium, a higher apparent external mass transfer resistance was found for ammonium. For the used aeration intensities, the external mass transfer coefficient for ammonium oxidation was ranging from 0.68 to 13.50 m d(-1) and for COD removal 2.9 to 22.4 m d(-1). The lower coefficient range for ammonium oxidation is likely related to the location of nitrifiers deeper in the biofilm. The measurement of external mass transfer rates in MBBR will help in better design and evaluation of MBBR system-based technologies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Study on Thermal Degradation Characteristics and Regression Rate Measurement of Paraffin-Based Fuel

Directory of Open Access Journals (Sweden)

Songqi Hu

2015-09-01

Full Text Available Paraffin fuel has been found to have a regression rate that is higher than conventional HTPB (hydroxyl-terminated polybutadiene fuel and, thus, presents itself as an ideal energy source for a hybrid rocket engine. The energy characteristics of paraffin-based fuel and HTPB fuel have been calculated by the method of minimum free energy. The thermal degradation characteristics were measured for paraffin, pretreated paraffin, HTPB and paraffin-based fuel in different working conditions by the using differential scanning calorimetry (DSC and a thermogravimetric analyzer (TGA. The regression rates of paraffin-based fuel and HTPB fuel were tested by a rectangular solid-gas hybrid engine. The research findings showed that: the specific impulse of paraffin-based fuel is almost the same as that of HTPB fuel; the decomposition temperature of pretreated paraffin is higher than that of the unprocessed paraffin, but lower than that of HTPB; with the increase of paraffin, the initial reaction exothermic peak of paraffin-based fuel is reached in advance, and the initial reaction heat release also increases; the regression rate of paraffin-based fuel is higher than the common HTPB fuel under the same conditions; with the increase of oxidizer mass flow rate, the regression rate of solid fuel increases accordingly for the same fuel formulation.

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

COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression

Directory of Open Access Journals (Sweden)

Leif E. Peterson

1997-11-01

Full Text Available A computer program for multifactor relative risks, confidence limits, and tests of hypotheses using regression coefficients and a variance-covariance matrix obtained from a previous additive or multiplicative regression analysis is described in detail. Data used by the program can be stored and input from an external disk-file or entered via the keyboard. The output contains a list of the input data, point estimates of single or joint effects, confidence intervals and tests of hypotheses based on a minimum modified chi-square statistic. Availability of the program is also discussed.

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

Science.gov (United States)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

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

Brightness-normalized Partial Least Squares Regression for hyperspectral data

International Nuclear Information System (INIS)

Feilhauer, Hannes; Asner, Gregory P.; Martin, Roberta E.; Schmidtlein, Sebastian

2010-01-01

Developed in the field of chemometrics, Partial Least Squares Regression (PLSR) has become an established technique in vegetation remote sensing. PLSR was primarily designed for laboratory analysis of prepared material samples. Under field conditions in vegetation remote sensing, the performance of the technique may be negatively affected by differences in brightness due to amount and orientation of plant tissues in canopies or the observing conditions. To minimize these effects, we introduced brightness normalization to the PLSR approach and tested whether this modification improves the performance under changing canopy and observing conditions. This test was carried out using high-fidelity spectral data (400-2510 nm) to model observed leaf chemistry. The spectral data was combined with a canopy radiative transfer model to simulate effects of varying canopy structure and viewing geometry. Brightness normalization enhanced the performance of PLSR by dampening the effects of canopy shade, thus providing a significant improvement in predictions of leaf chemistry (up to 3.6% additional explained variance in validation) compared to conventional PLSR. Little improvement was made on effects due to variable leaf area index, while minor improvement (mostly not significant) was observed for effects of variable viewing geometry. In general, brightness normalization increased the stability of model fits and regression coefficients for all canopy scenarios. Brightness-normalized PLSR is thus a promising approach for application on airborne and space-based imaging spectrometer data.

Influence of Graphene Oxide on the Tribological and Electrical Properties of PMMA Composites

Directory of Open Access Journals (Sweden)

Jiale Song

2013-01-01

Full Text Available The graphene oxide (GO was obtained by Hummers' method using natural graphite as raw materials. Then, the GO/poly(methyl methacrylate (PMMA nanocomposites were prepared by in situ polymerization. The tribological and electrical properties of nanocomposites were studied. As a result, the frictional coefficient of GO/PMMA nanocomposites was prominently improved with the content of the graphene oxide increasing. The electrical properties of nanocomposites were slightly increased when adding the graphene oxide.

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.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

Science.gov (United States)

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

A kinetic model of municipal sludge degradation during non-catalytic wet oxidation.

