A method for the selection of a functional form for a thermodynamic equation of state using weighted linear least squares stepwise regression

NASA Technical Reports Server (NTRS)

Jacobsen, R. T.; Stewart, R. B.; Crain, R. W., Jr.; Rose, G. L.; Myers, A. F.

1976-01-01

A method was developed for establishing a rational choice of the terms to be included in an equation of state with a large number of adjustable coefficients. The methods presented were developed for use in the determination of an equation of state for oxygen and nitrogen. However, a general application of the methods is possible in studies involving the determination of an optimum polynomial equation for fitting a large number of data points. The data considered in the least squares problem are experimental thermodynamic pressure-density-temperature data. Attention is given to a description of stepwise multiple regression and the use of stepwise regression in the determination of an equation of state for oxygen and nitrogen.

Using a Linear Regression Method to Detect Outliers in IRT Common Item Equating

ERIC Educational Resources Information Center

He, Yong; Cui, Zhongmin; Fang, Yu; Chen, Hanwei

2013-01-01

Common test items play an important role in equating alternate test forms under the common item nonequivalent groups design. When the item response theory (IRT) method is applied in equating, inconsistent item parameter estimates among common items can lead to large bias in equated scores. It is prudent to evaluate inconsistency in parameter…

Parameter estimation of Monod model by the Least-Squares method for microalgae Botryococcus Braunii sp

NASA Astrophysics Data System (ADS)

See, J. J.; Jamaian, S. S.; Salleh, R. M.; Nor, M. E.; Aman, F.

2018-04-01

This research aims to estimate the parameters of Monod model of microalgae Botryococcus Braunii sp growth by the Least-Squares method. Monod equation is a non-linear equation which can be transformed into a linear equation form and it is solved by implementing the Least-Squares linear regression method. Meanwhile, Gauss-Newton method is an alternative method to solve the non-linear Least-Squares problem with the aim to obtain the parameters value of Monod model by minimizing the sum of square error ( SSE). As the result, the parameters of the Monod model for microalgae Botryococcus Braunii sp can be estimated by the Least-Squares method. However, the estimated parameters value obtained by the non-linear Least-Squares method are more accurate compared to the linear Least-Squares method since the SSE of the non-linear Least-Squares method is less than the linear Least-Squares method.

Biomass equations for shrub species of Tamualipan thornscrub of North-Eastern Mexico

Treesearch

J. Navar; E. Mendez; A. Najera; J. Graciano; V. Dale; B. Parresol

2004-01-01

Nine additive allometric equations for computing above-ground, standing biomass were developed for the plant community and for each of 18 single species typical of the Tamaulipan thornscrub of north-eastern Mexico. Equations developed using additive procedures in seemingly unrelated linear regression provided statistical efficiency in total biomass estimates at the...

Diagnosis of Enzyme Inhibition Using Excel Solver: A Combined Dry and Wet Laboratory Exercise

ERIC Educational Resources Information Center

Dias, Albino A.; Pinto, Paula A.; Fraga, Irene; Bezerra, Rui M. F.

2014-01-01

In enzyme kinetic studies, linear transformations of the Michaelis-Menten equation, such as the Lineweaver-Burk double-reciprocal transformation, present some constraints. The linear transformation distorts the experimental error and the relationship between "x" and "y" axes; consequently, linear regression of transformed data…

Alternative Regression Equations for Estimation of Annual Peak-Streamflow Frequency for Undeveloped Watersheds in Texas using PRESS Minimization

USGS Publications Warehouse

Asquith, William H.; Thompson, David B.

2008-01-01

The U.S. Geological Survey, in cooperation with the Texas Department of Transportation and in partnership with Texas Tech University, investigated a refinement of the regional regression method and developed alternative equations for estimation of peak-streamflow frequency for undeveloped watersheds in Texas. A common model for estimation of peak-streamflow frequency is based on the regional regression method. The current (2008) regional regression equations for 11 regions of Texas are based on log10 transformations of all regression variables (drainage area, main-channel slope, and watershed shape). Exclusive use of log10-transformation does not fully linearize the relations between the variables. As a result, some systematic bias remains in the current equations. The bias results in overestimation of peak streamflow for both the smallest and largest watersheds. The bias increases with increasing recurrence interval. The primary source of the bias is the discernible curvilinear relation in log10 space between peak streamflow and drainage area. Bias is demonstrated by selected residual plots with superimposed LOWESS trend lines. To address the bias, a statistical framework based on minimization of the PRESS statistic through power transformation of drainage area is described and implemented, and the resulting regression equations are reported. Compared to log10-exclusive equations, the equations derived from PRESS minimization have PRESS statistics and residual standard errors less than the log10 exclusive equations. Selected residual plots for the PRESS-minimized equations are presented to demonstrate that systematic bias in regional regression equations for peak-streamflow frequency estimation in Texas can be reduced. Because the overall error is similar to the error associated with previous equations and because the bias is reduced, the PRESS-minimized equations reported here provide alternative equations for peak-streamflow frequency estimation.

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

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 < 0.05. A 95% confidence interval of the 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.

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

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 < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 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. PMID:27904175

The discovery of indicator variables for QSAR using inductive logic programming

NASA Astrophysics Data System (ADS)

King, Ross D.; Srinivasan, Ashwin

1997-11-01

A central problem in forming accurate regression equations in QSAR studies isthe selection of appropriate descriptors for the compounds under study. Wedescribe a novel procedure for using inductive logic programming (ILP) todiscover new indicator variables (attributes) for QSAR problems, and show thatthese improve the accuracy of the derived regression equations. ILP techniqueshave previously been shown to work well on drug design problems where thereis a large structural component or where clear comprehensible rules arerequired. However, ILP techniques have had the disadvantage of only being ableto make qualitative predictions (e.g. active, inactive) and not to predictreal numbers (regression). We unify ILP and linear regression techniques togive a QSAR method that has the strength of ILP at describing stericstructure, with the familiarity and power of linear regression. We evaluatedthe utility of this new QSAR technique by examining the prediction ofbiological activity with and without the addition of new structural indicatorvariables formed by ILP. In three out of five datasets examined the additionof ILP variables produced statistically better results (P < 0.01) over theoriginal description. The new ILP variables did not increase the overallcomplexity of the derived QSAR equations and added insight into possiblemechanisms of action. We conclude that ILP can aid in the process of drugdesign.

Application of stepwise multiple regression techniques to inversion of Nimbus 'IRIS' observations.

NASA Technical Reports Server (NTRS)

Ohring, G.

1972-01-01

Exploratory studies with Nimbus-3 infrared interferometer-spectrometer (IRIS) data indicate that, in addition to temperature, such meteorological parameters as geopotential heights of pressure surfaces, tropopause pressure, and tropopause temperature can be inferred from the observed spectra with the use of simple regression equations. The technique of screening the IRIS spectral data by means of stepwise regression to obtain the best radiation predictors of meteorological parameters is validated. The simplicity of application of the technique and the simplicity of the derived linear regression equations - which contain only a few terms - suggest usefulness for this approach. Based upon the results obtained, suggestions are made for further development and exploitation of the stepwise regression analysis technique.

Reversed inverse regression for the univariate linear calibration and its statistical properties derived using a new methodology

NASA Astrophysics Data System (ADS)

Kang, Pilsang; Koo, Changhoi; Roh, Hokyu

2017-11-01

Since simple linear regression theory was established at the beginning of the 1900s, it has been used in a variety of fields. Unfortunately, it cannot be used directly for calibration. In practical calibrations, the observed measurements (the inputs) are subject to errors, and hence they vary, thus violating the assumption that the inputs are fixed. Therefore, in the case of calibration, the regression line fitted using the method of least squares is not consistent with the statistical properties of simple linear regression as already established based on this assumption. To resolve this problem, "classical regression" and "inverse regression" have been proposed. However, they do not completely resolve the problem. As a fundamental solution, we introduce "reversed inverse regression" along with a new methodology for deriving its statistical properties. In this study, the statistical properties of this regression are derived using the "error propagation rule" and the "method of simultaneous error equations" and are compared with those of the existing regression approaches. The accuracy of the statistical properties thus derived is investigated in a simulation study. We conclude that the newly proposed regression and methodology constitute the complete regression approach for univariate linear calibrations.

Multiple regression technique for Pth degree polynominals with and without linear cross products

NASA Technical Reports Server (NTRS)

Davis, J. W.

1973-01-01

A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.

Deriving the Regression Equation without Using Calculus

ERIC Educational Resources Information Center

Gordon, Sheldon P.; Gordon, Florence S.

2004-01-01

Probably the one "new" mathematical topic that is most responsible for modernizing courses in college algebra and precalculus over the last few years is the idea of fitting a function to a set of data in the sense of a least squares fit. Whether it be simple linear regression or nonlinear regression, this topic opens the door to applying the…

Calibration Adjustment of the Mid-infrared Analyzer for an Accurate Determination of the Macronutrient Composition of Human Milk.

PubMed

Billard, Hélène; Simon, Laure; Desnots, Emmanuelle; Sochard, Agnès; Boscher, Cécile; Riaublanc, Alain; Alexandre-Gouabau, Marie-Cécile; Boquien, Clair-Yves

2016-08-01

Human milk composition analysis seems essential to adapt human milk fortification for preterm neonates. The Miris human milk analyzer (HMA), based on mid-infrared methodology, is convenient for a unique determination of macronutrients. However, HMA measurements are not totally comparable with reference methods (RMs). The primary aim of this study was to compare HMA results with results from biochemical RMs for a large range of protein, fat, and carbohydrate contents and to establish a calibration adjustment. Human milk was fractionated in protein, fat, and skim milk by covering large ranges of protein (0-3 g/100 mL), fat (0-8 g/100 mL), and carbohydrate (5-8 g/100 mL). For each macronutrient, a calibration curve was plotted by linear regression using measurements obtained using HMA and RMs. For fat, 53 measurements were performed, and the linear regression equation was HMA = 0.79RM + 0.28 (R(2) = 0.92). For true protein (29 measurements), the linear regression equation was HMA = 0.9RM + 0.23 (R(2) = 0.98). For carbohydrate (15 measurements), the linear regression equation was HMA = 0.59RM + 1.86 (R(2) = 0.95). A homogenization step with a disruptor coupled to a sonication step was necessary to obtain better accuracy of the measurements. Good repeatability (coefficient of variation < 7%) and reproducibility (coefficient of variation < 17%) were obtained after calibration adjustment. New calibration curves were developed for the Miris HMA, allowing accurate measurements in large ranges of macronutrient content. This is necessary for reliable use of this device in individualizing nutrition for preterm newborns. © The Author(s) 2015.

Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

PubMed

Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

2018-06-01

This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. Copyright © 2018 John Wiley & Sons, Ltd.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Linear models for calculating digestibile energy for sheep diets.

PubMed

Fonnesbeck, P V; Christiansen, M L; Harris, L E

1981-05-01

Equations for estimating the digestible energy (DE) content of sheep diets were generated from the chemical contents and a factorial description of diets fed to lambs in digestion trials. The diet factors were two forages (alfalfa and grass hay), harvested at three stages of maturity (late vegetative, early bloom and full bloom), fed in two ingredient combinations (all hay or a 50:50 hay and corn grain mixture) and prepared by two forage texture processes (coarsely chopped or finely chopped and pelleted). The 2 x 3 x 2 x 2 factorial arrangement produced 24 diet treatments. These were replicated twice, for a total of 48 lamb digestion trials. In model 1 regression equations, DE was calculated directly from chemical composition of the diet. In model 2, regression equations predicted the percentage of digested nutrient from the chemical contents of the diet and then DE of the diet was calculated as the sum of the gross energy of the digested organic components. Expanded forms of model 1 and model 2 were also developed that included diet factors as qualitative indicator variables to adjust the regression constant and regression coefficients for the diet description. The expanded forms of the equations accounted for significantly more variation in DE than did the simple models and more accurately estimated DE of the diet. Information provided by the diet description proved as useful as chemical analyses for the prediction of digestibility of nutrients. The statistics indicate that, with model 1, neutral detergent fiber and plant cell wall analyses provided as much information for the estimation of DE as did model 2 with the combined information from crude protein, available carbohydrate, total lipid, cellulose and hemicellulose. Regression equations are presented for estimating DE with the most currently analyzed organic components, including linear and curvilinear variables and diet factors that significantly reduce the standard error of the estimate. To estimate De of a diet, the user utilizes the equation that uses the chemical analysis information and diet description most effectively.

Whole stand volume tables for quaking aspen in the Rocky Mountains

Treesearch

Wayne D. Shepperd; H. Todd Mowrer

1984-01-01

Linear regression equations were developed to predict stand volumes for aspen given average stand basal area and average stand height. Tables constructed from these equations allow easy field estimation of gross merchantable cubic and board foot Scribner Rules per acre, and cubic meters per hectare using simple prism cruise data.

Data-driven discovery of partial differential equations.

PubMed

Rudy, Samuel H; Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2017-04-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg-de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

Using Linear Equating to Map PROMIS(®) Global Health Items and the PROMIS-29 V2.0 Profile Measure to the Health Utilities Index Mark 3.

PubMed

Hays, Ron D; Revicki, Dennis A; Feeny, David; Fayers, Peter; Spritzer, Karen L; Cella, David

2016-10-01

Preference-based health-related quality of life (HR-QOL) scores are useful as outcome measures in clinical studies, for monitoring the health of populations, and for estimating quality-adjusted life-years. This was a secondary analysis of data collected in an internet survey as part of the Patient-Reported Outcomes Measurement Information System (PROMIS(®)) project. To estimate Health Utilities Index Mark 3 (HUI-3) preference scores, we used the ten PROMIS(®) global health items, the PROMIS-29 V2.0 single pain intensity item and seven multi-item scales (physical functioning, fatigue, pain interference, depressive symptoms, anxiety, ability to participate in social roles and activities, sleep disturbance), and the PROMIS-29 V2.0 items. Linear regression analyses were used to identify significant predictors, followed by simple linear equating to avoid regression to the mean. The regression models explained 48 % (global health items), 61 % (PROMIS-29 V2.0 scales), and 64 % (PROMIS-29 V2.0 items) of the variance in the HUI-3 preference score. Linear equated scores were similar to observed scores, although differences tended to be larger for older study participants. HUI-3 preference scores can be estimated from the PROMIS(®) global health items or PROMIS-29 V2.0. The estimated HUI-3 scores from the PROMIS(®) health measures can be used for economic applications and as a measure of overall HR-QOL in research.

Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression.

PubMed

Crawford, John R; Garthwaite, Paul H; Denham, Annie K; Chelune, Gordon J

2012-12-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all psychologists are aware that regression equations can be built not only from raw data but also using only basic summary data for a sample, and (b) the computations involved are tedious and prone to error. In an attempt to overcome these barriers, Crawford and Garthwaite (2007) provided methods to build and apply simple linear regression models using summary statistics as data. In the present study, we extend this work to set out the steps required to build multiple regression models from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. We also develop, describe, and make available a computer program that implements these methods. Although there are caveats associated with the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate." Upgraded versions of earlier programs for regression in the single case are also provided; these add the point and interval estimates of effect size developed in the present article.

Minimizing bias in biomass allometry: Model selection and log transformation of data

Treesearch

Joseph Mascaro; undefined undefined; Flint Hughes; Amanda Uowolo; Stefan A. Schnitzer

2011-01-01

Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the raditional approach of log-transformation followed by linear regression). Most statistical software packages, however, assume additive errors by default, violating a key assumption of allometric theory and possibly producing spurious models....

40 CFR 91.321 - NDIR analyzer calibration.

Code of Federal Regulations, 2014 CFR

2014-07-01

... of full-scale concentration. A minimum of six evenly spaced points covering at least 80 percent of..., a linear calibration may be used. To determine if this criterion is met: (1) Perform a linear least-square regression on the data generated. Use an equation of the form y=mx, where x is the actual chart...

40 CFR 91.321 - NDIR analyzer calibration.

Code of Federal Regulations, 2012 CFR

2012-07-01

... of full-scale concentration. A minimum of six evenly spaced points covering at least 80 percent of..., a linear calibration may be used. To determine if this criterion is met: (1) Perform a linear least-square regression on the data generated. Use an equation of the form y=mx, where x is the actual chart...

40 CFR 91.321 - NDIR analyzer calibration.

Code of Federal Regulations, 2011 CFR

2011-07-01

... of full-scale concentration. A minimum of six evenly spaced points covering at least 80 percent of..., a linear calibration may be used. To determine if this criterion is met: (1) Perform a linear least-square regression on the data generated. Use an equation of the form y=mx, where x is the actual chart...

40 CFR 91.321 - NDIR analyzer calibration.

Code of Federal Regulations, 2013 CFR

2013-07-01

... of full-scale concentration. A minimum of six evenly spaced points covering at least 80 percent of..., a linear calibration may be used. To determine if this criterion is met: (1) Perform a linear least-square regression on the data generated. Use an equation of the form y=mx, where x is the actual chart...

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

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. PMID:29651366

Effect of removing the common mode errors on linear regression analysis of noise amplitudes in position time series of a regional GPS network & a case study of GPS stations in Southern California

NASA Astrophysics Data System (ADS)

Jiang, Weiping; Ma, Jun; Li, Zhao; Zhou, Xiaohui; Zhou, Boye

2018-05-01

The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126 GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components.

Comparative study of Poincaré plot analysis using short electroencephalogram signals during anaesthesia with spectral edge frequency 95 and bispectral index.

PubMed

Hayashi, K; Yamada, T; Sawa, T

2015-03-01

The return or Poincaré plot is a non-linear analytical approach in a two-dimensional plane, where a timed signal is plotted against itself after a time delay. Its scatter pattern reflects the randomness and variability in the signals. Quantification of a Poincaré plot of the electroencephalogram has potential to determine anaesthesia depth. We quantified the degree of dispersion (i.e. standard deviation, SD) along the diagonal line of the electroencephalogram-Poincaré plot (named as SD1/SD2), and compared SD1/SD2 values with spectral edge frequency 95 (SEF95) and bispectral index values. The regression analysis showed a tight linear regression equation with a coefficient of determination (R(2) ) value of 0.904 (p < 0.0001) between the Poincaré index (SD1/SD2) and SEF95, and a moderate linear regression equation between SD1/SD2 and bispectral index (R(2)  = 0.346, p < 0.0001). Quantification of the Poincaré plot tightly correlates with SEF95, reflecting anaesthesia-dependent changes in electroencephalogram oscillation. © 2014 The Association of Anaesthetists of Great Britain and Ireland.

Evaluation of Piecewise Polynomial Equations for Two Types of Thermocouples

PubMed Central

Chen, Andrew; Chen, Chiachung

2013-01-01

Thermocouples are the most frequently used sensors for temperature measurement because of their wide applicability, long-term stability and high reliability. However, one of the major utilization problems is the linearization of the transfer relation between temperature and output voltage of thermocouples. The linear calibration equation and its modules could be improved by using regression analysis to help solve this problem. In this study, two types of thermocouple and five temperature ranges were selected to evaluate the fitting agreement of different-order polynomial equations. Two quantitative criteria, the average of the absolute error values |e|ave and the standard deviation of calibration equation estd, were used to evaluate the accuracy and precision of these calibrations equations. The optimal order of polynomial equations differed with the temperature range. The accuracy and precision of the calibration equation could be improved significantly with an adequate higher degree polynomial equation. The technique could be applied with hardware modules to serve as an intelligent sensor for temperature measurement. PMID:24351627

Regional Regression Equations to Estimate Flow-Duration Statistics at Ungaged Stream Sites in Connecticut

USGS Publications Warehouse

Ahearn, Elizabeth A.

2010-01-01

Multiple linear regression equations for determining flow-duration statistics were developed to estimate select flow exceedances ranging from 25- to 99-percent for six 'bioperiods'-Salmonid Spawning (November), Overwinter (December-February), Habitat Forming (March-April), Clupeid Spawning (May), Resident Spawning (June), and Rearing and Growth (July-October)-in Connecticut. Regression equations also were developed to estimate the 25- and 99-percent flow exceedances without reference to a bioperiod. In total, 32 equations were developed. The predictive equations were based on regression analyses relating flow statistics from streamgages to GIS-determined basin and climatic characteristics for the drainage areas of those streamgages. Thirty-nine streamgages (and an additional 6 short-term streamgages and 28 partial-record sites for the non-bioperiod 99-percent exceedance) in Connecticut and adjacent areas of neighboring States were used in the regression analysis. Weighted least squares regression analysis was used to determine the predictive equations; weights were assigned based on record length. The basin characteristics-drainage area, percentage of area with coarse-grained stratified deposits, percentage of area with wetlands, mean monthly precipitation (November), mean seasonal precipitation (December, January, and February), and mean basin elevation-are used as explanatory variables in the equations. Standard errors of estimate of the 32 equations ranged from 10.7 to 156 percent with medians of 19.2 and 55.4 percent to predict the 25- and 99-percent exceedances, respectively. Regression equations to estimate high and median flows (25- to 75-percent exceedances) are better predictors (smaller variability of the residual values around the regression line) than the equations to estimate low flows (less than 75-percent exceedance). The Habitat Forming (March-April) bioperiod had the smallest standard errors of estimate, ranging from 10.7 to 20.9 percent. In contrast, the Rearing and Growth (July-October) bioperiod had the largest standard errors, ranging from 30.9 to 156 percent. The adjusted coefficient of determination of the equations ranged from 77.5 to 99.4 percent with medians of 98.5 and 90.6 percent to predict the 25- and 99-percent exceedances, respectively. Descriptive information on the streamgages used in the regression, measured basin and climatic characteristics, and estimated flow-duration statistics are provided in this report. Flow-duration statistics and the 32 regression equations for estimating flow-duration statistics in Connecticut are stored on the U.S. Geological Survey World Wide Web application ?StreamStats? (http://water.usgs.gov/osw/streamstats/index.html). The regression equations developed in this report can be used to produce unbiased estimates of select flow exceedances statewide.

Analysis of Binary Adherence Data in the Setting of Polypharmacy: A Comparison of Different Approaches

PubMed Central

Esserman, Denise A.; Moore, Charity G.; Roth, Mary T.

2009-01-01

Older community dwelling adults often take multiple medications for numerous chronic diseases. Non-adherence to these medications can have a large public health impact. Therefore, the measurement and modeling of medication adherence in the setting of polypharmacy is an important area of research. We apply a variety of different modeling techniques (standard linear regression; weighted linear regression; adjusted linear regression; naïve logistic regression; beta-binomial (BB) regression; generalized estimating equations (GEE)) to binary medication adherence data from a study in a North Carolina based population of older adults, where each medication an individual was taking was classified as adherent or non-adherent. In addition, through simulation we compare these different methods based on Type I error rates, bias, power, empirical 95% coverage, and goodness of fit. We find that estimation and inference using GEE is robust to a wide variety of scenarios and we recommend using this in the setting of polypharmacy when adherence is dichotomously measured for multiple medications per person. PMID:20414358

Weighted linear regression using D2H and D2 as the independent variables

Treesearch

Hans T. Schreuder; Michael S. Williams

1998-01-01

Several error structures for weighted regression equations used for predicting volume were examined for 2 large data sets of felled and standing loblolly pine trees (Pinus taeda L.). The generally accepted model with variance of error proportional to the value of the covariate squared ( D2H = diameter squared times height or D...

Fundamental Analysis of the Linear Multiple Regression Technique for Quantification of Water Quality Parameters from Remote Sensing Data. Ph.D. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Whitlock, C. H., III

1977-01-01

Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.

Relationships between otolith size and fish length in some mesopelagic teleosts (Myctophidae, Paralepididae, Phosichthyidae and Stomiidae).

PubMed

Battaglia, P; Malara, D; Ammendolia, G; Romeo, T; Andaloro, F

2015-09-01

Length-mass relationships and linear regressions are given for otolith size (length and height) and standard length (LS ) of certain mesopelagic fishes (Myctophidae, Paralepididae, Phosichthyidae and Stomiidae) living in the central Mediterranean Sea. The length-mass relationship showed isometric growth in six species, whereas linear regressions of LS and otolith size fit the data well for all species. These equations represent a useful tool for dietary studies on Mediterranean marine predators. © 2015 The Fisheries Society of the British Isles.

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models.

PubMed

Shah, A A; Xing, W W; Triantafyllidis, V

2017-04-01

In this paper, we develop reduced-order models for dynamic, parameter-dependent, linear and nonlinear partial differential equations using proper orthogonal decomposition (POD). The main challenges are to accurately and efficiently approximate the POD bases for new parameter values and, in the case of nonlinear problems, to efficiently handle the nonlinear terms. We use a Bayesian nonlinear regression approach to learn the snapshots of the solutions and the nonlinearities for new parameter values. Computational efficiency is ensured by using manifold learning to perform the emulation in a low-dimensional space. The accuracy of the method is demonstrated on a linear and a nonlinear example, with comparisons with a global basis approach.

Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models

PubMed Central

Xing, W. W.; Triantafyllidis, V.

2017-01-01

In this paper, we develop reduced-order models for dynamic, parameter-dependent, linear and nonlinear partial differential equations using proper orthogonal decomposition (POD). The main challenges are to accurately and efficiently approximate the POD bases for new parameter values and, in the case of nonlinear problems, to efficiently handle the nonlinear terms. We use a Bayesian nonlinear regression approach to learn the snapshots of the solutions and the nonlinearities for new parameter values. Computational efficiency is ensured by using manifold learning to perform the emulation in a low-dimensional space. The accuracy of the method is demonstrated on a linear and a nonlinear example, with comparisons with a global basis approach. PMID:28484327

Aspects of porosity prediction using multivariate linear regression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Byrnes, A.P.; Wilson, M.D.

1991-03-01

Highly accurate multiple linear regression models have been developed for sandstones of diverse compositions. Porosity reduction or enhancement processes are controlled by the fundamental variables, Pressure (P), Temperature (T), Time (t), and Composition (X), where composition includes mineralogy, size, sorting, fluid composition, etc. The multiple linear regression equation, of which all linear porosity prediction models are subsets, takes the generalized form: Porosity = C{sub 0} + C{sub 1}(P) + C{sub 2}(T) + C{sub 3}(X) + C{sub 4}(t) + C{sub 5}(PT) + C{sub 6}(PX) + C{sub 7}(Pt) + C{sub 8}(TX) + C{sub 9}(Tt) + C{sub 10}(Xt) + C{sub 11}(PTX) + C{submore » 12}(PXt) + C{sub 13}(PTt) + C{sub 14}(TXt) + C{sub 15}(PTXt). The first four primary variables are often interactive, thus requiring terms involving two or more primary variables (the form shown implies interaction and not necessarily multiplication). The final terms used may also involve simple mathematic transforms such as log X, e{sup T}, X{sup 2}, or more complex transformations such as the Time-Temperature Index (TTI). The X term in the equation above represents a suite of compositional variable and, therefore, a fully expanded equation may include a series of terms incorporating these variables. Numerous published bivariate porosity prediction models involving P (or depth) or Tt (TTI) are effective to a degree, largely because of the high degree of colinearity between p and TTI. However, all such bivariate models ignore the unique contributions of P and Tt, as well as various X terms. These simpler models become poor predictors in regions where colinear relations change, were important variables have been ignored, or where the database does not include a sufficient range or weight distribution for the critical variables.« less

Data-driven discovery of partial differential equations

PubMed Central

Rudy, Samuel H.; Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan

2017-01-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg–de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable. PMID:28508044

Use of Case History Data for the Development of Equations in Predicting High Risk, Reading Disabled Students.

ERIC Educational Resources Information Center

Stratton, Beverly D.; And Others

Demographic data on 92 subjects identified as having reading problems were used to develop equations useful in identifying high risk, reading disabled students. Multiple linear regression analysis of the data indicated that reading disability (1) had a significant positive relationship with birth order and number of siblings; (2) had a positive…

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

[Aboveground biomass of three conifers in Qianyanzhou plantation].

PubMed

Li, Xuanran; Liu, Qijing; Chen, Yongrui; Hu, Lile; Yang, Fengting

2006-08-01

In this paper, the regressive models of the aboveground biomass of Pinus elliottii, P. massoniana and Cunninghamia lanceolata in Qianyanzhou of subtropical China were established, and the regression analysis on the dry weight of leaf biomass and total biomass against branch diameter (d), branch length (L), d3 and d2L was conducted with linear, power and exponent functions. Power equation with single parameter (d) was proved to be better than the rests for P. massoniana and C. lanceolata, and linear equation with parameter (d3) was better for P. elliottii. The canopy biomass was derived by the regression equations for all branches. These equations were also used to fit the relationships of total tree biomass, branch biomass and foliage biomass with tree diameter at breast height (D), tree height (H), D3 and D2H, respectively. D2H was found to be the best parameter for estimating total biomass. For foliage-and branch biomass, both parameters and equation forms showed some differences among species. Correlations were highly significant (P <0.001) for foliage-, branch-and total biomass, with the highest for total biomass. By these equations, the aboveground biomass and its allocation were estimated, with the aboveground biomass of P. massoniana, P. elliottii, and C. lanceolata forests being 83.6, 72. 1 and 59 t x hm(-2), respectively, and more stem biomass than foliage-and branch biomass. According to the previous studies, the underground biomass of these three forests was estimated to be 10.44, 9.42 and 11.48 t x hm(-2), and the amount of fixed carbon was 47.94, 45.14 and 37.52 t x hm(-2), respectively.

Multiple regression equations modelling of groundwater of Ajmer-Pushkar railway line region, Rajasthan (India).

PubMed

Mathur, Praveen; Sharma, Sarita; Soni, Bhupendra

2010-01-01

In the present work, an attempt is made to formulate multiple regression equations using all possible regressions method for groundwater quality assessment of Ajmer-Pushkar railway line region in pre- and post-monsoon seasons. Correlation studies revealed the existence of linear relationships (r 0.7) for electrical conductivity (EC), total hardness (TH) and total dissolved solids (TDS) with other water quality parameters. The highest correlation was found between EC and TDS (r = 0.973). EC showed highly significant positive correlation with Na, K, Cl, TDS and total solids (TS). TH showed highest correlation with Ca and Mg. TDS showed significant correlation with Na, K, SO4, PO4 and Cl. The study indicated that most of the contamination present was water soluble or ionic in nature. Mg was present as MgCl2; K mainly as KCl and K2SO4, and Na was present as the salts of Cl, SO4 and PO4. On the other hand, F and NO3 showed no significant correlations. The r2 values and F values (at 95% confidence limit, alpha = 0.05) for the modelled equations indicated high degree of linearity among independent and dependent variables. Also the error % between calculated and experimental values was contained within +/- 15% limit.

Estimating basin lagtime and hydrograph-timing indexes used to characterize stormflows for runoff-quality analysis

USGS Publications Warehouse

Granato, Gregory E.

2012-01-01

A nationwide study to better define triangular-hydrograph statistics for use with runoff-quality and flood-flow studies was done by the U.S. Geological Survey (USGS) in cooperation with the Federal Highway Administration. Although the triangular hydrograph is a simple linear approximation, the cumulative distribution of stormflow with a triangular hydrograph is a curvilinear S-curve that closely approximates the cumulative distribution of stormflows from measured data. The temporal distribution of flow within a runoff event can be estimated using the basin lagtime, (which is the time from the centroid of rainfall excess to the centroid of the corresponding runoff hydrograph) and the hydrograph recession ratio (which is the ratio of the duration of the falling limb to the rising limb of the hydrograph). This report documents results of the study, methods used to estimate the variables, and electronic files that facilitate calculation of variables. Ten viable multiple-linear regression equations were developed to estimate basin lagtimes from readily determined drainage basin properties using data published in 37 stormflow studies. Regression equations using the basin lag factor (BLF, which is a variable calculated as the main-channel length, in miles, divided by the square root of the main-channel slope in feet per mile) and two variables describing development in the drainage basin were selected as the best candidates, because each equation explains about 70 percent of the variability in the data. The variables describing development are the USGS basin development factor (BDF, which is a function of the amount of channel modifications, storm sewers, and curb-and-gutter streets in a basin) and the total impervious area variable (IMPERV) in the basin. Two datasets were used to develop regression equations. The primary dataset included data from 493 sites that have values for the BLF, BDF, and IMPERV variables. This dataset was used to develop the best-fit regression equation using the BLF and BDF variables. The secondary dataset included data from 896 sites that have values for the BLF and IMPERV variables. This dataset was used to develop the best-fit regression equation using the BLF and IMPERV variables. Analysis of hydrograph recession ratios and basin characteristics for 41 sites indicated that recession ratios are random variables. Thus, recession ratios cannot be estimated quantitatively using multiple linear regression equations developed using the data available for these sites. The minimums of recession ratios for different streamgages are well characterized by a value of one. The most probable values and maximum values of recession ratios for different streamgages are, however, more variable than the minimums. The most probable values of recession ratios for the 41 streamgages analyzed ranged from 1.0 to 3.52 and had a median of 1.85. The maximum values ranged from 2.66 to 11.3 and had a median of 4.36.

Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.

ERIC Educational Resources Information Center

Vidal, Sherry

Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…

Closed-form solution for static pull-in voltage of electrostatically actuated clamped-clamped micro/nano beams under the effect of fringing field and van der Waals force

NASA Astrophysics Data System (ADS)

Bhojawala, V. M.; Vakharia, D. P.

2017-12-01

This investigation provides an accurate prediction of static pull-in voltage for clamped-clamped micro/nano beams based on distributed model. The Euler-Bernoulli beam theory is used adapting geometric non-linearity of beam, internal (residual) stress, van der Waals force, distributed electrostatic force and fringing field effects for deriving governing differential equation. The Galerkin discretisation method is used to make reduced-order model of the governing differential equation. A regime plot is presented in the current work for determining the number of modes required in reduced-order model to obtain completely converged pull-in voltage for micro/nano beams. A closed-form relation is developed based on the relationship obtained from curve fitting of pull-in instability plots and subsequent non-linear regression for the proposed relation. The output of regression analysis provides Chi-square (χ 2) tolerance value equals to 1  ×  10-9, adjusted R-square value equals to 0.999 29 and P-value equals to zero, these statistical parameters indicate the convergence of non-linear fit, accuracy of fitted data and significance of the proposed model respectively. The closed-form equation is validated using available data of experimental and numerical results. The relative maximum error of 4.08% in comparison to several available experimental and numerical data proves the reliability of the proposed closed-form equation.

Validity of bioelectrical impedance measurement in predicting fat-free mass of Chinese children and adolescents.

PubMed

Wang, Lin; Hui, Stanley Sai-chuen; Wong, Stephen Heung-sang

2014-11-15

The current study aimed to examine the validity of various published bioelectrical impedance analysis (BIA) equations in estimating FFM among Chinese children and adolescents and to develop BIA equations for the estimation of fat-free mass (FFM) appropriate for Chinese children and adolescents. A total of 255 healthy Chinese children and adolescents aged 9 to 19 years old (127 males and 128 females) from Tianjin, China, participated in the BIA measurement at 50 kHz between the hand and the foot. The criterion measure of FFM was also employed using dual-energy X-ray absorptiometry (DEXA). FFM estimated from 24 published BIA equations was cross-validated against the criterion measure from DEXA. Multiple linear regression was conducted to examine alternative BIA equation for the studied population. FFM estimated from the 24 published BIA equations yielded high correlations with the directly measured FFM from DEXA. However, none of the 24 equations was statistically equivalent with the DEXA-measured FFM. Using multiple linear regression and cross-validation against DEXA measurement, an alternative prediction equation was determined as follows: FFM (kg)=1.613+0.742×height (cm)2/impedance (Ω)+0.151×body weight (kg); R2=0.95; SEE=2.45 kg; CV=6.5, 93.7% of the residuals of all the participants fell within the 95% limits of agreement. BIA was highly correlated with FFM in Chinese children and adolescents. When the new developed BIA equations are applied, BIA can provide a practical and valid measurement of body composition in Chinese children and adolescents.

Validity of Bioelectrical Impedance Measurement in Predicting Fat-Free Mass of Chinese Children and Adolescents

PubMed Central

Wang, Lin; Hui, Stanley Sai-chuen; Wong, Stephen Heung-sang

2014-01-01

Background The current study aimed to examine the validity of various published bioelectrical impedance analysis (BIA) equations in estimating FFM among Chinese children and adolescents and to develop BIA equations for the estimation of fat-free mass (FFM) appropriate for Chinese children and adolescents. Material/Methods A total of 255 healthy Chinese children and adolescents aged 9 to 19 years old (127 males and 128 females) from Tianjin, China, participated in the BIA measurement at 50 kHz between the hand and the foot. The criterion measure of FFM was also employed using dual-energy X-ray absorptiometry (DEXA). FFM estimated from 24 published BIA equations was cross-validated against the criterion measure from DEXA. Multiple linear regression was conducted to examine alternative BIA equation for the studied population. Results FFM estimated from the 24 published BIA equations yielded high correlations with the directly measured FFM from DEXA. However, none of the 24 equations was statistically equivalent with the DEXA-measured FFM. Using multiple linear regression and cross-validation against DEXA measurement, an alternative prediction equation was determined as follows: FFM (kg)=1.613+0.742×height (cm)2/impedance (Ω)+0.151×body weight (kg); R2=0.95; SEE=2.45kg; CV=6.5, 93.7% of the residuals of all the participants fell within the 95% limits of agreement. Conclusions BIA was highly correlated with FFM in Chinese children and adolescents. When the new developed BIA equations are applied, BIA can provide a practical and valid measurement of body composition in Chinese children and adolescents. PMID:25398209

40 CFR 90.321 - NDIR analyzer calibration.

Code of Federal Regulations, 2012 CFR

2012-07-01

...) Perform a linear least-square regression on the data generated. Use an equation of the form y=mx, where x... covering at least 80 percent of the 10 to 90 range (64 percent) is required (see following table). Example...

40 CFR 90.321 - NDIR analyzer calibration.

Code of Federal Regulations, 2013 CFR

2013-07-01

...) Perform a linear least-square regression on the data generated. Use an equation of the form y=mx, where x... covering at least 80 percent of the 10 to 90 range (64 percent) is required (see following table). Example...

40 CFR 90.321 - NDIR analyzer calibration.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) Perform a linear least-square regression on the data generated. Use an equation of the form y=mx, where x... covering at least 80 percent of the 10 to 90 range (64 percent) is required (see following table). Example...

40 CFR 90.321 - NDIR analyzer calibration.

Code of Federal Regulations, 2014 CFR

2014-07-01

...) Perform a linear least-square regression on the data generated. Use an equation of the form y=mx, where x... covering at least 80 percent of the 10 to 90 range (64 percent) is required (see following table). Example...

Techniques for estimating magnitude and frequency of peak flows for Pennsylvania streams

USGS Publications Warehouse

Stuckey, Marla H.; Reed, Lloyd A.

2000-01-01

Regression equations for estimating the magnitude and frequency of floods on ungaged streams in Pennsylvania with drainage areas less that 2,000 square miles were developed on the basis of peak-flow data collected at 313 streamflow-gaging stations. All streamflow-gaging stations used in the development of the equations had 10 or more years of record and include active and discontinued continuous-record and crest-stage partial-record streamflow-gaging stations. Regional regression equations were developed for flood flows expected every 10, 25, 50, 100, and 500 years by the use of a weighted multiple linear regression model.The State was divided into two regions. The largest region, Region A, encompasses about 78 percent of Pennsylvania. The smaller region, Region B, includes only the northwestern part of the State. Basin characteristics used in the regression equations for Region A are drainage area, percentage of forest cover, percentage of urban development, percentage of basin underlain by carbonate bedrock, and percentage of basin controlled by lakes, swamps, and reservoirs. Basin characteristics used in the regression equations for Region B are drainage area and percentage of basin controlled by lakes, swamps, and reservoirs. The coefficient of determination (R2) values for the five flood-frequency equations for Region A range from 0.93 to 0.82, and for Region B, the range is from 0.96 to 0.89.While the regression equations can be used to predict the magnitude and frequency of peak flows for most streams in the State, they should not be used for streams with drainage areas greater than 2,000 square miles or less than 1.5 square miles, for streams that drain extensively mined areas, or for stream reaches immediately below flood-control reservoirs. In addition, the equations presented for Region B should not be used if the stream drains a basin with more than 5 percent urban development.

Prediction of elemental creep. [steady state and cyclic data from regression analysis

NASA Technical Reports Server (NTRS)

Davis, J. W.; Rummler, D. R.

1975-01-01

Cyclic and steady-state creep tests were performed to provide data which were used to develop predictive equations. These equations, describing creep as a function of stress, temperature, and time, were developed through the use of a least squares regression analyses computer program for both the steady-state and cyclic data sets. Comparison of the data from the two types of tests, revealed that there was no significant difference between the cyclic and steady-state creep strains for the L-605 sheet under the experimental conditions investigated (for the same total time at load). Attempts to develop a single linear equation describing the combined steady-state and cyclic creep data resulted in standard errors of estimates higher than obtained for the individual data sets. A proposed approach to predict elemental creep in metals uses the cyclic creep equation and a computer program which applies strain and time hardening theories of creep accumulation.

Experimental paleotemperature equation for planktonic foraminifera

NASA Astrophysics Data System (ADS)

Erez, Jonathan; Luz, Boaz

1983-06-01

Small live individuals of Globigerinoides sacculifer which were cultured in the laboratory reached maturity and produced garnets. Fifty to ninety percent of their skeleton weight was deposited under controlled water temperature (14° to 30°C) and water isotopic composition, and a correction was made to account for the isotopic composition of the original skeleton using control groups. Comparison of. the actual growth temperatures with the calculated temperature based on paleotemperature equations for inorganic CaCO 3 indicate that the foraminifera precipitate their CaCO 3 in isotopic equilibrium. Comparison with equations developed for biogenic calcite give a similarly good fit. Linear regression with CRAIG'S (1965) equation yields: t = -0.07 + 1.01 t̂ (r= 0.95) where t is the actual growth temperature and t̂ Is the calculated paleotemperature. The intercept and the slope of this linear equation show that the familiar paleotemperature equation developed originally for mollusca carbonate, is equally applicable for the planktonic foraminifer G. sacculifer. Second order regression of the culture temperature and the delta difference ( δ18Oc - δ18Ow) yield a correlation coefficient of r = 0.95: t̂ = 17.0 - 4.52(δ 18Oc - δ 18Ow) + 0.03(δ 18Oc - δ 18Ow) 2t̂, δ 18Oc and δ18Ow are the estimated temperature, the isotopic composition of the shell carbonate and the sea water respectively. A possible cause for nonequilibnum isotopic compositions reported earlier for living planktonic foraminifera is the improper combustion of the organic matter.