Science.gov (United States)

Prince-Pike, Arrian; Wilson, David I; Baroutian, Saeid; Andrews, John; Gapes, Daniel J

2015-12-15

Wet oxidation is a successful process for the treatment of municipal sludge. In addition, the resulting effluent from wet oxidation is a useful carbon source for subsequent biological nutrient removal processes in wastewater treatment. Owing to limitations with current kinetic models, this study produced a kinetic model which predicts the concentrations of key intermediate components during wet oxidation. The model was regressed from lab-scale experiments and then subsequently validated using data from a wet oxidation pilot plant. The model was shown to be accurate in predicting the concentrations of each component, and produced good results when applied to a plant 500 times larger in size. A statistical study was undertaken to investigate the validity of the regressed model parameters. Finally the usefulness of the model was demonstrated by suggesting optimum operating conditions such that volatile fatty acids were maximised. Copyright © 2015 Elsevier Ltd. All rights reserved.

Variation of solubility, biokinetics and dose coefficient of industrial uranium oxides according to the specific surface area

International Nuclear Information System (INIS)

Chazel, V.; Houpert, P.; Ansorbolo, E.; Henge-Napoli, M.H.; Paquet, F.

2000-01-01

The in vitro solubility, absorption to blood, lung retention and dose coefficient of industrial UO 2 samples were studied as a function of the specific surface area (SSA) of the particles. An in vitro study has been carried out on two samples of industrial UO 4 to compare the results with those obtained with UO 2 . Ten UO 2 samples supplied by different fuel factories or research laboratories, presented specific surface areas from 1.00 to 4.45 m 2 .g -1 . The wide range of values of SSA was due to the different conditions of fabrication. Dissolution tests in cell culture medium made on these ten samples have shown that the solubility increased 2.5-fold when the SSA increased 1.7-fold. The same tendency has been found for UO 4 , a soluble compound, and for U 3 O 8 , a moderately soluble compound. Four in vivo experiments carried out on rats by intratracheal instillation of dust suspensions of UO 2 , have highlighted the decrease in lung retention and the increase of absorption to blood with the SSA. The experimental absorption parameters calculated from the in vivo data allowed specific dose coefficients to be obtained which decreased from 6.6 to 4.3 μSv.Bq -1 when the SSA increased from 1.60 to 3.08 m 2 .g -1 . Thus, the medical monitoring of workers at the workplace has to take into account any change in the fabrication process of the uranium compound which can affect the physiochemical properties and consequently the dose coefficient. (author)

REGRESSIVE ANALYSIS OF BRAKING EFFICIENCY OF M1 CATEGORY VEHICLES WITH ANTI-BLOCKING BRAKE SYSTEM

Directory of Open Access Journals (Sweden)

О. Sarayev

2015-07-01

Full Text Available The problematics of assessing the effectiveness of vehicle braking after road accidentoccurrence is considered. For the first time in relation to the modern models of vehicles equipped with anti-lock brakes there were obtained regression models describing the relationship between the coefficient of traction and a random variable of steady deceleration. This does not contradict the essence of the stochastic physical object, which is the process of vehicle braking, unlike the previously adopted method of formalizing this process, using a deterministic function.

Mechanical Properties of Glass Surfaces Coated with Tin Oxide

DEFF Research Database (Denmark)

Swindlehurst, W. E.; Cantor, B.

1978-01-01

The effect of tin oxide coatings on the coefficient of friction and fracture strength of glass surfaces is studied. Experiments were performed partly on commercially treated glass bottles and partly on laboratory prepared microscope slides. Coatings were applied in the laboratory by decomposition...

Secondary electron emission characteristics of oxide electrodes in flat electron emission lamp

Directory of Open Access Journals (Sweden)

Chang-Lin Chiang

2016-01-01

Full Text Available The present study concerns with the secondary electron emission coefficient, γ, of the cathode materials used in the newly developed flat electron emission lamp (FEEL devices, which essentially integrates the concept of using cathode for fluorescent lamp and anode for cathode ray tube (CRT to obtain uniform planar lighting. Three different cathode materials, namely fluorine-doped tin oxide (FTO, aluminum oxide coated FTO (Al2O3/FTO and magnesium oxide coated FTO (MgO/FTO were prepared to investigate how the variations of γ and working gases influence the performance of FEEL devices, especially in lowering the breakdown voltage and pressure of the working gases. The results indicate that the MgO/FTO bilayer cathode exhibited a relatively larger effective secondary electron emission coefficient, resulting in significant reduction of breakdown voltage to about 3kV and allowing the device to be operated at the lower pressure to generate the higher lighting efficiency.

Secondary electron emission characteristics of oxide electrodes in flat electron emission lamp

Energy Technology Data Exchange (ETDEWEB)

Chiang, Chang-Lin, E-mail: CLChiang@itri.org.tw; Li, Chia-Hung [Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Road, Chutung 310, Taiwan (China); Department of Electrophysics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, Taiwan (China); Zeng, Hui-Kai [Department of Electronic Engineering, Chung Yuan Christian University, 200 Chung Pei Road, Chung Li 320, Taiwan (China); Li, Jung-Yu, E-mail: JY-Lee@itri.org.tw; Chen, Shih-Pu; Lin, Yi-Ping [Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Road, Chutung 310, Taiwan (China); Hsieh, Tai-Chiung; Juang, Jenh-Yih, E-mail: jyjuang@cc.nctu.edu.tw [Department of Electrophysics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, Taiwan (China)

2016-01-15

The present study concerns with the secondary electron emission coefficient, γ, of the cathode materials used in the newly developed flat electron emission lamp (FEEL) devices, which essentially integrates the concept of using cathode for fluorescent lamp and anode for cathode ray tube (CRT) to obtain uniform planar lighting. Three different cathode materials, namely fluorine-doped tin oxide (FTO), aluminum oxide coated FTO (Al{sub 2}O{sub 3}/FTO) and magnesium oxide coated FTO (MgO/FTO) were prepared to investigate how the variations of γ and working gases influence the performance of FEEL devices, especially in lowering the breakdown voltage and pressure of the working gases. The results indicate that the MgO/FTO bilayer cathode exhibited a relatively larger effective secondary electron emission coefficient, resulting in significant reduction of breakdown voltage to about 3kV and allowing the device to be operated at the lower pressure to generate the higher lighting efficiency.

Energy demand projection of China using a path-coefficient analysis and PSO–GA approach

International Nuclear Information System (INIS)

Yu Shiwei; Zhu Kejun; Zhang Xian

2012-01-01

Highlights: ► The effect mechanism of China’s energy demand is investigated detailedly. ► A hybrid algorithm PSO–GA optimal energy demands estimating model for China. ► China’s energy demand will reach 4.48 billion tce in 2015. ► The proposed method forecast shows its superiority compared with others. - Abstract: Energy demand projection is fundamental to rational energy planning formulation. The present study investigates the direct and indirect effects of five factors, namely GDP, population, proportion of industrial, proportion of urban population and coal percentage of total energy consumption on China’s energy demand, implementing a path-coefficient analysis. On this basis, a hybrid algorithm, Particle Swarm Optimization and Genetic Algorithm optimal Energy Demand Estimating (PSO–GA EDE) model, is proposed for China. The coefficients of the three forms of the model (linear, exponential and quadratic model) are optimized by proposed PSO–GA. To obtain a combinational prediction of three forms, a departure coefficient method is applied to get the combinational weights. The results show that the China’s energy demand will be 4.48 billion tce in 2015. Furthermore; the proposed method forecast shows its superiority compared with other single optimization method such as GA, PSO or ACO and multiple linear regressions.

Quadrature formulas for Fourier coefficients

KAUST Repository

Bojanov, Borislav

2009-09-01

We consider quadrature formulas of high degree of precision for the computation of the Fourier coefficients in expansions of functions with respect to a system of orthogonal polynomials. In particular, we show the uniqueness of a multiple node formula for the Fourier-Tchebycheff coefficients given by Micchelli and Sharma and construct new Gaussian formulas for the Fourier coefficients of a function, based on the values of the function and its derivatives. © 2009 Elsevier B.V. All rights reserved.

Crystal-free Formation of Non-Oxide Optical Fiber

Science.gov (United States)

Nabors, Sammy A.

2015-01-01

Researchers at NASA Marshall Space Flight Center have devised a method for the creation of crystal-free nonoxide optical fiber preforms. Non-oxide fiber optics are extensively used in infrared transmitting applications such as communication systems, chemical sensors, and laser fiber guides for cutting, welding and medical surgery. However, some of these glasses are very susceptible to crystallization. Even small crystals can lead to light scatter and a high attenuation coefficient, limiting their usefulness. NASA has developed a new method of non-oxide fiber formation that uses axial magnetic fields to suppress crystallization. The resulting non-oxide fibers are crystal free and have lower signal attenuation rates than silica based optical fibers.

Reproducibility of The Random Incidence Absorption Coefficient Converted From the Sabine Absorption Coefficient

DEFF Research Database (Denmark)

Jeong, Cheol-Ho; Chang, Ji-ho

2015-01-01

largely depending on the test room. Several conversion methods for porous absorbers from the Sabine absorption coefficient to the random incidence absorption coefficient were suggested by considering the finite size of a test specimen and non-uniformly incident energy onto the specimen, which turned out...... resistivity optimization outperforms the surface impedance optimization in terms of the reproducibility....

Functionalization of 2D macroporous silicon under the high-pressure oxidation

Science.gov (United States)

Karachevtseva, L.; Kartel, M.; Kladko, V.; Gudymenko, O.; Bo, Wang; Bratus, V.; Lytvynenko, O.; Onyshchenko, V.; Stronska, O.