Forecasting Air Force Logistics Command Second Destination Transportation: An Application of Multiple Regression Analysis and Neural Networks

DTIC Science & Technology

1990-09-01

without the help from the DSXR staff. William Lyons, Charles Ramsey , and Martin Meeks went above and beyond to help complete this research. Special...develop a valid forecasting model that is significantly more accurate than the one presently used by DSXR and suggested the development and testing of a...method, Strom tested DSXR’s iterative linear regression forecasting technique by examining P1 in the simple regression equation to determine whether

Response Surface Modeling Tolerance and Inference Error Risk Specifications: Proposed Industry Standards

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2012-01-01

This paper reviews the derivation of an equation for scaling response surface modeling experiments. The equation represents the smallest number of data points required to fit a linear regression polynomial so as to achieve certain specified model adequacy criteria. Specific criteria are proposed which simplify an otherwise rather complex equation, generating a practical rule of thumb for the minimum volume of data required to adequately fit a polynomial with a specified number of terms in the model. This equation and the simplified rule of thumb it produces can be applied to minimize the cost of wind tunnel testing.

Regression equations for estimation of annual peak-streamflow frequency for undeveloped watersheds in Texas using an L-moment-based, PRESS-minimized, residual-adjusted approach

USGS Publications Warehouse

Asquith, William H.; Roussel, Meghan C.

2009-01-01

Annual peak-streamflow frequency estimates are needed for flood-plain management; for objective assessment of flood risk; for cost-effective design of dams, levees, and other flood-control structures; and for design of roads, bridges, and culverts. Annual peak-streamflow frequency represents the peak streamflow for nine recurrence intervals of 2, 5, 10, 25, 50, 100, 200, 250, and 500 years. Common methods for estimation of peak-streamflow frequency for ungaged or unmonitored watersheds are regression equations for each recurrence interval developed for one or more regions; such regional equations are the subject of this report. The method is based on analysis of annual peak-streamflow data from U.S. Geological Survey streamflow-gaging stations (stations). Beginning in 2007, the U.S. Geological Survey, in cooperation with the Texas Department of Transportation and in partnership with Texas Tech University, began a 3-year investigation concerning the development of regional equations to estimate annual peak-streamflow frequency for undeveloped watersheds in Texas. The investigation focuses primarily on 638 stations with 8 or more years of data from undeveloped watersheds and other criteria. The general approach is explicitly limited to the use of L-moment statistics, which are used in conjunction with a technique of multi-linear regression referred to as PRESS minimization. The approach used to develop the regional equations, which was refined during the investigation, is referred to as the 'L-moment-based, PRESS-minimized, residual-adjusted approach'. For the approach, seven unique distributions are fit to the sample L-moments of the data for each of 638 stations and trimmed means of the seven results of the distributions for each recurrence interval are used to define the station specific, peak-streamflow frequency. As a first iteration of regression, nine weighted-least-squares, PRESS-minimized, multi-linear regression equations are computed using the watershed characteristics of drainage area, dimensionless main-channel slope, and mean annual precipitation. The residuals of the nine equations are spatially mapped, and residuals for the 10-year recurrence interval are selected for generalization to 1-degree latitude and longitude quadrangles. The generalized residual is referred to as the OmegaEM parameter and represents a generalized terrain and climate index that expresses peak-streamflow potential not otherwise represented in the three watershed characteristics. The OmegaEM parameter was assigned to each station, and using OmegaEM, nine additional regression equations are computed. Because of favorable diagnostics, the OmegaEM equations are expected to be generally reliable estimators of peak-streamflow frequency for undeveloped and ungaged stream locations in Texas. The mean residual standard error, adjusted R-squared, and percentage reduction of PRESS by use of OmegaEM are 0.30log10, 0.86, and -21 percent, respectively. Inclusion of the OmegaEM parameter provides a substantial reduction in the PRESS statistic of the regression equations and removes considerable spatial dependency in regression residuals. Although the OmegaEM parameter requires interpretation on the part of analysts and the potential exists that different analysts could estimate different values for a given watershed, the authors suggest that typical uncertainty in the OmegaEM estimate might be about +or-0.1010. Finally, given the two ensembles of equations reported herein and those in previous reports, hydrologic design engineers and other analysts have several different methods, which represent different analytical tracks, to make comparisons of peak-streamflow frequency estimates for ungaged watersheds in the study area.

Wind tunnel test of Teledyne Geotech model 1564B cup anemometer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parker, M.J.; Addis, R.P.

1991-04-04

The Department of Energy (DOE) Environment, Safety and Health Compliance Assessment (Tiger Team) of the Savannah River Site (SRS) questioned the method by which wind speed sensors (cup anemometers) are calibrated by the Environmental Technology Section (ETS). The Tiger Team member was concerned that calibration data was generated by running the wind tunnel to only 26 miles per hour (mph) when speeds exceeding 50 mph are readily obtainable. A wind tunnel experiment was conducted and confirmed the validity of the practice. Wind speeds common to SRS (6 mph) were predicted more accurately by 0--25 mph regression equations than 0--50 mphmore » regression equations. Higher wind speeds were slightly overpredicted by the 0--25 mph regression equations when compared to 0--50 mph regression equations. However, the greater benefit of more accurate lower wind speed predictions accuracy outweight the benefit of slightly better high (extreme) wind speed predictions. Therefore, it is concluded that 0--25 mph regression equations should continue to be utilized by ETS at SRS. During the Department of Energy Tiger Team audit, concerns were raised about the calibration of SRS cup anemometers. Wind speed is measured by ETS with Teledyne Geotech model 1564B cup anemometers, which are calibrated in the ETS wind tunnel. Linear regression lines are fitted to data points of tunnel speed versus anemometer output voltages up to 25 mph. The regression coefficients are then implemented into the data acquisition computer software when an instrument is installed in the field. The concern raised was that since the wind tunnel at SRS is able to generate a maximum wind speed higher than 25 mph, errors may be introduced in not using the full range of the wind tunnel.« less

Wind tunnel test of Teledyne Geotech model 1564B cup anemometer

NASA Astrophysics Data System (ADS)

Parker, M. J.; Addis, R. P.

1991-04-01

The Department of Energy (DOE) Environment, Safety, and Health Compliance Assessment (Tiger Team) of the Savannah River Site (SRS) questioned the method by which wind speed sensors (cup anemometers) are calibrated by the Environmental Technology Section (ETS). The Tiger Team member was concerned that calibration data was generated by running the wind tunnel to only 26 miles per hour (mph) when speeds exceeding 50 mph are readily obtainable. A wind tunnel experiment was conducted and confirmed the validity of the practice. Wind speeds common to SRS (6 mph) were predicted more accurately by 0-25 mph regression equations than 0-50 mph regression equations. Higher wind speeds were slightly overpredicted by the 0-25 mph regression equations when compared to 0-50 mph regression equations. However, the greater benefit of more accurate lower wind speed predictions accuracy outweigh the benefit of slightly better high (extreme) wind speed predictions. Therefore, it is concluded that 0-25 mph regression equations should continue to be utilized by ETS at SRS. During the Department of Energy Tiger Team audit, concerns were raised about the calibration of SRS cup anemometers. Wind speed is measured by ETS with Teledyne Geotech model 1564B cup anemometers, which are calibrated in the ETS wind tunnel. Linear regression lines are fitted to data points of tunnel speed versus anemometer output voltages up to 25 mph. The regression coefficients are then implemented into the data acquisition computer software when an instrument is installed in the field. The concern raised was that since the wind tunnel at SRS is able to generate a maximum wind speed higher than 25 mph, errors may be introduced in not using the full range of the wind tunnel.

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R.

1982-01-01

An examination of limitations, requirements, and precision of the linear multiple-regression technique for quantification of marine environmental parameters is conducted. Both environmental and optical physics conditions have been defined for which an exact solution to the signal response equations is of the same form as the multiple regression equation. Various statistical parameters are examined to define a criteria for selection of an unbiased fit when upwelled radiance values contain error and are correlated with each other. Field experimental data are examined to define data smoothing requirements in order to satisfy the criteria of Daniel and Wood (1971). Recommendations are made concerning improved selection of ground-truth locations to maximize variance and to minimize physical errors associated with the remote sensing experiment.

Fire spread in chaparral – a comparison of laboratory data and model predictions in burning live fuels

Treesearch

David R. Weise; Eunmo Koo; Xiangyang Zhou; Shankar Mahalingam; Frédéric Morandini; Jacques-Henri Balbi

2016-01-01

Fire behaviour data from 240 laboratory fires in high-density live chaparral fuel beds were compared with model predictions. Logistic regression was used to develop a model to predict fire spread success in the fuel beds and linear regression was used to predict rate of spread. Predictions from the Rothermel equation and three proposed changes as well as two physically...

Calculating the Solubilities of Drugs and Drug-Like Compounds in Octanol.

PubMed

Alantary, Doaa; Yalkowsky, Samuel

2016-09-01

A modification of the Van't Hoff equation is used to predict the solubility of organic compounds in dry octanol. The new equation describes a linear relationship between the logarithm of the solubility of a solute in octanol to its melting temperature. More than 620 experimentally measured octanol solubilities, collected from the literature, are used to validate the equation without using any regression or fitting. The average absolute error of the prediction is 0.66 log units. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Stature Estimation from Lower Limb Anthropometry using Linear Regression Analysis: A Study on the Malaysian Population.

PubMed

Abu Bakar, S N; Aspalilah, A; AbdelNasser, I; Nurliza, A; Hairuliza, M J; Swarhib, M; Das, S; Mohd Nor, F

2017-01-01

Stature is one of the characteristics that could be used to identify human, besides age, sex and racial affiliation. This is useful when the body found is either dismembered, mutilated or even decomposed, and helps in narrowing down the missing person's identity. The main aim of the present study was to construct regression functions for stature estimation by using lower limb bones in the Malaysian population. The sample comprised 87 adult individuals (81 males, 6 females) aged between 20 to 79 years. The parameters such as thigh length, lower leg length, leg length, foot length, foot height and foot breadth were measured. They were measured by a ruler and measuring tape. Statistical analysis involved independent t-test to analyse the difference between lower limbs in male and female. The Pearson's correlation test was used to analyse correlations between lower limb parameters and stature, and the linear regressions were used to form equations. The paired t-test was used to compare between actual stature and estimated stature by using the equations formed. Using independent t-test, there was a significant difference (p< 0.05) in the measurement between males and females with regard to leg length, thigh length, lower leg length, foot length and foot breadth. The thigh length, leg length and foot length were observed to have strong correlations with stature with p= 0.75, p= 0.81 and p= 0.69, respectively. Linear regressions were formulated for stature estimation. Paired t-test showed no significant difference between actual stature and estimated stature. It is concluded that regression functions can be used to estimate stature to identify skeletal remains in the Malaysia population.

Multiple regression for physiological data analysis: the problem of multicollinearity.

PubMed

Slinker, B K; Glantz, S A

1985-07-01

Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

PubMed

Meng, Yilin; Roux, Benoît

2015-08-11

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression

PubMed Central

2015-01-01

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost. PMID:26574437

Detection of changes in leaf water content using near- and middle-infrared reflectances

NASA Technical Reports Server (NTRS)

Hunt, E. Raymond, Jr.; Rock, Barrett N.

1989-01-01

A method to detect plant water stress by remote sensing is proposed using indices of near-IR and mid-IR wavelengths. The ability of the Leaf Water Content Index (LWCI) to determine leaf relative water content (RWC) is tested on species with different leaf morphologies. The way in which the Misture Stress Index (MSI) varies with RWC is studied. On test with several species, it is found that LWCI is equal to RWC, although the reflectances at 1.6 microns for two different RWC must be known to accurately predict unknown RWC. A linear correlation is found between MSI and RWC with each species having a different regression equation. Also, MSI is correlated with log sub 10 Equivalent Water Thickness (EWT) with data for all species falling on the same regression line. It is found that the minimum significant change of RWC that could be detected by appying the linear regression equation of MSI to EWT is 52 percent. Because the natural RWC variation from water stress is about 20 percent for most species, it is concluded that the near-IR and mid-IR reflectances cannot be used to remotely sense water stress.

Are Predictive Equations for Estimating Resting Energy Expenditure Accurate in Asian Indian Male Weightlifters?

PubMed

Joseph, Mini; Gupta, Riddhi Das; Prema, L; Inbakumari, Mercy; Thomas, Nihal

2017-01-01

The accuracy of existing predictive equations to determine the resting energy expenditure (REE) of professional weightlifters remains scarcely studied. Our study aimed at assessing the REE of male Asian Indian weightlifters with indirect calorimetry and to compare the measured REE (mREE) with published equations. A new equation using potential anthropometric variables to predict REE was also evaluated. REE was measured on 30 male professional weightlifters aged between 17 and 28 years using indirect calorimetry and compared with the eight formulas predicted by Harris-Benedicts, Mifflin-St. Jeor, FAO/WHO/UNU, ICMR, Cunninghams, Owen, Katch-McArdle, and Nelson. Pearson correlation coefficient, intraclass correlation coefficient, and multiple linear regression analysis were carried out to study the agreement between the different methods, association with anthropometric variables, and to formulate a new prediction equation for this population. Pearson correlation coefficients between mREE and the anthropometric variables showed positive significance with suprailiac skinfold thickness, lean body mass (LBM), waist circumference, hip circumference, bone mineral mass, and body mass. All eight predictive equations underestimated the REE of the weightlifters when compared with the mREE. The highest mean difference was 636 kcal/day (Owen, 1986) and the lowest difference was 375 kcal/day (Cunninghams, 1980). Multiple linear regression done stepwise showed that LBM was the only significant determinant of REE in this group of sportspersons. A new equation using LBM as the independent variable for calculating REE was computed. REE for weightlifters = -164.065 + 0.039 (LBM) (confidence interval -1122.984, 794.854]. This new equation reduced the mean difference with mREE by 2.36 + 369.15 kcal/day (standard error = 67.40). The significant finding of this study was that all the prediction equations underestimated the REE. The LBM was the sole determinant of REE in this population. In the absence of indirect calorimetry, the REE equation developed by us using LBM is a better predictor for calculating REE of professional male weightlifters of this region.

[Correlation between gaseous exchange rate, body temperature, and mitochondrial protein content in the liver of mice].

PubMed

Muradian, Kh K; Utko, N O; Mozzhukhina, T H; Pishel', I M; Litoshenko, O Ia; Bezrukov, V V; Fraĭfel'd, V E

2002-01-01

Correlative and regressive relations between the gaseous exchange, thermoregulation and mitochondrial protein content were analyzed by two- and three-dimensional statistics in mice. It has been shown that the pair wise linear methods of analysis did not reveal any significant correlation between the parameters under exploration. However, it became evident at three-dimensional and non-linear plotting for which the coefficients of multivariable correlation reached and even exceeded 0.7-0.8. The calculations based on partial differentiation of the multivariable regression equations allow to conclude that at certain values of VO2, VCO2 and body temperature negative relations between the systems of gaseous exchange and thermoregulation become dominating.

Comparison of linear and non-linear models for the adsorption of fluoride onto geo-material: limonite.

PubMed

Sahin, Rubina; Tapadia, Kavita

2015-01-01

The three widely used isotherms Langmuir, Freundlich and Temkin were examined in an experiment using fluoride (F⁻) ion adsorption on a geo-material (limonite) at four different temperatures by linear and non-linear models. Comparison of linear and non-linear regression models were given in selecting the optimum isotherm for the experimental results. The coefficient of determination, r², was used to select the best theoretical isotherm. The four Langmuir linear equations (1, 2, 3, and 4) are discussed. Langmuir isotherm parameters obtained from the four Langmuir linear equations using the linear model differed but they were the same when using the nonlinear model. Langmuir-2 isotherm is one of the linear forms, and it had the highest coefficient of determination (r² = 0.99) compared to the other Langmuir linear equations (1, 3 and 4) in linear form, whereas, for non-linear, Langmuir-4 fitted best among all the isotherms because it had the highest coefficient of determination (r² = 0.99). The results showed that the non-linear model may be a better way to obtain the parameters. In the present work, the thermodynamic parameters show that the absorption of fluoride onto limonite is both spontaneous (ΔG < 0) and endothermic (ΔH > 0). Scanning electron microscope and X-ray diffraction images also confirm the adsorption of F⁻ ion onto limonite. The isotherm and kinetic study reveals that limonite can be used as an adsorbent for fluoride removal. In future we can develop new technology for fluoride removal in large scale by using limonite which is cost-effective, eco-friendly and is easily available in the study area.

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

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

NASA Astrophysics Data System (ADS)

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.

Exact and Approximate Statistical Inference for Nonlinear Regression and the Estimating Equation Approach.

PubMed

Demidenko, Eugene

2017-09-01

The exact density distribution of the nonlinear least squares estimator in the one-parameter regression model is derived in closed form and expressed through the cumulative distribution function of the standard normal variable. Several proposals to generalize this result are discussed. The exact density is extended to the estimating equation (EE) approach and the nonlinear regression with an arbitrary number of linear parameters and one intrinsically nonlinear parameter. For a very special nonlinear regression model, the derived density coincides with the distribution of the ratio of two normally distributed random variables previously obtained by Fieller (1932), unlike other approximations previously suggested by other authors. Approximations to the density of the EE estimators are discussed in the multivariate case. Numerical complications associated with the nonlinear least squares are illustrated, such as nonexistence and/or multiple solutions, as major factors contributing to poor density approximation. The nonlinear Markov-Gauss theorem is formulated based on the near exact EE density approximation.

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

NASA Astrophysics Data System (ADS)

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.

Fungible weights in logistic regression.

PubMed

Jones, Jeff A; Waller, Niels G

2016-06-01

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

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

NASA Astrophysics Data System (ADS)

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.

Osmotically inactive sodium and potassium storage: lessons learned from the Edelman and Boling data.

PubMed

Nguyen, Minhtri K; Nguyen, Dai-Scott; Nguyen, Minh-Kevin

2016-09-01

Because changes in the plasma water sodium concentration ([Na(+)]pw) are clinically due to changes in the mass balance of Na(+), K(+), and H2O, the analysis and treatment of the dysnatremias are dependent on the validity of the Edelman equation in defining the quantitative interrelationship between the [Na(+)]pw and the total exchangeable sodium (Nae), total exchangeable potassium (Ke), and total body water (TBW) (Edelman IS, Leibman J, O'Meara MP, Birkenfeld LW. J Clin Invest 37: 1236-1256, 1958): [Na(+)]pw = 1.11(Nae + Ke)/TBW - 25.6. The interrelationship between [Na(+)]pw and Nae, Ke, and TBW in the Edelman equation is empirically determined by accounting for measurement errors in all of these variables. In contrast, linear regression analysis of the same data set using [Na(+)]pw as the dependent variable yields the following equation: [Na(+)]pw = 0.93(Nae + Ke)/TBW + 1.37. Moreover, based on the study by Boling et al. (Boling EA, Lipkind JB. 18: 943-949, 1963), the [Na(+)]pw is related to the Nae, Ke, and TBW by the following linear regression equation: [Na(+)]pw = 0.487(Nae + Ke)/TBW + 71.54. The disparities between the slope and y-intercept of these three equations are unknown. In this mathematical analysis, we demonstrate that the disparities between the slope and y-intercept in these three equations can be explained by how the osmotically inactive Na(+) and K(+) storage pool is quantitatively accounted for. Our analysis also indicates that the osmotically inactive Na(+) and K(+) storage pool is dynamically regulated and that changes in the [Na(+)]pw can be predicted based on changes in the Nae, Ke, and TBW despite dynamic changes in the osmotically inactive Na(+) and K(+) storage pool. Copyright © 2016 the American Physiological Society.

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China

PubMed Central

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R2 = 0.86–0.93 for 72 data sets collected in the industrial river and R2 = 0.60–0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals’ concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density. PMID:27028017

Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China.

PubMed

Yao, Hong; Zhuang, Wei; Qian, Yu; Xia, Bisheng; Yang, Yang; Qian, Xin

2016-01-01

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R(2) = 0.86-0.93 for 72 data sets collected in the industrial river and R(2) = 0.60-0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals' concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density.

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a stochastic generator of synthetic data was developed. Synthetic basin characteristics were randomised, keeping the statistical properties of observed physical and climatic variables in the homogeneous region. The synthetic flood quantiles were stochastically generated taking the regression equation as basis. The learnt Bayesian network was validated by the reliability diagram, the Brier Score and the ROC diagram, which are common measures used in the validation of probabilistic forecasts. Summarising, the flood quantile estimations through Bayesian networks supply information about the prediction uncertainty as a probability distribution function of discharges is given as result. Therefore, the Bayesian network model has application as a decision support for water resources and planning management.

Estimation of octanol/water partition coefficients using LSER parameters

USGS Publications Warehouse

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.

Bioassay of the Nucleopolyhedrosis Virus of Neodiprion sertifer (Hymenoptera: Diprionidae)

Treesearch

M.A. Mohamed; J.D. Podgwaite

1982-01-01

Linear regression analysis of probit mortality versus several concentrations of nucleopolyhedrosis virus of Neodiprion sertifer resulted in the equation Y = 2.170 + 0.872X. An LC50 was calculated at 1758 PIB/ml. Also, the incubation time of the virus was dependent on its concentration. Most insect viruses possess the potential...

[Selecting methods of controls concentration for internal quality control and continuity of control chart between different reagent lots for HBsAg qualitative detection].

PubMed

Li, Jin-ming; Zheng, Huai-jing; Wang, Lu-nan; Deng, Wei

2003-04-01

To establish a model for one choosing controls with a suitable concentration for internal quality control (IQC) with qualitative ELISA detection, and a consecutive plotting method on Levey-Jennings control chart when reagent kit lot is changed. First, a series of control serum with 0.2, 0.5, 1.0, 2.0 and 5.0ng/ml HBsAg respectively were assessed for within-run and between-run precision according to NCCLs EP5 document. Then, a linear regression equation (y=bx + a) with best correlation coefficient (r > 0.99) was established based on S/CO values of the series of control serum. Finally, one could choose controls with S/CO value calculated from the equation (y = bx + a) minus the product of the S/CO value multiplying three-fold between-run CV to be still more than 1.0 for IQC use. For consecutive plotting on Levey-Jennings control chart when ELISA kit lot was changed, the new lot kits were used to detect the same series of HBsAg control serum as above. Then, a new linear regression equation (y2 = b2x2 + a2) with best correlation coefficient was obtained. The old one (y1 =b1x1 + a1) could be obtained based on the mean values from above precision assessment. The S/CO value of a control serum detected by new lot kit could be changed to that detected by old kit lot based on the factor of y2/y1. Therefore, the plotting on primary Levey-Jennings control chart could be continued. The within-run coefficient of variation CV of the ELISA method for control serum with 0.2, 0.5, 1.0, 2.0 and 5.0ng/ml HBsAg were 11.08%, 9.49%, 9.83%, 9.18% and 7.25%, respectively, and between-run CV were 13.25%, 14.03%, 15.11%, 13.29% and 9.92%. The linear regression equation with best correlation coefficient from a test at random was y = 3.509x + 0.180. The suitable concentration of control serum for IQC could be 0.5ng/ml or 1.0ng/ml. The linear regression equation from the old lot and other two new lots of the ELISA kits were y1 = 3.550(x1) + 0.226, y2 = 3.238(x2) +0.388, and y3 =3.428(x3) + 0.148, respectively. Then, the transferring factors of 0.960 (y2/y1) and 0.908 (y3/y1) were obtained. The results shows that the model established for IQC control serum concentration selecting and for consecutive plotting on control chart when the reagent lot is changed is effective and practical.

The preliminary exploration of 64-slice volume computed tomography in the accurate measurement of pleural effusion.

PubMed

Guo, Zhi-Jun; Lin, Qiang; Liu, Hai-Tao; Lu, Jun-Ying; Zeng, Yan-Hong; Meng, Fan-Jie; Cao, Bin; Zi, Xue-Rong; Han, Shu-Ming; Zhang, Yu-Huan

2013-09-01

Using computed tomography (CT) to rapidly and accurately quantify pleural effusion volume benefits medical and scientific research. However, the precise volume of pleural effusions still involves many challenges and currently does not have a recognized accurate measuring. To explore the feasibility of using 64-slice CT volume-rendering technology to accurately measure pleural fluid volume and to then analyze the correlation between the volume of the free pleural effusion and the different diameters of the pleural effusion. The 64-slice CT volume-rendering technique was used to measure and analyze three parts. First, the fluid volume of a self-made thoracic model was measured and compared with the actual injected volume. Second, the pleural effusion volume was measured before and after pleural fluid drainage in 25 patients, and the volume reduction was compared with the actual volume of the liquid extract. Finally, the free pleural effusion volume was measured in 26 patients to analyze the correlation between it and the diameter of the effusion, which was then used to calculate the regression equation. After using the 64-slice CT volume-rendering technique to measure the fluid volume of the self-made thoracic model, the results were compared with the actual injection volume. No significant differences were found, P = 0.836. For the 25 patients with drained pleural effusions, the comparison of the reduction volume with the actual volume of the liquid extract revealed no significant differences, P = 0.989. The following linear regression equation was used to compare the pleural effusion volume (V) (measured by the CT volume-rendering technique) with the pleural effusion greatest depth (d): V = 158.16 × d - 116.01 (r = 0.91, P = 0.000). The following linear regression was used to compare the volume with the product of the pleural effusion diameters (l × h × d): V = 0.56 × (l × h × d) + 39.44 (r = 0.92, P = 0.000). The 64-slice CT volume-rendering technique can accurately measure the volume in pleural effusion patients, and a linear regression equation can be used to estimate the volume of the free pleural effusion.

Is adult gait less susceptible than paediatric gait to hip joint centre regression equation error?

PubMed

Kiernan, D; Hosking, J; O'Brien, T

2016-03-01

Hip joint centre (HJC) regression equation error during paediatric gait has recently been shown to have clinical significance. In relation to adult gait, it has been inferred that comparable errors with children in absolute HJC position may in fact result in less significant kinematic and kinetic error. This study investigated the clinical agreement of three commonly used regression equation sets (Bell et al., Davis et al. and Orthotrak) for adult subjects against the equations of Harrington et al. The relationship between HJC position error and subject size was also investigated for the Davis et al. set. Full 3-dimensional gait analysis was performed on 12 healthy adult subjects with data for each set compared to Harrington et al. The Gait Profile Score, Gait Variable Score and GDI-kinetic were used to assess clinical significance while differences in HJC position between the Davis and Harrington sets were compared to leg length and subject height using regression analysis. A number of statistically significant differences were present in absolute HJC position. However, all sets fell below the clinically significant thresholds (GPS <1.6°, GDI-Kinetic <3.6 points). Linear regression revealed a statistically significant relationship for both increasing leg length and increasing subject height with decreasing error in anterior/posterior and superior/inferior directions. Results confirm a negligible clinical error for adult subjects suggesting that any of the examined sets could be used interchangeably. Decreasing error with both increasing leg length and increasing subject height suggests that the Davis set should be used cautiously on smaller subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

Cooperation without culture? The null effect of generalized trust on intentional homicide: a cross-national panel analysis, 1995-2009.

PubMed

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation.

Empirical prediction of peak pressure levels in anthropogenic impulsive noise. Part I: Airgun arrays signals.

PubMed

Galindo-Romero, Marta; Lippert, Tristan; Gavrilov, Alexander

2015-12-01

This paper presents an empirical linear equation to predict peak pressure level of anthropogenic impulsive signals based on its correlation with the sound exposure level. The regression coefficients are shown to be weakly dependent on the environmental characteristics but governed by the source type and parameters. The equation can be applied to values of the sound exposure level predicted with a numerical model, which provides a significant improvement in the prediction of the peak pressure level. Part I presents the analysis for airgun arrays signals, and Part II considers the application of the empirical equation to offshore impact piling noise.

Non-linear quantitative structure-activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sadat Hayatshahi, Sayyed Hamed; Abdolmaleki, Parviz; Safarian, Shahrokh

2005-12-16

Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k {sub i} values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, themore » previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.« less

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Applicability of linear regression equation for prediction of chlorophyll content in rice leaves

NASA Astrophysics Data System (ADS)

Li, Yunmei

2005-09-01

A modeling approach is used to assess the applicability of the derived equations which are capable to predict chlorophyll content of rice leaves at a given view direction. Two radiative transfer models, including PROSPECT model operated at leaf level and FCR model operated at canopy level, are used in the study. The study is consisted of three steps: (1) Simulation of bidirectional reflectance from canopy with different leaf chlorophyll contents, leaf-area-index (LAI) and under storey configurations; (2) Establishment of prediction relations of chlorophyll content by stepwise regression; and (3) Assessment of the applicability of these relations. The result shows that the accuracy of prediction is affected by different under storey configurations and, however, the accuracy tends to be greatly improved with increase of LAI.

Simplified solution for point contact deformation between two elastic solids

NASA Technical Reports Server (NTRS)

Brewe, D. E.; Hamrock, B. J.

1976-01-01

A linear-regression by the method of least squares is made on the geometric variables that occur in the equation for point contact deformation. The ellipticity and the complete eliptic integrals of the first and second kind are expressed as a function of the x, y-plane principal radii. The ellipticity was varied from 1 (circular contact) to 10 (a configuration approaching line contact). These simplified equations enable one to calculate easily the point-contact deformation to within 3 percent without resorting to charts or numerical methods.

Predictive Regression Equations of Flowmetric and Spirometric Peak Expiratory Flow in Healthy Moroccan Children.

PubMed

Bouti, Khalid; Benamor, Jouda; Bourkadi, Jamal Eddine

2017-08-01

Peak Expiratory Flow (PEF) has never been characterised among healthy Moroccan school children. To study the relationship between PEF and anthropometric parameters (sex, age, height and weight) in healthy Moroccan school children, to establish predictive equations of PEF; and to compare flowmetric and spirometric PEF with Forced Expiratory Volume in 1 second (FEV1). This cross-sectional study was conducted between April, 2016 and May, 2016. It involved 222 (122 boys and 100 girls) healthy school children living in Ksar el-Kebir, Morocco. We used mobile equipments for realisation of spirometry and peak expiratory flow measurements. SPSS (Version 22.0) was used to calculate Student's t-test, Pearson's correlation coefficient and linear regression. Significant linear correlation was seen between PEF, age and height in boys and girls. The equation for prediction of flowmetric PEF in boys was calculated as 'F-PEF = -187+ 24.4 Age + 1.61 Height' (p-value<0.001, r=0.86), and for girls as 'F-PEF = -151 + 17Age + 1.59Height' (p-value<0.001, r=0.86). The equation for prediction of spirometric PEF in boys was calculated as 'S-PEF = -199+ 9.8Age + 2.67Height' (p-value<0.05, r=0.77), and for girls as 'S-PEF = -181 + 8.5Age + 2.5Height' (p-value<0.001, r=0.83). The boys had higher values than the girls. The performance of the Mini Wright Peak Flow Meter was lower than that of a spirometer. Our study established PEF predictive equations in Moroccan children. Our results appeared to be reliable, as evident by the high correlation coefficient in this sample. PEF can be an alternative of FEV1 in centers without spirometry.

Econometric models of road use, accidents, and road investment decisions. Volume 2 : an econometric model of car ownership, road use, accidents, and their severity (Essay 3)

DOT National Transportation Integrated Search

1999-11-01

Using a fairly large cross-section/time-series data base, covering all provinces of Norway and all months between January 1973 and December 1994, we estimate non-linear (Box-Cox) regression equations explaining aggregate car ownership, road use, seat...

Understory response following varying levels of overstory removal in mixed conifer stands

Treesearch

Fabian C.C. Uzoh; Leroy K. Dolph; John R. Anstead

1997-01-01

Diameter growth rates of understory trees were measured for periods both before and after overstory removal on six study areas in northern California. All the species responded with increased diameter growth after adjusting to their new environments. Linear regression equations that predict post treatment diameter growth increment of the residual trees are presented...

Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level

ERIC Educational Resources Information Center

Savalei, Victoria; Rhemtulla, Mijke

2017-01-01

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately…

Using twig diameters to estimate browse utilization on three shrub species in southeastern Montana

Treesearch

Mark A. Rumble

1987-01-01

Browse utilization estimates based on twig length and twig weight were compared for skunkbush sumac, wax currant, and chokecherry. Linear regression analysis was valid for twig length data; twig weight equations are nonlinear. Estimates of twig weight are more accurate. Problems encountered during development of a utilization model are discussed.

Methods for estimating tributary streamflow in the Chattahoochee River basin between Buford Dam and Franklin, Georgia

USGS Publications Warehouse

Stamey, Timothy C.

1998-01-01

Simple and reliable methods for estimating hourly streamflow are needed for the calibration and verification of a Chattahoochee River basin model between Buford Dam and Franklin, Ga. The river basin model is being developed by Georgia Department of Natural Resources, Environmental Protection Division, as part of their Chattahoochee River Modeling Project. Concurrent streamflow data collected at 19 continuous-record, and 31 partial-record streamflow stations, were used in ordinary least-squares linear regression analyses to define estimating equations, and in verifying drainage-area prorations. The resulting regression or drainage-area ratio estimating equations were used to compute hourly streamflow at the partial-record stations. The coefficients of determination (r-squared values) for the regression estimating equations ranged from 0.90 to 0.99. Observed and estimated hourly and daily streamflow data were computed for May 1, 1995, through October 31, 1995. Comparisons of observed and estimated daily streamflow data for 12 continuous-record tributary stations, that had available streamflow data for all or part of the period from May 1, 1995, to October 31, 1995, indicate that the mean error of estimate for the daily streamflow was about 25 percent.

Regression equations for estimating flood flows for the 2-, 10-, 25-, 50-, 100-, and 500-Year recurrence intervals in Connecticut

USGS Publications Warehouse

Ahearn, Elizabeth A.

2004-01-01

Multiple linear-regression equations were developed to estimate the magnitudes of floods in Connecticut for recurrence intervals ranging from 2 to 500 years. The equations can be used for nonurban, unregulated stream sites in Connecticut with drainage areas ranging from about 2 to 715 square miles. Flood-frequency data and hydrologic characteristics from 70 streamflow-gaging stations and the upstream drainage basins were used to develop the equations. The hydrologic characteristics?drainage area, mean basin elevation, and 24-hour rainfall?are used in the equations to estimate the magnitude of floods. Average standard errors of prediction for the equations are 31.8, 32.7, 34.4, 35.9, 37.6 and 45.0 percent for the 2-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals, respectively. Simplified equations using only one hydrologic characteristic?drainage area?also were developed. The regression analysis is based on generalized least-squares regression techniques. Observed flows (log-Pearson Type III analysis of the annual maximum flows) from five streamflow-gaging stations in urban basins in Connecticut were compared to flows estimated from national three-parameter and seven-parameter urban regression equations. The comparison shows that the three- and seven- parameter equations used in conjunction with the new statewide equations generally provide reasonable estimates of flood flows for urban sites in Connecticut, although a national urban flood-frequency study indicated that the three-parameter equations significantly underestimated flood flows in many regions of the country. Verification of the accuracy of the three-parameter or seven-parameter national regression equations using new data from Connecticut stations was beyond the scope of this study. A technique for calculating flood flows at streamflow-gaging stations using a weighted average also is described. Two estimates of flood flows?one estimate based on the log-Pearson Type III analyses of the annual maximum flows at the gaging station, and the other estimate from the regression equation?are weighted together based on the years of record at the gaging station and the equivalent years of record value determined from the regression. Weighted averages of flood flows for the 2-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals are tabulated for the 70 streamflow-gaging stations used in the regression analysis. Generally, weighted averages give the most accurate estimate of flood flows at gaging stations. An evaluation of the Connecticut's streamflow-gaging network was performed to determine whether the spatial coverage and range of geographic and hydrologic conditions are adequately represented for transferring flood characteristics from gaged to ungaged sites. Fifty-one of 54 stations in the current (2004) network support one or more flood needs of federal, state, and local agencies. Twenty-five of 54 stations in the current network are considered high-priority stations by the U.S. Geological Survey because of their contribution to the longterm understanding of floods, and their application for regionalflood analysis. Enhancements to the network to improve overall effectiveness for regionalization can be made by increasing the spatial coverage of gaging stations, establishing stations in regions of the state that are not well-represented, and adding stations in basins with drainage area sizes not represented. Additionally, the usefulness of the network for characterizing floods can be maintained and improved by continuing operation at the current stations because flood flows can be more accurately estimated at stations with continuous, long-term record.

Predicting hearing thresholds and occupational hearing loss with multiple-frequency auditory steady-state responses.

PubMed

Hsu, Ruey-Fen; Ho, Chi-Kung; Lu, Sheng-Nan; Chen, Shun-Sheng

2010-10-01

An objective investigation is needed to verify the existence and severity of hearing impairments resulting from work-related, noise-induced hearing loss in arbitration of medicolegal aspects. We investigated the accuracy of multiple-frequency auditory steady-state responses (Mf-ASSRs) between subjects with sensorineural hearing loss (SNHL) with and without occupational noise exposure. Cross-sectional study. Tertiary referral medical centre. Pure-tone audiometry and Mf-ASSRs were recorded in 88 subjects (34 patients had occupational noise-induced hearing loss [NIHL], 36 patients had SNHL without noise exposure, and 18 volunteers were normal controls). Inter- and intragroup comparisons were made. A predicting equation was derived using multiple linear regression analysis. ASSRs and pure-tone thresholds (PTTs) showed a strong correlation for all subjects (r = .77 ≈ .94). The relationship is demonstrated by the equationThe differences between the ASSR and PTT were significantly higher for the NIHL group than for the subjects with non-noise-induced SNHL (p < .001). Mf-ASSR is a promising tool for objectively evaluating hearing thresholds. Predictive value may be lower in subjects with occupational hearing loss. Regardless of carrier frequencies, the severity of hearing loss affects the steady-state response. Moreover, the ASSR may assist in detecting noise-induced injury of the auditory pathway. A multiple linear regression equation to accurately predict thresholds was shown that takes into consideration all effect factors.

Estimation of Compaction Parameters Based on Soil Classification

NASA Astrophysics Data System (ADS)

Lubis, A. S.; Muis, Z. A.; Hastuty, I. P.; Siregar, I. M.

2018-02-01

Factors that must be considered in compaction of the soil works were the type of soil material, field control, maintenance and availability of funds. Those problems then raised the idea of how to estimate the density of the soil with a proper implementation system, fast, and economical. This study aims to estimate the compaction parameter i.e. the maximum dry unit weight (γ dmax) and optimum water content (Wopt) based on soil classification. Each of 30 samples were being tested for its properties index and compaction test. All of the data’s from the laboratory test results, were used to estimate the compaction parameter values by using linear regression and Goswami Model. From the research result, the soil types were A4, A-6, and A-7 according to AASHTO and SC, SC-SM, and CL based on USCS. By linear regression, the equation for estimation of the maximum dry unit weight (γdmax *)=1,862-0,005*FINES- 0,003*LL and estimation of the optimum water content (wopt *)=- 0,607+0,362*FINES+0,161*LL. By Goswami Model (with equation Y=mLogG+k), for estimation of the maximum dry unit weight (γdmax *) with m=-0,376 and k=2,482, for estimation of the optimum water content (wopt *) with m=21,265 and k=-32,421. For both of these equations a 95% confidence interval was obtained.

A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

PubMed Central

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A.; Aguiló, Antoni

2015-01-01

Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF. PMID:25821960

A comparison between multiple regression models and CUN-BAE equation to predict body fat in adults.

PubMed

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A; Aguiló, Antoni

2015-01-01

Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.

QSTR of the toxicity of some organophosphorus compounds by using the quantum chemical and topological descriptors.

PubMed

Senior, Samir A; Madbouly, Magdy D; El massry, Abdel-Moneim

2011-09-01

Quantum chemical and topological descriptors of some organophosphorus compounds (OP) were correlated with their toxicity LD(50) as a dermal. The quantum chemical parameters were obtained using B3LYP/LANL2DZdp-ECP optimization. Using linear regression analysis, equations were derived to calculate the theoretical LD(50) of the studied compounds. The inclusion of quantum parameters, having both charge indices and topological indices, affects the toxicity of the studied compounds resulting in high correlation coefficient factors for the obtained equations. Two of the new four firstly supposed descriptors give higher correlation coefficients namely the Heteroatom Corrected Extended Connectivity Randic index ((1)X(HCEC)) and the Density Randic index ((1)X(Den)). The obtained linear equations were applied to predict the toxicity of some related structures. It was found that the sulfur atoms in these compounds must be replaced by oxygen atoms to achieve improved toxicity. Copyright © 2011 Elsevier Ltd. All rights reserved.

Cooperation without Culture? The Null Effect of Generalized Trust on Intentional Homicide: A Cross-National Panel Analysis, 1995–2009

PubMed Central

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation. PMID:23527211

The study of correlation among different scattering parameters in an aggregate dust model

NASA Astrophysics Data System (ADS)

Mazarbhuiya, A. M.; Das, H. S.