2018-03-01

Addition functionalization after high-pressure oxidation of 2D macroporous silicon structures is evaluated. X-ray diffractometry indicates formation of orthorhombic SiO2 phase on macroporous silicon at oxide thickness of 800-1200 nm due to cylindrical symmetry of macropores and high thermal expansion coefficient of SiO2. Pb center concentration grows with the splitting energy of LO- and TO-phonons and SiO2 thickness in oxidized macroporous silicon structures. This increase EPR signal amplitude and GHz radiation absorption and is promising for development of high-frequency devices and electronically controlled elements.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

Science.gov (United States)

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Drag Coefficient Estimation in Orbit Determination

Science.gov (United States)

McLaughlin, Craig A.; Manee, Steve; Lichtenberg, Travis

2011-07-01

Drag modeling is the greatest uncertainty in the dynamics of low Earth satellite orbits where ballistic coefficient and density errors dominate drag errors. This paper examines fitted drag coefficients found as part of a precision orbit determination process for Stella, Starlette, and the GEOSAT Follow-On satellites from 2000 to 2005. The drag coefficients for the spherical Stella and Starlette satellites are assumed to be highly correlated with density model error. The results using MSIS-86, NRLMSISE-00, and NRLMSISE-00 with dynamic calibration of the atmosphere (DCA) density corrections are compared. The DCA corrections were formulated for altitudes of 200-600 km and are found to be inappropriate when applied at 800 km. The yearly mean fitted drag coefficients are calculated for each satellite for each year studied. The yearly mean drag coefficients are higher for Starlette than Stella, where Starlette is at a higher altitude. The yearly mean fitted drag coefficients for all three satellites decrease as solar activity decreases after solar maximum.

Geoelectrical parameter-based multivariate regression borehole yield model for predicting aquifer yield in managing groundwater resource sustainability

Directory of Open Access Journals (Sweden)

Kehinde Anthony Mogaji

2016-07-01

Full Text Available This study developed a GIS-based multivariate regression (MVR yield rate prediction model of groundwater resource sustainability in the hard-rock geology terrain of southwestern Nigeria. This model can economically manage the aquifer yield rate potential predictions that are often overlooked in groundwater resources development. The proposed model relates the borehole yield rate inventory of the area to geoelectrically derived parameters. Three sets of borehole yield rate conditioning geoelectrically derived parameters—aquifer unit resistivity (ρ, aquifer unit thickness (D and coefficient of anisotropy (λ—were determined from the acquired and interpreted geophysical data. The extracted borehole yield rate values and the geoelectrically derived parameter values were regressed to develop the MVR relationship model by applying linear regression and GIS techniques. The sensitivity analysis results of the MVR model evaluated at P ⩽ 0.05 for the predictors ρ, D and λ provided values of 2.68 × 10−05, 2 × 10−02 and 2.09 × 10−06, respectively. The accuracy and predictive power tests conducted on the MVR model using the Theil inequality coefficient measurement approach, coupled with the sensitivity analysis results, confirmed the model yield rate estimation and prediction capability. The MVR borehole yield prediction model estimates were processed in a GIS environment to model an aquifer yield potential prediction map of the area. The information on the prediction map can serve as a scientific basis for predicting aquifer yield potential rates relevant in groundwater resources sustainability management. The developed MVR borehole yield rate prediction mode provides a good alternative to other methods used for this purpose.

Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks.

Science.gov (United States)

Zhang, Juping; Yang, Chan; Jin, Zhen; Li, Jia

2018-07-14

In this paper, the correlation coefficients between nodes in states are used as dynamic variables, and we construct SIR epidemic dynamic models with correlation coefficients by using the pair approximation method in static networks and dynamic networks, respectively. Considering the clustering coefficient of the network, we analytically investigate the existence and the local asymptotic stability of each equilibrium of these models and derive threshold values for the prevalence of diseases. Additionally, we obtain two equivalent epidemic thresholds in dynamic networks, which are compared with the results of the mean field equations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Boiling water reactors with uranium-plutonium mixed oxide fuel. Report 5: Analysis of the reactivity coefficients and the stability of a BWR loaded with MOx fuel

Energy Technology Data Exchange (ETDEWEB)

Demaziere, C. [CEA Centre d' Etudes de Cadarache, 13 - Saint-Paul-lez-Durance (France). Direction des Reacteurs Nucleaires