2017-09-01

We study the light scattering properties of aggregate particles in a wide range of complex refractive indices (m = n + i k, where 1.4 ≤ n ≤ 2.0, 0.001 ≤ k ≤1.0) and wavelengths (0.45 ≤ λ≤1.25 μ m) to investigate the correlation among different parameters e.g., the positive polarization maximum (P_{max}), the amplitude of the negative polarization (P_{min}), geometric albedo (A), (n,k) and λ. Numerical computations are performed by the Superposition T-matrix code with Ballistic Cluster-Cluster Aggregate (BCCA) particles of 128 monomers and Ballistic Aggregates (BA) particles of 512 monomers, where monomer's radius of aggregates is considered to be 0.1 μm. At a fixed value of k, P_{max} and n are correlated via a quadratic regression equation and this nature is observed at all wavelengths. Further, P_{max} and k are found to be related via a polynomial regression equation when n is taken to be fixed. The degree of the equation depends on the wavelength, higher the wavelength lower is the degree. We find that A and P_{max} are correlated via a cubic regression at λ= 0.45 μ m whereas this correlation is quadratic at higher wavelengths. We notice that |P_{min}| increases with the decrease of P_{max} and a strong linear correlation between them is observed when n is fixed at some value and k is changed from higher to lower value. Further, at a fix value of k, P_{min} and P_{max} can be fitted well via a quartic regression equation when n is changed from higher to lower value. We also find that P_{max} increases with λ and they are correlated via a quartic regression.

Deriving Hounsfield units using grey levels in cone beam computed tomography

PubMed Central

Mah, P; Reeves, T E; McDavid, W D

2010-01-01

Objectives An in vitro study was performed to investigate the relationship between grey levels in dental cone beam CT (CBCT) and Hounsfield units (HU) in CBCT scanners. Methods A phantom containing 8 different materials of known composition and density was imaged with 11 different dental CBCT scanners and 2 medical CT scanners. The phantom was scanned under three conditions: phantom alone and phantom in a small and large water container. The reconstructed data were exported as Digital Imaging and Communications in Medicine (DICOM) and analysed with On Demand 3D® by Cybermed, Seoul, Korea. The relationship between grey levels and linear attenuation coefficients was investigated. Results It was demonstrated that a linear relationship between the grey levels and the attenuation coefficients of each of the materials exists at some “effective” energy. From the linear regression equation of the reference materials, attenuation coefficients were obtained for each of the materials and CT numbers in HU were derived using the standard equation. Conclusions HU can be derived from the grey levels in dental CBCT scanners using linear attenuation coefficients as an intermediate step. PMID:20729181

Time-resolved flow reconstruction with indirect measurements using regression models and Kalman-filtered POD ROM

NASA Astrophysics Data System (ADS)

Leroux, Romain; Chatellier, Ludovic; David, Laurent

2018-01-01

This article is devoted to the estimation of time-resolved particle image velocimetry (TR-PIV) flow fields using a time-resolved point measurements of a voltage signal obtained by hot-film anemometry. A multiple linear regression model is first defined to map the TR-PIV flow fields onto the voltage signal. Due to the high temporal resolution of the signal acquired by the hot-film sensor, the estimates of the TR-PIV flow fields are obtained with a multiple linear regression method called orthonormalized partial least squares regression (OPLSR). Subsequently, this model is incorporated as the observation equation in an ensemble Kalman filter (EnKF) applied on a proper orthogonal decomposition reduced-order model to stabilize it while reducing the effects of the hot-film sensor noise. This method is assessed for the reconstruction of the flow around a NACA0012 airfoil at a Reynolds number of 1000 and an angle of attack of {20}°. Comparisons with multi-time delay-modified linear stochastic estimation show that both the OPLSR and EnKF combined with OPLSR are more accurate as they produce a much lower relative estimation error, and provide a faithful reconstruction of the time evolution of the velocity flow fields.

Bone mineral density and correlation factor analysis in normal Taiwanese children.

PubMed

Shu, San-Ging

2007-01-01

Our aim was to establish reference data and linear regression equations for lumbar bone mineral density (BMD) in normal Taiwanese children. Several influencing factors of lumbar BMD were investigated. Two hundred fifty-seven healthy children were recruited from schools, 136 boys and 121 girls, aged 4-18 years were enrolled on a voluntary basis with written consent. Their height, weight, blood pressure, puberty stage, bone age and lumbar BMD (L2-4) by dual energy x-ray absorptiometry (DEXA) were measured. Data were analyzed using Pearson correlation and stepwise regression tests. All measurements increased with age. Prior to age 8, there was no gender difference. Parameters such as height, weight, and bone age (BA) in girls surpassed boys between ages 8-13 without statistical significance (p> or =0.05). This was reversed subsequently after age 14 in height (p<0.05). BMD difference had the same trend but was not statistically significant either. The influencing power of puberty stage and bone age over BMD was almost equal to or higher than that of height and weight. All the other factors correlated with BMD to variable powers. Multiple linear regression equations for boys and girls were formulated. BMD reference data is provided and can be used to monitor childhood pathological conditions. However, BMD in those with abnormal bone age or pubertal development could need modifications to ensure accuracy.

Beyond logistic regression: structural equations modelling for binary variables and its application to investigating unobserved confounders.

PubMed

Kupek, Emil

2006-03-15

Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models. A large data set with a known structure among two related outcomes and three independent variables was generated to investigate the use of Yule's transformation of odds ratio (OR) into Q-metric by (OR-1)/(OR+1) to approximate Pearson's correlation coefficients between binary variables whose covariance structure can be further analysed by SEM. Percent of correctly classified events and non-events was compared with the classification obtained by logistic regression. The performance of SEM based on Q-metric was also checked on a small (N = 100) random sample of the data generated and on a real data set. SEM successfully recovered the generated model structure. SEM of real data suggested a significant influence of a latent confounding variable which would have not been detectable by standard logistic regression. SEM classification performance was broadly similar to that of the logistic regression. The analysis of binary data can be greatly enhanced by Yule's transformation of odds ratios into estimated correlation matrix that can be further analysed by SEM. The interpretation of results is aided by expressing them as odds ratios which are the most frequently used measure of effect in medical statistics.

Dimensional analysis of detrimental ozone generation by positive wire-to-plate corona discharge in air

NASA Astrophysics Data System (ADS)

Bo, Z.; Chen, J. H.

2010-02-01

The dimensional analysis technique is used to formulate a correlation between ozone generation rate and various parameters that are important in the design and operation of positive wire-to-plate corona discharges in indoor air. The dimensionless relation is determined by linear regression analysis based on the results from 36 laboratory-scale experiments. The derived equation is validated by experimental data and a numerical model published in the literature. Applications of such derived equation are illustrated through an example selection of the appropriate set of operating conditions in the design/operation of a photocopier to follow the federal regulations of ozone emission. Finally, a new current-voltage characteristic equation is proposed for positive wire-to-plate corona discharges based on the derived dimensionless equation.

Estimating Dbh of Trees Employing Multiple Linear Regression of the best Lidar-Derived Parameter Combination Automated in Python in a Natural Broadleaf Forest in the Philippines

NASA Astrophysics Data System (ADS)

Ibanez, C. A. G.; Carcellar, B. G., III; Paringit, E. C.; Argamosa, R. J. L.; Faelga, R. A. G.; Posilero, M. A. V.; Zaragosa, G. P.; Dimayacyac, N. A.

2016-06-01

Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem volume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters, except DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was done on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy cover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to get LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions. To know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and automated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The combination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike's Information Criterion) and BIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid size and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for further studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser- Meyer-Olkin (KMO) Coefficient and the Barlett's Test for Spherecity (BTS).

[Stature estimation for Sichuan Han nationality female based on X-ray technology with measurement of lumbar vertebrae].

PubMed

Qing, Si-han; Chang, Yun-feng; Dong, Xiao-ai; Li, Yuan; Chen, Xiao-gang; Shu, Yong-kang; Deng, Zhen-hua

2013-10-01

To establish the mathematical models of stature estimation for Sichuan Han female with measurement of lumbar vertebrae by X-ray to provide essential data for forensic anthropology research. The samples, 206 Sichuan Han females, were divided into three groups including group A, B and C according to the ages. Group A (206 samples) consisted of all ages, group B (116 samples) were 20-45 years old and 90 samples over 45 years old were group C. All the samples were examined lumbar vertebrae through CR technology, including the parameters of five centrums (L1-L5) as anterior border, posterior border and central heights (x1-x15), total central height of lumbar spine (x16), and the real height of every sample. The linear regression analysis was produced using the parameters to establish the mathematical models of stature estimation. Sixty-two trained subjects were tested to verify the accuracy of the mathematical models. The established mathematical models by hypothesis test of linear regression equation model were statistically significant (P<0.05). The standard errors of the equation were 2.982-5.004 cm, while correlation coefficients were 0.370-0.779 and multiple correlation coefficients were 0.533-0.834. The return tests of the highest correlation coefficient and multiple correlation coefficient of each group showed that the highest accuracy of the multiple regression equation, y = 100.33 + 1.489 x3 - 0.548 x6 + 0.772 x9 + 0.058 x12 + 0.645 x15, in group A were 80.6% (+/- lSE) and 100% (+/- 2SE). The established mathematical models in this study could be applied for the stature estimation for Sichuan Han females.

An overview of longitudinal data analysis methods for neurological research.

PubMed

Locascio, Joseph J; Atri, Alireza

2011-01-01

The purpose of this article is to provide a concise, broad and readily accessible overview of longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in neurology. In general, we advise that older, traditional methods, including (1) simple regression of the dependent variable on a time measure, (2) analyzing a single summary subject level number that indexes changes for each subject and (3) a general linear model approach with a fixed-subject effect, should be reserved for quick, simple or preliminary analyses. We advocate the general use of mixed-random and fixed-effect regression models for analyses of most longitudinal clinical studies. Under restrictive situations or to provide validation, we recommend: (1) repeated-measure analysis of covariance (ANCOVA), (2) ANCOVA for two time points, (3) generalized estimating equations and (4) latent growth curve/structural equation models.

Effect of the physicochemical parameters of benzimidazole molecules on their retention by a nonpolar sorbent from an aqueous acetonitrile solution

NASA Astrophysics Data System (ADS)

Shafigulin, R. V.; Safonova, I. A.; Bulanova, A. V.

2015-09-01

The effect of the structure of benzimidazoles on their chromatographic retention on octadecyl silica gel from an aqueous acetonitrile eluent was studied. One- and many-parameter correlation equations were obtained by linear regression analysis, and their prognostic potential in determining the retention factors of benzimidazoles under study was analyzed.

An Examination of Pennsylvania's Classroom Diagnostic Testing as a Predictive Model of Pennsylvania System of School Assessment Performance

ERIC Educational Resources Information Center

Matsanka, Christopher

2017-01-01

The purpose of this non-experimental quantitative study was to investigate the relationship between Pennsylvania's Classroom Diagnostic Tools (CDT) interim assessments and the state-mandated Pennsylvania System of School Assessment (PSSA) and to create linear regression equations that could be used as models to predict student performance on the…

The Relationship of Bole Diameters and Crown Widths of Seven Bottomland Hardwood Species

Treesearch

John K. Francis

1988-01-01

Diameters, heights, and eight crown radii per tree were measured on 75 individuals from each of seven bottomland hardwood species in Mississippi. It was determined that the seven species could not be described by a single regression equation. Crown class was tested to see whether it significantly influenced the slope or intercept of the linear relationship. Three of...

[Research on the method of interference correction for nondispersive infrared multi-component gas analysis].

PubMed

Sun, You-Wen; Liu, Wen-Qing; Wang, Shi-Mei; Huang, Shu-Hua; Yu, Xiao-Man

2011-10-01

A method of interference correction for nondispersive infrared multi-component gas analysis was described. According to the successive integral gas absorption models and methods, the influence of temperature and air pressure on the integral line strengths and linetype was considered, and based on Lorentz detuning linetypes, the absorption cross sections and response coefficients of H2O, CO2, CO, and NO on each filter channel were obtained. The four dimension linear regression equations for interference correction were established by response coefficients, the absorption cross interference was corrected by solving the multi-dimensional linear regression equations, and after interference correction, the pure absorbance signal on each filter channel was only controlled by the corresponding target gas concentration. When the sample cell was filled with gas mixture with a certain concentration proportion of CO, NO and CO2, the pure absorbance after interference correction was used for concentration inversion, the inversion concentration error for CO2 is 2.0%, the inversion concentration error for CO is 1.6%, and the inversion concentration error for NO is 1.7%. Both the theory and experiment prove that the interference correction method proposed for NDIR multi-component gas analysis is feasible.

Preliminary study of the association between the elimination parameters of phenytoin and phenobarbital.

PubMed

Methaneethorn, Janthima; Panomvana, Duangchit; Vachirayonstien, Thaveechai

2017-09-26

Therapeutic drug monitoring is essential for both phenytoin and phenobarbital therapy given their narrow therapeutic indexes. Nevertheless, the measurement of either phenytoin or phenobarbital concentrations might not be available in some rural hospitals. Information assisting individualized phenytoin and phenobarbital combination therapy is important. This study's objective was to determine the relationship between the maximum rate of metabolism of phenytoin (Vmax) and phenobarbital clearance (CLPB), which can serve as a guide to individualized drug therapy. Data on phenytoin and phenobarbital concentrations of 19 epileptic patients concurrently receiving both drugs were obtained from medical records. Phenytoin and phenobarbital pharmacokinetic parameters were studied at steady-state conditions. The relationship between the elimination parameters of both drugs was determined using simple linear regression. A high correlation coefficient between Vmax and CLPB was found [r=0.744; p<0.001 for Vmax (mg/kg/day) vs. CLPB (L/kg/day)]. Such a relatively strong linear relationship between the elimination parameters of both drugs indicates that Vmax might be predicted from CLPB and vice versa. Regression equations were established for estimating Vmax from CLPB, and vice versa in patients treated with combination of phenytoin and phenobarbital. These proposed equations can be of use in aiding individualized drug therapy.

Predicting terrestrial gamma dose rate based on geological and soil information: case study of Perak state, Malaysia.

PubMed

Ramli, A T; Apriantoro, N H; Heryansyah, A; Basri, N A; Sanusi, M S M; Abu Hanifah, N Z H

2016-03-01

An extensive terrestrial gamma radiation dose (TGRD) rate survey has been conducted in Perak State, Peninsular Malaysia. The survey has been carried out taking into account geological and soil information, involving 2930 in situ surveys. Based on geological and soil information collected during TGRD rate measurements, TGRD rates have been predicted in Perak State using a statistical regression analysis which would be helpful to focus surveys in areas that are difficult to access. An equation was formulated according to a linear relationship between TGRD rates, geological contexts and soil types. The comparison of in situ measurements and predicted TGRD dose rates was tabulated and showed good agreement with the linear regression equation. The TGRD rates in the study area ranged from 38 nGy h(-1) to 1039 nGy h(-1) with a mean value of 224  ±  138 nGy h(-1). This value is higher than the world average as reported in UNSCEAR 2000. The TGRD rates contribute an average dose rate of 1.37 mSv per year. An isodose map for the study area was developed using a Kriging method based on predicted and in situ TGRD rate values.

New method for calculating a mathematical expression for streamflow recession

USGS Publications Warehouse

Rutledge, Albert T.

1991-01-01

An empirical method has been devised to calculate the master recession curve, which is a mathematical expression for streamflow recession during times of negligible direct runoff. The method is based on the assumption that the storage-delay factor, which is the time per log cycle of streamflow recession, varies linearly with the logarithm of streamflow. The resulting master recession curve can be nonlinear. The method can be executed by a computer program that reads a data file of daily mean streamflow, then allows the user to select several near-linear segments of streamflow recession. The storage-delay factor for each segment is one of the coefficients of the equation that results from linear least-squares regression. Using results for each recession segment, a mathematical expression of the storage-delay factor as a function of the log of streamflow is determined by linear least-squares regression. The master recession curve, which is a second-order polynomial expression for time as a function of log of streamflow, is then derived using the coefficients of this function.

Estimating Causal Effects with Ancestral Graph Markov Models

PubMed Central

Malinsky, Daniel; Spirtes, Peter

2017-01-01

We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equation model with no feedback, and we allow for the possibility of latent variables. Under assumptions standard in the causal search literature, we use conditional independence constraints to search for an equivalence class of ancestral graphs. Then, for each model in the equivalence class, we perform the appropriate regression (using causal structure information to determine which covariates to include in the regression) to estimate a set of possible causal effects. Our approach is based on the “IDA” procedure of Maathuis et al. (2009), which assumes that all relevant variables have been measured (i.e., no unmeasured confounders). We generalize their work by relaxing this assumption, which is often violated in applied contexts. We validate the performance of our algorithm on simulated data and demonstrate improved precision over IDA when latent variables are present. PMID:28217244

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

PubMed

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

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, 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.

Mapping Soil pH Buffering Capacity of Selected Fields

NASA Technical Reports Server (NTRS)

Weaver, A. R.; Kissel, D. E.; Chen, F.; West, L. T.; Adkins, W.; Rickman, D.; Luvall, J. C.

2003-01-01

Soil pH buffering capacity, since it varies spatially within crop production fields, may be used to define sampling zones to assess lime requirement, or for modeling changes in soil pH when acid forming fertilizers or manures are added to a field. Our objective was to develop a procedure to map this soil property. One hundred thirty six soil samples (0 to 15 cm depth) from three Georgia Coastal Plain fields were titrated with calcium hydroxide to characterize differences in pH buffering capacity of the soils. Since the relationship between soil pH and added calcium hydroxide was approximately linear for all samples up to pH 6.5, the slope values of these linear relationships for all soils were regressed on the organic C and clay contents of the 136 soil samples using multiple linear regression. The equation that fit the data best was b (slope of pH vs. lime added) = 0.00029 - 0.00003 * % clay + 0.00135 * % O/C, r(exp 2) = 0.68. This equation was applied within geographic information system (GIS) software to create maps of soil pH buffering capacity for the three fields. When the mapped values of the pH buffering capacity were compared with measured values for a total of 18 locations in the three fields, there was good general agreement. A regression of directly measured pH buffering capacities on mapped pH buffering capacities at the field locations for these samples gave an r(exp 2) of 0.88 with a slope of 1.04 for a group of soils that varied approximately tenfold in their pH buffering capacities.

A rotor optimization using regression analysis

NASA Technical Reports Server (NTRS)

Giansante, N.

1984-01-01

The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

Correlation between lung to thorax transverse area ratio and observed/expected lung area to head circumference ratio in fetuses with left-sided diaphragmatic hernia.

PubMed

Hidaka, Nobuhiro; Murata, Masaharu; Sasahara, Jun; Ishii, Keisuke; Mitsuda, Nobuaki

2015-05-01

Observed/expected lung area to head circumference ratio (o/e LHR) and lung to thorax transverse area ratio (LTR) are the sonographic indicators of postnatal outcome in fetuses with congenital diaphragmatic hernia (CDH), and they are not influenced by gestational age. We aimed to evaluate the relationship between these two parameters in the same subjects with fetal left-sided CDH. Fetuses with left-sided CDH managed between 2005 and 2012 were included. Data of LTR and o/e LHR values measured on the same day prior to 33 weeks' gestation in target fetuses were retrospectively collected. The correlation between the two parameters was estimated using the Spearman's rank-correlation coefficient, and linear regression analysis was used to assess the relationship between them. Data on 61 measurements from 36 CDH fetuses were analyzed to obtain a Spearman's rank-correlation coefficient of 0.74 with the following linear equation: LTR = 0.002 × (o/e LHR) + 0.005. The determination coefficient of this linear equation was sufficiently high at 0.712, and the prediction accuracy obtained with this regression formula was considered satisfactory. A good linear correlation between the LTR and the o/e LHR was obtained, suggesting that we can translate the predictive parameters for each other. This information is expected to be useful to improve our understanding of different investigations focusing on LTR or o/e LHR as a predictor of postnatal outcome in CDH. © 2014 Japanese Teratology Society.

TSS concentration in sewers estimated from turbidity measurements by means of linear regression accounting for uncertainties in both variables.

PubMed

Bertrand-Krajewski, J L

2004-01-01

In order to replace traditional sampling and analysis techniques, turbidimeters can be used to estimate TSS concentration in sewers, by means of sensor and site specific empirical equations established by linear regression of on-site turbidity Tvalues with TSS concentrations C measured in corresponding samples. As the ordinary least-squares method is not able to account for measurement uncertainties in both T and C variables, an appropriate regression method is used to solve this difficulty and to evaluate correctly the uncertainty in TSS concentrations estimated from measured turbidity. The regression method is described, including detailed calculations of variances and covariance in the regression parameters. An example of application is given for a calibrated turbidimeter used in a combined sewer system, with data collected during three dry weather days. In order to show how the established regression could be used, an independent 24 hours long dry weather turbidity data series recorded at 2 min time interval is used, transformed into estimated TSS concentrations, and compared to TSS concentrations measured in samples. The comparison appears as satisfactory and suggests that turbidity measurements could replace traditional samples. Further developments, including wet weather periods and other types of sensors, are suggested.

Using structural equation modeling to construct calibration equations relating PM2.5 mass concentration samplers to the federal reference method sampler

NASA Astrophysics Data System (ADS)

Bilonick, Richard A.; Connell, Daniel P.; Talbott, Evelyn O.; Rager, Judith R.; Xue, Tao

2015-02-01

The objective of this study was to remove systematic bias among fine particulate matter (PM2.5) mass concentration measurements made by different types of samplers used in the Pittsburgh Aerosol Research and Inhalation Epidemiology Study (PARIES). PARIES is a retrospective epidemiology study that aims to provide a comprehensive analysis of the associations between air quality and human health effects in the Pittsburgh, Pennsylvania, region from 1999 to 2008. Calibration was needed in order to minimize the amount of systematic error in PM2.5 exposure estimation as a result of including data from 97 different PM2.5 samplers at 47 monitoring sites. Ordinary regression often has been used for calibrating air quality measurements from pairs of measurement devices; however, this is only appropriate when one of the two devices (the "independent" variable) is free from random error, which is rarely the case. A group of methods known as "errors-in-variables" (e.g., Deming regression, reduced major axis regression) has been developed to handle calibration between two devices when both are subject to random error, but these methods require information on the relative sizes of the random errors for each device, which typically cannot be obtained from the observed data. When data from more than two devices (or repeats of the same device) are available, the additional information is not used to inform the calibration. A more general approach that often has been overlooked is the use of a measurement error structural equation model (SEM) that allows the simultaneous comparison of three or more devices (or repeats). The theoretical underpinnings of all of these approaches to calibration are described, and the pros and cons of each are discussed. In particular, it is shown that both ordinary regression (when used for calibration) and Deming regression are particular examples of SEMs but with substantial deficiencies. To illustrate the use of SEMs, the 7865 daily average PM2.5 mass concentration measurements made by seven collocated samplers at an urban monitoring site in Pittsburgh, Pennsylvania, were used. These samplers, which included three federal reference method (FRM) samplers, three speciation samplers, and a tapered element oscillating microbalance (TEOM), operated at various times during the 10-year PARIES study period. Because TEOM measurements are known to depend on temperature, the constructed SEM provided calibration equations relating the TEOM to the FRM and speciation samplers as a function of ambient temperature. It was shown that TEOM imprecision and TEOM bias (relative to the FRM) both decreased as temperature increased. It also was shown that the temperature dependency for bias was non-linear and followed a sigmoidal (logistic) pattern. The speciation samplers exhibited only small bias relative to the FRM samplers, although the FRM samplers were shown to be substantially more precise than both the TEOM and the speciation samplers. Comparison of the SEM results to pairwise simple linear regression results showed that the regression results can differ substantially from the correctly-derived calibration equations, especially if the less-precise device is used as the independent variable in the regression.

Generalized Onsager's reciprocal relations for the master and Fokker-Planck equations

NASA Astrophysics Data System (ADS)

Peng, Liangrong; Zhu, Yi; Hong, Liu

2018-06-01

The Onsager's reciprocal relation plays a fundamental role in the nonequilibrium thermodynamics. However, unfortunately, its classical version is valid only within a narrow region near equilibrium due to the linear regression hypothesis, which largely restricts its usage. In this paper, based on the conservation-dissipation formalism, a generalized version of Onsager's relations for the master equations and Fokker-Planck equations was derived. Nonlinear constitutive relations with nonsymmetric and positively stable operators, which become symmetric under the detailed balance condition, constitute key features of this new generalization. Similar conclusions also hold for many other classical models in physics and chemistry, which in turn make the current study as a benchmark for the application of generalized Onsager's relations in nonequilibrium thermodynamics.

Probabilistic lifetime strength of aerospace materials via computational simulation

NASA Technical Reports Server (NTRS)

Boyce, Lola; Keating, Jerome P.; Lovelace, Thomas B.; Bast, Callie C.

1991-01-01

The results of a second year effort of a research program are presented. The research included development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic phenomenological constitutive relationship, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects of primitive variables. These primitive variables often originate in the environment and may include stress from loading, temperature, chemical, or radiation attack. This multifactor interaction constitutive equation is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the constitutive equation using actual experimental materials data together with the multiple linear regression of that data.

Linear regression techniques for use in the EC tracer method of secondary organic aerosol estimation

NASA Astrophysics Data System (ADS)

Saylor, Rick D.; Edgerton, Eric S.; Hartsell, Benjamin E.

A variety of linear regression techniques and simple slope estimators are evaluated for use in the elemental carbon (EC) tracer method of secondary organic carbon (OC) estimation. Linear regression techniques based on ordinary least squares are not suitable for situations where measurement uncertainties exist in both regressed variables. In the past, regression based on the method of Deming [1943. Statistical Adjustment of Data. Wiley, London] has been the preferred choice for EC tracer method parameter estimation. In agreement with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], we find that in the limited case where primary non-combustion OC (OC non-comb) is assumed to be zero, the ratio of averages (ROA) approach provides a stable and reliable estimate of the primary OC-EC ratio, (OC/EC) pri. In contrast with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], however, we find that the optimal use of Deming regression (and the more general York et al. [2004. Unified equations for the slope, intercept, and standard errors of the best straight line. American Journal of Physics 72, 367-375] regression) provides excellent results as well. For the more typical case where OC non-comb is allowed to obtain a non-zero value, we find that regression based on the method of York is the preferred choice for EC tracer method parameter estimation. In the York regression technique, detailed information on uncertainties in the measurement of OC and EC is used to improve the linear best fit to the given data. If only limited information is available on the relative uncertainties of OC and EC, then Deming regression should be used. On the other hand, use of ROA in the estimation of secondary OC, and thus the assumption of a zero OC non-comb value, generally leads to an overestimation of the contribution of secondary OC to total measured OC.

New Generalized Equation for Predicting Maximal Oxygen Uptake (from the Fitness Registry and the Importance of Exercise National Database).

PubMed

Kokkinos, Peter; Kaminsky, Leonard A; Arena, Ross; Zhang, Jiajia; Myers, Jonathan

2017-08-15

Impaired cardiorespiratory fitness (CRF) is closely linked to chronic illness and associated with adverse events. The American College of Sports Medicine (ACSM) regression equations (ACSM equations) developed to estimate oxygen uptake have known limitations leading to well-documented overestimation of CRF, especially at higher work rates. Thus, there is a need to explore alternative equations to more accurately predict CRF. We assessed maximal oxygen uptake (VO 2 max) obtained directly by open-circuit spirometry in 7,983 apparently healthy subjects who participated in the Fitness Registry and the Importance of Exercise National Database (FRIEND). We randomly sampled 70% of the participants from each of the following age categories: <40, 40 to 50, 50 to 70, and ≥70 and used the remaining 30% for validation. Multivariable linear regression analysis was applied to identify the most relevant variables and construct the best prediction model for VO 2 max. Treadmill speed and treadmill speed × grade were considered in the final model as predictors of measured VO 2 max and the following equation was generated: VO 2 max in ml O 2 /kg/min = speed (m/min) × (0.17 + fractional grade × 0.79) + 3.5. The FRIEND equation predicted VO 2 max with an overall error >4 times lower than the error associated with the traditional ACSM equations (5.1 ± 18.3% vs 21.4 ± 24.9%, respectively). Overestimation associated with the ACSM equation was accentuated when different protocols were considered separately. In conclusion, The FRIEND equation predicts VO 2 max more precisely than the traditional ACSM equations with an overall error >4 times lower than that associated with the ACSM equations. Published by Elsevier Inc.

Effects of alloy composition on cyclic flame hot-corrosion attack of cast nickel-base superalloys at 900 deg C

NASA Technical Reports Server (NTRS)

Deadmore, D. L.

1984-01-01

The effects of Cr, Al, Ti, Mo, Ta, Nb, and W content on the hot corrosion of nickel base alloys were investigated. The alloys were tested in a Mach 0.3 flame with 0.5 ppmw sodium at a temperature of 900 C. One nondestructive and three destructive tests were conducted. The best corrosion resistance was achieved when the Cr content was 12 wt %. However, some lower-Cr-content alloys ( 10 wt%) exhibited reasonable resistance provided that the Al content alloys ( 10 wt %) exhibited reasonable resistance provided that the Al content was 2.5 wt % and the Ti content was Aa wt %. The effect of W, Ta, Mo, and Nb contents on the hot-corrosion resistance varied depending on the Al and Ti contents. Several commercial alloy compositions were also tested and the corrosion attack was measured. Predicted attack was calculated for these alloys from derived regression equations and was in reasonable agreement with that experimentally measured. The regression equations were derived from measurements made on alloys in a one-quarter replicate of a 2(7) statistical design alloy composition experiment. These regression equations represent a simple linear model and are only a very preliminary analysis of the data needed to provide insights into the experimental method.

An Overview of Longitudinal Data Analysis Methods for Neurological Research

PubMed Central

Locascio, Joseph J.; Atri, Alireza

2011-01-01

The purpose of this article is to provide a concise, broad and readily accessible overview of longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in neurology. In general, we advise that older, traditional methods, including (1) simple regression of the dependent variable on a time measure, (2) analyzing a single summary subject level number that indexes changes for each subject and (3) a general linear model approach with a fixed-subject effect, should be reserved for quick, simple or preliminary analyses. We advocate the general use of mixed-random and fixed-effect regression models for analyses of most longitudinal clinical studies. Under restrictive situations or to provide validation, we recommend: (1) repeated-measure analysis of covariance (ANCOVA), (2) ANCOVA for two time points, (3) generalized estimating equations and (4) latent growth curve/structural equation models. PMID:22203825

Simple method for quick estimation of aquifer hydrogeological parameters

NASA Astrophysics Data System (ADS)

Ma, C.; Li, Y. Y.

2017-08-01

Development of simple and accurate methods to determine the aquifer hydrogeological parameters was of importance for groundwater resources assessment and management. Aiming at the present issue of estimating aquifer parameters based on some data of the unsteady pumping test, a fitting function of Theis well function was proposed using fitting optimization method and then a unitary linear regression equation was established. The aquifer parameters could be obtained by solving coefficients of the regression equation. The application of the proposed method was illustrated, using two published data sets. By the error statistics and analysis on the pumping drawdown, it showed that the method proposed in this paper yielded quick and accurate estimates of the aquifer parameters. The proposed method could reliably identify the aquifer parameters from long distance observed drawdowns and early drawdowns. It was hoped that the proposed method in this paper would be helpful for practicing hydrogeologists and hydrologists.

Environmental Regulations and Changes in Petroleum Refining Operations (Short-Term Energy Outlook Supplement June 1998)

EIA Publications

1998-01-01

Changes in domestic refining operations are identified and related to the summer Reid vapor pressure (RVP) restrictions and oxygenate blending requirements. This analysis uses published Energy Information Administration survey data and linear regression equations from the Short-Term Integrated Forecasting System (STIFS). The STIFS model is used for producing forecasts appearing in the Short-Term Energy Outlook.

Regression models to predict hip joint centers in pathological hip population.

PubMed

Mantovani, Giulia; Ng, K C Geoffrey; Lamontagne, Mario

2016-02-01

The purpose was to investigate the validity of Harrington's and Davis's hip joint center (HJC) regression equations on a population affected by a hip deformity, (i.e., femoroacetabular impingement). Sixty-seven participants (21 healthy controls, 46 with a cam-type deformity) underwent pelvic CT imaging. Relevant bony landmarks and geometric HJCs were digitized from the images, and skin thickness was measured for the anterior and posterior superior iliac spines. Non-parametric statistical and Bland-Altman tests analyzed differences between the predicted HJC (from regression equations) and the actual HJC (from CT images). The error from Davis's model (25.0 ± 6.7 mm) was larger than Harrington's (12.3 ± 5.9 mm, p<0.001). There were no differences between groups, thus, studies on femoroacetabular impingement can implement conventional regression models. Measured skin thickness was 9.7 ± 7.0mm and 19.6 ± 10.9 mm for the anterior and posterior bony landmarks, respectively, and correlated with body mass index. Skin thickness estimates can be considered to reduce the systematic error introduced by surface markers. New adult-specific regression equations were developed from the CT dataset, with the hypothesis that they could provide better estimates when tuned to a larger adult-specific dataset. The linear models were validated on external datasets and using leave-one-out cross-validation techniques; Prediction errors were comparable to those of Harrington's model, despite the adult-specific population and the larger sample size, thus, prediction accuracy obtained from these parameters could not be improved. Copyright © 2015 Elsevier B.V. All rights reserved.

Interpreting experimental data on egg production--applications of dynamic differential equations.

PubMed

France, J; Lopez, S; Kebreab, E; Dijkstra, J

2013-09-01

This contribution focuses on applying mathematical models based on systems of ordinary first-order differential equations to synthesize and interpret data from egg production experiments. Models based on linear systems of differential equations are contrasted with those based on nonlinear systems. Regression equations arising from analytical solutions to linear compartmental schemes are considered as candidate functions for describing egg production curves, together with aspects of parameter estimation. Extant candidate functions are reviewed, a role for growth functions such as the Gompertz equation suggested, and a function based on a simple new model outlined. Structurally, the new model comprises a single pool with an inflow and an outflow. Compartmental simulation models based on nonlinear systems of differential equations, and thus requiring numerical solution, are next discussed, and aspects of parameter estimation considered. This type of model is illustrated in relation to development and evaluation of a dynamic model of calcium and phosphorus flows in layers. The model consists of 8 state variables representing calcium and phosphorus pools in the crop, stomachs, plasma, and bone. The flow equations are described by Michaelis-Menten or mass action forms. Experiments that measure Ca and P uptake in layers fed different calcium concentrations during shell-forming days are used to evaluate the model. In addition to providing a useful management tool, such a simulation model also provides a means to evaluate feeding strategies aimed at reducing excretion of potential pollutants in poultry manure to the environment.

Computational simulation of coupled material degradation processes for probabilistic lifetime strength of aerospace materials

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.

1992-01-01

The research included ongoing development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primative variables. These primative variable may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above described constitutive equation using actual experimental materials data together with linear regression of that data, thereby predicting values for the empirical material constraints for each effect or primative variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from the open literature for materials typically of interest to those studying aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Linear and nonlinear spectroscopy from quantum master equations.

PubMed

Fetherolf, Jonathan H; Berkelbach, Timothy C

2017-12-28

We investigate the accuracy of the second-order time-convolutionless (TCL2) quantum master equation for the calculation of linear and nonlinear spectroscopies of multichromophore systems. We show that even for systems with non-adiabatic coupling, the TCL2 master equation predicts linear absorption spectra that are accurate over an extremely broad range of parameters and well beyond what would be expected based on the perturbative nature of the approach; non-equilibrium population dynamics calculated with TCL2 for identical parameters are significantly less accurate. For third-order (two-dimensional) spectroscopy, the importance of population dynamics and the violation of the so-called quantum regression theorem degrade the accuracy of TCL2 dynamics. To correct these failures, we combine the TCL2 approach with a classical ensemble sampling of slow microscopic bath degrees of freedom, leading to an efficient hybrid quantum-classical scheme that displays excellent accuracy over a wide range of parameters. In the spectroscopic setting, the success of such a hybrid scheme can be understood through its separate treatment of homogeneous and inhomogeneous broadening. Importantly, the presented approach has the computational scaling of TCL2, with the modest addition of an embarrassingly parallel prefactor associated with ensemble sampling. The presented approach can be understood as a generalized inhomogeneous cumulant expansion technique, capable of treating multilevel systems with non-adiabatic dynamics.

Linear and nonlinear spectroscopy from quantum master equations

NASA Astrophysics Data System (ADS)

Fetherolf, Jonathan H.; Berkelbach, Timothy C.

2017-12-01

We investigate the accuracy of the second-order time-convolutionless (TCL2) quantum master equation for the calculation of linear and nonlinear spectroscopies of multichromophore systems. We show that even for systems with non-adiabatic coupling, the TCL2 master equation predicts linear absorption spectra that are accurate over an extremely broad range of parameters and well beyond what would be expected based on the perturbative nature of the approach; non-equilibrium population dynamics calculated with TCL2 for identical parameters are significantly less accurate. For third-order (two-dimensional) spectroscopy, the importance of population dynamics and the violation of the so-called quantum regression theorem degrade the accuracy of TCL2 dynamics. To correct these failures, we combine the TCL2 approach with a classical ensemble sampling of slow microscopic bath degrees of freedom, leading to an efficient hybrid quantum-classical scheme that displays excellent accuracy over a wide range of parameters. In the spectroscopic setting, the success of such a hybrid scheme can be understood through its separate treatment of homogeneous and inhomogeneous broadening. Importantly, the presented approach has the computational scaling of TCL2, with the modest addition of an embarrassingly parallel prefactor associated with ensemble sampling. The presented approach can be understood as a generalized inhomogeneous cumulant expansion technique, capable of treating multilevel systems with non-adiabatic dynamics.

Prediction of fat-free body mass from bioelectrical impedance and anthropometry among 3-year-old children using DXA

PubMed Central

Ejlerskov, Katrine T.; Jensen, Signe M.; Christensen, Line B.; Ritz, Christian; Michaelsen, Kim F.; Mølgaard, Christian

2014-01-01

For 3-year-old children suitable methods to estimate body composition are sparse. We aimed to develop predictive equations for estimating fat-free mass (FFM) from bioelectrical impedance (BIA) and anthropometry using dual-energy X-ray absorptiometry (DXA) as reference method using data from 99 healthy 3-year-old Danish children. Predictive equations were derived from two multiple linear regression models, a comprehensive model (height2/resistance (RI), six anthropometric measurements) and a simple model (RI, height, weight). Their uncertainty was quantified by means of 10-fold cross-validation approach. Prediction error of FFM was 3.0% for both equations (root mean square error: 360 and 356 g, respectively). The derived equations produced BIA-based prediction of FFM and FM near DXA scan results. We suggest that the predictive equations can be applied in similar population samples aged 2–4 years. The derived equations may prove useful for studies linking body composition to early risk factors and early onset of obesity. PMID:24463487

Prediction of fat-free body mass from bioelectrical impedance and anthropometry among 3-year-old children using DXA.

PubMed

Ejlerskov, Katrine T; Jensen, Signe M; Christensen, Line B; Ritz, Christian; Michaelsen, Kim F; Mølgaard, Christian

2014-01-27

For 3-year-old children suitable methods to estimate body composition are sparse. We aimed to develop predictive equations for estimating fat-free mass (FFM) from bioelectrical impedance (BIA) and anthropometry using dual-energy X-ray absorptiometry (DXA) as reference method using data from 99 healthy 3-year-old Danish children. Predictive equations were derived from two multiple linear regression models, a comprehensive model (height(2)/resistance (RI), six anthropometric measurements) and a simple model (RI, height, weight). Their uncertainty was quantified by means of 10-fold cross-validation approach. Prediction error of FFM was 3.0% for both equations (root mean square error: 360 and 356 g, respectively). The derived equations produced BIA-based prediction of FFM and FM near DXA scan results. We suggest that the predictive equations can be applied in similar population samples aged 2-4 years. The derived equations may prove useful for studies linking body composition to early risk factors and early onset of obesity.

The dissolved organic matter as a potential soil quality indicator in arable soils of Hungary.

PubMed

Filep, Tibor; Draskovits, Eszter; Szabó, József; Koós, Sándor; László, Péter; Szalai, Zoltán

2015-07-01

Although several authors have suggested that the labile fraction of soils could be a potential soil quality indicator, the possibilities and limitations of using the dissolved organic matter (DOM) fraction for this purpose have not yet been investigated. The objective of this study was to evaluate the hypothesis that DOM is an adequate indicator of soil quality. To test this, the soil quality indices (SQI) of 190 arable soils from a Hungarian dataset were estimated, and these values were compared to DOM parameters (DOC and SUVA254). A clear difference in soil quality was found between the soil types, with low soil quality for arenosols (average SQI 0.5) and significantly higher values for gleysols, vertisols, regosols, solonetzes and chernozems. The SQI-DOC relationship could be described by non-linear regression, while a linear connection was observed between SQI and SUVA. The regression equations obtained for the dataset showed only one relatively weak significant correlation between the variables, for DOC (R (2) = 0.157(***); n = 190), while non-significant relationships were found for the DOC and SUVA254 values. However, an envelope curve operated with the datasets showed the robust potential of DOC to indicate soil quality changes, with a high R (2) value for the envelope curve regression equation. The limitations to using the DOM fraction of soils as a quality indicator are due to the contradictory processes which take place in soils in many cases.

Validation of a single-stage fixed-rate step test for the prediction of maximal oxygen uptake in healthy adults.

PubMed

Hansen, Dominique; Jacobs, Nele; Thijs, Herbert; Dendale, Paul; Claes, Neree

2016-09-01

Healthcare professionals with limited access to ergospirometry remain in need of valid and simple submaximal exercise tests to predict maximal oxygen uptake (VO2max ). Despite previous validation studies concerning fixed-rate step tests, accurate equations for the estimation of VO2max remain to be formulated from a large sample of healthy adults between age 18-75 years (n > 100). The aim of this study was to develop a valid equation to estimate VO2max from a fixed-rate step test in a larger sample of healthy adults. A maximal ergospirometry test, with assessment of cardiopulmonary parameters and VO2max , and a 5-min fixed-rate single-stage step test were executed in 112 healthy adults (age 18-75 years). During the step test and subsequent recovery, heart rate was monitored continuously. By linear regression analysis, an equation to predict VO2max from the step test was formulated. This equation was assessed for level of agreement by displaying Bland-Altman plots and calculation of intraclass correlations with measured VO2max . Validity further was assessed by employing a Jackknife procedure. The linear regression analysis generated the following equation to predict VO2max (l min(-1) ) from the step test: 0·054(BMI)+0·612(gender)+3·359(body height in m)+0·019(fitness index)-0·012(HRmax)-0·011(age)-3·475. This equation explained 78% of the variance in measured VO2max (F = 66·15, P<0·001). The level of agreement and intraclass correlation was high (ICC = 0·94, P<0·001) between measured and predicted VO2max . From this study, a valid fixed-rate single-stage step test equation has been developed to estimate VO2max in healthy adults. This tool could be employed by healthcare professionals with limited access to ergospirometry. © 2015 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Common y-intercept and single compound regressions of gas-particle partitioning data vs 1/T

NASA Astrophysics Data System (ADS)

Pankow, James F.