2000-01-01

This report is a part of the project titled 'Boiling Water Reactors With Uranium-Plutonium Mixed Oxide (MOx) Fuel'. The aim of this study is to model the impact of a core loading pattern containing MOx bundles upon the main characteristics of a BWR (reactivity coefficients, stability, etc.). For this purpose, the Core Management System (CMS) codes of Studsvik Scandpower are used. This package is constituted by CASMO-4/TABLES-3/SIMULATE-3. It has been shown in previous reports that these codes are able to accurately represent and model MOx bundles. This report is thus devoted to the study of BWR cores loaded (partially or totally) with MOx bundles. The plutonium quality used is the Pu type 2016 (mostly Pu-239, 56 %, and Pu-240, 26 %), but a variation of the plutonium isotopic vector was also investigated, in case of a partial MOx loading. One notices that the reactivity coefficients do not present significant changes in comparison with a full UOx loading. Nevertheless, two main problems arise: the shutdown margin at BOC is lower than 1 % and the stability to in-phase oscillations is slightly decreased. (The SIMULATE-3 version used for this study does not contain the latest MOx enhancements described in literature, since these code developments have not been provided to the department. Nevertheless, as the nominal average enrichment of the MOx bundles is 5.41 % (total amount of plutonium), which can still be considered as a relatively low enrichment, the accuracy of the CMS codes is acceptable without the use of the MOx improvements for this level of Pu enrichment.

Variation in aerodynamic coefficients with altitude

Science.gov (United States)

Shahid, Faiza; Hussain, Mukkarum; Baig, Mirza Mehmood; Haq, Ihtram ul

Precise aerodynamics performance prediction plays key role for a flying vehicle to get its mission completed within desired accuracy. Aerodynamic coefficients for same Mach number can be different at different altitude due to difference in Reynolds number. Prediction of these aerodynamics coefficients can be made through experiments, analytical solution or Computational Fluid Dynamics (CFD). Advancements in computational power have generated the concept of using CFD as a virtual Wind Tunnel (WT), hence aerodynamic performance prediction in present study is based upon CFD (numerical test rig). Simulations at different altitudes for a range of Mach numbers with zero angle of attack are performed to predict axial force coefficient behavior with altitude (Reynolds number). Similar simulations for a fixed Mach number '3' and a range of angle of attacks are also carried out to envisage the variation in normal force and pitching moment coefficients with altitude (Reynolds number). Results clearly depict that the axial force coefficient is a function of altitude (Reynolds number) and increase as altitude increases, especially for subsonic region. Variation in axial force coefficient with altitude (Reynolds number) slightly increases for larger values of angle of attacks. Normal force and pitching moment coefficients do not depend on altitude (Reynolds number) at smaller values of angle of attacks but show slight decrease as altitude increases. Present study suggests that variation of normal force and pitching moment coefficients with altitude can be neglected but the variation of axial force coefficient with altitude should be considered for vehicle fly in dense atmosphere. It is recommended to continue this study to more complex configurations for various Mach numbers with side slip and real gas effects.

Implications of NGA for NEHRP site coefficients

Science.gov (United States)

Borcherdt, Roger D.

2012-01-01

Three proposals are provided to update tables 11.4-1 and 11.4-2 of Minimum Design Loads for Buildings and Other Structures (7-10), by the American Society of Civil Engineers (2010) (ASCE/SEI 7-10), with site coefficients implied directly by NGA (Next Generation Attenuation) ground motion prediction equations (GMPEs). Proposals include a recommendation to use straight-line interpolation to infer site coefficients at intermediate values of ̅vs (average shear velocity). Site coefficients are recommended to ensure consistency with ASCE/SEI 7-10 MCER (Maximum Considered Earthquake) seismic-design maps and simplified site-specific design spectra procedures requiring site classes with associated tabulated site coefficients and a reference site class with unity site coefficients. Recommended site coefficients are confirmed by independent observations of average site amplification coefficients inferred with respect to an average ground condition consistent with that used for the MCER maps. The NGA coefficients recommended for consideration are implied directly by the NGA GMPEs and do not require introduction of additional models.

Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network

International Nuclear Information System (INIS)

Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui

2010-01-01

Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)

Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

Directory of Open Access Journals (Sweden)

Yunfeng Wu

2014-01-01

Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Directory of Open Access Journals (Sweden)

J. Chardon

2018-01-01

Full Text Available Statistical downscaling models (SDMs are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

Determination of equilibration kinetics of oxide electrode materials using a manometric method

International Nuclear Information System (INIS)

Badwal, S.P.S.; Jiang, S.P.; Love, J.; Nowotny, J.; Rekas, M.

1998-01-01

The gas/solid equilibration kinetics for electrode oxide materials, such as (La 0.8 Sr 0.2 )MnO 3 , using a manometric method, was determined. The reaction kinetics between oxygen and the oxide material was monitored using the measurements of the P(O 2 ) changes during isothermic experiments of oxidation and reduction. The procedure of the determination will be described and relevant kinetic equations was derived. The equilibration kinetic data obtained can be used to determine the chemical diffusion coefficient. Copyright (1998) Australasian Ceramic Society

Simplified method of ''push-pull'' test data analysis for determining in situ reaction rate coefficients

International Nuclear Information System (INIS)

Haggerty, R.; Schroth, M.H.; Istok, J.D.