Confidence intervals are placed around the log Kp vs 1/ T correlation equations obtained using simple linear regressions (SLR) with the gas-particle partitioning data set of Yamasaki et al. [(1982) Env. Sci. Technol.16, 189-194]. The compounds and groups of compounds studied include the polycylic aromatic hydrocarbons phenanthrene + anthracene, me-phenanthrene + me-anthracene, fluoranthene, pyrene, benzo[ a]fluorene + benzo[ b]fluorene, chrysene + benz[ a]anthracene + triphenylene, benzo[ b]fluoranthene + benzo[ k]fluoranthene, and benzo[ a]pyrene + benzo[ e]pyrene (note: me = methyl). For any given compound, at equilibrium, the partition coefficient Kp equals ( F/ TSP)/ A where F is the particulate-matter associated concentration (ng m -3), A is the gas-phase concentration (ng m -3), and TSP is the concentration of particulate matter (μg m -3). At temperatures more than 10°C from the mean sampling temperature of 17°C, the confidence intervals are quite wide. Since theory predicts that similar compounds sorbing on the same particulate matter should possess very similar y-intercepts, the data set was also fitted using a special common y-intercept regression (CYIR). For most of the compounds, the CYIR equations fell inside of the SLR 95% confidence intervals. The CYIR y-intercept value is -18.48, and is reasonably close to the type of value that can be predicted for PAH compounds. The set of CYIR regression equations is probably more reliable than the set of SLR equations. For example, the CYIR-derived desorption enthalpies are much more highly correlated with vaporization enthalpies than are the SLR-derived desorption enthalpies. It is recommended that the CYIR approach be considered whenever analysing temperature-dependent gas-particle partitioning data.

New methodology for modeling annual-aircraft emissions at airports

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodmansey, B.G.; Patterson, J.G.

An as-accurate-as-possible estimation of total-aircraft emissions are an essential component of any environmental-impact assessment done for proposed expansions at major airports. To determine the amount of emissions generated by aircraft using present models it is necessary to know the emission characteristics of all engines that are on all planes using the airport. However, the published data base does not cover all engine types and, therefore, a new methodology is needed to assist in estimating annual emissions from aircraft at airports. Linear regression equations relating quantity of emissions to aircraft weight using a known-fleet mix are developed in this paper. Total-annualmore » emissions for CO, NO[sub x], NMHC, SO[sub x], CO[sub 2], and N[sub 2]O are tabulated for Toronto's international airport for 1990. The regression equations are statistically significant for all emissions except for NMHC from large jets and NO[sub x] and NMHC for piston-engine aircraft. This regression model is a relatively simple, fast, and inexpensive method of obtaining an annual-emission inventory for an airport.« less

Area under the curve predictions of dalbavancin, a new lipoglycopeptide agent, using the end of intravenous infusion concentration data point by regression analyses such as linear, log-linear and power models.

PubMed

Bhamidipati, Ravi Kanth; Syed, Muzeeb; Mullangi, Ramesh; Srinivas, Nuggehally

2018-02-01

1. Dalbavancin, a lipoglycopeptide, is approved for treating gram-positive bacterial infections. Area under plasma concentration versus time curve (AUC inf ) of dalbavancin is a key parameter and AUC inf /MIC ratio is a critical pharmacodynamic marker. 2. Using end of intravenous infusion concentration (i.e. C max ) C max versus AUC inf relationship for dalbavancin was established by regression analyses (i.e. linear, log-log, log-linear and power models) using 21 pairs of subject data. 3. The predictions of the AUC inf were performed using published C max data by application of regression equations. The quotient of observed/predicted values rendered fold difference. The mean absolute error (MAE)/root mean square error (RMSE) and correlation coefficient (r) were used in the assessment. 4. MAE and RMSE values for the various models were comparable. The C max versus AUC inf exhibited excellent correlation (r > 0.9488). The internal data evaluation showed narrow confinement (0.84-1.14-fold difference) with a RMSE < 10.3%. The external data evaluation showed that the models predicted AUC inf with a RMSE of 3.02-27.46% with fold difference largely contained within 0.64-1.48. 5. Regardless of the regression models, a single time point strategy of using C max (i.e. end of 30-min infusion) is amenable as a prospective tool for predicting AUC inf of dalbavancin in patients.

Model test on the relationship feed energy and protein ratio to the production and quality of milk protein

NASA Astrophysics Data System (ADS)

Hartanto, R.; Jantra, M. A. C.; Santosa, S. A. B.; Purnomoadi, A.

2018-01-01

The purpose of this research was to find an appropriate relationship model between the feed energy and protein ratio with the amount of production and quality of milk proteins. This research was conducted at Getasan Sub-district, Semarang Regency, Central Java Province, Indonesia using 40 samples (Holstein Friesian cattle, lactation period II-III and lactation month 3-4). Data were analyzed using linear and quadratic regressions, to predict the production and quality of milk protein from feed energy and protein ratio that describe the diet. The significance of model was tested using analysis of variance. Coefficient of determination (R2), residual variance (RV) and root mean square prediction error (RMSPE) were reported for the developed equations as an indicator of the goodness of model fit. The results showed no relationship in milk protein (kg), milk casein (%), milk casein (kg) and milk urea N (mg/dl) as function of CP/TDN. The significant relationship was observed in milk production (L or kg) and milk protein (%) as function of CP/TDN, both in linear and quadratic models. In addition, a quadratic change in milk production (L) (P = 0.003), milk production (kg) (P = 0.003) and milk protein concentration (%) (P = 0.026) were observed with increase of CP/TDN. It can be concluded that quadratic equation was the good fitting model for this research, because quadratic equation has larger R2, smaller RV and smaller RMSPE than those of linear equation.

Stress Regression Analysis of Asphalt Concrete Deck Pavement Based on Orthogonal Experimental Design and Interlayer Contact

NASA Astrophysics Data System (ADS)

Wang, Xuntao; Feng, Jianhu; Wang, Hu; Hong, Shidi; Zheng, Supei

2018-03-01

A three-dimensional finite element box girder bridge and its asphalt concrete deck pavement were established by ANSYS software, and the interlayer bonding condition of asphalt concrete deck pavement was assumed to be contact bonding condition. Orthogonal experimental design is used to arrange the testing plans of material parameters, and an evaluation of the effect of different material parameters in the mechanical response of asphalt concrete surface layer was conducted by multiple linear regression model and using the results from the finite element analysis. Results indicated that stress regression equations can well predict the stress of the asphalt concrete surface layer, and elastic modulus of waterproof layer has a significant influence on stress values of asphalt concrete surface layer.

Correlation among extinction efficiency and other parameters in an aggregate dust model

NASA Astrophysics Data System (ADS)

Dhar, Tanuj Kumar; Sekhar Das, Himadri

2017-10-01

We study the extinction properties of highly porous Ballistic Cluster-Cluster Aggregate dust aggregates in a wide range of complex refractive indices (1.4≤ n≤ 2.0, 0.001≤ k≤ 1.0) and wavelengths (0.11 {{μ }}{{m}}≤ {{λ }}≤ 3.4 {{μ }} m). An attempt has been made for the first time to investigate the correlation among extinction efficiency ({Q}{ext}), composition of dust aggregates (n,k), wavelength of radiation (λ) and size parameter of the monomers (x). If k is fixed at any value between 0.001 and 1.0, {Q}{ext} increases with increase of n from 1.4 to 2.0. {Q}{ext} and n are correlated via linear regression when the cluster size is small, whereas the correlation is quadratic at moderate and higher sizes of the cluster. This feature is observed at all wavelengths (ultraviolet to optical to infrared). We also find that the variation of {Q}{ext} with n is very small when λ is high. When n is fixed at any value between 1.4 and 2.0, it is observed that {Q}{ext} and k are correlated via a polynomial regression equation (of degree 1, 2, 3 or 4), where the degree of the equation depends on the cluster size, n and λ. The correlation is linear for small size and quadratic/cubic/quartic for moderate and higher sizes. We have also found that {Q}{ext} and x are correlated via a polynomial regression (of degree 3, 4 or 5) for all values of n. The degree of regression is found to be n and k-dependent. The set of relations obtained from our work can be used to model interstellar extinction for dust aggregates in a wide range of wavelengths and complex refractive indices.

A Study of the Effect of the Front-End Styling of Sport Utility Vehicles on Pedestrian Head Injuries

PubMed Central

Qin, Qin; Chen, Zheng; Bai, Zhonghao; Cao, Libo

2018-01-01

Background The number of sport utility vehicles (SUVs) on China market is continuously increasing. It is necessary to investigate the relationships between the front-end styling features of SUVs and head injuries at the styling design stage for improving the pedestrian protection performance and product development efficiency. Methods Styling feature parameters were extracted from the SUV side contour line. And simplified finite element models were established based on the 78 SUV side contour lines. Pedestrian headform impact simulations were performed and validated. The head injury criterion of 15 ms (HIC15) at four wrap-around distances was obtained. A multiple linear regression analysis method was employed to describe the relationships between the styling feature parameters and the HIC15 at each impact point. Results The relationship between the selected styling features and the HIC15 showed reasonable correlations, and the regression models and the selected independent variables showed statistical significance. Conclusions The regression equations obtained by multiple linear regression can be used to assess the performance of SUV styling in protecting pedestrians' heads and provide styling designers with technical guidance regarding their artistic creations.

Experimental determination of a Viviparus contectus thermometry equation.

PubMed

Bugler, Melanie J; Grimes, Stephen T; Leng, Melanie J; Rundle, Simon D; Price, Gregory D; Hooker, Jerry J; Collinson, Margaret E

2009-09-01

Experimental measurements of the (18)O/(16)O isotope fractionation between the biogenic aragonite of Viviparus contectus (Gastropoda) and its host freshwater were undertaken to generate a species-specific thermometry equation. The temperature dependence of the fractionation factor and the relationship between Deltadelta(18)O (delta(18)O(carb.) - delta(18)O(water)) and temperature were calculated from specimens maintained under laboratory and field (collection and cage) conditions. The field specimens were grown (Somerset, UK) between August 2007 and August 2008, with water samples and temperature measurements taken monthly. Specimens grown in the laboratory experiment were maintained under constant temperatures (15 degrees C, 20 degrees C and 25 degrees C) with water samples collected weekly. Application of a linear regression to the datasets indicated that the gradients of all three experiments were within experimental error of each other (+/-2 times the standard error); therefore, a combined (laboratory and field data) correlation could be applied. The relationship between Deltadelta(18)O (delta(18)O(carb.) - delta(18)O(water)) and temperature (T) for this combined dataset is given by: T = - 7.43( + 0.87, - 1.13)*Deltadelta18O + 22.89(+/- 2.09) (T is in degrees C, delta(18)O(carb.) is with respect to Vienna Pee Dee Belemnite (VPDB) and delta(18)O(water) is with respect to Vienna Standard Mean Ocean Water (VSMOW). Quoted errors are 2 times standard error).Comparisons made with existing aragonitic thermometry equations reveal that the linear regression for the combined Viviparus contectus equation is within 2 times the standard error of previously reported aragonitic thermometry equations. This suggests there are no species-specific vital effects for Viviparus contectus. Seasonal delta(18)O(carb.) profiles from specimens retrieved from the field cage experiment indicate that during shell secretion the delta(18)O(carb.) of the shell carbonate is not influenced by size, sex or whether females contained eggs or juveniles. Copyright (c) 2009 John Wiley & Sons, Ltd.

ELASTIC NET FOR COX'S PROPORTIONAL HAZARDS MODEL WITH A SOLUTION PATH ALGORITHM.

PubMed

Wu, Yichao

2012-01-01

For least squares regression, Efron et al. (2004) proposed an efficient solution path algorithm, the least angle regression (LAR). They showed that a slight modification of the LAR leads to the whole LASSO solution path. Both the LAR and LASSO solution paths are piecewise linear. Recently Wu (2011) extended the LAR to generalized linear models and the quasi-likelihood method. In this work we extend the LAR further to handle Cox's proportional hazards model. The goal is to develop a solution path algorithm for the elastic net penalty (Zou and Hastie (2005)) in Cox's proportional hazards model. This goal is achieved in two steps. First we extend the LAR to optimizing the log partial likelihood plus a fixed small ridge term. Then we define a path modification, which leads to the solution path of the elastic net regularized log partial likelihood. Our solution path is exact and piecewise determined by ordinary differential equation systems.

On the use of regression analysis for the estimation of human biological age.

PubMed

Krøll, J; Saxtrup, O

2000-01-01

The present investigation compares three linear regression procedures for the definition of human biological age (bioage). As a model system for bioage definition is used the variations with age of blood hemoglobin (B-hemoglobin) in males in the age range 50-95 years. The bioage measures compared are: 1: P-bioage; defined from regression of chronological age on B-hemoglobin results. 2: AC-bioage; obtained by indirect regression, using in reverse the equation describing the regression of B-hemoglobin on age in a reference population. 3: BC-bioage; defined by orthogonal regression on the reference regression line of B-hemoglobin on age. It is demonstrated that the P-bioage measure gives an overestimation of the bioage in the younger and an underestimation in the older individuals. This 'regression to the mean' is avoided using the indirect regression procedures. Here the relatively low SD of the BC-bioage measure results from the inclusion of individual chronological age in the orthogonal regression procedure. Observations on male blood donors illustrates the variation of the AC- and BC-bioage measures in the individual.

Statistical analysis of water-level, springflow, and streamflow data for the Edwards Aquifer in south-central Texas

USGS Publications Warehouse

Puente, Celso

1976-01-01

Water-level, springflow, and streamflow data were used to develop simple and multiple linear-regression equations for use in estimating water levels in wells and the flow of three major springs in the Edwards aquifer in the eastern San Antonio area. The equations provide daily, monthly, and annual estimates that compare very favorably with observed data. Analyses of geologic and hydrologic data indicate that the water discharged by the major springs is supplied primarily by regional underflow from the west and southwest and by local recharge in the infiltration area in northern Bexar, Comal, and Hays Counties.

Graphical Tools for Linear Structural Equation Modeling

DTIC Science & Technology

2014-06-01

others. 4Kenny and Milan (2011) write, “Identification is perhaps the most difficult concept for SEM researchers to understand. We have seen SEM...model to using typical SEM software to determine model identifia- bility. Kenny and Milan (2011) list the following drawbacks: (i) If poor starting...the well known recursive and null rules (Bollen, 1989) and the regression rule (Kenny and Milan , 2011). A Simple Criterion for Identifying Individual

Validity of Bioelectrical Impedance Analysis to Estimation Fat-Free Mass in the Army Cadets.

PubMed

Langer, Raquel D; Borges, Juliano H; Pascoa, Mauro A; Cirolini, Vagner X; Guerra-Júnior, Gil; Gonçalves, Ezequiel M

2016-03-11

Bioelectrical Impedance Analysis (BIA) is a fast, practical, non-invasive, and frequently used method for fat-free mass (FFM) estimation. The aims of this study were to validate predictive equations of BIA to FFM estimation in Army cadets and to develop and validate a specific BIA equation for this population. A total of 396 males, Brazilian Army cadets, aged 17-24 years were included. The study used eight published predictive BIA equations, a specific equation in FFM estimation, and dual-energy X-ray absorptiometry (DXA) as a reference method. Student's t-test (for paired sample), linear regression analysis, and Bland-Altman method were used to test the validity of the BIA equations. Predictive BIA equations showed significant differences in FFM compared to DXA (p < 0.05) and large limits of agreement by Bland-Altman. Predictive BIA equations explained 68% to 88% of FFM variance. Specific BIA equations showed no significant differences in FFM, compared to DXA values. Published BIA predictive equations showed poor accuracy in this sample. The specific BIA equations, developed in this study, demonstrated validity for this sample, although should be used with caution in samples with a large range of FFM.

Metabolic control analysis using transient metabolite concentrations. Determination of metabolite concentration control coefficients.

PubMed Central

Delgado, J; Liao, J C

1992-01-01

The methodology previously developed for determining the Flux Control Coefficients [Delgado & Liao (1992) Biochem. J. 282, 919-927] is extended to the calculation of metabolite Concentration Control Coefficients. It is shown that the transient metabolite concentrations are related by a few algebraic equations, attributed to mass balance, stoichiometric constraints, quasi-equilibrium or quasi-steady states, and kinetic regulations. The coefficients in these relations can be estimated using linear regression, and can be used to calculate the Control Coefficients. The theoretical basis and two examples are discussed. Although the methodology is derived based on the linear approximation of enzyme kinetics, it yields reasonably good estimates of the Control Coefficients for systems with non-linear kinetics. PMID:1497632

Dosing algorithm for warfarin using CYP2C9 and VKORC1 genotyping from a multi-ethnic population: comparison with other equations.

PubMed

Wu, Alan H B; Wang, Ping; Smith, Andrew; Haller, Christine; Drake, Katherine; Linder, Mark; Valdes, Roland

2008-02-01

Polymorphism in the genes for cytochrome (CYP)2C9 and the vitamin K epoxide reductase complex subunit 1 (VKORC1) affect the pharmacokinetics and pharmacodynamics of warfarin. We developed and validated a warfarin-dosing algorithm for a multi-ethnic population that predicts the best dose for stable anticoagulation, and compared its performance against other regression equations. We determined the allele and haplotype frequencies of genes for CYP2C9 and VKORC1 on 167 Caucasian, African-American, Asian and Hispanic patients on warfarin. On a subset where complete data were available (n=92), we developed a dosing equation that predicts the actual dose needed to maintain target anticoagulation using demographic variables and genotypes. This regression was validated against an independent group of subjects. We also applied our data to five other published warfarin-dosing equations. The allele frequency for CYP2C9*2 and *3 and the A allele for VKORC1 3673 was similar to previously published reports. For Caucasians and Asians, VKORC1 SNPs were in Hardy-Weinberg linkage equilibrium. Some VKORC1 SNPs among the African-American population and one SNP among Hispanics were not in equilibrium. The linear regression of predicted versus actual warfarin dose produced r-values of 0.71 for the training set and 0.67 for the validation set. The regression coefficient improved (to r=0.78 and 0.75, respectively) when rare genotypes were eliminated or when the 7566 VKORC1 genotype was added to the model. All of the regression models tested produced a similar degree of correlation. The exclusion of rare genotypes that are more associated with certain ethnicities improved the model. Minor improvements in algorithms can be observed with the inclusion of ethnicity and more CYP2C9 and VKORC1 SNPs as variables. Major improvements will likely require the identification of new gene associations with warfarin dosing.

Converting positive and negative symptom scores between PANSS and SAPS/SANS.

PubMed

van Erp, Theo G M; Preda, Adrian; Nguyen, Dana; Faziola, Lawrence; Turner, Jessica; Bustillo, Juan; Belger, Aysenil; Lim, Kelvin O; McEwen, Sarah; Voyvodic, James; Mathalon, Daniel H; Ford, Judith; Potkin, Steven G; Fbirn

2014-01-01

The Scale for the Assessment of Positive Symptoms (SAPS), the Scale for the Assessment of Negative Symptoms (SANS), and the Positive and Negative Syndrome Scale for Schizophrenia (PANSS) are the most widely used schizophrenia symptom rating scales, but despite their co-existence for 25 years no easily usable between-scale conversion mechanism exists. The aim of this study was to provide equations for between-scale symptom rating conversions. Two-hundred-and-five schizophrenia patients [mean age±SD=39.5±11.6, 156 males] were assessed with the SANS, SAPS, and PANSS. Pearson's correlations between symptom scores from each of the scales were computed. Linear regression analyses, on data from 176 randomly selected patients, were performed to derive equations for converting ratings between the scales. Intraclass correlations, on data from the remaining 29 patients, not part of the regression analyses, were performed to determine rating conversion accuracy. Between-scale positive and negative symptom ratings were highly correlated. Intraclass correlations between the original positive and negative symptom ratings and those obtained via conversion of alternative ratings using the conversion equations were moderate to high (ICCs=0.65 to 0.91). Regression-based equations may be useful for conversion between schizophrenia symptom severity as measured by the SANS/SAPS and PANSS, though additional validation is warranted. This study's conversion equations, implemented at http:/converteasy.org, may aid in the comparison of medication efficacy studies, in meta- and mega-analyses examining symptoms as moderator variables, and in retrospective combination of symptom data in multi-center data sharing projects that need to pool symptom rating data when such data are obtained using different scales. Copyright © 2013 Elsevier B.V. All rights reserved.

Cotton-type and joint invariants for linear elliptic systems.

PubMed

Aslam, A; Mahomed, F M

2013-01-01

Cotton-type invariants for a subclass of a system of two linear elliptic equations, obtainable from a complex base linear elliptic equation, are derived both by spliting of the corresponding complex Cotton invariants of the base complex equation and from the Laplace-type invariants of the system of linear hyperbolic equations equivalent to the system of linear elliptic equations via linear complex transformations of the independent variables. It is shown that Cotton-type invariants derived from these two approaches are identical. Furthermore, Cotton-type and joint invariants for a general system of two linear elliptic equations are also obtained from the Laplace-type and joint invariants for a system of two linear hyperbolic equations equivalent to the system of linear elliptic equations by complex changes of the independent variables. Examples are presented to illustrate the results.

Cotton-Type and Joint Invariants for Linear Elliptic Systems

PubMed Central

Aslam, A.; Mahomed, F. M.

2013-01-01

Cotton-type invariants for a subclass of a system of two linear elliptic equations, obtainable from a complex base linear elliptic equation, are derived both by spliting of the corresponding complex Cotton invariants of the base complex equation and from the Laplace-type invariants of the system of linear hyperbolic equations equivalent to the system of linear elliptic equations via linear complex transformations of the independent variables. It is shown that Cotton-type invariants derived from these two approaches are identical. Furthermore, Cotton-type and joint invariants for a general system of two linear elliptic equations are also obtained from the Laplace-type and joint invariants for a system of two linear hyperbolic equations equivalent to the system of linear elliptic equations by complex changes of the independent variables. Examples are presented to illustrate the results. PMID:24453871

A proposed method to detect kinematic differences between and within individuals.

PubMed

Frost, David M; Beach, Tyson A C; McGill, Stuart M; Callaghan, Jack P

2015-06-01

The primary objective was to examine the utility of a novel method of detecting "actual" kinematic changes using the within-subject variation. Twenty firefighters were assigned to one of two groups (lifting or firefighting). Participants performed 25 repetitions of two lifting or firefighting tasks, in three sessions. The magnitude and within-subject variation of several discrete kinematic measures were computed. Sequential averages of each variable were used to derive a cubic, quadratic and linear regression equation. The efficacy of each equation was examined by contrasting participants' sequential means to their 25-trial mean±1SD and 2SD. The magnitude and within-subject variation of each dependent measure was repeatable for all tasks; however, each participant did not exhibit the same movement patterns as the group. The number of instances across all variables, tasks and testing sessions whereby the 25-trial mean±1SD was contained within the boundaries established by the regression equations increased as the aggregate scores included more trials. Each equation achieved success in at least 88% of all instances when three trials were included in the sequential mean (95% with five trials). The within-subject variation may offer a means to examine participant-specific changes without having to collect a large number of trials. Copyright © 2015 Elsevier Ltd. All rights reserved.

Clinical laboratory investigation of the Sanofi ACCESS CK-MB procedure and comparison to electrophoresis and Abbott IMx.

PubMed

Mao, G D; Adeli, K; Eisenbrey, A B; Artiss, J D

1996-07-01

This evaluation was undertaken to verify the application protocol for the CK-MB assay on the ACCESS Immunoassay Analyzer (Sanofi Diagnostics Pasteur, Chaska, MN). The results show that the ACCESS CK-MB assay total imprecision was 6.8% to 9.1%. Analytical linearity of the ACCESS CK-MB assay was excellent in the range of < 1-214 micrograms/L. A comparison of the ACCESS CK-MB assay with the IMx (Abbott Laboratories, Abbott Park, IL) method shows good correlation r = 0.990 (n = 108). Linear regression analysis yielded Y = 1.36X-0.3, Sx/y = 7.2. ACCESS CK-MB values also correlated well with CK-MB by electrophoresis with r = 0.968 (n = 132). The linear regression equation for this comparison was Y = 1.08X + 1.4, Sx/y = 14.1. The expected non-myocardial infarction range of CK-MB determined by the ACCESS system was 1.3-9.4 micrograms/L (mean = 4.0, n = 58). The ACCESS CK-MB assay would appear to be rapid, precise and clinically useful.

Humidity and Gravimetric Equivalency Adjustments for Nephelometer-Based Particulate Matter Measurements of Emissions from Solid Biomass Fuel Use in Cookstoves

PubMed Central

Soneja, Sutyajeet; Chen, Chen; Tielsch, James M.; Katz, Joanne; Zeger, Scott L.; Checkley, William; Curriero, Frank C.; Breysse, Patrick N.

2014-01-01

Great uncertainty exists around indoor biomass burning exposure-disease relationships due to lack of detailed exposure data in large health outcome studies. Passive nephelometers can be used to estimate high particulate matter (PM) concentrations during cooking in low resource environments. Since passive nephelometers do not have a collection filter they are not subject to sampler overload. Nephelometric concentration readings can be biased due to particle growth in high humid environments and differences in compositional and size dependent aerosol characteristics. This paper explores relative humidity (RH) and gravimetric equivalency adjustment approaches to be used for the pDR-1000 used to assess indoor PM concentrations for a cookstove intervention trial in Nepal. Three approaches to humidity adjustment performed equivalently (similar root mean squared error). For gravimetric conversion, the new linear regression equation with log-transformed variables performed better than the traditional linear equation. In addition, gravimetric conversion equations utilizing a spline or quadratic term were examined. We propose a humidity adjustment equation encompassing the entire RH range instead of adjusting for RH above an arbitrary 60% threshold. Furthermore, we propose new integrated RH and gravimetric conversion methods because they have one response variable (gravimetric PM2.5 concentration), do not contain an RH threshold, and is straightforward. PMID:24950062

Humidity and gravimetric equivalency adjustments for nephelometer-based particulate matter measurements of emissions from solid biomass fuel use in cookstoves.

PubMed

Soneja, Sutyajeet; Chen, Chen; Tielsch, James M; Katz, Joanne; Zeger, Scott L; Checkley, William; Curriero, Frank C; Breysse, Patrick N

2014-06-19

Great uncertainty exists around indoor biomass burning exposure-disease relationships due to lack of detailed exposure data in large health outcome studies. Passive nephelometers can be used to estimate high particulate matter (PM) concentrations during cooking in low resource environments. Since passive nephelometers do not have a collection filter they are not subject to sampler overload. Nephelometric concentration readings can be biased due to particle growth in high humid environments and differences in compositional and size dependent aerosol characteristics. This paper explores relative humidity (RH) and gravimetric equivalency adjustment approaches to be used for the pDR-1000 used to assess indoor PM concentrations for a cookstove intervention trial in Nepal. Three approaches to humidity adjustment performed equivalently (similar root mean squared error). For gravimetric conversion, the new linear regression equation with log-transformed variables performed better than the traditional linear equation. In addition, gravimetric conversion equations utilizing a spline or quadratic term were examined. We propose a humidity adjustment equation encompassing the entire RH range instead of adjusting for RH above an arbitrary 60% threshold. Furthermore, we propose new integrated RH and gravimetric conversion methods because they have one response variable (gravimetric PM2.5 concentration), do not contain an RH threshold, and is straightforward.

A theory of fine structure image models with an application to detection and classification of dementia.

PubMed

O'Neill, William; Penn, Richard; Werner, Michael; Thomas, Justin

2015-06-01

Estimation of stochastic process models from data is a common application of time series analysis methods. Such system identification processes are often cast as hypothesis testing exercises whose intent is to estimate model parameters and test them for statistical significance. Ordinary least squares (OLS) regression and the Levenberg-Marquardt algorithm (LMA) have proven invaluable computational tools for models being described by non-homogeneous, linear, stationary, ordinary differential equations. In this paper we extend stochastic model identification to linear, stationary, partial differential equations in two independent variables (2D) and show that OLS and LMA apply equally well to these systems. The method employs an original nonparametric statistic as a test for the significance of estimated parameters. We show gray scale and color images are special cases of 2D systems satisfying a particular autoregressive partial difference equation which estimates an analogous partial differential equation. Several applications to medical image modeling and classification illustrate the method by correctly classifying demented and normal OLS models of axial magnetic resonance brain scans according to subject Mini Mental State Exam (MMSE) scores. Comparison with 13 image classifiers from the literature indicates our classifier is at least 14 times faster than any of them and has a classification accuracy better than all but one. Our modeling method applies to any linear, stationary, partial differential equation and the method is readily extended to 3D whole-organ systems. Further, in addition to being a robust image classifier, estimated image models offer insights into which parameters carry the most diagnostic image information and thereby suggest finer divisions could be made within a class. Image models can be estimated in milliseconds which translate to whole-organ models in seconds; such runtimes could make real-time medicine and surgery modeling possible.

Validity of two methods for estimation of vertical jump height.

PubMed

Dias, Jonathan Ache; Dal Pupo, Juliano; Reis, Diogo C; Borges, Lucas; Santos, Saray G; Moro, Antônio R P; Borges, Noé G

2011-07-01

The objectives of this study were (a) to determine the concurrent validity of the flight time (FT) and double integration of vertical reaction force (DIF) methods in the estimation of vertical jump height with the video method (VID) as reference; (b) to verify the degree of agreement among the 3 methods; (c) to propose regression equations to predict the jump height using the FT and DIF. Twenty healthy male and female nonathlete college students participated in this study. The experiment involved positioning a contact mat (CTM) on the force platform (FP), with a video camera 3 m from the FP and perpendicular to the sagittal plane of the subject being assessed. Each participant performed 15 countermovement jumps with 60-second intervals between the trials. Significant differences were found between the jump height obtained by VID and the results with FT (p ≤ 0.01) and DIF (p ≤ 0.01), showing that the methods are not valid. Additionally, the DIF showed a greater degree of agreement with the reference method than the FT did, and both presented a systematic error. From the linear regression test was determined the prediction equations with a high degree of linearity between the methods VID vs. DIF (R = 0.988) and VID vs. FT (R = 0.979). Therefore, the prediction equations suggested may allow coaches to measure the vertical jump performance of athletes by the FT and DIF, using a CTM or an FP, which represents more practical and viable approaches in the sports field; comparisons can then be made with the results of other athletes evaluated by VID.

The Hydrothermal Chemistry of Gold, Arsenic, Antimony, Mercury and Silver

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessinger, Brad; Apps, John A.

2003-03-23

A comprehensive thermodynamic database based on the Helgeson-Kirkham-Flowers (HKF) equation of state was developed for metal complexes in hydrothermal systems. Because this equation of state has been shown to accurately predict standard partial molal thermodynamic properties of aqueous species at elevated temperatures and pressures, this study provides the necessary foundation for future exploration into transport and depositional processes in polymetallic ore deposits. The HKF equation of state parameters for gold, arsenic, antimony, mercury, and silver sulfide and hydroxide complexes were derived from experimental equilibrium constants using nonlinear regression calculations. In order to ensure that the resulting parameters were internally consistent,more » those experiments utilizing incompatible thermodynamic data were re-speciated prior to regression. Because new experimental studies were used to revise the HKF parameters for H2S0 and HS-1, those metal complexes for which HKF parameters had been previously derived were also updated. It was found that predicted thermodynamic properties of metal complexes are consistent with linear correlations between standard partial molal thermodynamic properties. This result allowed assessment of several complexes for which experimental data necessary to perform regression calculations was limited. Oxygen fugacity-temperature diagrams were calculated to illustrate how thermodynamic data improves our understanding of depositional processes. Predicted thermodynamic properties were used to investigate metal transport in Carlin-type gold deposits. Assuming a linear relationship between temperature and pressure, metals are predicted to predominantly be transported as sulfide complexes at a total aqueous sulfur concentration of 0.05 m. Also, the presence of arsenic and antimony mineral phases in the deposits are shown to restrict mineralization within a limited range of chemical conditions. Finally, at a lesser aqueous sulfur concentration of 0.01 m, host rock sulfidation can explain the origin of arsenic and antimony minerals within the paragenetic sequence.« less

Variability of creatinine measurements in clinical laboratories: results from the CRIC study.

PubMed

Joffe, Marshall; Hsu, Chi-yuan; Feldman, Harold I; Weir, Matthew; Landis, J R; Hamm, L Lee

2010-01-01

Estimating equations using serum creatinine (SCr) are often used to assess glomerular filtration rate (GFR). Such creatinine (Cr)-based formulae may produce biased estimates of GFR when using Cr measurements that have not been calibrated to reference laboratories. In this paper, we sought to examine the degree of this variation in Cr assays in several laboratories associated with academic medical centers affiliated with the Chronic Renal Insufficiency Cohort (CRIC) Study; to consider how best to correct for this variation, and to quantify the impact of such corrections on eligibility for participation in CRIC. Variability of Cr is of particular concern in the conduct of CRIC, a large multicenter study of subjects with chronic renal disease, because eligibility for the study depends on Cr-based assessment of GFR. A library of 5 large volume plasma specimens from apheresis patients was assembled, representing levels of plasma Cr from 0.8 to 2.4 mg/dl. Samples from this library were used for measurement of Cr at each of the 14 CRIC laboratories repetitively over time. We used graphical displays and linear regression methods to examine the variability in Cr, and used linear regression to develop calibration equations. We also examined the impact of the various calibration equations on the proportion of subjects screened as potential participants who were actually eligible for the study. There was substantial variability in Cr assays across laboratories and over time. We developed calibration equations for each laboratory; these equations varied substantially among laboratories and somewhat over time in some laboratories. The laboratory site contributed the most to variability (51% of the variance unexplained by the specimen) and variation with time accounted for another 15%. In some laboratories, calibration equations resulted in differences in eligibility for CRIC of as much as 20%. The substantial variability in SCr assays across laboratories necessitates calibration of SCr measures to a common standard. Failing to do so may substantially affect study eligibility and clinical interpretations when they are determined by Cr-based estimates of GFR. 2010 S. Karger AG, Basel.

How many stakes are required to measure the mass balance of a glacier?

USGS Publications Warehouse

Fountain, A.G.; Vecchia, A.

1999-01-01

Glacier mass balance is estimated for South Cascade Glacier and Maclure Glacier using a one-dimensional regression of mass balance with altitude as an alternative to the traditional approach of contouring mass balance values. One attractive feature of regression is that it can be applied to sparse data sets where contouring is not possible and can provide an objective error of the resulting estimate. Regression methods yielded mass balance values equivalent to contouring methods. The effect of the number of mass balance measurements on the final value for the glacier showed that sample sizes as small as five stakes provided reasonable estimates, although the error estimates were greater than for larger sample sizes. Different spatial patterns of measurement locations showed no appreciable influence on the final value as long as different surface altitudes were intermittently sampled over the altitude range of the glacier. Two different regression equations were examined, a quadratic, and a piecewise linear spline, and comparison of results showed little sensitivity to the type of equation. These results point to the dominant effect of the gradient of mass balance with altitude of alpine glaciers compared to transverse variations. The number of mass balance measurements required to determine the glacier balance appears to be scale invariant for small glaciers and five to ten stakes are sufficient.

Development and Validation of a Prototype Vacuum Sensing Unit for the DD2011 Chairside Amalgam Separators

DTIC Science & Technology

2015-10-30

pressure values onto the SD card. The addition of free and open-source Arduino libraries allowed for the seamless integration of the shield into the...alert the user when replacing the separator is necessary. Methods: A sensor was built to measure and record differential pressure values within the...from the transducers during simulated blockages were transformed into pressure values using linear regression equations from the calibration data

Methods for estimating low-flow statistics for Massachusetts streams

USGS Publications Warehouse

Ries, Kernell G.; Friesz, Paul J.

2000-01-01

Methods and computer software are described in this report for determining flow duration, low-flow frequency statistics, and August median flows. These low-flow statistics can be estimated for unregulated streams in Massachusetts using different methods depending on whether the location of interest is at a streamgaging station, a low-flow partial-record station, or an ungaged site where no data are available. Low-flow statistics for streamgaging stations can be estimated using standard U.S. Geological Survey methods described in the report. The MOVE.1 mathematical method and a graphical correlation method can be used to estimate low-flow statistics for low-flow partial-record stations. The MOVE.1 method is recommended when the relation between measured flows at a partial-record station and daily mean flows at a nearby, hydrologically similar streamgaging station is linear, and the graphical method is recommended when the relation is curved. Equations are presented for computing the variance and equivalent years of record for estimates of low-flow statistics for low-flow partial-record stations when either a single or multiple index stations are used to determine the estimates. The drainage-area ratio method or regression equations can be used to estimate low-flow statistics for ungaged sites where no data are available. The drainage-area ratio method is generally as accurate as or more accurate than regression estimates when the drainage-area ratio for an ungaged site is between 0.3 and 1.5 times the drainage area of the index data-collection site. Regression equations were developed to estimate the natural, long-term 99-, 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, and 50-percent duration flows; the 7-day, 2-year and the 7-day, 10-year low flows; and the August median flow for ungaged sites in Massachusetts. Streamflow statistics and basin characteristics for 87 to 133 streamgaging stations and low-flow partial-record stations were used to develop the equations. The streamgaging stations had from 2 to 81 years of record, with a mean record length of 37 years. The low-flow partial-record stations had from 8 to 36 streamflow measurements, with a median of 14 measurements. All basin characteristics were determined from digital map data. The basin characteristics that were statistically significant in most of the final regression equations were drainage area, the area of stratified-drift deposits per unit of stream length plus 0.1, mean basin slope, and an indicator variable that was 0 in the eastern region and 1 in the western region of Massachusetts. The equations were developed by use of weighted-least-squares regression analyses, with weights assigned proportional to the years of record and inversely proportional to the variances of the streamflow statistics for the stations. Standard errors of prediction ranged from 70.7 to 17.5 percent for the equations to predict the 7-day, 10-year low flow and 50-percent duration flow, respectively. The equations are not applicable for use in the Southeast Coastal region of the State, or where basin characteristics for the selected ungaged site are outside the ranges of those for the stations used in the regression analyses. A World Wide Web application was developed that provides streamflow statistics for data collection stations from a data base and for ungaged sites by measuring the necessary basin characteristics for the site and solving the regression equations. Output provided by the Web application for ungaged sites includes a map of the drainage-basin boundary determined for the site, the measured basin characteristics, the estimated streamflow statistics, and 90-percent prediction intervals for the estimates. An equation is provided for combining regression and correlation estimates to obtain improved estimates of the streamflow statistics for low-flow partial-record stations. An equation is also provided for combining regression and drainage-area ratio estimates to obtain improved e

[Prevalence of chronic kidney disease in hypertensive persons attended in primary care from Spain determined by application of estimating equations].

PubMed

Gómez Navarro, Rafael

2009-01-01

To study the renal function (FR) of the hypertensive patients by means of estimating equations and serum creatinine (Crp). To calculate the percentage of patients with chronic kidney disease (ERC) that present normal values of Crp. To analyze which factors collaborate in the deterioration of the FR. Descriptive cross-sectional study of patients with HTA. Crp and arterial tension (TA) were determined. The glomerular filtration rate was calculated by means of Cockroft-Gault and MDRD's formula. The years of evolution of the HTA were registered. A descriptive study of the variables and the possible dependence among them was completed, using several times linear multiple regression. 52 patients were studied (57,7% women). Average age 72,4 +/- 10,8. 32,6% (Cockcroft-Gault) or 21,5% (MDRD) were fulfilling ERC criterion. The ERC was mainly diagnosed in females. 21,4% (Cockcroft-Gault) and 9,5 % patients (MDRD) with ERC had normal Crp values. We do not find linear dependence between the numbers of TA and the FR. The TA check-up objectives do not suppose less development of ERC. In males we find linear dependence within the FR (MDRD) and the years of evolution of the HTA. The ERC is a frequent pathology in the hypertense persons. The systematical utilization of estimating equations facilitates the detection of hidden ERC in patients with normal Crp.

Estimation of stature from radiologic anthropometry of the lumbar vertebral dimensions in Chinese.

PubMed

Zhang, Kui; Chang, Yun-feng; Fan, Fei; Deng, Zhen-hua

2015-11-01

The recent study was to assess the relationship between the radiologic anthropometry of the lumbar vertebral dimensions and stature in Chinese and to develop regression formulae to estimate stature from these dimensions. A total of 412 normal, healthy volunteers, comprising 206 males and 206 females, were recruited. The linear regression analysis were performed to assess the correlation between the stature and lengths of various segments of the lumbar vertebral column. Among the regression equations created for single variable, the predictive value was greatest for the reconstruction of stature from the lumbar segment in both sexes and subgroup analysis. When individual vertebral body was used, the heights of posterior vertebral body of L3 gave the most accurate results for male group, the heights of central vertebral body of L1 provided the most accurate results for female group and female group with age above 45 years, the heights of central vertebral body of L3 gave the most accurate results for the groups with age from 20-45 years for both sexes and the male group with age above 45 years. The heights of anterior vertebral body of L5 gave the less accurate results except for the heights of anterior vertebral body of L4 provided the less accurate result for the male group with age above 45 years. As expected, multiple regression equations were more successful than equations derived from a single variable. The research observations suggest lumbar vertebral dimensions to be useful in stature estimation among Chinese population. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Verification of spectrophotometric method for nitrate analysis in water samples

NASA Astrophysics Data System (ADS)

Kurniawati, Puji; Gusrianti, Reny; Dwisiwi, Bledug Bernanti; Purbaningtias, Tri Esti; Wiyantoko, Bayu

2017-12-01

The aim of this research was to verify the spectrophotometric method to analyze nitrate in water samples using APHA 2012 Section 4500 NO3-B method. The verification parameters used were: linearity, method detection limit, level of quantitation, level of linearity, accuracy and precision. Linearity was obtained by using 0 to 50 mg/L nitrate standard solution and the correlation coefficient of standard calibration linear regression equation was 0.9981. The method detection limit (MDL) was defined as 0,1294 mg/L and limit of quantitation (LOQ) was 0,4117 mg/L. The result of a level of linearity (LOL) was 50 mg/L and nitrate concentration 10 to 50 mg/L was linear with a level of confidence was 99%. The accuracy was determined through recovery value was 109.1907%. The precision value was observed using % relative standard deviation (%RSD) from repeatability and its result was 1.0886%. The tested performance criteria showed that the methodology was verified under the laboratory conditions.

Addressing the unemployment-mortality conundrum: non-linearity is the answer.

PubMed

Bonamore, Giorgio; Carmignani, Fabrizio; Colombo, Emilio

2015-02-01

The effect of unemployment on mortality is the object of a lively literature. However, this literature is characterized by sharply conflicting results. We revisit this issue and suggest that the relationship might be non-linear. We use data for 265 territorial units (regions) within 23 European countries over the period 2000-2012 to estimate a multivariate regression of mortality. The estimating equation allows for a quadratic relationship between unemployment and mortality. We control for various other determinants of mortality at regional and national level and we include region-specific and time-specific fixed effects. The model is also extended to account for the dynamic adjustment of mortality and possible lagged effects of unemployment. We find that the relationship between mortality and unemployment is U shaped. In the benchmark regression, when the unemployment rate is low, at 3%, an increase by one percentage point decreases average mortality by 0.7%. As unemployment increases, the effect decays: when the unemployment rate is 8% (sample average) a further increase by one percentage point decreases average mortality by 0.4%. The effect changes sign, turning from negative to positive, when unemployment is around 17%. When the unemployment rate is 25%, a further increase by one percentage point raises average mortality by 0.4%. Results hold for different causes of death and across different specifications of the estimating equation. We argue that the non-linearity arises because the level of unemployment affects the psychological and behavioural response of individuals to worsening economic conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Parallels between control PDE's (Partial Differential Equations) and systems of ODE's (Ordinary Differential Equations)

NASA Technical Reports Server (NTRS)

Hunt, L. R.; Villarreal, Ramiro

1987-01-01

System theorists understand that the same mathematical objects which determine controllability for nonlinear control systems of ordinary differential equations (ODEs) also determine hypoellipticity for linear partial differentail equations (PDEs). Moreover, almost any study of ODE systems begins with linear systems. It is remarkable that Hormander's paper on hypoellipticity of second order linear p.d.e.'s starts with equations due to Kolmogorov, which are shown to be analogous to the linear PDEs. Eigenvalue placement by state feedback for a controllable linear system can be paralleled for a Kolmogorov equation if an appropriate type of feedback is introduced. Results concerning transformations of nonlinear systems to linear systems are similar to results for transforming a linear PDE to a Kolmogorov equation.

Acidity in DMSO from the embedded cluster integral equation quantum solvation model.

PubMed

Heil, Jochen; Tomazic, Daniel; Egbers, Simon; Kast, Stefan M

2014-04-01

The embedded cluster reference interaction site model (EC-RISM) is applied to the prediction of acidity constants of organic molecules in dimethyl sulfoxide (DMSO) solution. EC-RISM is based on a self-consistent treatment of the solute's electronic structure and the solvent's structure by coupling quantum-chemical calculations with three-dimensional (3D) RISM integral equation theory. We compare available DMSO force fields with reference calculations obtained using the polarizable continuum model (PCM). The results are evaluated statistically using two different approaches to eliminating the proton contribution: a linear regression model and an analysis of pK(a) shifts for compound pairs. Suitable levels of theory for the integral equation methodology are benchmarked. The results are further analyzed and illustrated by visualizing solvent site distribution functions and comparing them with an aqueous environment.

Collisional redistribution of radiation. III - The equation of motion for the correlation function and the scattered spectrum

NASA Technical Reports Server (NTRS)

Burnett, K.; Cooper, J.

1980-01-01

Computations were made of the scattering of monochromatic radiation by a degenerate atom in the binary-collision approximation for field strengths whose products of the Rabi frequency for atomic transition and the duration of a strong collision are much less than 1. An expression of motion for the correlation function is derived which does not exclude the region where thermal correlations may be neglected; the equation is valid outside the quantum-regression regime, and has a straightforward solution for practical cases. Solutions for the weak-field linear response regime are presented in terms of generalized absorption and emission profiles which depend on the indices of the atomic multipoles.

Calibration of diatom-pH-alkalinity methodology for the interpretation of the sedimentary record in Emerald Lake Integrated watershed study. Final report, 6 May 1985-10 October 1986

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holmes, R.W.

1986-10-10

The present study was designed to establish quantitative relationships between lake air-equilibrated pH, alkalinity, and diatoms occurring in the surface sediments in high-elevation Sierra Nevada Lakes. These relationships provided the necessary information to develop predictive equations relating lake pH to the composition of surface-sediment diatom assemblages in 27 study lakes. Using the Hustedt diatom pH classification system, Index B of Renberg and Hellberg, and multiple linear regression analysis, two equations were developed which predict lake pH from the relative abundance of sediment diatoms occurring in each of four diatom pH groupings.

Modelling of capital asset pricing by considering the lagged effects

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Bon, A. Talib bin; Supian, S.

2017-01-01

In this paper the problem of modelling the Capital Asset Pricing Model (CAPM) with the effect of the lagged is discussed. It is assumed that asset returns are analysed influenced by the market return and the return of risk-free assets. To analyse the relationship between asset returns, the market return, and the return of risk-free assets, it is conducted by using a regression equation of CAPM, and regression equation of lagged distributed CAPM. Associated with the regression equation lagged CAPM distributed, this paper also developed a regression equation of Koyck transformation CAPM. Results of development show that the regression equation of Koyck transformation CAPM has advantages, namely simple as it only requires three parameters, compared with regression equation of lagged distributed CAPM.

Influence of Primary Gage Sensitivities on the Convergence of Balance Load Iterations

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred

2012-01-01

The connection between the convergence of wind tunnel balance load iterations and the existence of the primary gage sensitivities of a balance is discussed. First, basic elements of two load iteration equations that the iterative method uses in combination with results of a calibration data analysis for the prediction of balance loads are reviewed. Then, the connection between the primary gage sensitivities, the load format, the gage output format, and the convergence characteristics of the load iteration equation choices is investigated. A new criterion is also introduced that may be used to objectively determine if the primary gage sensitivity of a balance gage exists. Then, it is shown that both load iteration equations will converge as long as a suitable regression model is used for the analysis of the balance calibration data, the combined influence of non linear terms of the regression model is very small, and the primary gage sensitivities of all balance gages exist. The last requirement is fulfilled, e.g., if force balance calibration data is analyzed in force balance format. Finally, it is demonstrated that only one of the two load iteration equation choices, i.e., the iteration equation used by the primary load iteration method, converges if one or more primary gage sensitivities are missing. This situation may occur, e.g., if force balance calibration data is analyzed in direct read format using the original gage outputs. Data from the calibration of a six component force balance is used to illustrate the connection between the convergence of the load iteration equation choices and the existence of the primary gage sensitivities.

Prediction equations for maximal respiratory pressures of Brazilian adolescents.

PubMed

Mendes, Raquel E F; Campos, Tania F; Macêdo, Thalita M F; Borja, Raíssa O; Parreira, Verônica F; Mendonça, Karla M P P

2013-01-01

The literature emphasizes the need for studies to provide reference values and equations able to predict respiratory muscle strength of Brazilian subjects at different ages and from different regions of Brazil. To develop prediction equations for maximal respiratory pressures (MRP) of Brazilian adolescents. In total, 182 healthy adolescents (98 boys and 84 girls) aged between 12 and 18 years, enrolled in public and private schools in the city of Natal-RN, were evaluated using an MVD300 digital manometer (Globalmed®) according to a standardized protocol. Statistical analysis was performed using SPSS Statistics 17.0 software, with a significance level of 5%. Data normality was verified using the Kolmogorov-Smirnov test, and descriptive analysis results were expressed as the mean and standard deviation. To verify the correlation between the MRP and the independent variables (age, weight, height and sex), the Pearson correlation test was used. To obtain the prediction equations, stepwise multiple linear regression was used. The variables height, weight and sex were correlated to MRP. However, weight and sex explained part of the variability of MRP, and the regression analysis in this study indicated that these variables contributed significantly in predicting maximal inspiratory pressure, and only sex contributed significantly to maximal expiratory pressure. This study provides reference values and two models of prediction equations for maximal inspiratory and expiratory pressures and sets the necessary normal lower limits for the assessment of the respiratory muscle strength of Brazilian adolescents.

Validation and application of single breath cardiac output determinations in man

NASA Technical Reports Server (NTRS)

Loeppky, J. A.; Fletcher, E. R.; Myhre, L. G.; Luft, U. C.

1986-01-01

The results of a procedure for estimating cardiac output by a single-breath technique (Qsb), obtained in healthy males during supine rest and during exercise on a bicycle ergometer, were compared with the results on cardiac output obtained by the direct Fick method (QF). The single breath maneuver consisted of a slow exhalation to near residual volume following an inspiration somewhat deeper than normal. The Qsb calculations incorporated an equation of the CO2 dissociation curve and a 'moving spline' sequential curve-fitting technique to calculate the instantaneous R from points on the original expirogram. The resulting linear regression equation indicated a 24-percent underestimation of QF by the Qsb technique. After applying a correction, the Qsb-QF relationship was improved. A subsequent study during upright rest and exercise to 80 percent of VO2(max) in 6 subjects indicated a close linear relationship between Qsb and VO2 for all 95 values obtained, with slope and intercept close to those in published studies in which invasive cardiac output measurements were used.

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Wheat flour dough Alveograph characteristics predicted by Mixolab regression models.

PubMed

Codină, Georgiana Gabriela; Mironeasa, Silvia; Mironeasa, Costel; Popa, Ciprian N; Tamba-Berehoiu, Radiana

2012-02-01

In Romania, the Alveograph is the most used device to evaluate the rheological properties of wheat flour dough, but lately the Mixolab device has begun to play an important role in the breadmaking industry. These two instruments are based on different principles but there are some correlations that can be found between the parameters determined by the Mixolab and the rheological properties of wheat dough measured with the Alveograph. Statistical analysis on 80 wheat flour samples using the backward stepwise multiple regression method showed that Mixolab values using the ‘Chopin S’ protocol (40 samples) and ‘Chopin + ’ protocol (40 samples) can be used to elaborate predictive models for estimating the value of the rheological properties of wheat dough: baking strength (W), dough tenacity (P) and extensibility (L). The correlation analysis confirmed significant findings (P < 0.05 and P < 0.01) between the parameters of wheat dough studied by the Mixolab and its rheological properties measured with the Alveograph. A number of six predictive linear equations were obtained. Linear regression models gave multiple regression coefficients with R²(adjusted) > 0.70 for P, R²(adjusted) > 0.70 for W and R²(adjusted) > 0.38 for L, at a 95% confidence interval. Copyright © 2011 Society of Chemical Industry.

[Predicting the probability of development and progression of primary open angle glaucoma by regression modeling].

PubMed

Likhvantseva, V G; Sokolov, V A; Levanova, O N; Kovelenova, I V

2018-01-01

Prediction of the clinical course of primary open-angle glaucoma (POAG) is one of the main directions in solving the problem of vision loss prevention and stabilization of the pathological process. Simple statistical methods of correlation analysis show the extent of each risk factor's impact, but do not indicate the total impact of these factors in personalized combinations. The relationships between the risk factors is subject to correlation and regression analysis. The regression equation represents the dependence of the mathematical expectation of the resulting sign on the combination of factor signs. To develop a technique for predicting the probability of development and progression of primary open-angle glaucoma based on a personalized combination of risk factors by linear multivariate regression analysis. The study included 66 patients (23 female and 43 male; 132 eyes) with newly diagnosed primary open-angle glaucoma. The control group consisted of 14 patients (8 male and 6 female). Standard ophthalmic examination was supplemented with biochemical study of lacrimal fluid. Concentration of matrix metalloproteinase MMP-2 and MMP-9 in tear fluid in both eyes was determined using 'sandwich' enzyme-linked immunosorbent assay (ELISA) method. The study resulted in the development of regression equations and step-by-step multivariate logistic models that can help calculate the risk of development and progression of POAG. Those models are based on expert evaluation of clinical and instrumental indicators of hydrodynamic disturbances (coefficient of outflow ease - C, volume of intraocular fluid secretion - F, fluctuation of intraocular pressure), as well as personalized morphometric parameters of the retina (central retinal thickness in the macular area) and concentration of MMP-2 and MMP-9 in the tear film. The newly developed regression equations are highly informative and can be a reliable tool for studying of the influence vector and assessment of pathogenic potential of the independent risk factors in specific personalized combinations.

A Solution to Separation and Multicollinearity in Multiple Logistic Regression

PubMed Central

Shen, Jianzhao; Gao, Sujuan

2010-01-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27–38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth’s penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study. PMID:20376286

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

PubMed

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

Accuracy of an equation for estimating age from mandibular third molar development in a Thai population

PubMed Central

Verochana, Karune; Prapayasatok, Sangsom; Mahasantipiya, Phattaranant May; Korwanich, Narumanas

2016-01-01

Purpose This study assessed the accuracy of age estimates produced by a regression equation derived from lower third molar development in a Thai population. Materials and Methods The first part of this study relied on measurements taken from panoramic radiographs of 614 Thai patients aged from 9 to 20. The stage of lower left and right third molar development was observed in each radiograph and a modified Gat score was assigned. Linear regression on this data produced the following equation: Y=9.309+1.673 mG+0.303S (Y=age; mG=modified Gat score; S=sex). In the second part of this study, the predictive accuracy of this equation was evaluated using data from a second set of panoramic radiographs (539 Thai subjects, 9 to 24 years old). Each subject's age was estimated using the above equation and compared against age calculated from a provided date of birth. Estimated and known age data were analyzed using the Pearson correlation coefficient and descriptive statistics. Results Ages estimated from lower left and lower right third molar development stage were significantly correlated with the known ages (r=0.818, 0.808, respectively, P≤0.01). 50% of age estimates in the second part of the study fell within a range of error of ±1 year, while 75% fell within a range of error of ±2 years. The study found that the equation tends to estimate age accurately when individuals are 9 to 20 years of age. Conclusion The equation can be used for age estimation for Thai populations when the individuals are 9 to 20 years of age. PMID:27051633

Accuracy of an equation for estimating age from mandibular third molar development in a Thai population.

PubMed

Verochana, Karune; Prapayasatok, Sangsom; Janhom, Apirum; Mahasantipiya, Phattaranant May; Korwanich, Narumanas

2016-03-01

This study assessed the accuracy of age estimates produced by a regression equation derived from lower third molar development in a Thai population. The first part of this study relied on measurements taken from panoramic radiographs of 614 Thai patients aged from 9 to 20. The stage of lower left and right third molar development was observed in each radiograph and a modified Gat score was assigned. Linear regression on this data produced the following equation: Y=9.309+1.673 mG+0.303S (Y=age; mG=modified Gat score; S=sex). In the second part of this study, the predictive accuracy of this equation was evaluated using data from a second set of panoramic radiographs (539 Thai subjects, 9 to 24 years old). Each subject's age was estimated using the above equation and compared against age calculated from a provided date of birth. Estimated and known age data were analyzed using the Pearson correlation coefficient and descriptive statistics. Ages estimated from lower left and lower right third molar development stage were significantly correlated with the known ages (r=0.818, 0.808, respectively, P≤0.01). 50% of age estimates in the second part of the study fell within a range of error of ±1 year, while 75% fell within a range of error of ±2 years. The study found that the equation tends to estimate age accurately when individuals are 9 to 20 years of age. The equation can be used for age estimation for Thai populations when the individuals are 9 to 20 years of age.

Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

ERIC Educational Resources Information Center

Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

2012-01-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

Estimates of Flow Duration, Mean Flow, and Peak-Discharge Frequency Values for Kansas Stream Locations

USGS Publications Warehouse

Perry, Charles A.; Wolock, David M.; Artman, Joshua C.

2004-01-01

Streamflow statistics of flow duration and peak-discharge frequency were estimated for 4,771 individual locations on streams listed on the 1999 Kansas Surface Water Register. These statistics included the flow-duration values of 90, 75, 50, 25, and 10 percent, as well as the mean flow value. Peak-discharge frequency values were estimated for the 2-, 5-, 10-, 25-, 50-, and 100-year floods. Least-squares multiple regression techniques were used, along with Tobit analyses, to develop equations for estimating flow-duration values of 90, 75, 50, 25, and 10 percent and the mean flow for uncontrolled flow stream locations. The contributing-drainage areas of 149 U.S. Geological Survey streamflow-gaging stations in Kansas and parts of surrounding States that had flow uncontrolled by Federal reservoirs and used in the regression analyses ranged from 2.06 to 12,004 square miles. Logarithmic transformations of climatic and basin data were performed to yield the best linear relation for developing equations to compute flow durations and mean flow. In the regression analyses, the significant climatic and basin characteristics, in order of importance, were contributing-drainage area, mean annual precipitation, mean basin permeability, and mean basin slope. The analyses yielded a model standard error of prediction range of 0.43 logarithmic units for the 90-percent duration analysis to 0.15 logarithmic units for the 10-percent duration analysis. The model standard error of prediction was 0.14 logarithmic units for the mean flow. Regression equations used to estimate peak-discharge frequency values were obtained from a previous report, and estimates for the 2-, 5-, 10-, 25-, 50-, and 100-year floods were determined for this report. The regression equations and an interpolation procedure were used to compute flow durations, mean flow, and estimates of peak-discharge frequency for locations along uncontrolled flow streams on the 1999 Kansas Surface Water Register. Flow durations, mean flow, and peak-discharge frequency values determined at available gaging stations were used to interpolate the regression-estimated flows for the stream locations where available. Streamflow statistics for locations that had uncontrolled flow were interpolated using data from gaging stations weighted according to the drainage area and the bias between the regression-estimated and gaged flow information. On controlled reaches of Kansas streams, the streamflow statistics were interpolated between gaging stations using only gaged data weighted by drainage area.

Analysis of methods to estimate spring flows in a karst aquifer

USGS Publications Warehouse

Sepulveda, N.

2009-01-01

Hydraulically and statistically based methods were analyzed to identify the most reliable method to predict spring flows in a karst aquifer. Measured water levels at nearby observation wells, measured spring pool altitudes, and the distance between observation wells and the spring pool were the parameters used to match measured spring flows. Measured spring flows at six Upper Floridan aquifer springs in central Florida were used to assess the reliability of these methods to predict spring flows. Hydraulically based methods involved the application of the Theis, Hantush-Jacob, and Darcy-Weisbach equations, whereas the statistically based methods were the multiple linear regressions and the technology of artificial neural networks (ANNs). Root mean square errors between measured and predicted spring flows using the Darcy-Weisbach method ranged between 5% and 15% of the measured flows, lower than the 7% to 27% range for the Theis or Hantush-Jacob methods. Flows at all springs were estimated to be turbulent based on the Reynolds number derived from the Darcy-Weisbach equation for conduit flow. The multiple linear regression and the Darcy-Weisbach methods had similar spring flow prediction capabilities. The ANNs provided the lowest residuals between measured and predicted spring flows, ranging from 1.6% to 5.3% of the measured flows. The model prediction efficiency criteria also indicated that the ANNs were the most accurate method predicting spring flows in a karst aquifer. ?? 2008 National Ground Water Association.

Analysis of methods to estimate spring flows in a karst aquifer.

PubMed

Sepúlveda, Nicasio

2009-01-01

Hydraulically and statistically based methods were analyzed to identify the most reliable method to predict spring flows in a karst aquifer. Measured water levels at nearby observation wells, measured spring pool altitudes, and the distance between observation wells and the spring pool were the parameters used to match measured spring flows. Measured spring flows at six Upper Floridan aquifer springs in central Florida were used to assess the reliability of these methods to predict spring flows. Hydraulically based methods involved the application of the Theis, Hantush-Jacob, and Darcy-Weisbach equations, whereas the statistically based methods were the multiple linear regressions and the technology of artificial neural networks (ANNs). Root mean square errors between measured and predicted spring flows using the Darcy-Weisbach method ranged between 5% and 15% of the measured flows, lower than the 7% to 27% range for the Theis or Hantush-Jacob methods. Flows at all springs were estimated to be turbulent based on the Reynolds number derived from the Darcy-Weisbach equation for conduit flow. The multiple linear regression and the Darcy-Weisbach methods had similar spring flow prediction capabilities. The ANNs provided the lowest residuals between measured and predicted spring flows, ranging from 1.6% to 5.3% of the measured flows. The model prediction efficiency criteria also indicated that the ANNs were the most accurate method predicting spring flows in a karst aquifer.

Technical note: Evaluation of urinary purine derivatives in comparison with duodenal purines for estimating rumen microbial protein supply in sheep.

PubMed

Kozloski, G V; Stefanello, C M; Oliveira, L; Filho, H M N Ribeiro; Klopfenstein, T J

2017-02-01

A data set of individual observations was compiled from digestibility trials to examine the relationship between the duodenal purine bases (PB) flow and urinary purine derivatives (PD) excretion and the validity of different equations for estimating rumen microbial N (Nm) supply based on urinary PD in comparison with estimates based on duodenal PB. Trials (8 trials, = 185) were conducted with male sheep fitted with a duodenal T-type cannula, housed in metabolic cages, and fed forage alone or with supplements. The amount of PD excreted in urine was linearly related to the amount of PB flowing to the duodenum ( < 0.05). The intercept of the linear regression was 0.180 mmol/(d·kg), representing the endogenous excretion of PD, and the slope was lower than 1 ( < 0.05), indicating that only 0.43% of the PB in the duodenum was excreted as PD in urine. The Nm supply estimated by either approach was linearly related ( < 0.05) to the digestible OM intake. However, the Nm supply estimated through either of 3 published PD-based equations probably underestimated the Nm supply in sheep.

Technological pedagogical content knowledge of junior high school mathematics teachers in teaching linear equation

NASA Astrophysics Data System (ADS)

Wati, S.; Fitriana, L.; Mardiyana

2018-04-01

Linear equation is one of the topics in mathematics that are considered difficult. Student difficulties of understanding linear equation can be caused by lack of understanding this concept and the way of teachers teach. TPACK is a way to understand the complex relationships between teaching and content taught through the use of specific teaching approaches and supported by the right technology tools. This study aims to identify TPACK of junior high school mathematics teachers in teaching linear equation. The method used in the study was descriptive. In the first phase, a survey using a questionnaire was carried out on 45 junior high school mathematics teachers in teaching linear equation. While in the second phase, the interview involved three teachers. The analysis of data used were quantitative and qualitative technique. The result PCK revealed teachers emphasized developing procedural and conceptual knowledge through reliance on traditional in teaching linear equation. The result of TPK revealed teachers’ lower capacity to deal with the general information and communications technologies goals across the curriculum in teaching linear equation. The result indicated that PowerPoint constitutes TCK modal technological capability in teaching linear equation. The result of TPACK seems to suggest a low standard in teachers’ technological skills across a variety of mathematics education goals in teaching linear equation. This means that the ability of teachers’ TPACK in teaching linear equation still needs to be improved.

Resistance of nickel-chromium-aluminum alloys to cyclic oxidation at 1100 C and 1200 C

NASA Technical Reports Server (NTRS)

Barrett, C. A.; Lowell, C. E.

1976-01-01

Nickel-rich alloys in the Ni-Cr-Al system were evaluated for cyclic oxidation resistance in still air at 1,100 and 1,200 C. A first approximation oxidation attack parameter Ka was derived from specific weight change data involving both a scaling growth constant and a spalling constant. An estimating equation was derived with Ka as a function of the Cr and Al content by multiple linear regression and translated into countour ternary diagrams showing regions of minimum attack. An additional factor inferred from the regression analysis was that alloys melted in zirconia crucibles had significantly greater oxidation resistance than comparable alloys melted otherwise.

A Simple and Specific Stability- Indicating RP-HPLC Method for Routine Assay of Adefovir Dipivoxil in Bulk and Tablet Dosage Form.

PubMed

Darsazan, Bahar; Shafaati, Alireza; Mortazavi, Seyed Alireza; Zarghi, Afshin

2017-01-01

A simple and reliable stability-indicating RP-HPLC method was developed and validated for analysis of adefovir dipivoxil (ADV).The chromatographic separation was performed on a C 18 column using a mixture of acetonitrile-citrate buffer (10 mM at pH 5.2) 36:64 (%v/v) as mobile phase, at a flow rate of 1.5 mL/min. Detection was carried out at 260 nm and a sharp peak was obtained for ADV at a retention time of 5.8 ± 0.01 min. No interferences were observed from its stress degradation products. The method was validated according to the international guidelines. Linear regression analysis of data for the calibration plot showed a linear relationship between peak area and concentration over the range of 0.5-16 μg/mL; the regression coefficient was 0.9999and the linear regression equation was y = 24844x-2941.3. The detection (LOD) and quantification (LOQ) limits were 0.12 and 0.35 μg/mL, respectively. The results proved the method was fast (analysis time less than 7 min), precise, reproducible, and accurate for analysis of ADV over a wide range of concentration. The proposed specific method was used for routine quantification of ADV in pharmaceutical bulk and a tablet dosage form.

Effective Surfactants Blend Concentration Determination for O/W Emulsion Stabilization by Two Nonionic Surfactants by Simple Linear Regression.

PubMed

Hassan, A K

2015-01-01

In this work, O/W emulsion sets were prepared by using different concentrations of two nonionic surfactants. The two surfactants, tween 80(HLB=15.0) and span 80(HLB=4.3) were used in a fixed proportions equal to 0.55:0.45 respectively. HLB value of the surfactants blends were fixed at 10.185. The surfactants blend concentration is starting from 3% up to 19%. For each O/W emulsion set the conductivity was measured at room temperature (25±2°), 40, 50, 60, 70 and 80°. Applying the simple linear regression least squares method statistical analysis to the temperature-conductivity obtained data determines the effective surfactants blend concentration required for preparing the most stable O/W emulsion. These results were confirmed by applying the physical stability centrifugation testing and the phase inversion temperature range measurements. The results indicated that, the relation which represents the most stable O/W emulsion has the strongest direct linear relationship between temperature and conductivity. This relationship is linear up to 80°. This work proves that, the most stable O/W emulsion is determined via the determination of the maximum R² value by applying of the simple linear regression least squares method to the temperature-conductivity obtained data up to 80°, in addition to, the true maximum slope is represented by the equation which has the maximum R² value. Because the conditions would be changed in a more complex formulation, the method of the determination of the effective surfactants blend concentration was verified by applying it for more complex formulations of 2% O/W miconazole nitrate cream and the results indicate its reproducibility.

Optimal Estimation of Clock Values and Trends from Finite Data

NASA Technical Reports Server (NTRS)

Greenhall, Charles

2005-01-01

We show how to solve two problems of optimal linear estimation from a finite set of phase data. Clock noise is modeled as a stochastic process with stationary dth increments. The covariance properties of such a process are contained in the generalized autocovariance function (GACV). We set up two principles for optimal estimation: with the help of the GACV, these principles lead to a set of linear equations for the regression coefficients and some auxiliary parameters. The mean square errors of the estimators are easily calculated. The method can be used to check the results of other methods and to find good suboptimal estimators based on a small subset of the available data.

On estimation of linear transformation models with nested case–control sampling

PubMed Central

Liu, Mengling

2011-01-01

Nested case–control (NCC) sampling is widely used in large epidemiological cohort studies for its cost effectiveness, but its data analysis primarily relies on the Cox proportional hazards model. In this paper, we consider a family of linear transformation models for analyzing NCC data and propose an inverse selection probability weighted estimating equation method for inference. Consistency and asymptotic normality of our estimators for regression coefficients are established. We show that the asymptotic variance has a closed analytic form and can be easily estimated. Numerical studies are conducted to support the theory and an application to the Wilms’ Tumor Study is also given to illustrate the methodology. PMID:21912975

ELASTIC NET FOR COX’S PROPORTIONAL HAZARDS MODEL WITH A SOLUTION PATH ALGORITHM

PubMed Central

Wu, Yichao

2012-01-01

For least squares regression, Efron et al. (2004) proposed an efficient solution path algorithm, the least angle regression (LAR). They showed that a slight modification of the LAR leads to the whole LASSO solution path. Both the LAR and LASSO solution paths are piecewise linear. Recently Wu (2011) extended the LAR to generalized linear models and the quasi-likelihood method. In this work we extend the LAR further to handle Cox’s proportional hazards model. The goal is to develop a solution path algorithm for the elastic net penalty (Zou and Hastie (2005)) in Cox’s proportional hazards model. This goal is achieved in two steps. First we extend the LAR to optimizing the log partial likelihood plus a fixed small ridge term. Then we define a path modification, which leads to the solution path of the elastic net regularized log partial likelihood. Our solution path is exact and piecewise determined by ordinary differential equation systems. PMID:23226932

Predicting major element mineral/melt equilibria - A statistical approach

NASA Technical Reports Server (NTRS)

Hostetler, C. J.; Drake, M. J.

1980-01-01

Empirical equations have been developed for calculating the mole fractions of NaO0.5, MgO, AlO1.5, SiO2, KO0.5, CaO, TiO2, and FeO in a solid phase of initially unknown identity given only the composition of the coexisting silicate melt. The approach involves a linear multivariate regression analysis in which solid composition is expressed as a Taylor series expansion of the liquid compositions. An internally consistent precision of approximately 0.94 is obtained, that is, the nature of the liquidus phase in the input data set can be correctly predicted for approximately 94% of the entries. The composition of the liquidus phase may be calculated to better than 5 mol % absolute. An important feature of this 'generalized solid' model is its reversibility; that is, the dependent and independent variables in the linear multivariate regression may be inverted to permit prediction of the composition of a silicate liquid produced by equilibrium partial melting of a polymineralic source assemblage.

Relationships between age and dental attrition in Australian aboriginals.

PubMed

Richards, L C; Miller, S L

1991-02-01

Tooth wear scores (ratios of exposed dentin to total crown area) were calculated from dental casts of Australian Aboriginal subjects of known age from three populations. Linear regression equations relating attrition scores to age were derived. The slope of the regression line reflects the rate of tooth wear, and the intercept is related to the timing of first exposure of dentin. Differences in morphology between anterior and posterior teeth are reflected in a linear relationship between attrition scores and age for anterior teeth but a logarithmic relationship for posterior teeth. Correlations between age and attrition range from less than 0.40 for third molars (where differences in the eruption and occlusion of the teeth resulted in different patterns of wear) to greater than 0.80 for the premolars and first molars. Because of the generally high correlations between age and attrition, it is possible to estimate age from the extent of tooth wear with confidence limits of the order of +/- 10 years.

Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

PubMed

Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

2017-12-01

We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

Application of factor analysis of infrared spectra for quantitative determination of beta-tricalcium phosphate in calcium hydroxylapatite.

PubMed

Arsenyev, P A; Trezvov, V V; Saratovskaya, N V

1997-01-01

This work represents a method, which allows to determine phase composition of calcium hydroxylapatite basing on its infrared spectrum. The method uses factor analysis of the spectral data of calibration set of samples to determine minimal number of factors required to reproduce the spectra within experimental error. Multiple linear regression is applied to establish correlation between factor scores of calibration standards and their properties. The regression equations can be used to predict the property value of unknown sample. The regression model was built for determination of beta-tricalcium phosphate content in hydroxylapatite. Statistical estimation of quality of the model was carried out. Application of the factor analysis on spectral data allows to increase accuracy of beta-tricalcium phosphate determination and expand the range of determination towards its less concentration. Reproducibility of results is retained.

Morphometric analysis of the relationships between intervertebral disc and vertebral body heights: an anatomical and radiographic study of the human thoracic spine

PubMed Central

Kunkel, Maria E; Herkommer, Andrea; Reinehr, Michael; Böckers, Tobias M; Wilke, Hans-Joachim

2011-01-01

The main aim of this study was to provide anatomical data on the heights of the human intervertebral discs for all levels of the thoracic spine by direct and radiographic measurements. Additionally, the heights of the neighboring vertebral bodies were measured, and the prediction of the disc heights based only on the size of the vertebral bodies was investigated. The anterior (ADH), middle (MDH) and posterior heights (PDH) of the discs were measured directly and on radiographs of 72 spine segments from 30 donors (age 57.43 ± 11.27 years). The radiographic measurement error and the reliability of the measurements were calculated. Linear and non-linear regression analyses were employed for investigation of statistical correlations between the heights of the thoracic disc and vertebrae. Radiographic measurements displayed lower repeatability and were shorter than the anatomical ones (approximately 9% for ADH and 37% for PDH). The thickness of the discs varied from 4.5 to 7.2 mm, with the MDH approximately 22.7% greater. The disc heights showed good correlations with the vertebral body heights (R2, 0.659–0.835, P-values < 0.005; anova), allowing the generation of 10 prediction equations. New data on thoracic disc morphometry were provided in this study. The generated set of regression equations could be used to predict thoracic disc heights from radiographic measurement of the vertebral body height posterior. For the creation of parameterized models of the human thoracic discs, the use of the prediction equations could eliminate the need for direct measurement on intervertebral discs. Moreover, the error produced by radiographic measurements could be reduced at least for the PDH. PMID:21615399

Simulating sunflower canopy temperatures to infer root-zone soil water potential

NASA Technical Reports Server (NTRS)

Choudhury, B. J.; Idso, S. B.

1983-01-01

A soil-plant-atmosphere model for sunflower (Helianthus annuus L.), together with clear sky weather data for several days, is used to study the relationship between canopy temperature and root-zone soil water potential. Considering the empirical dependence of stomatal resistance on insolation, air temperature and leaf water potential, a continuity equation for water flux in the soil-plant-atmosphere system is solved for the leaf water potential. The transpirational flux is calculated using Monteith's combination equation, while the canopy temperature is calculated from the energy balance equation. The simulation shows that, at high soil water potentials, canopy temperature is determined primarily by air and dew point temperatures. These results agree with an empirically derived linear regression equation relating canopy-air temperature differential to air vapor pressure deficit. The model predictions of leaf water potential are also in agreement with observations, indicating that measurements of canopy temperature together with a knowledge of air and dew point temperatures can provide a reliable estimate of the root-zone soil water potential.

Estimation of stature using hand and foot dimensions in Slovak adults.

PubMed

Uhrová, Petra; Beňuš, Radoslav; Masnicová, Soňa; Obertová, Zuzana; Kramárová, Daniela; Kyselicová, Klaudia; Dörnhöferová, Michaela; Bodoriková, Silvia; Neščáková, Eva

2015-03-01

Hand and foot dimensions used for stature estimation help to formulate a biological profile in the process of personal identification. Morphological variability of hands and feet shows the importance of generating population-specific equations to estimate stature. The stature, hand length, hand breadth, foot length and foot breadth of 250 young Slovak males and females, aged 18-24 years, were measured according to standard anthropometric procedures. The data were statistically analyzed using independent t-test for sex and bilateral differences. Pearson correlation coefficient was used for assessing relationship between stature and hand/foot parameters, and subsequently linear regression analysis was used to estimate stature. The results revealed significant sex differences in hand and foot dimensions as well as in stature (p<0.05). There was a positive and statistically significant correlation between stature and all measurements in both sexes (p<0.01). The highest correlation coefficient was found for foot length in males (r=0.71) as well as in females (r=0.63). Regression equations were computed separately for each sex. The accuracy of stature prediction ranged from ±4.6 to ±6.1cm. The results of this study indicate that hand and foot dimension can be used to estimate stature for Slovak for the purpose of forensic field. The regression equations can be of use for stature estimation particularly in cases of dismembered bodies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Techniques for estimating flood-peak discharges from urban basins in Missouri

USGS Publications Warehouse

Becker, L.D.

1986-01-01

Techniques are defined for estimating the magnitude and frequency of future flood peak discharges of rainfall-induced runoff from small urban basins in Missouri. These techniques were developed from an initial analysis of flood records of 96 gaged sites in Missouri and adjacent states. Final regression equations are based on a balanced, representative sampling of 37 gaged sites in Missouri. This sample included 9 statewide urban study sites, 18 urban sites in St. Louis County, and 10 predominantly rural sites statewide. Short-term records were extended on the basis of long-term climatic records and use of a rainfall-runoff model. Linear least-squares regression analyses were used with log-transformed variables to relate flood magnitudes of selected recurrence intervals (dependent variables) to selected drainage basin indexes (independent variables). For gaged urban study sites within the State, the flood peak estimates are from the frequency curves defined from the synthesized long-term discharge records. Flood frequency estimates are made for ungaged sites by using regression equations that require determination of the drainage basin size and either the percentage of impervious area or a basin development factor. Alternative sets of equations are given for the 2-, 5-, 10-, 25-, 50-, and 100-yr recurrence interval floods. The average standard errors of estimate range from about 33% for the 2-yr flood to 26% for the 100-yr flood. The techniques for estimation are applicable to flood flows that are not significantly affected by storage caused by manmade activities. Flood peak discharge estimating equations are considered applicable for sites on basins draining approximately 0.25 to 40 sq mi. (Author 's abstract)

On supporting students' understanding of solving linear equation by using flowchart

NASA Astrophysics Data System (ADS)

Toyib, Muhamad; Kusmayadi, Tri Atmojo; Riyadi

2017-05-01

The aim of this study was to support 7th graders to gradually understand the concepts and procedures of solving linear equation. Thirty-two 7th graders of a Junior High School in Surakarta, Indonesia were involved in this study. Design research was used as the research approach to achieve the aim. A set of learning activities in solving linear equation with one unknown were designed based on Realistic Mathematics Education (RME) approach. The activities were started by playing LEGO to find a linear equation then solve the equation by using flowchart. The results indicate that using the realistic problems, playing LEGO could stimulate students to construct linear equation. Furthermore, Flowchart used to encourage students' reasoning and understanding on the concepts and procedures of solving linear equation with one unknown.

Adjustment of regional regression equations for urban storm-runoff quality using at-site data

USGS Publications Warehouse

Barks, C.S.

1996-01-01

Regional regression equations have been developed to estimate urban storm-runoff loads and mean concentrations using a national data base. Four statistical methods using at-site data to adjust the regional equation predictions were developed to provide better local estimates. The four adjustment procedures are a single-factor adjustment, a regression of the observed data against the predicted values, a regression of the observed values against the predicted values and additional local independent variables, and a weighted combination of a local regression with the regional prediction. Data collected at five representative storm-runoff sites during 22 storms in Little Rock, Arkansas, were used to verify, and, when appropriate, adjust the regional regression equation predictions. Comparison of observed values of stormrunoff loads and mean concentrations to the predicted values from the regional regression equations for nine constituents (chemical oxygen demand, suspended solids, total nitrogen as N, total ammonia plus organic nitrogen as N, total phosphorus as P, dissolved phosphorus as P, total recoverable copper, total recoverable lead, and total recoverable zinc) showed large prediction errors ranging from 63 percent to more than several thousand percent. Prediction errors for 6 of the 18 regional regression equations were less than 100 percent and could be considered reasonable for water-quality prediction equations. The regression adjustment procedure was used to adjust five of the regional equation predictions to improve the predictive accuracy. For seven of the regional equations the observed and the predicted values are not significantly correlated. Thus neither the unadjusted regional equations nor any of the adjustments were appropriate. The mean of the observed values was used as a simple estimator when the regional equation predictions and adjusted predictions were not appropriate.

Profile local linear estimation of generalized semiparametric regression model for longitudinal data.

PubMed

Sun, Yanqing; Sun, Liuquan; Zhou, Jie

2013-07-01

This paper studies the generalized semiparametric regression model for longitudinal data where the covariate effects are constant for some and time-varying for others. Different link functions can be used to allow more flexible modelling of longitudinal data. The nonparametric components of the model are estimated using a local linear estimating equation and the parametric components are estimated through a profile estimating function. The method automatically adjusts for heterogeneity of sampling times, allowing the sampling strategy to depend on the past sampling history as well as possibly time-dependent covariates without specifically model such dependence. A [Formula: see text]-fold cross-validation bandwidth selection is proposed as a working tool for locating an appropriate bandwidth. A criteria for selecting the link function is proposed to provide better fit of the data. Large sample properties of the proposed estimators are investigated. Large sample pointwise and simultaneous confidence intervals for the regression coefficients are constructed. Formal hypothesis testing procedures are proposed to check for the covariate effects and whether the effects are time-varying. A simulation study is conducted to examine the finite sample performances of the proposed estimation and hypothesis testing procedures. The methods are illustrated with a data example.

Unpacking the Complexity of Linear Equations from a Cognitive Load Theory Perspective

ERIC Educational Resources Information Center

Ngu, Bing Hiong; Phan, Huy P.

2016-01-01

The degree of element interactivity determines the complexity and therefore the intrinsic cognitive load of linear equations. The unpacking of linear equations at the level of operational and relational lines allows the classification of linear equations in a hierarchical level of complexity. Mapping similar operational and relational lines across…

Schwarz maps of algebraic linear ordinary differential equations

NASA Astrophysics Data System (ADS)

Sanabria Malagón, Camilo

2017-12-01

A linear ordinary differential equation is called algebraic if all its solution are algebraic over its field of definition. In this paper we solve the problem of finding closed form solution to algebraic linear ordinary differential equations in terms of standard equations. Furthermore, we obtain a method to compute all algebraic linear ordinary differential equations with rational coefficients by studying their associated Schwarz map through the Picard-Vessiot Theory.