1998-01-01

The single-well, ''''push-pull'''' test method is useful for obtaining information on a wide variety of aquifer physical, chemical, and microbiological characteristics. A push-pull test consists of the pulse-type injection of a prepared test solution into a single monitoring well followed by the extraction of the test solution/ground water mixture from the same well. The test solution contains a conservative tracer and one or more reactants selected to investigate a particular process. During the extraction phase, the concentrations of tracer, reactants, and possible reaction products are measured to obtain breakthrough curves for all solutes. This paper presents a simplified method of data analysis that can be used to estimate a first-order reaction rate coefficient from these breakthrough curves. Rate coefficients are obtained by fitting a regression line to a plot of normalized concentrations versus elapsed time, requiring no knowledge of aquifer porosity, dispersivity, or hydraulic conductivity. A semi-analytical solution to the advective-dispersion equation is derived and used in a sensitivity analysis to evaluate the ability of the simplified method to estimate reaction rate coefficients in simulated push-pull tests in a homogeneous, confined aquifer with a fully-penetrating injection/extraction well and varying porosity, dispersivity, test duration, and reaction rate. A numerical flow and transport code (SUTRA) is used to evaluate the ability of the simplified method to estimate reaction rate coefficients in simulated push-pull tests in a heterogeneous, unconfined aquifer with a partially penetrating well. In all cases the simplified method provides accurate estimates of reaction rate coefficients; estimation errors ranged from 0.1 to 8.9% with most errors less than 5%

Uso de regressões logísticas múltiplas para mapeamento digital de solos no Planalto Médio do RS Multiple logistic regression applied to soil survey in rio grande do sul state, Brazil

Directory of Open Access Journals (Sweden)

Samuel Ribeiro Figueiredo

2008-12-01

hydrographic variables (distance to rivers, flow length, topographical wetness index, and stream power index. Multiple logistic regressions were established between the soil classes mapped on the basis of a traditional survey at a scale of 1:80.000 and the land variables calculated using the DEM. The regressions were used to calculate the probability of occurrence of each soil class. The final estimated soil map was drawn by assigning the soil class with highest probability of occurrence to each cell. The general accuracy was evaluated at 58 % and the Kappa coefficient at 38 % in a comparison of the original soil map with the map estimated at the original scale. A legend simplification had little effect to increase the general accuracy of the map (general accuracy of 61 % and Kappa coefficient of 39 %. It was concluded that multiple logistic regressions have a predictive potential as tool of supervised soil mapping.

Statistical experiments using the multiple regression research for prediction of proper hardness in areas of phosphorus cast-iron brake shoes manufacturing

Science.gov (United States)

Kiss, I.; Cioată, V. G.; Ratiu, S. A.; Rackov, M.; Penčić, M.

2018-01-01

Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. This article focuses on expressing the multiple linear regression model related to the hardness assurance by the chemical composition of the phosphorous cast irons destined to the brake shoes, having in view that the regression coefficients will illustrate the unrelated contributions of each independent variable towards predicting the dependent variable. In order to settle the multiple correlations between the hardness of the cast-iron brake shoes, and their chemical compositions several regression equations has been proposed. Is searched a mathematical solution which can determine the optimum chemical composition for the hardness desirable values. Starting from the above-mentioned affirmations two new statistical experiments are effectuated related to the values of Phosphorus [P], Manganese [Mn] and Silicon [Si]. Therefore, the regression equations, which describe the mathematical dependency between the above-mentioned elements and the hardness, are determined. As result, several correlation charts will be revealed.

Evaluating the Performance of Polynomial Regression Method with Different Parameters during Color Characterization

Directory of Open Access Journals (Sweden)

Bangyong Sun

2014-01-01

Full Text Available The polynomial regression method is employed to calculate the relationship of device color space and CIE color space for color characterization, and the performance of different expressions with specific parameters is evaluated. Firstly, the polynomial equation for color conversion is established and the computation of polynomial coefficients is analysed. And then different forms of polynomial equations are used to calculate the RGB and CMYK’s CIE color values, while the corresponding color errors are compared. At last, an optimal polynomial expression is obtained by analysing several related parameters during color conversion, including polynomial numbers, the degree of polynomial terms, the selection of CIE visual spaces, and the linearization.

Experience with oxide fuel for advanced reactors

International Nuclear Information System (INIS)

Leggett, R.D.