Nonlinear GARCH model and 1 / f noise

NASA Astrophysics Data System (ADS)

Kononovicius, A.; Ruseckas, J.

2015-06-01

Auto-regressive conditionally heteroskedastic (ARCH) family models are still used, by practitioners in business and economic policy making, as a conditional volatility forecasting models. Furthermore ARCH models still are attracting an interest of the researchers. In this contribution we consider the well known GARCH(1,1) process and its nonlinear modifications, reminiscent of NGARCH model. We investigate the possibility to reproduce power law statistics, probability density function and power spectral density, using ARCH family models. For this purpose we derive stochastic differential equations from the GARCH processes in consideration. We find the obtained equations to be similar to a general class of stochastic differential equations known to reproduce power law statistics. We show that linear GARCH(1,1) process has power law distribution, but its power spectral density is Brownian noise-like. However, the nonlinear modifications exhibit both power law distribution and power spectral density of the 1 /fβ form, including 1 / f noise.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Flood Nowcasting With Linear Catchment Models, Radar and Kalman Filters

NASA Astrophysics Data System (ADS)

Pegram, Geoff; Sinclair, Scott

A pilot study using real time rainfall data as input to a parsimonious linear distributed flood forecasting model is presented. The aim of the study is to deliver an operational system capable of producing flood forecasts, in real time, for the Mgeni and Mlazi catchments near the city of Durban in South Africa. The forecasts can be made at time steps which are of the order of a fraction of the catchment response time. To this end, the model is formulated in Finite Difference form in an equation similar to an Auto Regressive Moving Average (ARMA) model; it is this formulation which provides the required computational efficiency. The ARMA equation is a discretely coincident form of the State-Space equations that govern the response of an arrangement of linear reservoirs. This results in a functional relationship between the reservoir response con- stants and the ARMA coefficients, which guarantees stationarity of the ARMA model. Input to the model is a combined "Best Estimate" spatial rainfall field, derived from a combination of weather RADAR and Satellite rainfield estimates with point rain- fall given by a network of telemetering raingauges. Several strategies are employed to overcome the uncertainties associated with forecasting. Principle among these are the use of optimal (double Kalman) filtering techniques to update the model states and parameters in response to current streamflow observations and the application of short term forecasting techniques to provide future estimates of the rainfield as input to the model.

Geographical variation of cerebrovascular disease in New York State: the correlation with income

PubMed Central

Han, Daikwon; Carrow, Shannon S; Rogerson, Peter A; Munschauer, Frederick E

2005-01-01

Background Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predicted by a nonlinear model using income as a surrogate socioeconomic risk factor. Results We used spatial clustering methods to identify areas with high and low prevalence of cerebrovascular disease at the ZIP code level after smoothing rates and correcting for edge effects; geographic locations of high and low clusters of cerebrovascular disease in New York State were identified with and without income adjustment. To examine effects of income, we calculated the excess number of cases using a non-linear regression with cerebrovascular disease rates taken as the dependent variable and income and income squared taken as independent variables. The resulting regression equation was: excess rate = 32.075 - 1.22*10-4(income) + 8.068*10-10(income2), and both income and income squared variables were significant at the 0.01 level. When income was included as a covariate in the non-linear regression, the number and size of clusters of high cerebrovascular disease prevalence decreased. Some 87 ZIP codes exceeded the critical value of the local statistic yielding a relative risk of 1.2. The majority of low cerebrovascular disease prevalence geographic clusters disappeared when the non-linear income effect was included. For linear regression, the excess rate of cerebrovascular disease falls with income; each $10,000 increase in median income of each ZIP code resulted in an average reduction of 3.83 observed cases. The significant nonlinear effect indicates a lessening of this income effect with increasing income. Conclusion Income is a non-linear predictor of excess cerebrovascular disease rates, with both low and high observed cerebrovascular disease rate areas associated with higher income. Income alone explains a significant amount of the geographical variance in cerebrovascular disease across New York State since both high and low clusters of cerebrovascular disease dissipate or disappear with income adjustment. Geographical modeling, including non-linear effects of income, may allow for better identification of other non-traditional risk factors. PMID:16242043

A regularization corrected score method for nonlinear regression models with covariate error.

PubMed

Zucker, David M; Gorfine, Malka; Li, Yi; Tadesse, Mahlet G; Spiegelman, Donna

2013-03-01

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer. Copyright © 2013, The International Biometric Society.

New exact solutions of the Tzitzéica-type equations in non-linear optics using the expa function method

NASA Astrophysics Data System (ADS)

Hosseini, K.; Ayati, Z.; Ansari, R.

2018-04-01

One specific class of non-linear evolution equations, known as the Tzitzéica-type equations, has received great attention from a group of researchers involved in non-linear science. In this article, new exact solutions of the Tzitzéica-type equations arising in non-linear optics, including the Tzitzéica, Dodd-Bullough-Mikhailov and Tzitzéica-Dodd-Bullough equations, are obtained using the expa function method. The integration technique actually suggests a useful and reliable method to extract new exact solutions of a wide range of non-linear evolution equations.

Computer Mapping of Water Quality in Saginaw Bay with LANDSAT Digital Data

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator); Shah, N. J.; Smith, V. E.; Mckeon, J. B.

1976-01-01

The author has identified the following significant results. LANDSAT digital data and ground truth measurements for Saginaw Bay (Lake Huron), Michigan, for 31 July 1975 were correlated by stepwise linear regression and the resulting equations used to estimate invisible water quality parameters in nonsampled areas. Chloride, conductivity, total Kjeldahl nitrogen, total phosphorus, and chlorophyll a were best correlated with the ratio of LANDSAT Band 4 to Band 5. Temperature and Secchi depth correlate best with Band 5.

Quality-of-water data and statistical summary for selected coal-mined strip pits in Crawford and Cherokee counties, southeastern Kansas

USGS Publications Warehouse

Pope, Larry M.; Diaz, A.M.

1982-01-01

Quality-of-water data, collected October 21-23, 1980, and a statistical summary are presented for 42 coal-mined strip pits in Crawford and Cherokee Counties, Southeastern Kansas. The statistical summary includes minimum and maximum observed values , mean, and standard deviation. Simple linear regression equations relating specific conductance, dissolved solids, and acidity to concentrations of dissolved solids, sulfate, calcium, and magnesium, potassium, aluminum, and iron are also presented. (USGS)

Local Linear Observed-Score Equating

ERIC Educational Resources Information Center

Wiberg, Marie; van der Linden, Wim J.

2011-01-01

Two methods of local linear observed-score equating for use with anchor-test and single-group designs are introduced. In an empirical study, the two methods were compared with the current traditional linear methods for observed-score equating. As a criterion, the bias in the equated scores relative to true equating based on Lord's (1980)…

Modeling The Skeleton Weight of an Adult Caucasian Man.

PubMed

Avtandilashvili, Maia; Tolmachev, Sergei Y

2018-05-17

The reference value for the skeleton weight of an adult male (10.5 kg) recommended by the International Commission on Radiological Protection in Publication 70 is based on weights of dissected skeletons from 44 individuals, including two U.S. Transuranium and Uranium Registries whole-body donors. The International Commission on Radiological Protection analysis of anatomical data from 31 individuals with known values of body height demonstrated significant correlation between skeleton weight and body height. The corresponding regression equation, Wskel (kg) = -10.7 + 0.119 × H (cm), published in International Commission on Radiological Protection Publication 70 is typically used to estimate the skeleton weight from body height. Currently, the U.S. Transuranium and Uranium Registries holds data on individual bone weights from a total of 40 male whole-body donors, which has provided a unique opportunity to update the International Commission on Radiological Protection skeleton weight vs. body height equation. The original International Commission on Radiological Protection Publication 70 and the new U.S. Transuranium and Uranium Registries data were combined in a set of 69 data points representing a group of 33- to 95-y-old individuals with body heights and skeleton weights ranging from 155 to 188 cm and 6.5 to 13.4 kg, respectively. Data were fitted with a linear least-squares regression. A significant correlation between the two parameters was observed (r = 0.28), and an updated skeleton weight vs. body height equation was derived: Wskel (kg) = -6.5 + 0.093 × H (cm). In addition, a correlation of skeleton weight with multiple variables including body height, body weight, and age was evaluated using multiple regression analysis, and a corresponding fit equation was derived: Wskel (kg) = -0.25 + 0.046 × H (cm) + 0.036 × Wbody (kg) - 0.012 × A (y). These equations will be used to estimate skeleton weights and, ultimately, total skeletal actinide activities for biokinetic modeling of U.S. Transuranium and Uranium Registries partial-body donation cases.

Students’ difficulties in solving linear equation problems

NASA Astrophysics Data System (ADS)

Wati, S.; Fitriana, L.; Mardiyana

2018-03-01

A linear equation is an algebra material that exists in junior high school to university. It is a very important material for students in order to learn more advanced mathematics topics. Therefore, linear equation material is essential to be mastered. However, the result of 2016 national examination in Indonesia showed that students’ achievement in solving linear equation problem was low. This fact became a background to investigate students’ difficulties in solving linear equation problems. This study used qualitative descriptive method. An individual written test on linear equation tasks was administered, followed by interviews. Twenty-one sample students of grade VIII of SMPIT Insan Kamil Karanganyar did the written test, and 6 of them were interviewed afterward. The result showed that students with high mathematics achievement donot have difficulties, students with medium mathematics achievement have factual difficulties, and students with low mathematics achievement have factual, conceptual, operational, and principle difficulties. Based on the result there is a need of meaningfulness teaching strategy to help students to overcome difficulties in solving linear equation problems.

A theory of fine structure image models with an application to detection and classification of dementia

PubMed Central

Penn, Richard; Werner, Michael; Thomas, Justin

2015-01-01

Background Estimation of stochastic process models from data is a common application of time series analysis methods. Such system identification processes are often cast as hypothesis testing exercises whose intent is to estimate model parameters and test them for statistical significance. Ordinary least squares (OLS) regression and the Levenberg-Marquardt algorithm (LMA) have proven invaluable computational tools for models being described by non-homogeneous, linear, stationary, ordinary differential equations. Methods In this paper we extend stochastic model identification to linear, stationary, partial differential equations in two independent variables (2D) and show that OLS and LMA apply equally well to these systems. The method employs an original nonparametric statistic as a test for the significance of estimated parameters. Results We show gray scale and color images are special cases of 2D systems satisfying a particular autoregressive partial difference equation which estimates an analogous partial differential equation. Several applications to medical image modeling and classification illustrate the method by correctly classifying demented and normal OLS models of axial magnetic resonance brain scans according to subject Mini Mental State Exam (MMSE) scores. Comparison with 13 image classifiers from the literature indicates our classifier is at least 14 times faster than any of them and has a classification accuracy better than all but one. Conclusions Our modeling method applies to any linear, stationary, partial differential equation and the method is readily extended to 3D whole-organ systems. Further, in addition to being a robust image classifier, estimated image models offer insights into which parameters carry the most diagnostic image information and thereby suggest finer divisions could be made within a class. Image models can be estimated in milliseconds which translate to whole-organ models in seconds; such runtimes could make real-time medicine and surgery modeling possible. PMID:26029638

On solutions of the fifth-order dispersive equations with porous medium type non-linearity

NASA Astrophysics Data System (ADS)

Kocak, Huseyin; Pinar, Zehra

2018-07-01

In this work, we focus on obtaining the exact solutions of the fifth-order semi-linear and non-linear dispersive partial differential equations, which have the second-order diffusion-like (porous-type) non-linearity. The proposed equations were not studied in the literature in the sense of the exact solutions. We reveal solutions of the proposed equations using the classical Riccati equations method. The obtained exact solutions, which can play a key role to simulate non-linear waves in the medium with dispersion and diffusion, are illustrated and discussed in details.

Resting Energy Expenditure Prediction in Recreational Athletes of 18–35 Years: Confirmation of Cunningham Equation and an Improved Weight-Based Alternative

PubMed Central

ten Haaf, Twan; Weijs, Peter J. M.

2014-01-01

Introduction Resting energy expenditure (REE) is expected to be higher in athletes because of their relatively high fat free mass (FFM). Therefore, REE predictive equation for recreational athletes may be required. The aim of this study was to validate existing REE predictive equations and to develop a new recreational athlete specific equation. Methods 90 (53M, 37F) adult athletes, exercising on average 9.1±5.0 hours a week and 5.0±1.8 times a week, were included. REE was measured using indirect calorimetry (Vmax Encore n29), FFM and FM were measured using air displacement plethysmography. Multiple linear regression analysis was used to develop a new FFM-based and weight-based REE predictive equation. The percentage accurate predictions (within 10% of measured REE), percentage bias, root mean square error and limits of agreement were calculated. Results The Cunningham equation and the new weight-based equation and the new FFM-based equation performed equally well. De Lorenzo's equation predicted REE less accurate, but better than the other generally used REE predictive equations. Harris-Benedict, WHO, Schofield, Mifflin and Owen all showed less than 50% accuracy. Conclusion For a population of (Dutch) recreational athletes, the REE can accurately be predicted with the existing Cunningham equation. Since body composition measurement is not always possible, and other generally used equations fail, the new weight-based equation is advised for use in sports nutrition. PMID:25275434

Determination of suitable drying curve model for bread moisture loss during baking

NASA Astrophysics Data System (ADS)

Soleimani Pour-Damanab, A. R.; Jafary, A.; Rafiee, S.

2013-03-01

This study presents mathematical modelling of bread moisture loss or drying during baking in a conventional bread baking process. In order to estimate and select the appropriate moisture loss curve equation, 11 different models, semi-theoretical and empirical, were applied to the experimental data and compared according to their correlation coefficients, chi-squared test and root mean square error which were predicted by nonlinear regression analysis. Consequently, of all the drying models, a Page model was selected as the best one, according to the correlation coefficients, chi-squared test, and root mean square error values and its simplicity. Mean absolute estimation error of the proposed model by linear regression analysis for natural and forced convection modes was 2.43, 4.74%, respectively.

Dry season mean monthly flow and harmonic mean flow regression equations for selected ungaged basins in Arkansas

USGS Publications Warehouse

Breaker, Brian K.

2015-01-01

Equations for two regions were found to be statistically significant for developing regression equations for estimating harmonic mean flows at ungaged basins; thus, equations are applicable only to streams in those respective regions in Arkansas. Regression equations for dry season mean monthly flows are applicable only to streams located throughout Arkansas. All regression equations are applicable only to unaltered streams where flows were not significantly affected by regulation, diversion, or urbanization. The median number of years used for dry season mean monthly flow calculation was 43, and the median number of years used for harmonic mean flow calculations was 34 for region 1 and 43 for region 2.

Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans

PubMed Central

Villa, Chiara; Brůžek, Jaroslav

2017-01-01

Background Estimating volumes and masses of total body components is important for the study and treatment monitoring of nutrition and nutrition-related disorders, cancer, joint replacement, energy-expenditure and exercise physiology. While several equations have been offered for estimating total body components from MRI slices, no reliable and tested method exists for CT scans. For the first time, body composition data was derived from 41 high-resolution whole-body CT scans. From these data, we defined equations for estimating volumes and masses of total body AT and LT from corresponding tissue areas measured in selected CT scan slices. Methods We present a new semi-automatic approach to defining the density cutoff between adipose tissue (AT) and lean tissue (LT) in such material. An intra-class correlation coefficient (ICC) was used to validate the method. The equations for estimating the whole-body composition volume and mass from areas measured in selected slices were modeled with ordinary least squares (OLS) linear regressions and support vector machine regression (SVMR). Results and Discussion The best predictive equation for total body AT volume was based on the AT area of a single slice located between the 4th and 5th lumbar vertebrae (L4-L5) and produced lower prediction errors (|PE| = 1.86 liters, %PE = 8.77) than previous equations also based on CT scans. The LT area of the mid-thigh provided the lowest prediction errors (|PE| = 2.52 liters, %PE = 7.08) for estimating whole-body LT volume. We also present equations to predict total body AT and LT masses from a slice located at L4-L5 that resulted in reduced error compared with the previously published equations based on CT scans. The multislice SVMR predictor gave the theoretical upper limit for prediction precision of volumes and cross-validated the results. PMID:28533960

Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans.

PubMed

Lacoste Jeanson, Alizé; Dupej, Ján; Villa, Chiara; Brůžek, Jaroslav

2017-01-01

Estimating volumes and masses of total body components is important for the study and treatment monitoring of nutrition and nutrition-related disorders, cancer, joint replacement, energy-expenditure and exercise physiology. While several equations have been offered for estimating total body components from MRI slices, no reliable and tested method exists for CT scans. For the first time, body composition data was derived from 41 high-resolution whole-body CT scans. From these data, we defined equations for estimating volumes and masses of total body AT and LT from corresponding tissue areas measured in selected CT scan slices. We present a new semi-automatic approach to defining the density cutoff between adipose tissue (AT) and lean tissue (LT) in such material. An intra-class correlation coefficient (ICC) was used to validate the method. The equations for estimating the whole-body composition volume and mass from areas measured in selected slices were modeled with ordinary least squares (OLS) linear regressions and support vector machine regression (SVMR). The best predictive equation for total body AT volume was based on the AT area of a single slice located between the 4th and 5th lumbar vertebrae (L4-L5) and produced lower prediction errors (|PE| = 1.86 liters, %PE = 8.77) than previous equations also based on CT scans. The LT area of the mid-thigh provided the lowest prediction errors (|PE| = 2.52 liters, %PE = 7.08) for estimating whole-body LT volume. We also present equations to predict total body AT and LT masses from a slice located at L4-L5 that resulted in reduced error compared with the previously published equations based on CT scans. The multislice SVMR predictor gave the theoretical upper limit for prediction precision of volumes and cross-validated the results.

Application of LANDSAT to the surveillance and control of lake eutrophication in the Great Lakes basin. [Saginaw Bay, Michigan and Wisconsin

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Computer techniques were developed for mapping water quality parameters from LANDSAT data, using surface samples collected in an ongoing survey of water quality in Saginaw Bay. Chemical and biological parameters were measured on 31 July 1975 at 16 bay stations in concert with the LANDSAT overflight. Application of stepwise linear regression bands to nine of these parameters and corresponding LANDSAT measurements for bands 4 and 5 only resulted in regression correlation coefficients that varied from 0.94 for temperature to 0.73 for Secchi depth. Regression equations expressed with the pair of bands 4 and 5, rather than the ratio band 4/band 5, provided higher correlation coefficients for all the water quality parameters studied (temperature, Secchi depth, chloride, conductivity, total kjeldahl nitrogen, total phosphorus, chlorophyll a, total solids, and suspended solids).

Pulmonary Hypertension and Computed Tomography Measurement of Small Pulmonary Vessels in Severe Emphysema

PubMed Central

Matsuoka, Shin; Washko, George R.; Yamashiro, Tsuneo; Estepar, Raul San Jose; Diaz, Alejandro; Silverman, Edwin K.; Hoffman, Eric; Fessler, Henry E.; Criner, Gerard J.; Marchetti, Nathaniel; Scharf, Steven M.; Martinez, Fernando J.; Reilly, John J.; Hatabu, Hiroto

2010-01-01

Rationale: Vascular alteration of small pulmonary vessels is one of the characteristic features of pulmonary hypertension in chronic obstructive pulmonary disease. The in vivo relationship between pulmonary hypertension and morphological alteration of the small pulmonary vessels has not been assessed in patients with severe emphysema. Objectives: We evaluated the correlation of total cross-sectional area of small pulmonary vessels (CSA) assessed on computed tomography (CT) scans with the degree of pulmonary hypertension estimated by right heart catheterization. Methods: In 79 patients with severe emphysema enrolled in the National Emphysema Treatment Trial (NETT), we measured CSA less than 5 mm2 (CSA<5) and 5 to 10 mm2 (CSA5−10), and calculated the percentage of total CSA for the lung area (%CSA<5 and %CSA5–10, respectively). The correlations of %CSA<5 and %CSA5–10 with pulmonary arterial mean pressure (\\documentclass[10pt]{article} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{pmc} \\usepackage[Euler]{upgreek} \\pagestyle{empty} \\oddsidemargin -1.0in \\begin{document} \\begin{equation*}\\overline{Ppa}\\end{equation*}\\end{document}) obtained by right heart catheterization were evaluated. Multiple linear regression analysis using \\documentclass[10pt]{article} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{pmc} \\usepackage[Euler]{upgreek} \\pagestyle{empty} \\oddsidemargin -1.0in \\begin{document} \\begin{equation*}\\overline{Ppa}\\end{equation*}\\end{document} as the dependent outcome was also performed. Measurements and Main Results: The %CSA<5 had a significant negative correlation with \\documentclass[10pt]{article} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{pmc} \\usepackage[Euler]{upgreek} \\pagestyle{empty} \\oddsidemargin -1.0in \\begin{document} \\begin{equation*}\\overline{Ppa}\\end{equation*}\\end{document} (r = −0.512, P < 0.0001), whereas the correlation between %CSA5–10 and \\documentclass[10pt]{article} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{pmc} \\usepackage[Euler]{upgreek} \\pagestyle{empty} \\oddsidemargin -1.0in \\begin{document} \\begin{equation*}\\overline{Ppa}\\end{equation*}\\end{document} did not reach statistical significance (r = −0.196, P = 0.083). Multiple linear regression analysis showed that %CSA<5 and diffusing capacity of carbon monoxide (DlCO) % predicted were independent predictors of \\documentclass[10pt]{article} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{pmc} \\usepackage[Euler]{upgreek} \\pagestyle{empty} \\oddsidemargin -1.0in \\begin{document} \\begin{equation*}\\overline{Ppa}\\end{equation*}\\end{document} (r2 = 0.541): %CSA <5 (P < 0.0001), and DlCO % predicted (P = 0.022). Conclusions: The %CSA<5 measured on CT images is significantly correlated to \\documentclass[10pt]{article} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{pmc} \\usepackage[Euler]{upgreek} \\pagestyle{empty} \\oddsidemargin -1.0in \\begin{document} \\begin{equation*}\\overline{Ppa}\\end{equation*}\\end{document} in severe emphysema and can estimate the degree of pulmonary hypertension. PMID:19875683

On differential operators generating iterative systems of linear ODEs of maximal symmetry algebra

NASA Astrophysics Data System (ADS)

Ndogmo, J. C.

2017-06-01

Although every iterative scalar linear ordinary differential equation is of maximal symmetry algebra, the situation is different and far more complex for systems of linear ordinary differential equations, and an iterative system of linear equations need not be of maximal symmetry algebra. We illustrate these facts by examples and derive families of vector differential operators whose iterations are all linear systems of equations of maximal symmetry algebra. Some consequences of these results are also discussed.

Low-flow, base-flow, and mean-flow regression equations for Pennsylvania streams

USGS Publications Warehouse

Stuckey, Marla H.

2006-01-01

Low-flow, base-flow, and mean-flow characteristics are an important part of assessing water resources in a watershed. These streamflow characteristics can be used by watershed planners and regulators to determine water availability, water-use allocations, assimilative capacities of streams, and aquatic-habitat needs. Streamflow characteristics are commonly predicted by use of regression equations when a nearby streamflow-gaging station is not available. Regression equations for predicting low-flow, base-flow, and mean-flow characteristics for Pennsylvania streams were developed from data collected at 293 continuous- and partial-record streamflow-gaging stations with flow unaffected by upstream regulation, diversion, or mining. Continuous-record stations used in the regression analysis had 9 years or more of data, and partial-record stations used had seven or more measurements collected during base-flow conditions. The state was divided into five low-flow regions and regional regression equations were developed for the 7-day, 10-year; 7-day, 2-year; 30-day, 10-year; 30-day, 2-year; and 90-day, 10-year low flows using generalized least-squares regression. Statewide regression equations were developed for the 10-year, 25-year, and 50-year base flows using generalized least-squares regression. Statewide regression equations were developed for harmonic mean and mean annual flow using weighted least-squares regression. Basin characteristics found to be significant explanatory variables at the 95-percent confidence level for one or more regression equations were drainage area, basin slope, thickness of soil, stream density, mean annual precipitation, mean elevation, and the percentage of glaciation, carbonate bedrock, forested area, and urban area within a basin. Standard errors of prediction ranged from 33 to 66 percent for the n-day, T-year low flows; 21 to 23 percent for the base flows; and 12 to 38 percent for the mean annual flow and harmonic mean, respectively. The regression equations are not valid in watersheds with upstream regulation, diversions, or mining activities. Watersheds with karst features need close examination as to the applicability of the regression-equation results.

The Relationship Between Surface Curvature and Abdominal Aortic Aneurysm Wall Stress.

PubMed

de Galarreta, Sergio Ruiz; Cazón, Aitor; Antón, Raúl; Finol, Ender A

2017-08-01

The maximum diameter (MD) criterion is the most important factor when predicting risk of rupture of abdominal aortic aneurysms (AAAs). An elevated wall stress has also been linked to a high risk of aneurysm rupture, yet is an uncommon clinical practice to compute AAA wall stress. The purpose of this study is to assess whether other characteristics of the AAA geometry are statistically correlated with wall stress. Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis (FEA). These models were subsequently used to estimate wall stress and maximum diameter and to evaluate the spatial distributions of wall thickness, cross-sectional diameter, mean curvature, and Gaussian curvature. Data analysis consisted of statistical correlations of the aforementioned geometry metrics with wall stress for the 30 AAA inner and outer wall surfaces. In addition, a linear regression analysis was performed with all the AAA wall surfaces to quantify the relationship of the geometric indices with wall stress. These analyses indicated that while all the geometry metrics have statistically significant correlations with wall stress, the local mean curvature (LMC) exhibits the highest average Pearson's correlation coefficient for both inner and outer wall surfaces. The linear regression analysis revealed coefficients of determination for the outer and inner wall surfaces of 0.712 and 0.516, respectively, with LMC having the largest effect on the linear regression equation with wall stress. This work underscores the importance of evaluating AAA mean wall curvature as a potential surrogate for wall stress.

Estimating global per-capita carbon emissions with VIIRS nighttime lights satellite data

NASA Astrophysics Data System (ADS)

Jasmin, T.; Desai, A. R.; Pierce, R. B.

2015-12-01

With the launch of the Suomi National Polar-orbiting Partnership (NPP) satellite in November 2011, we now have nighttime lights remote sensing capability vastly improved over the predecessor Defense Meteorological Satellite Program (DMSP), owing to improved spatial and radiometric resolution provided by the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band (DNB) along with technology improvements in data transfer, processing, and storage. This development opens doors for improving novel scientific applications utilizing remotely sensed low-level visible light, for purposes ranging from estimating population to inferring factors relating to economic development. For example, the success of future international agreements to reduce greenhouse gas emissions will be dependent on mechanisms to monitor remotely for compliance. Here, we discuss implementation and evaluation of the VRCE system (VIIRS Remote Carbon Estimates), developed at the University of Wisconsin-Madison, which provides monthly independent, unbiased estimates of per-capita carbon emissions. Cloud-free global composites of Earth nocturnal lighting are generated from VIIRS DNB at full spatial resolution (750 meter). A population equation is derived from a linear regression of DNB radiance sums at state level to U.S. Census data. CO2 emissions are derived from a linear regression of VIIRS DNB radiance sums to U.S. Department of Energy emission estimates. Regional coefficients for factors such as percentage of energy use from renewable sources are factored in, and together these equations are used to generate per-capita CO2 emission estimates at the country level.

Normative values for the spine shape parameters using 3D standing analysis from a database of 268 asymptomatic Caucasian and Japanese subjects.

PubMed

Le Huec, Jean Charles; Hasegawa, Kazuhiro

2016-11-01

Sagittal balance analysis has gained importance and the measure of the radiographic spinopelvic parameters is now a routine part of many interventions of spine surgery. Indeed, surgical correction of lumbar lordosis must be proportional to the pelvic incidence (PI). The compensatory mechanisms [pelvic retroversion with increased pelvic tilt (PT) and decreased thoracic kyphosis] spontaneously reverse after successful surgery. This study is the first to provide 3D standing spinopelvic reference values from a large database of Caucasian (n = 137) and Japanese (n = 131) asymptomatic subjects. The key spinopelvic parameters [e.g., PI, PT, sacral slope (SS)] were comparable in Japanese and Caucasian populations. Three equations, namely lumbar lordosis based on PI, PT based on PI and SS based on PI, were calculated after linear regression modeling and were comparable in both populations: lumbar lordosis (L1-S1) = 0.54*PI + 27.6, PT = 0.44*PI - 11.4 and SS = 0.54*PI + 11.90. We showed that the key spinopelvic parameters obtained from a large database of healthy subjects were comparable for Causasian and Japanese populations. The normative values provided in this study and the equations obtained after linear regression modeling could help to estimate pre-operatively the lumbar lordosis restoration and could be also used as guidelines for spinopelvic sagittal balance.

Development and validation of a predictive equation for lean body mass in children and adolescents.

PubMed

Foster, Bethany J; Platt, Robert W; Zemel, Babette S

2012-05-01

Lean body mass (LBM) is not easy to measure directly in the field or clinical setting. Equations to predict LBM from simple anthropometric measures, which account for the differing contributions of fat and lean to body weight at different ages and levels of adiposity, would be useful to both human biologists and clinicians. To develop and validate equations to predict LBM in children and adolescents across the entire range of the adiposity spectrum. Dual energy X-ray absorptiometry was used to measure LBM in 836 healthy children (437 females) and linear regression was used to develop sex-specific equations to estimate LBM from height, weight, age, body mass index (BMI) for age z-score and population ancestry. Equations were validated using bootstrapping methods and in a local independent sample of 332 children and in national data collected by NHANES. The mean difference between measured and predicted LBM was - 0.12% (95% limits of agreement - 11.3% to 8.5%) for males and - 0.14% ( - 11.9% to 10.9%) for females. Equations performed equally well across the entire adiposity spectrum, as estimated by BMI z-score. Validation indicated no over-fitting. LBM was predicted within 5% of measured LBM in the validation sample. The equations estimate LBM accurately from simple anthropometric measures.

A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty

USGS Publications Warehouse

Friedel, Michael J.

2011-01-01

This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimizes root-mean squared and unit errors for the evolution of fittest equations. An optimization technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterized as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modeling approach can be applied to nonlinear multivariate problems in all fields of study.

Transformation matrices between non-linear and linear differential equations

NASA Technical Reports Server (NTRS)

Sartain, R. L.

1983-01-01

In the linearization of systems of non-linear differential equations, those systems which can be exactly transformed into the second order linear differential equation Y"-AY'-BY=0 where Y, Y', and Y" are n x 1 vectors and A and B are constant n x n matrices of real numbers were considered. The 2n x 2n matrix was used to transform the above matrix equation into the first order matrix equation X' = MX. Specially the matrix M and the conditions which will diagonalize or triangularize M were studied. Transformation matrices P and P sub -1 were used to accomplish this diagonalization or triangularization to return to the solution of the second order matrix differential equation system from the first order system.

Effects of oxygen content on the oxidation process of Si-containing steel during anisothermal heating

NASA Astrophysics Data System (ADS)

Yuan, Qing; Xu, Guang; Liang, Wei-cheng; He, Bei; Zhou, Ming-xing

2018-02-01

The oxidizing behavior of Si-containing steel was investigated in an O2 and N2 binary-component gas with oxygen contents ranging between 0.5vol% and 4.0vol% under anisothermal-oxidation conditions. A simultaneous thermal analyzer was employed to simulate the heating process of Si-containing steel in industrial reheating furnaces. The oxidation gas mixtures were introduced from the commencement of heating. The results show that the oxidizing rate remains constant in the isothermal holding process at high temperatures; therefore, the mass change versus time presents a linear law. A linear relation also exists between the oxidizing rate and the oxygen content. Using the linear regression equation, the oxidation rate at different oxygen contents can be predicted. In addition, the relationship between the total mass gain and the oxygen content is linear; thus, the total mass gain at oxygen contents between 0.5vol%-4.0vol% can be determined. These results enrich the theoretical studies of the oxidation process in Si-containing steels.

Using Bar Velocity to Predict the Maximum Dynamic Strength in the Half-Squat Exercise.

PubMed

Loturco, Irineu; Pereira, Lucas A; Cal Abad, Cesar C; Gil, Saulo; Kitamura, Katia; Kobal, Ronaldo; Nakamura, Fábio Y

2016-07-01

To determine whether athletes from different sport disciplines present similar mean propulsive velocity (MPV) in the half-squat (HS) during submaximal and maximal tests, enabling prediction of 1-repetition maximum (1-RM) from MPV at any given submaximal load. Sixty-four male athletes, comprising American football, rugby, and soccer players; sprinters and jumpers; and combat-sport strikers attended 2 testing sessions separated by 2-4 wk. On the first visit, a standardized 1-RM test was performed. On the second, athletes performed HSs on Smith-machine equipment, using relative percentages of 1-RM to determine the respective MPV of submaximal and maximal loads. Linear regression established the relationship between MPV and percentage of 1-RM. A very strong linear relationship (R2 ≈ .96) was observed between the MPV and the percentages of HS 1-RM, resulting in the following equation: %HS 1-RM = -105.05 × MPV + 131.75. The MPV at HS 1-RM was ~0.3 m/s. This equation can be used to predict HS 1-RM on a Smith machine with a high degree of accuracy.

Quasi-Newton methods for parameter estimation in functional differential equations

NASA Technical Reports Server (NTRS)

Brewer, Dennis W.

1988-01-01

A state-space approach to parameter estimation in linear functional differential equations is developed using the theory of linear evolution equations. A locally convergent quasi-Newton type algorithm is applied to distributed systems with particular emphasis on parameters that induce unbounded perturbations of the state. The algorithm is computationally implemented on several functional differential equations, including coefficient and delay estimation in linear delay-differential equations.

Estimation of Total Length of Femur from its Proximal and Distal Segmental Measurements of Disarticulated Femur Bones of Nepalese Population using Regression Equation Method.

PubMed

Khanal, Laxman; Shah, Sandip; Koirala, Sarun

2017-03-01

Length of long bones is taken as an important contributor for estimating one of the four elements of forensic anthropology i.e., stature of the individual. Since physical characteristics of the individual differ among different groups of population, population specific studies are needed for estimating the total length of femur from its segment measurements. Since femur is not always recovered intact in forensic cases, it was the aim of this study to derive regression equations from measurements of proximal and distal fragments in Nepalese population. A cross-sectional study was done among 60 dry femora (30 from each side) without sex determination in anthropometry laboratory. Along with maximum femoral length, four proximal and four distal segmental measurements were measured following the standard method with the help of osteometric board, measuring tape and digital Vernier's caliper. Bones with gross defects were excluded from the study. Measured values were recorded separately for right and left side. Statistical Package for Social Science (SPSS version 11.5) was used for statistical analysis. The value of segmental measurements were different between right and left side but statistical difference was not significant except for depth of medial condyle (p=0.02). All the measurements were positively correlated and found to have linear relationship with the femoral length. With the help of regression equation, femoral length can be calculated from the segmental measurements; and then femoral length can be used to calculate the stature of the individual. The data collected may contribute in the analysis of forensic bone remains in study population.

Gradient-Induced Voltages on 12-Lead ECGs during High Duty-Cycle MRI Sequences and a Method for Their Removal considering Linear and Concomitant Gradient Terms

PubMed Central

Zhang, Shelley HuaLei; Ho Tse, Zion Tsz; Dumoulin, Charles L.; Kwong, Raymond Y.; Stevenson, William G.; Watkins, Ronald; Ward, Jay; Wang, Wei; Schmidt, Ehud J.

2015-01-01

Purpose To restore 12-lead ECG signal fidelity inside MRI by removing magnetic-field gradient induced-voltages during high gradient-duty-cycle sequences. Theory and Methods A theoretical equation was derived, providing first- and second-order electrical fields induced at individual ECG electrode as a function of gradient fields. Experiments were performed at 3T on healthy volunteers, using a customized acquisition system which captured full amplitude and frequency response of ECGs, or a commercial recording system. The 19 equation coefficients were derived by linear regression of data from accelerated sequences, and used to compute induced-voltages in real-time during full-resolution sequences to remove ECG artifacts. Restored traces were evaluated relative to ones acquired without imaging. Results Measured induced-voltages were 0.7V peak-to-peak during balanced Steady-State Free Precession (bSSFP) with heart at the isocenter. Applying the equation during gradient echo sequencing, three-dimensional fast spin echo and multi-slice bSSFP imaging restored nonsaturated traces and second-order concomitant terms showed larger contributions in electrodes farther from the magnet isocenter. Equation coefficients are evaluated with high repeatability (ρ = 0.996) and are subject, sequence, and slice-orientation dependent. Conclusion Close agreement between theoretical and measured gradient-induced voltages allowed for real-time removal. Prospective estimation of sequence-periods where large induced-voltages occur may allow hardware removal of these signals. PMID:26101951

Nationwide summary of US Geological Survey regional regression equations for estimating magnitude and frequency of floods for ungaged sites, 1993

USGS Publications Warehouse

Jennings, M.E.; Thomas, W.O.; Riggs, H.C.

1994-01-01

For many years, the U.S. Geological Survey (USGS) has been involved in the development of regional regression equations for estimating flood magnitude and frequency at ungaged sites. These regression equations are used to transfer flood characteristics from gaged to ungaged sites through the use of watershed and climatic characteristics as explanatory or predictor variables. Generally these equations have been developed on a statewide or metropolitan area basis as part of cooperative study programs with specific State Departments of Transportation or specific cities. The USGS, in cooperation with the Federal Highway Administration and the Federal Emergency Management Agency, has compiled all the current (as of September 1993) statewide and metropolitan area regression equations into a micro-computer program titled the National Flood Frequency Program.This program includes regression equations for estimating flood-peak discharges and techniques for estimating a typical flood hydrograph for a given recurrence interval peak discharge for unregulated rural and urban watersheds. These techniques should be useful to engineers and hydrologists for planning and design applications. This report summarizes the statewide regression equations for rural watersheds in each State, summarizes the applicable metropolitan area or statewide regression equations for urban watersheds, describes the National Flood Frequency Program for making these computations, and provides much of the reference information on the extrapolation variables needed to run the program.

Equating Scores from Adaptive to Linear Tests

ERIC Educational Resources Information Center

van der Linden, Wim J.

2006-01-01

Two local methods for observed-score equating are applied to the problem of equating an adaptive test to a linear test. In an empirical study, the methods were evaluated against a method based on the test characteristic function (TCF) of the linear test and traditional equipercentile equating applied to the ability estimates on the adaptive test…

Mean Velocity vs. Mean Propulsive Velocity vs. Peak Velocity: Which Variable Determines Bench Press Relative Load With Higher Reliability?

PubMed

García-Ramos, Amador; Pestaña-Melero, Francisco L; Pérez-Castilla, Alejandro; Rojas, Francisco J; Gregory Haff, G

2018-05-01

García-Ramos, A, Pestaña-Melero, FL, Pérez-Castilla, A, Rojas, FJ, and Haff, GG. Mean velocity vs. mean propulsive velocity vs. peak velocity: which variable determines bench press relative load with higher reliability? J Strength Cond Res 32(5): 1273-1279, 2018-This study aimed to compare between 3 velocity variables (mean velocity [MV], mean propulsive velocity [MPV], and peak velocity [PV]): (a) the linearity of the load-velocity relationship, (b) the accuracy of general regression equations to predict relative load (%1RM), and (c) the between-session reliability of the velocity attained at each percentage of the 1-repetition maximum (%1RM). The full load-velocity relationship of 30 men was evaluated by means of linear regression models in the concentric-only and eccentric-concentric bench press throw (BPT) variants performed with a Smith machine. The 2 sessions of each BPT variant were performed within the same week separated by 48-72 hours. The main findings were as follows: (a) the MV showed the strongest linearity of the load-velocity relationship (median r = 0.989 for concentric-only BPT and 0.993 for eccentric-concentric BPT), followed by MPV (median r = 0.983 for concentric-only BPT and 0.980 for eccentric-concentric BPT), and finally PV (median r = 0.974 for concentric-only BPT and 0.969 for eccentric-concentric BPT); (b) the accuracy of the general regression equations to predict relative load (%1RM) from movement velocity was higher for MV (SEE = 3.80-4.76%1RM) than for MPV (SEE = 4.91-5.56%1RM) and PV (SEE = 5.36-5.77%1RM); and (c) the PV showed the lowest within-subjects coefficient of variation (3.50%-3.87%), followed by MV (4.05%-4.93%), and finally MPV (5.11%-6.03%). Taken together, these results suggest that the MV could be the most appropriate variable for monitoring the relative load (%1RM) in the BPT exercise performed in a Smith machine.

Spacecraft Constrained Maneuver Planning Using Positively Invariant Constraint Admissible Sets (Postprint)

DTIC Science & Technology

2013-08-14

Connectivity Graph; Graph Search; Bounded Disturbances; Linear Time-Varying (LTV); Clohessy - Wiltshire -Hill (CWH) 16. SECURITY CLASSIFICATION OF: 17...the linearization of the relative motion model given by the Hill- Clohessy - Wiltshire (CWH) equations is used [14]. A. Nonlinear equations of motion...equations can be used to describe the motion of the debris. B. Linearized HCW equations in discrete-time For δr << R, the linearized Hill- Clohessy

Newton's method: A link between continuous and discrete solutions of nonlinear problems

NASA Technical Reports Server (NTRS)

Thurston, G. A.

1980-01-01

Newton's method for nonlinear mechanics problems replaces the governing nonlinear equations by an iterative sequence of linear equations. When the linear equations are linear differential equations, the equations are usually solved by numerical methods. The iterative sequence in Newton's method can exhibit poor convergence properties when the nonlinear problem has multiple solutions for a fixed set of parameters, unless the iterative sequences are aimed at solving for each solution separately. The theory of the linear differential operators is often a better guide for solution strategies in applying Newton's method than the theory of linear algebra associated with the numerical analogs of the differential operators. In fact, the theory for the differential operators can suggest the choice of numerical linear operators. In this paper the method of variation of parameters from the theory of linear ordinary differential equations is examined in detail in the context of Newton's method to demonstrate how it might be used as a guide for numerical solutions.

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

PubMed

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.

The Cockroft and Gault formula for estimation of creatinine clearance: a friendly deconstruction.

PubMed

Millar, J Alasdair

2012-02-24

To review the derivation of the Cockroft and Gault formula for estimating creatinine clearance from serum creatinine in a historical context. The derivation described by Cockroft and Gault was reviewed, and an alternative formula was sought using the data reported in the paper. Cockroft and Gault used 24 hour urine creatinine data expressed as mg/kg body weight and mathematical manipulation of a linear regression equation which introduced body weight as an independent variable into the formula. This involved a circular logic and may have been mathematically invalid. A more logical equation not containing body weight was derived from the data. The Cockcroft and Gault formula has been validated by long usage but the derivation appears logically insecure. Nevertheless, its role in estimating renal function at the bedside is established.

The Rayleigh-Taylor instability in a self-gravitating two-layer viscous sphere

NASA Astrophysics Data System (ADS)

Mondal, Puskar; Korenaga, Jun

2018-03-01

The dispersion relation of the Rayleigh-Taylor instability in the spherical geometry is of profound importance in the context of the Earth's core formation. Here we present a complete derivation of this dispersion relation for a self-gravitating two-layer viscous sphere. Such relation is, however, obtained through the solution of a complex transcendental equation, and it is difficult to gain physical insights directly from the transcendental equation itself. We thus also derive an empirical formula to compute the growth rate, by combining the Monte Carlo sampling of the relevant model parameter space with linear regression. Our analysis indicates that the growth rate of Rayleigh-Taylor instability is most sensitive to the viscosity of inner layer in a physical setting that is most relevant to the core formation.

Temporal Stability of the NDVI-LAI Relationship in a Napa Valley Vineyard

NASA Technical Reports Server (NTRS)

Johnson, L. F.

2003-01-01

Remotely sensed normalized difference vegetation index (NDVI) values, derived from high-resolution satellite images, were compared with ground measurements of vineyard leaf area index (LAI) periodically during the 2001 growing season. The two variables were strongly related at six ground calibration sites on each of four occasions (r squared = 0.91 to 0.98). Linear regression equations relating the two variables did not significantly differ by observation date, and a single equation accounted for 92 percent of the variance in the combined dataset. Temporal stability of the relationship opens the possibility of transforming NDVI maps to LAI in the absence of repeated ground calibration fieldwork. In order to take advantage of this circumstance, however, steps should be taken to assure temporal consistency in spectral data values comprising the NDVI.

User's manual for interactive LINEAR: A FORTRAN program to derive linear aircraft models

NASA Technical Reports Server (NTRS)

Antoniewicz, Robert F.; Duke, Eugene L.; Patterson, Brian P.

1988-01-01

An interactive FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models is documented in this report. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.

Supporting second grade lower secondary school students’ understanding of linear equation system in two variables using ethnomathematics

NASA Astrophysics Data System (ADS)

Nursyahidah, F.; Saputro, B. A.; Rubowo, M. R.

2018-03-01

The aim of this research is to know the students’ understanding of linear equation system in two variables using Ethnomathematics and to acquire learning trajectory of linear equation system in two variables for the second grade of lower secondary school students. This research used methodology of design research that consists of three phases, there are preliminary design, teaching experiment, and retrospective analysis. Subject of this study is 28 second grade students of Sekolah Menengah Pertama (SMP) 37 Semarang. The result of this research shows that the students’ understanding in linear equation system in two variables can be stimulated by using Ethnomathematics in selling buying tradition in Peterongan traditional market in Central Java as a context. All of strategies and model that was applied by students and also their result discussion shows how construction and contribution of students can help them to understand concept of linear equation system in two variables. All the activities that were done by students produce learning trajectory to gain the goal of learning. Each steps of learning trajectory of students have an important role in understanding the concept from informal to the formal level. Learning trajectory using Ethnomathematics that is produced consist of watching video of selling buying activity in Peterongan traditional market to construct linear equation in two variables, determine the solution of linear equation in two variables, construct model of linear equation system in two variables from contextual problem, and solving a contextual problem related to linear equation system in two variables.

Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014

USGS Publications Warehouse

Eash, David A.; Barnes, Kimberlee K.; O'Shea, Padraic S.

2016-09-19

A statewide study was led to develop regression equations for estimating three selected spring and three selected fall low-flow frequency statistics for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include spring (April through June) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and fall (October through December) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years. Estimates of the three selected spring statistics are provided for 241 U.S. Geological Survey continuous-record streamgages, and estimates of the three selected fall statistics are provided for 238 of these streamgages, using data through June 2014. Because only 9 years of fall streamflow record were available, three streamgages included in the development of the spring regression equations were not included in the development of the fall regression equations. Because of regulation, diversion, or urbanization, 30 of the 241 streamgages were not included in the development of the regression equations. The study area includes Iowa and adjacent areas within 50 miles of the Iowa border. Because trend analyses indicated statistically significant positive trends when considering the period of record for most of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. Geographic information system software was used to measure 63 selected basin characteristics for each of the 211streamgages used to develop the regional regression equations. The study area was divided into three low-flow regions that were defined in a previous study for the development of regional regression equations.Because several streamgages included in the development of regional regression equations have estimates of zero flow calculated from observed streamflow for selected spring and fall low-flow frequency statistics, the final equations for the three low-flow regions were developed using two types of regression analyses—left-censored and generalized-least-squares regression analyses. A total of 211 streamgages were included in the development of nine spring regression equations—three equations for each of the three low-flow regions. A total of 208 streamgages were included in the development of nine fall regression equations—three equations for each of the three low-flow regions. A censoring threshold was used to develop 15 left-censored regression equations to estimate the three fall low-flow frequency statistics for each of the three low-flow regions and to estimate the three spring low-flow frequency statistics for the southern and northwest regions. For the northeast region, generalized-least-squares regression was used to develop three equations to estimate the three spring low-flow frequency statistics. For the northeast region, average standard errors of prediction range from 32.4 to 48.4 percent for the spring equations and average standard errors of estimate range from 56.4 to 73.8 percent for the fall equations. For the northwest region, average standard errors of estimate range from 58.9 to 62.1 percent for the spring equations and from 83.2 to 109.4 percent for the fall equations. For the southern region, average standard errors of estimate range from 43.2 to 64.0 percent for the spring equations and from 78.1 to 78.7 percent for the fall equations.The regression equations are applicable only to stream sites in Iowa with low flows not substantially affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. The regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system application. StreamStats allows users to click on any ungaged stream site and compute estimates of the six selected spring and fall low-flow statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged site are provided. StreamStats also allows users to click on any Iowa streamgage to obtain computed estimates for the six selected spring and fall low-flow statistics.

Prediction of pulmonary hypertension in idiopathic pulmonary fibrosis☆

PubMed Central

Zisman, David A.; Ross, David J.; Belperio, John A.; Saggar, Rajan; Lynch, Joseph P.; Ardehali, Abbas; Karlamangla, Arun S.

2007-01-01

Summary Background Reliable, noninvasive approaches to the diagnosis of pulmonary hypertension in idiopathic pulmonary fibrosis are needed. We tested the hypothesis that the forced vital capacity to diffusing capacity ratio and room air resting pulse oximetry may be combined to predict mean pulmonary artery pressure (MPAP) in idiopathic pulmonary fibrosis. Methods Sixty-one idiopathic pulmonary fibrosis patients with available right-heart catheterization were studied. We regressed measured MPAP as a continuous variable on pulse oximetry (SpO2) and percent predicted forced vital capacity (FVC) to percent-predicted diffusing capacity ratio (% FVC/% DLco) in a multivariable linear regression model. Results Linear regression generated the following equation: MPAP = −11.9+0.272 × SpO2+0.0659 × (100−SpO2)2+3.06 × (% FVC/% DLco); adjusted R2 = 0.55, p<0.0001. The sensitivity, specificity, positive predictive and negative predictive value of model-predicted pulmonary hypertension were 71% (95% confidence interval (CI): 50–89%), 81% (95% CI: 68–92%), 71% (95% CI: 51–87%) and 81% (95% CI: 68–94%). Conclusions A pulmonary hypertension predictor based on room air resting pulse oximetry and FVC to diffusing capacity ratio has a relatively high negative predictive value. However, this model will require external validation before it can be used in clinical practice. PMID:17604151

Estimation of selected flow and water-quality characteristics of Alaskan streams

USGS Publications Warehouse

Parks, Bruce; Madison, R.J.

1985-01-01

Although hydrologic data are either sparse or nonexistent for large areas of Alaska, the drainage area, area of lakes, glacier and forest cover, and average precipitation in a hydrologic basin of interest can be measured or estimated from existing maps. Application of multiple linear regression techniques indicates that statistically significant correlations exist between properties of basins determined from maps and measured streamflow characteristics. This suggests that corresponding characteristics of ungaged basins can be estimated. Streamflow frequency characteristics can be estimated from regional equations developed for southeast, south-central and Yukon regions. Statewide or modified regional equations must be used, however, for the southwest, northwest, and Arctic Slope regions where there is a paucity of data. Equations developed from basin characteristics are given to estimate suspended-sediment values for glacial streams and, with less reliability, for nonglacial streams. Equations developed from available specific conductance data are given to estimate concentrations of major dissolved inorganic constituents. Suggestions are made for expanding the existing data base and thus improving the ability to estimate hydrologic characteristics for Alaskan streams. (USGS)

Estimation of Missing Water-Level Data for the Everglades Depth Estimation Network (EDEN)

USGS Publications Warehouse

Conrads, Paul; Petkewich, Matthew D.

2009-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and water-surface elevation models designed to provide scientists, engineers, and water-resource managers with current (2000-2009) water-depth information for the entire freshwater portion of the greater Everglades. The U.S. Geological Survey Greater Everglades Priority Ecosystems Science provides support for EDEN and their goal of providing quality-assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. To increase the accuracy of the daily water-surface elevation model, water-level estimation equations were developed to fill missing data. To minimize the occurrences of no estimation of data due to missing data for an input station, a minimum of three linear regression equations were developed for each station using different input stations. Of the 726 water-level estimation equations developed to fill missing data at 239 stations, more than 60 percent of the equations have coefficients of determination greater than 0.90, and 92 percent have an coefficient of determination greater than 0.70.

Localization of the eigenvalues of linear integral equations with applications to linear ordinary differential equations.

NASA Technical Reports Server (NTRS)

Sloss, J. M.; Kranzler, S. K.

1972-01-01

The equivalence of a considered integral equation form with an infinite system of linear equations is proved, and the localization of the eigenvalues of the infinite system is expressed. Error estimates are derived, and the problems of finding upper bounds and lower bounds for the eigenvalues are solved simultaneously.

A frequency-duty cycle equation for the ACGIH hand activity level.

PubMed

Radwin, Robert G; Azari, David P; Lindstrom, Mary J; Ulin, Sheryl S; Armstrong, Thomas J; Rempel, David

2015-01-01

A new equation for predicting the hand activity level (HAL) used in the American Conference for Government Industrial Hygienists threshold limit value®(TLV®) was based on exertion frequency (F) and percentage duty cycle (D). The TLV® includes a table for estimating HAL from F and D originating from data in Latko et al. (Latko WA, Armstrong TJ, Foulke JA, Herrin GD, Rabourn RA, Ulin SS, Development and evaluation of an observational method for assessing repetition in hand tasks. American Industrial Hygiene Association Journal, 58(4):278-285, 1997) and post hoc adjustments that include extrapolations outside of the data range. Multimedia video task analysis determined D for two additional jobs from Latko's study not in the original data-set, and a new nonlinear regression equation was developed to better fit the data and create a more accurate table. The equation, HAL = 6:56 ln D[F(1:31) /1+3:18 F(1:31), generally matches the TLV® HAL lookup table, and is a substantial improvement over the linear model, particularly for F>1.25 Hz and D>60% jobs. The equation more closely fits the data and applies the TLV® using a continuous function.

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

NASA Astrophysics Data System (ADS)

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).

A generalized partially linear mean-covariance regression model for longitudinal proportional data, with applications to the analysis of quality of life data from cancer clinical trials.

PubMed

Zheng, Xueying; Qin, Guoyou; Tu, Dongsheng

2017-05-30

Motivated by the analysis of quality of life data from a clinical trial on early breast cancer, we propose in this paper a generalized partially linear mean-covariance regression model for longitudinal proportional data, which are bounded in a closed interval. Cholesky decomposition of the covariance matrix for within-subject responses and generalized estimation equations are used to estimate unknown parameters and the nonlinear function in the model. Simulation studies are performed to evaluate the performance of the proposed estimation procedures. Our new model is also applied to analyze the data from the cancer clinical trial that motivated this research. In comparison with available models in the literature, the proposed model does not require specific parametric assumptions on the density function of the longitudinal responses and the probability function of the boundary values and can capture dynamic changes of time or other interested variables on both mean and covariance of the correlated proportional responses. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

New Results on the Linear Equating Methods for the Non-Equivalent-Groups Design

ERIC Educational Resources Information Center

von Davier, Alina A.

2008-01-01

The two most common observed-score equating functions are the linear and equipercentile functions. These are often seen as different methods, but von Davier, Holland, and Thayer showed that any equipercentile equating function can be decomposed into linear and nonlinear parts. They emphasized the dominant role of the linear part of the nonlinear…

Temperature preference of the white perch, Morone americana, collected in the Wicomico River, Maryland

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hall, L.W. Jr.; Hocutt, C.H.; Stauffer, J.R. Jr.

1979-06-01

Temperature preference tests were conducted on fresh water white perch (Morone americana), collected from the Wicomico River, Maryland. Collection temperature was 27/sup 0/C and acclimation temperatures used in temperature preference tests were 6, 12, 18, 24, 30, and 33/sup 0/C. The following methods were used to determine the final temperature preference:linear regression, quadratic equation, and eyeball plots. Recorded final temperature preference values were 28.9, 29.3, and 30.6/sup 0/C using each method respectively.

Peak flow regression equations For small, ungaged streams in Maine: Comparing map-based to field-based variables

USGS Publications Warehouse

Lombard, Pamela J.; Hodgkins, Glenn A.

2015-01-01

Regression equations to estimate peak streamflows with 1- to 500-year recurrence intervals (annual exceedance probabilities from 99 to 0.2 percent, respectively) were developed for small, ungaged streams in Maine. Equations presented here are the best available equations for estimating peak flows at ungaged basins in Maine with drainage areas from 0.3 to 12 square miles (mi2). Previously developed equations continue to be the best available equations for estimating peak flows for basin areas greater than 12 mi2. New equations presented here are based on streamflow records at 40 U.S. Geological Survey streamgages with a minimum of 10 years of recorded peak flows between 1963 and 2012. Ordinary least-squares regression techniques were used to determine the best explanatory variables for the regression equations. Traditional map-based explanatory variables were compared to variables requiring field measurements. Two field-based variables—culvert rust lines and bankfull channel widths—either were not commonly found or did not explain enough of the variability in the peak flows to warrant inclusion in the equations. The best explanatory variables were drainage area and percent basin wetlands; values for these variables were determined with a geographic information system. Generalized least-squares regression was used with these two variables to determine the equation coefficients and estimates of accuracy for the final equations.

Linear stability analysis of the Vlasov-Poisson equations in high density plasmas in the presence of crossed fields and density gradients

NASA Technical Reports Server (NTRS)

Kaup, D. J.; Hansen, P. J.; Choudhury, S. Roy; Thomas, Gary E.

1986-01-01

The equations for the single-particle orbits in a nonneutral high density plasma in the presence of inhomogeneous crossed fields are obtained. Using these orbits, the linearized Vlasov equation is solved as an expansion in the orbital radii in the presence of inhomogeneities and density gradients. A model distribution function is introduced whose cold-fluid limit is exactly the same as that used in many previous studies of the cold-fluid equations. This model function is used to reduce the linearized Vlasov-Poisson equations to a second-order ordinary differential equation for the linearized electrostatic potential whose eigenvalue is the perturbation frequency.

Determination of saleable product in finished cattle and beef carcasses utilizing bioelectrical impedance technology.

PubMed

Marchello, M J; McLennan, J E; Dhuyvetter, D V; Slanger, W D

1999-11-01

Two experiments were performed to develop prediction equations of saleable beef and to validate the prediction equations. In Exp. 1, 50 beef cattle were finished to typical slaughter weights, and multiple linear regression equations were developed to predict kilograms of trimmed boneless, retail product of live cattle, and hot and cold carcasses. A four-terminal bioelectrical impedance analyzer (BIA) was used to measure resistance (Rs) and reactance (Xc) on each animal and processed carcass. The IMPS cuts plus trim were weighed and recorded. Distance between detector terminals (Lg) and carcass temperature (Tp) at time of BIA readings were recorded. Other variables included live weight (BW), hot carcass weight (HCW), cold carcass weight (CCW), and volume (Lg2/Rs). Regression equations for predicting kilograms of saleable product were [11.87 + (.409 x BW) - (.335 x Lg) + (.0518 x volume)] for live (R2 = .80); [-58.83 + (.589 x HCW) - (.846 x Rs) + (1.152 x Xc) + (.142 x Lg) + (2.608 x Tp)] for hot carcass (R2 = .95); and [32.15 + (.633 x CCW) + (.33 x Xc) - (.83 x Lg) + (.677 x volume)] for cold carcass (R2 = .93). In Exp. 2, 27 beef cattle were finished in a manner similar to Exp. 1, and the prediction equations from Exp. 1 were used to predict the saleable product of these animals. The Pearson correlations between actual saleable product and the predictions based on live and cold carcass data were .91 and .95, respectively. The Spearman and Kendall rank correlations were .95 and .83, respectively, for the cold carcass data. These results provide a practical application of bioelectrical impedance for market-based pricing. They complement previous studies that assessed fat-free mass.

Age prediction formulae from radiographic assessment of skeletal maturation at the knee in an Irish population.

PubMed

O'Connor, Jean E; Coyle, Joseph; Bogue, Conor; Spence, Liam D; Last, Jason

2014-01-01

Age estimation in living subjects is primarily achieved through assessment of a hand-wrist radiograph and comparison with a standard reference atlas. Recently, maturation of other regions of the skeleton has also been assessed in an attempt to refine the age estimates. The current study presents a method to predict bone age directly from the knee in a modern Irish sample. Ten maturity indicators (A-J) at the knee were examined from radiographs of 221 subjects (137 males; 84 females). Each indicator was assigned a maturity score. Scores for indicators A-G, H-J and A-J, respectively, were totalled to provide a cumulative maturity score for change in morphology of the epiphyses (AG), epiphyseal union (HJ) and the combination of both (AJ). Linear regression equations to predict age from the maturity scores (AG, HJ, AJ) were constructed for males and females. For males, equation-AJ demonstrated the greatest predictive capability (R(2)=0.775) while for females equation-HJ had the strongest capacity for prediction (R(2)=0.815). When equation-AJ for males and equation-HJ for females were applied to the current sample, the predicted age of 90% of subjects was within ±1.5 years of actual age for male subjects and within +2.0 to -1.9 years of actual age for female subjects. The regression formulae and associated charts represent the most contemporary method of age prediction currently available for an Irish population, and provide a further technique which can contribute to a multifactorial approach to age estimation in non-adults. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Chemical Equation Balancing.

ERIC Educational Resources Information Center

Blakley, G. R.

1982-01-01

Reviews mathematical techniques for solving systems of homogeneous linear equations and demonstrates that the algebraic method of balancing chemical equations is a matter of solving a system of homogeneous linear equations. FORTRAN programs using this matrix method to chemical equation balancing are available from the author. (JN)

Reaeration equations derived from U.S. geological survey database

USGS Publications Warehouse

Melching, C.S.; Flores, H.E.

1999-01-01

Accurate estimation of the reaeration-rate coefficient (K2) is extremely important for waste-load allocation. Currently, available K2 estimation equations generally yield poor estimates when applied to stream conditions different from those for which the equations were derived because they were derived from small databases composed of potentially highly inaccurate measurements. A large data set of K2 measurements made with tracer-gas methods was compiled from U.S. Geological Survey studies. This compilation included 493 reaches on 166 streams in 23 states. Careful screening to detect and eliminate erroneous measurements reduced the date set to 371 measurements. These measurements were divided into four subgroups on the basis of flow regime (channel control or pool and riffle) and stream scale (discharge greater than or less than 0.556 m3/s). Multiple linear regression in logarithms was applied to relate K2 to 12 stream hydraulic and water-quality characteristics. The resulting best-estimation equations had the form of semiempirical equations that included the rate of energy dissipation and discharge or depth and width as variables. For equation verification, a data set of K2 measurements made with tracer-gas procedures by other agencies was compiled from the literature. This compilation included 127 reaches on at least 24 streams in at least seven states. The standard error of estimate obtained when applying the developed equations to the U.S. Geological Survey data set ranged from 44 to 61%, whereas the standard error of estimate was 78% when applied to the verification data set.Accurate estimation of the reaeration-rate coefficient (K2) is extremely important for waste-load allocation. Currently, available K2 estimation equations generally yield poor estimates when applied to stream conditions different from those for which the equations were derived because they were derived from small databases composed of potentially highly inaccurate measurements. A large data set of K2 measurements made with tracer-gas methods was compiled from U.S. Geological Survey studies. This compilation included 493 reaches on 166 streams in 23 states. Careful screening to detect and eliminate erroneous measurements reduced the data set to 371 measurements. These measurements were divided into four subgroups on the basis of flow regime (channel control or pool and riffle) and stream scale (discharge greater than or less than 0.556 m3/s). Multiple linear regression in logarithms was applied to relate K2 to 12 stream hydraulic and water-quality characteristics. The resulting best-estimation equations had the form of semiempirical equations that included the rate of energy dissipation and discharge or depth and width as variables. For equation verification, a data set of K2 measurements made with tracer-gas procedures by other agencies was compiled from the literature. This compilation included 127 reaches on at least 24 streams in at least seven states. The standard error of estimate obtained when applying the developed equations to the U.S. Geological Survey data set ranged from 44 to 61%, whereas the standard error of estimate was 78% when applied to the verification data set.

A Comparison between Linear IRT Observed-Score Equating and Levine Observed-Score Equating under the Generalized Kernel Equating Framework

ERIC Educational Resources Information Center

Chen, Haiwen

2012-01-01

In this article, linear item response theory (IRT) observed-score equating is compared under a generalized kernel equating framework with Levine observed-score equating for nonequivalent groups with anchor test design. Interestingly, these two equating methods are closely related despite being based on different methodologies. Specifically, when…

Linear ordinary differential equations with constant coefficients. Revisiting the impulsive response method using factorization

NASA Astrophysics Data System (ADS)

Camporesi, Roberto

2011-06-01

We present an approach to the impulsive response method for solving linear constant-coefficient ordinary differential equations based on the factorization of the differential operator. The approach is elementary, we only assume a basic knowledge of calculus and linear algebra. In particular, we avoid the use of distribution theory, as well as of the other more advanced approaches: Laplace transform, linear systems, the general theory of linear equations with variable coefficients and the variation of constants method. The approach presented here can be used in a first course on differential equations for science and engineering majors.

Alternative approaches to predicting methane emissions from dairy cows.

PubMed

Mills, J A N; Kebreab, E; Yates, C M; Crompton, L A; Cammell, S B; Dhanoa, M S; Agnew, R E; France, J

2003-12-01

Previous attempts to apply statistical models, which correlate nutrient intake with methane production, have been of limited value where predictions are obtained for nutrient intakes and diet types outside those used in model construction. Dynamic mechanistic models have proved more suitable for extrapolation, but they remain computationally expensive and are not applied easily in practical situations. The first objective of this research focused on employing conventional techniques to generate statistical models of methane production appropriate to United Kingdom dairy systems. The second objective was to evaluate these models and a model published previously using both United Kingdom and North American data sets. Thirdly, nonlinear models were considered as alternatives to the conventional linear regressions. The United Kingdom calorimetry data used to construct the linear models also were used to develop the three nonlinear alternatives that were all of modified Mitscherlich (monomolecular) form. Of the linear models tested, an equation from the literature proved most reliable across the full range of evaluation data (root mean square prediction error = 21.3%). However, the Mitscherlich models demonstrated the greatest degree of adaptability across diet types and intake level. The most successful model for simulating the independent data was a modified Mitscherlich equation with the steepness parameter set to represent dietary starch-to-ADF ratio (root mean square prediction error = 20.6%). However, when such data were unavailable, simpler Mitscherlich forms relating dry matter or metabolizable energy intake to methane production remained better alternatives relative to their linear counterparts.

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma

USGS Publications Warehouse

Lewis, Jason M.

2010-01-01

Peak-streamflow regression equations were determined for estimating flows with exceedance probabilities from 50 to 0.2 percent for the state of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the state by use of a generalized least squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for watershed basins with drainage areas less than 2,510 square miles that are not affected by regulation. The resulting regression equations had a standard model error ranging from 31 to 46 percent. Annual-maximum peak flows observed at 231 streamflow-gaging stations through water year 2008 were used for the regression analysis. Gage peak-streamflow estimates were used from previous work unless 2008 gaging-station data were available, in which new peak-streamflow estimates were calculated. The U.S. Geological Survey StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 231 gaging stations in and near Oklahoma are listed. Methodologies are presented to estimate peak streamflows at ungaged sites by using estimates from gaging stations on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Deep Wavelet Scattering for Quantum Energy Regression

NASA Astrophysics Data System (ADS)

Hirn, Matthew

Physical functionals are usually computed as solutions of variational problems or from solutions of partial differential equations, which may require huge computations for complex systems. Quantum chemistry calculations of ground state molecular energies is such an example. Indeed, if x is a quantum molecular state, then the ground state energy E0 (x) is the minimum eigenvalue solution of the time independent Schrödinger Equation, which is computationally intensive for large systems. Machine learning algorithms do not simulate the physical system but estimate solutions by interpolating values provided by a training set of known examples {(xi ,E0 (xi) } i <= n . However, precise interpolations may require a number of examples that is exponential in the system dimension, and are thus intractable. This curse of dimensionality may be circumvented by computing interpolations in smaller approximation spaces, which take advantage of physical invariants. Linear regressions of E0 over a dictionary Φ ={ϕk } k compute an approximation E 0 as: E 0 (x) =∑kwkϕk (x) , where the weights {wk } k are selected to minimize the error between E0 and E 0 on the training set. The key to such a regression approach then lies in the design of the dictionary Φ. It must be intricate enough to capture the essential variability of E0 (x) over the molecular states x of interest, while simple enough so that evaluation of Φ (x) is significantly less intensive than a direct quantum mechanical computation (or approximation) of E0 (x) . In this talk we present a novel dictionary Φ for the regression of quantum mechanical energies based on the scattering transform of an intermediate, approximate electron density representation ρx of the state x. The scattering transform has the architecture of a deep convolutional network, composed of an alternating sequence of linear filters and nonlinear maps. Whereas in many deep learning tasks the linear filters are learned from the training data, here the physical properties of E0 (invariance to isometric transformations of the state x, stable to deformations of x) are leveraged to design a collection of linear filters ρx *ψλ for an appropriate wavelet ψ. These linear filters are composed with the nonlinear modulus operator, and the process is iterated upon so that at each layer stable, invariant features are extracted: ϕk (x) = ∥ | | ρx *ψλ1 | * ψλ2 | * ... *ψλm ∥ , k = (λ1 , ... ,λm) , m = 1 , 2 , ... The scattering transform thus encodes not only interactions at multiple scales (in the first layer, m = 1), but also features that encode complex phenomena resulting from a cascade of interactions across scales (in subsequent layers, m >= 2). Numerical experiments give state of the art accuracy over data bases of organic molecules, while theoretical results guarantee performance for the component of the ground state energy resulting from Coulombic interactions. Supported by the ERC InvariantClass 320959 Grant.

Body Fat Equations and Electrical Bioimpedance Values in Prediction of Cardiovascular Risk Factors in Eutrophic and Overweight Adolescents

PubMed Central

Faria, Franciane Rocha; Faria, Eliane Rodrigues; Cecon, Roberta Stofeles; Barbosa Júnior, Djalma Adão; Franceschini, Sylvia do Carmo Castro; Peluzio, Maria do Carmo Gouveia; Ribeiro, Andréia Queiroz; Lira, Pedro Israel Cabral; Cecon, Paulo Roberto; Priore, Silvia Eloiza

2013-01-01

The aim of this study was to analyze body fat anthropometric equations and electrical bioimpedance analysis (BIA) in the prediction of cardiovascular risk factors in eutrophic and overweight adolescents. 210 adolescents were divided into eutrophic group (G1) and overweight group (G2). The percentage of body fat (% BF) was estimated using 10 body fat anthropometric equations and 2 BIA. We measured lipid profiles, uric acid, insulin, fasting glucose, homeostasis model assessment-insulin resistance (HOMA-IR), and blood pressure. We found that 76.7% of the adolescents exhibited inadequacy of at least one biochemical parameter or clinical cardiovascular risk. Higher values of triglycerides (TG) (P = 0.001), insulin, and HOMA-IR (P < 0.001) were observed in the G2 adolescents. In multivariate linear regression analysis, the % BF from equation (5) was associated with TG, diastolic blood pressure, and insulin in G1. Among the G2 adolescents, the % BF estimated by (5) and (9) was associated with LDL, TG, insulin, and the HOMA-IR. Body fat anthropometric equations were associated with cardiovascular risk factors and should be used to assess the nutritional status of adolescents. In this study, equation (5) was associated with a higher number of cardiovascular risk factors independent of the nutritional status of adolescents. PMID:23762051

Techniques for estimating streamflow characteristics in the Eastern and Interior coal provinces of the United States

USGS Publications Warehouse

Wetzel, Kim L.; Bettandorff, J.M.

1986-01-01

Techniques are presented for estimating various streamflow characteristics, such as peak flows, mean monthly and annual flows, flow durations, and flow volumes, at ungaged sites on unregulated streams in the Eastern Coal region. Streamflow data and basin characteristics for 629 gaging stations were used to develop multiple-linear-regression equations. Separate equations were developed for the Eastern and Interior Coal Provinces. Drainage area is an independent variable common to all equations. Other variables needed, depending on the streamflow characteristic, are mean annual precipitation, mean basin elevation, main channel length, basin storage, main channel slope, and forest cover. A ratio of the observed 50- to 90-percent flow durations was used in the development of relations to estimate low-flow frequencies in the Eastern Coal Province. Relations to estimate low flows in the Interior Coal Province are not presented because the standard errors were greater than 0.7500 log units and were considered to be of poor reliability.

A quantitative description of normal AV nodal conduction curve in man.

PubMed

Teague, S; Collins, S; Wu, D; Denes, P; Rosen, K; Arzbaecher, R

1976-01-01

The AV nodal conduction curve generated by the atrial extrastimulus technique has been described only qualitatively in man, making clinical comparison of known normal curves with those of suspected AV nodal dysfunction difficult. Also, the effects of physiological and pharmacological interventions have not been quantifiable. In 50 patients with normal AV conduction as defined by normal AH (less than 130 ms), normal AV nodal effective and functional refractory periods (less than 380 and less than 500 ms), and absence of demonstrable dual AV nodal pathways, we found that conduction curves (at sinus rhythm or longest paced cycle length) can be described by an exponential equation of the form delta = Ae-Bx. In this equation, delta is the increase in AV nodal conduction time of an extrastimulus compared to that of a regular beat and x is extrastimulus interval. The natural logarithm of this equation is linear in the semilogarithmic plane, thus permitting the constants A and B to be easily determined by a least-squares regression analysis with a hand calculator.

Fast wavelet based algorithms for linear evolution equations

NASA Technical Reports Server (NTRS)

Engquist, Bjorn; Osher, Stanley; Zhong, Sifen

1992-01-01

A class was devised of fast wavelet based algorithms for linear evolution equations whose coefficients are time independent. The method draws on the work of Beylkin, Coifman, and Rokhlin which they applied to general Calderon-Zygmund type integral operators. A modification of their idea is applied to linear hyperbolic and parabolic equations, with spatially varying coefficients. A significant speedup over standard methods is obtained when applied to hyperbolic equations in one space dimension and parabolic equations in multidimensions.

Note on Solutions to a Class of Nonlinear Singular Integro-Differential Equations,

DTIC Science & Technology

1986-08-01

KdV) ut + 2uu x +Uxx x a 0, (1) the sine-Gordon equation Uxt a sin u, (2) and the Kadomtsev - Petviashvili (KP) equation (Ut + 2uu x + UXXx)x -3a 2u yy...SOUIN OA LSFNN ! /" / M.. \\boiz A.S ::-:- and ,M.O.. .- :1/1 / NOTE ON SOLUTIONS TO A CLASS OF NON \\ / LINEAR SINGULAR INTEGRO-DIFFERENTIA[ EQUATIONS by...important nonlinear evolution equations which can be linearized. Many of these equations fall into the category of linearization via soliton theory and

Major controlling factors and prediction models for arsenic uptake from soil to wheat plants.

PubMed

Dai, Yunchao; Lv, Jialong; Liu, Ke; Zhao, Xiaoyan; Cao, Yingfei

2016-08-01

The application of current Chinese agriculture soil quality standards fails to evaluate the land utilization functions appropriately due to the diversity of soil properties and plant species. Therefore, the standards should be amended. A greenhouse experiment was conducted to investigate arsenic (As) enrichment in various soils from 18 Chinese provinces in parallel with As transfer to 8 wheat varieties. The goal of the study was to build and calibrate soil-wheat threshold models to forecast the As threshold of wheat soils. In Shaanxi soils, Wanmai and Jimai were the most sensitive and insensitive wheat varieties, respectively; and in Jiangxi soils, Zhengmai and Xumai were the most sensitive and insensitive wheat varieties, respectively. Relationships between soil properties and the bioconcentration factor (BCF) were built based on stepwise multiple linear regressions. Soil pH was the best predictor of BCF, and after normalizing the regression equation (Log BCF=0.2054 pH- 3.2055, R(2)=0.8474, n=14, p<0.001), we obtained a calibrated model. Using the calibrated model, a continuous soil-wheat threshold equation (HC5=10((-0.2054 pH+2.9935))+9.2) was obtained for the species-sensitive distribution curve, which was built on Chinese food safety standards. The threshold equation is a helpful tool that can be applied to estimate As uptake from soil to wheat. Copyright © 2016 Elsevier Inc. All rights reserved.

Development of a Standalone Thermal Wellbore Simulator

NASA Astrophysics Data System (ADS)

Xiong, Wanqiang

With continuous developments of various different sophisticated wells in the petroleum industry, wellbore modeling and simulation have increasingly received more attention. Especially in unconventional oil and gas recovery processes, there is a growing demand for more accurate wellbore modeling. Despite notable advancements made in wellbore modeling, none of the existing wellbore simulators has been as successful as reservoir simulators such as Eclipse and CMG's and further research works on handling issues such as accurate heat loss modeling and multi-tubing wellbore modeling are really necessary. A series of mathematical equations including main governing equations, auxiliary equations, PVT equations, thermodynamic equations, drift-flux model equations, and wellbore heat loss calculation equations are collected and screened from publications. Based on these modeling equations, workflows for wellbore simulation and software development are proposed. Research works are conducted in key steps for developing a wellbore simulator: discretization, a grid system, a solution method, a linear equation solver, and computer language. A standalone thermal wellbore simulator is developed by using standard C++ language. This wellbore simulator can simulate single-phase injection and production, two-phase steam injection and two-phase oil and water production. By implementing a multi-part scheme which divides a wellbore with sophisticated configuration into several relative simple simulation running units, this simulator can handle different complex wellbores: wellbore with multistage casings, horizontal wells, multilateral wells and double tubing. In pursuance of improved accuracy of heat loss calculations to surrounding formations, a semi-numerical method is proposed and a series of FLUENT simulations have been conducted in this study. This semi-numerical method involves extending the 2D formation heat transfer simulation to include a casing wall and cement and adopting new correlations regressed by this study. Meanwhile, a correlation for handling heat transfer in double-tubing annulus is regressed. This work initiates the research on heat transfer in a double-tubing wellbore system. A series of validation and test works are performed in hot water injection, steam injection, real filed data, a horizontal well, a double-tubing well and comparison with the Ramey method. The program in this study also performs well in matching with real measured field data, simulation in horizontal wells and double-tubing wells.

Application of conditional moment tests to model checking for generalized linear models.

PubMed

Pan, Wei

2002-06-01

Generalized linear models (GLMs) are increasingly being used in daily data analysis. However, model checking for GLMs with correlated discrete response data remains difficult. In this paper, through a case study on marginal logistic regression using a real data set, we illustrate the flexibility and effectiveness of using conditional moment tests (CMTs), along with other graphical methods, to do model checking for generalized estimation equation (GEE) analyses. Although CMTs provide an array of powerful diagnostic tests for model checking, they were originally proposed in the econometrics literature and, to our knowledge, have never been applied to GEE analyses. CMTs cover many existing tests, including the (generalized) score test for an omitted covariate, as special cases. In summary, we believe that CMTs provide a class of useful model checking tools.

Regression Equations for Estimating Flood Flows at Selected Recurrence Intervals for Ungaged Streams in Pennsylvania

USGS Publications Warehouse

Roland, Mark A.; Stuckey, Marla H.

2008-01-01

Regression equations were developed for estimating flood flows at selected recurrence intervals for ungaged streams in Pennsylvania with drainage areas less than 2,000 square miles. These equations were developed utilizing peak-flow data from 322 streamflow-gaging stations within Pennsylvania and surrounding states. All stations used in the development of the equations had 10 or more years of record and included active and discontinued continuous-record as well as crest-stage partial-record stations. The state was divided into four regions, and regional regression equations were developed to estimate the 2-, 5-, 10-, 50-, 100-, and 500-year recurrence-interval flood flows. The equations were developed by means of a regression analysis that utilized basin characteristics and flow data associated with the stations. Significant explanatory variables at the 95-percent confidence level for one or more regression equations included the following basin characteristics: drainage area; mean basin elevation; and the percentages of carbonate bedrock, urban area, and storage within a basin. The regression equations can be used to predict the magnitude of flood flows for specified recurrence intervals for most streams in the state; however, they are not valid for streams with drainage areas generally greater than 2,000 square miles or with substantial regulation, diversion, or mining activity within the basin. Estimates of flood-flow magnitude and frequency for streamflow-gaging stations substantially affected by upstream regulation are also presented.

Modeling of thermal degradation kinetics of the C-glucosyl xanthone mangiferin in an aqueous model solution as a function of pH and temperature and protective effect of honeybush extract matrix.

PubMed

Beelders, Theresa; de Beer, Dalene; Kidd, Martin; Joubert, Elizabeth

2018-01-01

Mangiferin, a C-glucosyl xanthone, abundant in mango and honeybush, is increasingly targeted for its bioactive properties and thus to enhance functional properties of food. The thermal degradation kinetics of mangiferin at pH3, 4, 5, 6 and 7 were each modeled at five temperatures ranging between 60 and 140°C. First-order reaction models were fitted to the data using non-linear regression to determine the reaction rate constant at each pH-temperature combination. The reaction rate constant increased with increasing temperature and pH. Comparison of the reaction rate constants at 100°C revealed an exponential relationship between the reaction rate constant and pH. The data for each pH were also modeled with the Arrhenius equation using non-linear and linear regression to determine the activation energy and pre-exponential factor. Activation energies decreased slightly with increasing pH. Finally, a multi-linear model taking into account both temperature and pH was developed for mangiferin degradation. Sterilization (121°C for 4min) of honeybush extracts dissolved at pH4, 5 and 7 did not cause noticeable degradation of mangiferin, although the multi-linear model predicted 34% degradation at pH7. The extract matrix is postulated to exert a protective effect as changes in potential precursor content could not fully explain the stability of mangiferin. Copyright © 2017 Elsevier Ltd. All rights reserved.

FAST TRACK COMMUNICATION: On the Liouvillian solution of second-order linear differential equations and algebraic invariant curves

NASA Astrophysics Data System (ADS)

Man, Yiu-Kwong

2010-10-01

In this communication, we present a method for computing the Liouvillian solution of second-order linear differential equations via algebraic invariant curves. The main idea is to integrate Kovacic's results on second-order linear differential equations with the Prelle-Singer method for computing first integrals of differential equations. Some examples on using this approach are provided.

A refinement of the combination equations for evaporation

USGS Publications Warehouse

Milly, P.C.D.

1991-01-01

Most combination equations for evaporation rely on a linear expansion of the saturation vapor-pressure curve around the air temperature. Because the temperature at the surface may differ from this temperature by several degrees, and because the saturation vapor-pressure curve is nonlinear, this approximation leads to a certain degree of error in those evaporation equations. It is possible, however, to introduce higher-order polynomial approximations for the saturation vapor-pressure curve and to derive a family of explicit equations for evaporation, having any desired degree of accuracy. Under the linear approximation, the new family of equations for evaporation reduces, in particular cases, to the combination equations of H. L. Penman (Natural evaporation from open water, bare soil and grass, Proc. R. Soc. London, Ser. A193, 120-145, 1948) and of subsequent workers. Comparison of the linear and quadratic approximations leads to a simple approximate expression for the error associated with the linear case. Equations based on the conventional linear approximation consistently underestimate evaporation, sometimes by a substantial amount. ?? 1991 Kluwer Academic Publishers.

Nonlinear Diophantine equation 11 x +13 y = z 2

NASA Astrophysics Data System (ADS)

Sugandha, A.; Tripena, A.; Prabowo, A.; Sukono, F.

2018-03-01

This research aims to obtaining the solutions (if any) from the Non Linear Diophantine equation of 11 x + 13 y = z 2. There are 3 possibilities to obtain the solutions (if any) from the Non Linear Diophantine equation, namely single, multiple, and no solution. This research is conducted in two stages: (1) by utilizing simulation to obtain the solutions (if any) from the Non Linear Diophantine equation of 11 x + 13 y = z 2 and (2) by utilizing congruency theory with its characteristics proven that the Non Linear Diophantine equation has no solution for non negative whole numbers (integers) of x, y, z.