1984-01-01

This paper focuses on the use and potential of oxide fuel systems for the LMFBR. The flawless performance of mixed oxide (UO 2 -PuO 2 ) fuel in FFTF to 100,000 MWd/MTM is reviewed and means for achieving 200,000 MWd/MTM are presented. This includes using non-swelling alloys for cladding and ducts to overcome the limitations caused by swelling of the current alloys. Examples are provided of the inherently safe characteristics of oxide fuel including a large negative Doppler coefficient, its dispersive nature under hypothetical accident scenarios, and the low energy molten fuel-coolant interaction. Developments in fuel fabrication and reprocessing that stress safety and reduced personnel exposure are presented. Lastly, the flexibility to design for maximum fuel supply (high breeding gain) or minimum fuel cost (long lifetime) is shown

Experience with oxide fuel for advanced reactors

International Nuclear Information System (INIS)

Leggett, R.D.

1984-04-01

This paper focuses on the use and potential of oxide fuel system for the LMFBR. The flawless performance of mixed oxide (UO 2 -PuO 2 ) fuel in FFTF to 100,000 MWd/MTM is reviewed and means for achieving 200,000 MWd/MTM are presented. This includes using non-swelling alloys for cladding and ducts to overcome the limitations caused by swelling of the current alloys. Exampled are provided of the inherently safe characteristics of oxide fuel including a large negative Doppler coefficient, its dispersive nature under hypothetical accident scenarios, and the low energy molten fuel-coolant interaction. Developments in fuel fabrication and reprocessing that stress safety and reduced personnel exposure are presented. Lastly, the flexibility to design for maximum fuel supply (high breeding gain) or minimum fuel cost (long lifetime) is shown

Probabilistic optimization of safety coefficients

International Nuclear Information System (INIS)

Marques, M.; Devictor, N.; Magistris, F. de

1999-01-01

This article describes a reliability-based method for the optimization of safety coefficients defined and used in design codes. The purpose of the optimization is to determine the partial safety coefficients which minimize an objective function for sets of components and loading situations covered by a design rule. This objective function is a sum of distances between the reliability of the components designed using the safety coefficients and a target reliability. The advantage of this method is shown on the examples of the reactor vessel, a vapour pipe and the safety injection circuit. (authors)

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.

Reduction-oxidation Enabled Glass-ceramics to Stainless Steel Bonding Part I: screening of doping oxidants

Energy Technology Data Exchange (ETDEWEB)

Dai, Steve Xunhu [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-09-01

Lithium silicate-based glass-ceramics with high coefficients of thermal expansion, designed to form matched hermetic seals in 304L stainless steel housing, show little evidence of interfacial chemical bonding, despite extensive inter-diffusion at the glass-ceramic-stainless steel (GC-SS) interface. A series of glass-ceramic compositions modified with a variety of oxidants, AgO, FeO, NiO, PbO, SnO, CuO, CoO, MoO₃ and WO₃, are examined for the feasibility of forming bonding oxides through reduction-oxidation (redox) at the GC-SS interface. The oxidants were selected according to their Gibbs free energy to allow for oxidation of Cr/Mn/Si from stainless steel, and yet to prevent a reduction of P2O5 in the glass-ceramic where the P₂O₅ is to form Li₃PO₄ nuclei for growth of high expansion crystalline SiO₂ phases. Other than the CuO and CoO modified glass-ceramics, bonding from interfacial redox reactions were not achieved in the modified glass-ceramics, either because of poor wetting on the stainless steel or a reduction of the oxidants at the surface of glass-ceramic specimens rather than the GC-SS interface.

Logic regression and its extensions.

Science.gov (United States)

Schwender, Holger; Ruczinski, Ingo

2010-01-01

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

Variation in aerodynamic coefficients with altitude

Directory of Open Access Journals (Sweden)

Faiza Shahid

Full Text Available Precise aerodynamics performance prediction plays key role for a flying vehicle to get its mission completed within desired accuracy. Aerodynamic coefficients for same Mach number can be different at different altitude due to difference in Reynolds number. Prediction of these aerodynamics coefficients can be made through experiments, analytical solution or Computational Fluid Dynamics (CFD. Advancements in computational power have generated the concept of using CFD as a virtual Wind Tunnel (WT, hence aerodynamic performance prediction in present study is based upon CFD (numerical test rig. Simulations at different altitudes for a range of Mach numbers with zero angle of attack are performed to predict axial force coefficient behavior with altitude (Reynolds number. Similar simulations for a fixed Mach number ‘3’ and a range of angle of attacks are also carried out to envisage the variation in normal force and pitching moment coefficients with altitude (Reynolds number. Results clearly depict that the axial force coefficient is a function of altitude (Reynolds number and increase as altitude increases, especially for subsonic region. Variation in axial force coefficient with altitude (Reynolds number slightly increases for larger values of angle of attacks. Normal force and pitching moment coefficients do not depend on altitude (Reynolds number at smaller values of angle of attacks but show slight decrease as altitude increases. Present study suggests that variation of normal force and pitching moment coefficients with altitude can be neglected but the variation of axial force coefficient with altitude should be considered for vehicle fly in dense atmosphere. It is recommended to continue this study to more complex configurations for various Mach numbers with side slip and real gas effects. Keywords: Mach number, Reynolds number, Blunt body, Altitude effect, Angle of attacks

Anomalous Seebeck coefficient in boron carbides

International Nuclear Information System (INIS)

Aselage, T.L.; Emin, D.; Wood, C.; Mackinnon, I.D.R.; Howard, I.A.

1987-01-01

Boron carbides exhibit an anomalously large Seebeck coefficient with a temperature coefficient that is characteristic of polaronic hopping between inequivalent sites. The inequivalence in the sites is associated with disorder in the solid. The temperature dependence of the Seebeck coefficient for materials prepared by different techniques provides insight into the nature of the disorder

Tumor regression patterns in retinoblastoma

International Nuclear Information System (INIS)

Zafar, S.N.; Siddique, S.N.; Zaheer, N.

2016-01-01

To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)

Einstein coefficients for rotational lines of the (0,0) band of the NO A2sigma(+)-X2Pi system

Science.gov (United States)

Reisel, John R.; Carter, Campbell D.; Laurendeau, Normand M.

1992-01-01

A summary of the spectroscopic equations necessary for prediction of the molecular transition energies and the Einstein A and B coefficients for rovibronic lines of the gamma(0,0) band of nitric oxide (NO) is presented. The calculated molecular transition energies are all within 0.57/cm of published experimental values; in addition, over 95 percent of the calculated energies give agreement with measured results within 0.25/cm. Einstein coefficients are calculated from the band A00 value and the known Hoenl-London factors and are tabulated for individual rovibronic transitions in the NO A2sigma(+)-X2Pi(0,0) band.

Combining Alphas via Bounded Regression

Directory of Open Access Journals (Sweden)

Zura Kakushadze

2015-11-01

Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.

Gini coefficient as a life table function

Directory of Open Access Journals (Sweden)

2003-06-01

Full Text Available This paper presents a toolkit for measuring and analyzing inter-individual inequality in length of life by Gini coefficient. Gini coefficient and four other inequality measures are defined on the length-of-life distribution. Properties of these measures and their empirical testing on mortality data suggest a possibility for different judgements about the direction of changes in the degree of inequality by using different measures. A new computational procedure for the estimation of Gini coefficient from life tables is developed and tested on about four hundred real life tables. The estimates of Gini coefficient are precise enough even for abridged life tables with the final age group of 85+. New formulae have been developed for the decomposition of differences between Gini coefficients by age and cause of death. A new method for decomposition of age-components into effects of mortality and composition of population by group is developed. Temporal changes in the effects of elimination of causes of death on Gini coefficient are analyzed. Numerous empirical examples show: Lorenz curves for Sweden, Russia and Bangladesh in 1995, proportional changes in Gini coefficient and four other measures of inequality for the USA in 1950-1995 and for Russia in 1959-2000. Further shown are errors of estimates of Gini coefficient when computed from various types of mortality data of France, Japan, Sweden and the USA in 1900-95, decompositions of the USA-UK difference in life expectancies and Gini coefficients by age and cause of death in 1997. As well, effects of elimination of major causes of death in the UK in 1951-96 on Gini coefficient, age-specific effects of mortality and educational composition of the Russian population on changes in life expectancy and Gini coefficient between 1979 and 1989. Illustrated as well are variations in life expectancy and Gini coefficient across 32 countries in 1996-1999 and associated changes in life expectancy and Gini

Exploring the negative temperature coefficient behavior of acetaldehyde based on detailed intermediate measurements in a jet-stirred reactor

KAUST Repository

Tao, Tao; Sun, Wenyu; Hansen, Nils; Jasper, Ahren W.; Moshammer, Kai; Chen, Bingjie; Wang, Zhandong; Huang, Can; Dagaut, Philippe; Yang, Bin

2018-01-01

Acetaldehyde is an observed emission species and a key intermediate produced during the combustion and low-temperature oxidation of fossil and bio-derived fuels. Investigations into the low-temperature oxidation chemistry of acetaldehyde are essential to develop a better core mechanism and to better understand auto-ignition and cool flame phenomena. Here, the oxidation of acetaldehyde was studied at low-temperatures (528–946 K) in a jet-stirred reactor (JSR) with the corrected residence time of 2.7 s at 700 Torr. This work describes a detailed set of experimental results that capture the negative temperature coefficient (NTC) behavior in the low-temperature oxidation of acetaldehyde. The mole fractions of 28 species were measured as functions of the temperature by employing a vacuum ultra-violet photoionization molecular-beam mass spectrometer. To explain the observed NTC behavior, an updated mechanism was proposed, which well reproduces the concentration profiles of many observed peroxide intermediates. The kinetic analysis based on the updated mechanism reveals that the NTC behavior of acetaldehyde oxidation is caused by the competition between the O-addition to and the decomposition of the CHCO radical.