Lie algebras and linear differential equations.

NASA Technical Reports Server (NTRS)

Brockett, R. W.; Rahimi, A.

1972-01-01

Certain symmetry properties possessed by the solutions of linear differential equations are examined. For this purpose, some basic ideas from the theory of finite dimensional linear systems are used together with the work of Wei and Norman on the use of Lie algebraic methods in differential equation theory.

Dairy manure nutrient analysis using quick tests.

PubMed

Singh, A; Bicudo, J R

2005-05-01

Rapid on-farm assessment of manure nutrient content can be achieved with the use of quick tests. These tests can be used to indirectly measure the nutrient content in animal slurries immediately before manure is applied on agricultural fields. The objective of this study was to assess the reliability of hydrometers, electrical conductivity meter and pens, and Agros N meter against standard laboratory methods. Manure samples were collected from 34 dairy farms in the Mammoth Cave area in central Kentucky. Regression equations were developed for combined and individual counties located In the area (Barren, Hart and Monroe). Our results indicated that accuracy in nutrient estimation could be improved if separate linear regressions were developed for farms with similar facilities in a county. Direct hydrometer estimates of total nitrogen were among the most accurate when separate regression equations were developed for each county (R2 = 0.61, 0.93, and 0.74 for Barren, Hart and Monroe county, respectively). Reasonably accurate estimates (R2 > 0.70) were also obtained for total nitrogen and total phosphorus using hydrometers, either by relating specific gravity to nutrient content or to total solids content. Estimation of ammoniacal nitrogen with Agros N meter and electrical conductivity meter/pens correlated well with standard laboratory determinations, especially while using the individual data sets from Hart County (R2 = 0.70 to 0.87). This study indicates that the use of quick test calibration equations developed for a small area or region where farms are similar in terms of manure handling and management, housing, and feed ration are more appropriate than using "universal" equations usually developed with combined data sets. Accuracy is expected to improve if individual farms develop their own calibration curves. Nevertheless, we suggest confidence intervals always be specified for nutrients estimated through quick testing for any specific region, county, or farm.

Simple taper: Taper equations for the field forester

Treesearch

David R. Larsen

2017-01-01

"Simple taper" is set of linear equations that are based on stem taper rates; the intent is to provide taper equation functionality to field foresters. The equation parameters are two taper rates based on differences in diameter outside bark at two points on a tree. The simple taper equations are statistically equivalent to more complex equations. The linear...

Standard Errors of Equating for the Percentile Rank-Based Equipercentile Equating with Log-Linear Presmoothing

ERIC Educational Resources Information Center

Wang, Tianyou

2009-01-01

Holland and colleagues derived a formula for analytical standard error of equating using the delta-method for the kernel equating method. Extending their derivation, this article derives an analytical standard error of equating procedure for the conventional percentile rank-based equipercentile equating with log-linear smoothing. This procedure is…

A fresh look at linear ordinary differential equations with constant coefficients. Revisiting the impulsive response method using factorization

NASA Astrophysics Data System (ADS)

Camporesi, Roberto

2016-01-01

We present an approach to the impulsive response method for solving linear constant-coefficient ordinary differential equations of any order based on the factorization of the differential operator. The approach is elementary, we only assume a basic knowledge of calculus and linear algebra. In particular, we avoid the use of distribution theory, as well as of the other more advanced approaches: Laplace transform, linear systems, the general theory of linear equations with variable coefficients and variation of parameters. The approach presented here can be used in a first course on differential equations for science and engineering majors.

The spectral applications of Beer-Lambert law for some biological and dosimetric materials

NASA Astrophysics Data System (ADS)

Içelli, Orhan; Yalçin, Zeynel; Karakaya, Vatan; Ilgaz, Işıl P.

2014-08-01

The aim of this study is to conduct quantitative and qualitative analysis of biological and dosimetric materials which contain organic and inorganic materials and to make the determination by using the spectral theorem Beer-Lambert law. Beer-Lambert law is a system of linear equations for the spectral theory. It is possible to solve linear equations with a non-zero coefficient matrix determinant forming linear equations. Characteristic matrix of the linear equation with zero determinant is called point spectrum at the spectral theory.

Role of anthropometric data in the prediction of 4-stranded hamstring graft size in anterior cruciate ligament reconstruction.

PubMed

Ho, Sean Wei Loong; Tan, Teong Jin Lester; Lee, Keng Thiam

2016-03-01

To evaluate whether pre-operative anthropometric data can predict the optimal diameter and length of hamstring tendon autograft for anterior cruciate ligament (ACL) reconstruction. This was a cohort study that involved 169 patients who underwent single-bundle ACL reconstruction (single surgeon) with 4-stranded MM Gracilis and MM Semi-Tendinosus autografts. Height, weight, body mass index (BMI), gender, race, age and -smoking status were recorded pre-operatively. Intra-operatively, the diameter and functional length of the 4-stranded autograft was recorded. Multiple regression analysis was used to determine the relationship between the anthropometric measurements and the length and diameter of the implanted autografts. The strongest correlation between 4-stranded hamstring autograft diameter was height and weight. This correlation was stronger in females than males. BMI had a moderate correlation with the diameter of the graft in females. Females had a significantly smaller graft both in diameter and length when compared with males. Linear regression models did not show any significant correlation between hamstring autograft length with height and weight (p>0.05). Simple regression analysis demonstrated that height and weight can be used to predict hamstring graft diameter. The following regression equation was obtained for females: Graft diameter=0.012+0.034*Height+0.026*Weight (R2=0.358, p=0.004) The following regression equation was obtained for males: Graft diameter=5.130+0.012*Height+0.007*Weight (R2=0.086, p=0.002). Pre-operative anthropometric data has a positive correlation with the diameter of 4 stranded hamstring autografts but no significant correlation with the length. This data can be utilised to predict the autograft diameter and may be useful for pre-operative planning and patient counseling for graft selection.

Regression equations to estimate seasonal flow duration, n-day high-flow frequency, and n-day low-flow frequency at sites in North Dakota using data through water year 2009

USGS Publications Warehouse

Williams-Sether, Tara; Gross, Tara A.

2016-02-09

Seasonal mean daily flow data from 119 U.S. Geological Survey streamflow-gaging stations in North Dakota; the surrounding states of Montana, Minnesota, and South Dakota; and the Canadian provinces of Manitoba and Saskatchewan with 10 or more years of unregulated flow record were used to develop regression equations for flow duration, n-day high flow and n-day low flow using ordinary least-squares and Tobit regression techniques. Regression equations were developed for seasonal flow durations at the 10th, 25th, 50th, 75th, and 90th percent exceedances; the 1-, 7-, and 30-day seasonal mean high flows for the 10-, 25-, and 50-year recurrence intervals; and the 1-, 7-, and 30-day seasonal mean low flows for the 2-, 5-, and 10-year recurrence intervals. Basin and climatic characteristics determined to be significant explanatory variables in one or more regression equations included drainage area, percentage of basin drainage area that drains to isolated lakes and ponds, ruggedness number, stream length, basin compactness ratio, minimum basin elevation, precipitation, slope ratio, stream slope, and soil permeability. The adjusted coefficient of determination for the n-day high-flow regression equations ranged from 55.87 to 94.53 percent. The Chi2 values for the duration regression equations ranged from 13.49 to 117.94, whereas the Chi2 values for the n-day low-flow regression equations ranged from 4.20 to 49.68.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

Simultaneous determination of selegiline and desmethylselegiline in human body fluids by headspace solid-phase microextraction and gas chromatography-mass spectrometry.

PubMed

Kuriki, Ayako; Kumazawa, Takeshi; Lee, Xiao-Pen; Hasegawa, Chika; Kawamura, Mitsuru; Suzuki, Osamu; Sato, Keizo

2006-12-05

A method for the simultaneous determination of selegiline and its metabolite, desmethylselegiline, in human whole blood and urine is presented. The method, which combines a fiber-based headspace solid-phase microextraction (SPME) technique with gas chromatography-mass spectrometry (GC-MS), required optimization of various parameters (e.g., salt additives, extraction temperatures, extraction times and the extraction properties of the SPME fiber coatings). Pargyline was used as the internal standard. Extraction efficiencies for both selegiline and desmethylselegiline were 2.0-3.4% for whole blood, and 8.0-13.2% for urine. The regression equations for selegiline and desmethylselegiline extracted from whole blood were linear (r(2)=0.996 and 0.995) within the concentration ranges 0.1-10 and 0.2-20 ng/ml, respectively. For urine, the regression equations for selegiline and desmethylselegiline were linear (r(2)=0.999 and 0.998) within the concentration ranges 0.05-5.0 and 0.1-10 ng/ml, respectively. The limit of detection for selegiline and desmethylselegiline was 0.01-0.05 ng/ml for both samples. The lower and upper limits of quantification for each compound were 0.05-0.2 and 5-20 ng/ml, respectively. Intra- and inter-day coefficients of variation for selegiline and desmethylselegiline in both samples were not greater than 8.7 and 11.7%, respectively. The determination of selegiline and desmethylselegiline concentrations in Parkinson's disease patients undergoing continuous selegiline treatment is presented and is shown to validate the present methodology.

Reference values for airway resistance in newborns, infants and preschoolers from a Latin American population.

PubMed

Gochicoa, Laura G; Thomé-Ortiz, Laura P; Furuya, María E Y; Canto, Raquel; Ruiz-García, Martha E; Zúñiga-Vázquez, Guillermo; Martínez-Ramírez, Filiberto; Vargas, Mario H

2012-05-01

Several studies have determined reference values for airway resistance measured by the interrupter technique (Rint) in paediatric populations, but only one has been done on Latin American children, and no studies have been performed on Mexican children. Moreover, these previous studies mostly included children aged 3 years and older; therefore, information regarding Rint reference values for newborns and infants is scarce. Rint measurements were performed on preschool children attending eight kindergartens (Group 1) and also on sedated newborns, infants and preschool children admitted to a tertiary-level paediatric hospital due to non-cardiopulmonary disorders (Group 2). In both groups, Rint values were inversely associated with age, weight and height, but the strongest association was with height. The linear regression equation for Group 1 (n = 209, height 86-129 cm) was Rint = 2.153 - 0.012 × height (cm) (standard deviation of residuals 0.181 kPa/L/s). The linear regression equation for Group 2 (n = 55, height 52-113 cm) was Rint = 4.575 - 0.035 × height (cm) (standard deviation of residuals 0.567 kPa/L/s). Girls tended to have slightly higher Rint values than boys, a difference that diminished with increasing height. In this study, Rint reference values applicable to Mexican children were determined, and these values are probably also applicable to other paediatric populations with similar Spanish-Amerindian ancestries. There was an inverse relationship between Rint and height, with relatively large between-subject variability. © 2012 The Authors. Respirology © 2012 Asian Pacific Society of Respirology.

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

PubMed

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

The numerical solution of linear multi-term fractional differential equations: systems of equations

NASA Astrophysics Data System (ADS)

Edwards, John T.; Ford, Neville J.; Simpson, A. Charles

2002-11-01

In this paper, we show how the numerical approximation of the solution of a linear multi-term fractional differential equation can be calculated by reduction of the problem to a system of ordinary and fractional differential equations each of order at most unity. We begin by showing how our method applies to a simple class of problems and we give a convergence result. We solve the Bagley Torvik equation as an example. We show how the method can be applied to a general linear multi-term equation and give two further examples.

Comparison between light scattering and gravimetric samplers for PM10 mass concentration in poultry and pig houses

NASA Astrophysics Data System (ADS)

Cambra-López, María; Winkel, Albert; Mosquera, Julio; Ogink, Nico W. M.; Aarnink, André J. A.

2015-06-01

The objective of this study was to compare co-located real-time light scattering devices and equivalent gravimetric samplers in poultry and pig houses for PM10 mass concentration, and to develop animal-specific calibration factors for light scattering samplers. These results will contribute to evaluate the comparability of different sampling instruments for PM10 concentrations. Paired DustTrak light scattering device (DustTrak aerosol monitor, TSI, U.S.) and PM10 gravimetric cyclone sampler were used for measuring PM10 mass concentrations during 24 h periods (from noon to noon) inside animal houses. Sampling was conducted in 32 animal houses in the Netherlands, including broilers, broiler breeders, layers in floor and in aviary system, turkeys, piglets, growing-finishing pigs in traditional and low emission housing with dry and liquid feed, and sows in individual and group housing. A total of 119 pairs of 24 h measurements (55 for poultry and 64 for pigs) were recorded and analyzed using linear regression analysis. Deviations between samplers were calculated and discussed. In poultry, cyclone sampler and DustTrak data fitted well to a linear regression, with a regression coefficient equal to 0.41, an intercept of 0.16 mg m-3 and a correlation coefficient of 0.91 (excluding turkeys). Results in turkeys showed a regression coefficient equal to 1.1 (P = 0.49), an intercept of 0.06 mg m-3 (P < 0.0001) and a correlation coefficient of 0.98. In pigs, we found a regression coefficient equal to 0.61, an intercept of 0.05 mg m-3 and a correlation coefficient of 0.84. Measured PM10 concentrations using DustTraks were clearly underestimated (approx. by a factor 2) in both poultry and pig housing systems compared with cyclone pre-separators. Absolute, relative, and random deviations increased with concentration. DustTrak light scattering devices should be self-calibrated to investigate PM10 mass concentrations accurately in animal houses. We recommend linear regression equations as animal-specific calibration factors for DustTraks instead of manufacturer calibration factors, especially in heavily dusty environments such as animal houses.

New Equating Methods and Their Relationships with Levine Observed Score Linear Equating under the Kernel Equating Framework

ERIC Educational Resources Information Center

Chen, Haiwen; Holland, Paul

2010-01-01

In this paper, we develop a new curvilinear equating for the nonequivalent groups with anchor test (NEAT) design under the assumption of the classical test theory model, that we name curvilinear Levine observed score equating. In fact, by applying both the kernel equating framework and the mean preserving linear transformation of…

Adjustment of ionized calcium concentration for serum pH is not a valid marker of calcium homeostasis: implications for identifying individuals at risk of calcium metabolic disorders.

PubMed

Lam, Virginie; Dhaliwal, Satvinder S; Mamo, John C

2013-05-01

Ionized calcium (iCa) is the biologically active form of this micronutrient. Serum determination of iCa is measured via ion-electrode potentiometry (IEP) and reporting iCa relative to pH 7.4 is normally utilized to avoid the potential confounding effects of ex vivo changes to serum pH. Adjustment of iCa for pH has not been adequately justified. In this study, utilizing carefully standardized protocols for blood collection, the preparation of serum and controlling time of collection-to-analysis, we determined serum iCa and pH utilizing an IEP-analyser hosted at an accredited diagnostic laboratory. Regression analysis of unadjusted-iCa (iCa(raw)) concentration versus pH was described by linear regression and accounted for 37% of serum iCa(raw) variability. iCa(raw) was then expressed at pH 7.4 by either adjusting iCa(raw) based on the linear regression equation describing the association of iCa with serum pH (iCa(regr)) or using IEP coded published normative equations (iCa(pub)). iCa(regr) was comparable to iCa(raw), indicating that blood collection and processing methodologies were sound. However, iCa(pub) yielded values that were significantly lower than iCa(raw). iCa(pub) did not identify 15% subjects who had greater than desirable serum concentration of iCa based on iCa(raw). Sixty percent of subjects with low levels of iCa(raw) were also not detected by iCa(pub). Determination of the kappa value measure of agreement for iCa(raw) versus iCa(pub) showed relatively poor concordance (κ = 0.42). With simple protocols that avoid sampling artefacts, expressing iCa(raw) is likely to be a more valid and physiologically relevant marker of calcium homeostasis than is iCa(pub).

[Association of occupational chronic psychological stress with heat shock protein 70 in serum and tumor necrosis factor-alpha expression levels].

PubMed

Qiu, F Y; Tian, R L; Qiang, Y; He, K P; Liu, H R; Zhang, W; Song, H

2016-05-01

To investigate the relationship between occupational chronic psychological stress with heat shock protein 70 (HSP70) and tumor necrosis factor-alpha (TNF-α). Using case-control study design, we selected 622 cases in 20 to 60 years old and unrelated patients with metabolic syndrome as the case group between October 2011 and October 2012 at two hospitals of Ningxia hui autonomous region. At the same time, we selected 600 healthy people from health check-up crowd in the above two hospitals as control group. The the research objects were sex, age, nation, height, weight, smoking, drinking, exercise, and so on. After informed consent, all the research objects were collected fasting venous blood samples 10 ml in order to proceed laboratory testing of biochemical indicators. The expression of HSP70 and TNF-α in serum was determined by ELISA. Using the revised occupational stress inventory (OSI) to survey the occupational chronic psychological stress factors and stress level of research object. The correlation of occupational chronic psychological stress scores with HSP70 and TNF-α was investigated by partial correlation analysis. We built a multivariate linear regression equation With HSP70 and TNF alpha as the independent variable and occupational chronic psychological stress scores as the dependent variable, using equation of the determination coefficient R(2) to judge the degree of fitting equation. The total points of chronic stress factors in all respondents was (136.65±16.19). Among them, the mild stress level group was 313, moderate was 588, severe was 321, chronic heart stress factors scores were (119.96±13.30), (135.33±3.23), (155.33±13.55) points, respectively. In the case group subjects, the expression of HSP70 in mild, moderate and severe occupational chronic psychological stress levels were (29.88±30.08), (36.38±30.08), (27.16±23.77) ng/ml (F=6.85, P=0.001). The control group were (27.64±9.89), (39.78±29.77), (3.94±3.09) ng/ml (F=125.71, P<0.001). Multiple linear regression analysis showed the expression of psychological stress and HSP70 was a negative linear relationship, while positive linear relationship with TNF-α, the fitting of the regression equation was y=-0.07x1+ 0.011x2+ 136.88. Partial correlation analysis results showed that the occupational chronic psychological stress scores and negatively correlated with HSP70 (r=-0.11, P<0.001) and was positively related with TNF-α (r=0.11, P<0.001). In all survey respondents, the expression of HSP70 in mild, moderate and severe occupational chronic psychological stress levels group were (28.49±20.10), (37.99±29.96), (17.98±21.77) ng/ml (F=64.08, P<0.001). The expression of TNF-α were (133.61±129.51), (171.23±133.69), (169.31±196.09) pg/ml (F=6.93, P=0.001). The expression levels of HSP70 and TNF-α in serum were affected by occupational chronic psychological stress. While the level of occupational chronic psychological stress increased, the expression level of HSP70 in serum reduced, the expression level of TNF-α raised.

Gap-filling methods to impute eddy covariance flux data by preserving variance.

NASA Astrophysics Data System (ADS)

Kunwor, S.; Staudhammer, C. L.; Starr, G.; Loescher, H. W.

2015-12-01

To represent carbon dynamics, in terms of exchange of CO2 between the terrestrial ecosystem and the atmosphere, eddy covariance (EC) data has been collected using eddy flux towers from various sites across globe for more than two decades. However, measurements from EC data are missing for various reasons: precipitation, routine maintenance, or lack of vertical turbulence. In order to have estimates of net ecosystem exchange of carbon dioxide (NEE) with high precision and accuracy, robust gap-filling methods to impute missing data are required. While the methods used so far have provided robust estimates of the mean value of NEE, little attention has been paid to preserving the variance structures embodied by the flux data. Preserving the variance of these data will provide unbiased and precise estimates of NEE over time, which mimic natural fluctuations. We used a non-linear regression approach with moving windows of different lengths (15, 30, and 60-days) to estimate non-linear regression parameters for one year of flux data from a long-leaf pine site at the Joseph Jones Ecological Research Center. We used as our base the Michaelis-Menten and Van't Hoff functions. We assessed the potential physiological drivers of these parameters with linear models using micrometeorological predictors. We then used a parameter prediction approach to refine the non-linear gap-filling equations based on micrometeorological conditions. This provides us an opportunity to incorporate additional variables, such as vapor pressure deficit (VPD) and volumetric water content (VWC) into the equations. Our preliminary results indicate that improvements in gap-filling can be gained with a 30-day moving window with additional micrometeorological predictors (as indicated by lower root mean square error (RMSE) of the predicted values of NEE). Our next steps are to use these parameter predictions from moving windows to gap-fill the data with and without incorporation of potential driver variables of the parameters traditionally used. Then, comparisons of the predicted values from these methods and 'traditional' gap-filling methods (using 12 fixed monthly windows) will be assessed to show the scale of preserving variance. Further, this method will be applied to impute artificially created gaps for analyzing if variance is preserved.

A canonical form of the equation of motion of linear dynamical systems

NASA Astrophysics Data System (ADS)

Kawano, Daniel T.; Salsa, Rubens Goncalves; Ma, Fai; Morzfeld, Matthias

2018-03-01

The equation of motion of a discrete linear system has the form of a second-order ordinary differential equation with three real and square coefficient matrices. It is shown that, for almost all linear systems, such an equation can always be converted by an invertible transformation into a canonical form specified by two diagonal coefficient matrices associated with the generalized acceleration and displacement. This canonical form of the equation of motion is unique up to an equivalence class for non-defective systems. As an important by-product, a damped linear system that possesses three symmetric and positive definite coefficients can always be recast as an undamped and decoupled system.

An application of the Maslov complex germ method to the one-dimensional nonlocal Fisher-KPP equation

NASA Astrophysics Data System (ADS)

Shapovalov, A. V.; Trifonov, A. Yu.

A semiclassical approximation approach based on the Maslov complex germ method is considered in detail for the one-dimensional nonlocal Fisher-Kolmogorov-Petrovskii-Piskunov (Fisher-KPP) equation under the supposition of weak diffusion. In terms of the semiclassical formalism developed, the original nonlinear equation is reduced to an associated linear partial differential equation and some algebraic equations for the coefficients of the linear equation with a given accuracy of the asymptotic parameter. The solutions of the nonlinear equation are constructed from the solutions of both the linear equation and the algebraic equations. The solutions of the linear problem are found with the use of symmetry operators. A countable family of the leading terms of the semiclassical asymptotics is constructed in explicit form. The semiclassical asymptotics are valid by construction in a finite time interval. We construct asymptotics which are different from the semiclassical ones and can describe evolution of the solutions of the Fisher-KPP equation at large times. In the example considered, an initial unimodal distribution becomes multimodal, which can be treated as an example of a space structure.

Dynamic Density: An Air Traffic Management Metric

NASA Technical Reports Server (NTRS)

Laudeman, I. V.; Shelden, S. G.; Branstrom, R.; Brasil, C. L.

1998-01-01

The definition of a metric of air traffic controller workload based on air traffic characteristics is essential to the development of both air traffic management automation and air traffic procedures. Dynamic density is a proposed concept for a metric that includes both traffic density (a count of aircraft in a volume of airspace) and traffic complexity (a measure of the complexity of the air traffic in a volume of airspace). It was hypothesized that a metric that includes terms that capture air traffic complexity will be a better measure of air traffic controller workload than current measures based only on traffic density. A weighted linear dynamic density function was developed and validated operationally. The proposed dynamic density function includes a traffic density term and eight traffic complexity terms. A unit-weighted dynamic density function was able to account for an average of 22% of the variance in observed controller activity not accounted for by traffic density alone. A comparative analysis of unit weights, subjective weights, and regression weights for the terms in the dynamic density equation was conducted. The best predictor of controller activity was the dynamic density equation with regression-weighted complexity terms.

Prediction of rectal temperature using non-invasive physiologic variable measurements in hair pregnant ewes subjected to natural conditions of heat stress.

PubMed

Vicente-Pérez, Ricardo; Avendaño-Reyes, Leonel; Mejía-Vázquez, Ángel; Álvarez-Valenzuela, F Daniel; Correa-Calderón, Abelardo; Mellado, Miguel; Meza-Herrera, Cesar A; Guerra-Liera, Juan E; Robinson, P H; Macías-Cruz, Ulises

2016-01-01

Rectal temperature (RT) is the foremost physiological variable indicating if an animal is suffering hyperthermia. However, this variable is traditionally measured by invasive methods, which may compromise animal welfare. Models to predict RT have been developed for growing pigs and lactating dairy cows, but not for pregnant heat-stressed ewes. Our aim was to develop a prediction equation for RT using non-invasive physiological variables in pregnant ewes under heat stress. A total of 192 records of respiratory frequency (RF) and hair coat temperature in various body regions (i.e., head, rump, flank, shoulder, and belly) obtained from 24 Katahdin × Pelibuey pregnant multiparous ewes were collected during the last third of gestation (i.e., d 100 to lambing) with a 15 d sampling interval. Hair coat temperatures were taken using infrared thermal imaging technology. Initially, a Pearson correlation analysis examined the relationship among variables, and then multiple linear regression analysis was used to develop the prediction equations. All predictor variables were positively correlated (P<0.01; r=0.59-0.67) with RT. The adjusted equation which best predicted RT (P<0.01; Radj(2)=56.15%; CV=0.65%) included as predictors RF and head and belly temperatures. Comparison of predicted and observed values for RT indicates a suitable agreement (P<0.01) between them with moderate accuracy (Radj(2)=56.15%) when RT was calculated with the adjusted equation. In general, the final equation does not violate any assumption of multiple regression analysis. The RT in heat-stressed pregnant ewes can be predicted with an adequate accuracy using non-invasive physiologic variables, and the final equation was: RT=35.57+0.004 (RF)+0.067 (heat temperature)+0.028 (belly temperature). Copyright © 2015 Elsevier Ltd. All rights reserved.

Operator Factorization and the Solution of Second-Order Linear Ordinary Differential Equations

ERIC Educational Resources Information Center

Robin, W.

2007-01-01

The theory and application of second-order linear ordinary differential equations is reviewed from the standpoint of the operator factorization approach to the solution of ordinary differential equations (ODE). Using the operator factorization approach, the general second-order linear ODE is solved, exactly, in quadratures and the resulting…

Estimating the total energy demand for supra-maximal exercise using the VO2-power regression from an incremental exercise test.

PubMed

Aisbett, B; Le Rossignol, P

2003-09-01

The VO2-power regression and estimated total energy demand for a 6-minute supra-maximal exercise test was predicted from a continuous incremental exercise test. Sub-maximal VO2-power co-ordinates were established from the last 40 seconds (s) of 150-second exercise stages. The precision of the estimated total energy demand was determined using the 95% confidence interval (95% CI) of the estimated total energy demand. The linearity of the individual VO2-power regression equations was determined using Pearson's correlation coefficient. The mean 95% CI of the estimated total energy demand was 5.9 +/- 2.5 mL O2 Eq x kg(-1) x min(-1), and the mean correlation coefficient was 0.9942 +/- 0.0042. The current study contends that the sub-maximal VO2-power co-ordinates from a continuous incremental exercise test can be used to estimate supra-maximal energy demand without compromising the precision of the accumulated oxygen deficit (AOD) method.

Fractional differential equations based modeling of microbial survival and growth curves: model development and experimental validation.

PubMed

Kaur, A; Takhar, P S; Smith, D M; Mann, J E; Brashears, M M

2008-10-01

A fractional differential equations (FDEs)-based theory involving 1- and 2-term equations was developed to predict the nonlinear survival and growth curves of foodborne pathogens. It is interesting to note that the solution of 1-term FDE leads to the Weibull model. Nonlinear regression (Gauss-Newton method) was performed to calculate the parameters of the 1-term and 2-term FDEs. The experimental inactivation data of Salmonella cocktail in ground turkey breast, ground turkey thigh, and pork shoulder; and cocktail of Salmonella, E. coli, and Listeria monocytogenes in ground beef exposed at isothermal cooking conditions of 50 to 66 degrees C were used for validation. To evaluate the performance of 2-term FDE in predicting the growth curves-growth of Salmonella typhimurium, Salmonella Enteritidis, and background flora in ground pork and boneless pork chops; and E. coli O157:H7 in ground beef in the temperature range of 22.2 to 4.4 degrees C were chosen. A program was written in Matlab to predict the model parameters and survival and growth curves. Two-term FDE was more successful in describing the complex shapes of microbial survival and growth curves as compared to the linear and Weibull models. Predicted curves of 2-term FDE had higher magnitudes of R(2) (0.89 to 0.99) and lower magnitudes of root mean square error (0.0182 to 0.5461) for all experimental cases in comparison to the linear and Weibull models. This model was capable of predicting the tails in survival curves, which was not possible using Weibull and linear models. The developed model can be used for other foodborne pathogens in a variety of food products to study the destruction and growth behavior.

Resting energy expenditure prediction in recreational athletes of 18-35 years: confirmation of Cunningham equation and an improved weight-based alternative.

PubMed

ten Haaf, Twan; Weijs, Peter J M

2014-01-01

Resting energy expenditure (REE) is expected to be higher in athletes because of their relatively high fat free mass (FFM). Therefore, REE predictive equation for recreational athletes may be required. The aim of this study was to validate existing REE predictive equations and to develop a new recreational athlete specific equation. 90 (53 M, 37 F) adult athletes, exercising on average 9.1 ± 5.0 hours a week and 5.0 ± 1.8 times a week, were included. REE was measured using indirect calorimetry (Vmax Encore n29), FFM and FM were measured using air displacement plethysmography. Multiple linear regression analysis was used to develop a new FFM-based and weight-based REE predictive equation. The percentage accurate predictions (within 10% of measured REE), percentage bias, root mean square error and limits of agreement were calculated. Results: The Cunningham equation and the new weight-based equation REE(kJ / d) = 49.940* weight(kg) + 2459.053* height(m) - 34.014* age(y) + 799.257* sex(M = 1,F = 0) + 122.502 and the new FFM-based equation REE(kJ / d) = 95.272*FFM(kg) + 2026.161 performed equally well. De Lorenzo's equation predicted REE less accurate, but better than the other generally used REE predictive equations. Harris-Benedict, WHO, Schofield, Mifflin and Owen all showed less than 50% accuracy. For a population of (Dutch) recreational athletes, the REE can accurately be predicted with the existing Cunningham equation. Since body composition measurement is not always possible, and other generally used equations fail, the new weight-based equation is advised for use in sports nutrition.

Fast and local non-linear evolution of steep wave-groups on deep water: A comparison of approximate models to fully non-linear simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adcock, T. A. A.; Taylor, P. H.

2016-01-15

The non-linear Schrödinger equation and its higher order extensions are routinely used for analysis of extreme ocean waves. This paper compares the evolution of individual wave-packets modelled using non-linear Schrödinger type equations with packets modelled using fully non-linear potential flow models. The modified non-linear Schrödinger Equation accurately models the relatively large scale non-linear changes to the shape of wave-groups, with a dramatic contraction of the group along the mean propagation direction and a corresponding extension of the width of the wave-crests. In addition, as extreme wave form, there is a local non-linear contraction of the wave-group around the crest whichmore » leads to a localised broadening of the wave spectrum which the bandwidth limited non-linear Schrödinger Equations struggle to capture. This limitation occurs for waves of moderate steepness and a narrow underlying spectrum.« less

The condition of regular degeneration for singularly perturbed systems of linear differential-difference equations.

NASA Technical Reports Server (NTRS)

Cooke, K. L.; Meyer, K. R.

1966-01-01

Extension of problem of singular perturbation for linear scalar constant coefficient differential- difference equation with single retardation to several retardations, noting degenerate equation solution

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

A Few New 2+1-Dimensional Nonlinear Dynamics and the Representation of Riemann Curvature Tensors

NASA Astrophysics Data System (ADS)

Wang, Yan; Zhang, Yufeng; Zhang, Xiangzhi

2016-09-01

We first introduced a linear stationary equation with a quadratic operator in ∂x and ∂y, then a linear evolution equation is given by N-order polynomials of eigenfunctions. As applications, by taking N=2, we derived a (2+1)-dimensional generalized linear heat equation with two constant parameters associative with a symmetric space. When taking N=3, a pair of generalized Kadomtsev-Petviashvili equations with the same eigenvalues with the case of N=2 are generated. Similarly, a second-order flow associative with a homogeneous space is derived from the integrability condition of the two linear equations, which is a (2+1)-dimensional hyperbolic equation. When N=3, the third second flow associative with the homogeneous space is generated, which is a pair of new generalized Kadomtsev-Petviashvili equations. Finally, as an application of a Hermitian symmetric space, we established a pair of spectral problems to obtain a new (2+1)-dimensional generalized Schrödinger equation, which is expressed by the Riemann curvature tensors.

Solution of Volterra and Fredholm Classes of Equations via Triangular Orthogonal Function (A Combination of Right Hand Triangular Function and Left Hand Triangular Function) and Hybrid Orthogonal Function (A Combination of Sample Hold Function and Right Hand Triangular Function)

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Anirban; Ganguly, Anindita; Chatterjee, Saumya Deep

2018-04-01

In this paper the authors have dealt with seven kinds of non-linear Volterra and Fredholm classes of equations. The authors have formulated an algorithm for solving the aforementioned equation types via Hybrid Function (HF) and Triangular Function (TF) piecewise-linear orthogonal approach. In this approach the authors have reduced integral equation or integro-differential equation into equivalent system of simultaneous non-linear equation and have employed either Newton's method or Broyden's method to solve the simultaneous non-linear equations. The authors have calculated the L2-norm error and the max-norm error for both HF and TF method for each kind of equations. Through the illustrated examples, the authors have shown that the HF based algorithm produces stable result, on the contrary TF-computational method yields either stable, anomalous or unstable results.

Simulation of relationship between river discharge and sediment yield in the semi-arid river watersheds

NASA Astrophysics Data System (ADS)

Khaleghi, Mohammad Reza; Varvani, Javad

2018-02-01

Complex and variable nature of the river sediment yield caused many problems in estimating the long-term sediment yield and problems input into the reservoirs. Sediment Rating Curves (SRCs) are generally used to estimate the suspended sediment load of the rivers and drainage watersheds. Since the regression equations of the SRCs are obtained by logarithmic retransformation and have a little independent variable in this equation, they also overestimate or underestimate the true sediment load of the rivers. To evaluate the bias correction factors in Kalshor and Kashafroud watersheds, seven hydrometric stations of this region with suitable upstream watershed and spatial distribution were selected. Investigation of the accuracy index (ratio of estimated sediment yield to observed sediment yield) and the precision index of different bias correction factors of FAO, Quasi-Maximum Likelihood Estimator (QMLE), Smearing, and Minimum-Variance Unbiased Estimator (MVUE) with LSD test showed that FAO coefficient increases the estimated error in all of the stations. Application of MVUE in linear and mean load rating curves has not statistically meaningful effects. QMLE and smearing factors increased the estimated error in mean load rating curve, but that does not have any effect on linear rating curve estimation.

Linearized-moment analysis of the temperature jump and temperature defect in the Knudsen layer of a rarefied gas.

PubMed

Gu, Xiao-Jun; Emerson, David R

2014-06-01

Understanding the thermal behavior of a rarefied gas remains a fundamental problem. In the present study, we investigate the predictive capabilities of the regularized 13 and 26 moment equations. In this paper, we consider low-speed problems with small gradients, and to simplify the analysis, a linearized set of moment equations is derived to explore a classic temperature problem. Analytical solutions obtained for the linearized 26 moment equations are compared with available kinetic models and can reliably capture all qualitative trends for the temperature-jump coefficient and the associated temperature defect in the thermal Knudsen layer. In contrast, the linearized 13 moment equations lack the necessary physics to capture these effects and consistently underpredict kinetic theory. The deviation from kinetic theory for the 13 moment equations increases significantly for specular reflection of gas molecules, whereas the 26 moment equations compare well with results from kinetic theory. To improve engineering analyses, expressions for the effective thermal conductivity and Prandtl number in the Knudsen layer are derived with the linearized 26 moment equations.

Steady-state flow distribution and monthly flow duration in selected branches of St. Clair and Detroit rivers within the Great Lakes waterway

USGS Publications Warehouse

Holtschlag, D.J.; Koschik, J.A.

2001-01-01

St. Clair and Detroit Rivers are connecting channels between Lake Huron and Lake Erie in the Great Lakes waterway, and form part of the boundary between the United States and Canada. St. Clair River, the upper connecting channel, drains 222,400 square miles and has an average flow of about 182,000 cubic feet per second. Water from St. Clair River combines with local inflows and discharges into Lake St. Clair before flowing into Detroit River. In some reaches of St. Clair and Detroit Rivers, islands and dikes split the flow into two to four branches. Even when the flow in a reach is known, proportions of flows within individual branches of a reach are uncertain. Simple linear regression equations, subject to a flow continuity constraint, are developed to provide estimators of these proportions and flows. The equations are based on 533 paired measurements of flow in 13 reaches forming 31 branches. The equations provide a means for computing the expected values and uncertainties of steady-state flows on the basis of flow conditions specified at the upstream boundaries of the waterway. In 7 upstream reaches, flow is considered fixed because it can be determined on the basis of flows specified at waterway boundaries and flow continuity. In these reaches, the uncertainties of flow proportions indicated by the regression equations can be used directly to determine the uncertainties of the corresponding flows. In the remaining 6 downstream reaches, flow is considered uncertain because these reaches do not receive flow from all the branches of an upstream reach, or they receive flow from some branches of more than one upstream reach. Monte Carlo simulation analysis is used to quantify this increase in uncertainty associated with the propagation of uncertainties from upstream reaches to downstream reaches. To eliminate the need for Monte Carlo simulations for routine calculations, polynomial regression equations are developed to approximate the variation in uncertainties as a function of flow at the headwaters of St. Clair River. Finally, monthly flow-duration data on the main channels of St. Clair and Detroit Rivers are used with the equations developed in this report to estimate the steady-state flow-duration characteristics of selected branches.

Estimating design-flood discharges for streams in Iowa using drainage-basin and channel-geometry characteristics

USGS Publications Warehouse

Eash, D.A.

1993-01-01

Procedures provided for applying the drainage-basin and channel-geometry regression equations depend on whether the design-flood discharge estimate is for a site on an ungaged stream, an ungaged site on a gaged stream, or a gaged site. When both a drainage-basin and a channel-geometry regression-equation estimate are available for a stream site, a procedure is presented for determining a weighted average of the two flood estimates. The drainage-basin regression equations are applicable to unregulated rural drainage areas less than 1,060 square miles, and the channel-geometry regression equations are applicable to unregulated rural streams in Iowa with stabilized channels.

Estimating the effects of wages on obesity.

PubMed

Kim, DaeHwan; Leigh, John Paul

2010-05-01

To estimate the effects of wages on obesity and body mass. Data on household heads, aged 20 to 65 years, with full-time jobs, were drawn from the Panel Study of Income Dynamics for 2003 to 2007. The Panel Study of Income Dynamics is a nationally representative sample. Instrumental variables (IV) for wages were created using knowledge of computer software and state legal minimum wages. Least squares (linear regression) with corrected standard errors were used to estimate the equations. Statistical tests revealed both instruments were strong and tests for over-identifying restrictions were favorable. Wages were found to be predictive (P < 0.05) of obesity and body mass in regressions both before and after applying IVs. Coefficient estimates suggested stronger effects in the IV models. Results are consistent with the hypothesis that low wages increase obesity prevalence and body mass.

Encouraging Students to Think Strategically when Learning to Solve Linear Equations

ERIC Educational Resources Information Center

Robson, Daphne; Abell, Walt; Boustead, Therese

2012-01-01

Students who are preparing to study science and engineering need to understand equation solving but adult students returning to study can find this difficult. In this paper, the design of an online resource, Equations2go, for helping students learn to solve linear equations is investigated. Students learning to solve equations need to consider…

FAST TRACK COMMUNICATION Quasi self-adjoint nonlinear wave equations

NASA Astrophysics Data System (ADS)

Ibragimov, N. H.; Torrisi, M.; Tracinà, R.

2010-11-01

In this paper we generalize the classification of self-adjoint second-order linear partial differential equation to a family of nonlinear wave equations with two independent variables. We find a class of quasi self-adjoint nonlinear equations which includes the self-adjoint linear equations as a particular case. The property of a differential equation to be quasi self-adjoint is important, e.g. for constructing conservation laws associated with symmetries of the differential equation.

ADM For Solving Linear Second-Order Fredholm Integro-Differential Equations

NASA Astrophysics Data System (ADS)

Karim, Mohd F.; Mohamad, Mahathir; Saifullah Rusiman, Mohd; Che-Him, Norziha; Roslan, Rozaini; Khalid, Kamil

2018-04-01

In this paper, we apply Adomian Decomposition Method (ADM) as numerically analyse linear second-order Fredholm Integro-differential Equations. The approximate solutions of the problems are calculated by Maple package. Some numerical examples have been considered to illustrate the ADM for solving this equation. The results are compared with the existing exact solution. Thus, the Adomian decomposition method can be the best alternative method for solving linear second-order Fredholm Integro-Differential equation. It converges to the exact solution quickly and in the same time reduces computational work for solving the equation. The result obtained by ADM shows the ability and efficiency for solving these equations.

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Barrett, Joe H., III; Roeder, William P.

2010-01-01

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

The association of genetic variants of type 2 diabetes with kidney function.

PubMed

Franceschini, Nora; Shara, Nawar M; Wang, Hong; Voruganti, V Saroja; Laston, Sandy; Haack, Karin; Lee, Elisa T; Best, Lyle G; Maccluer, Jean W; Cochran, Barbara J; Dyer, Thomas D; Howard, Barbara V; Cole, Shelley A; North, Kari E; Umans, Jason G

2012-07-01

Type 2 diabetes is highly prevalent and is the major cause of progressive chronic kidney disease in American Indians. Genome-wide association studies identified several loci associated with diabetes but their impact on susceptibility to diabetic complications is unknown. We studied the association of 18 type 2 diabetes genome-wide association single-nucleotide polymorphisms (SNPs) with estimated glomerular filtration rate (eGFR; MDRD equation) and urine albumin-to-creatinine ratio in 6958 Strong Heart Study family and cohort participants. Center-specific residuals of eGFR and log urine albumin-to-creatinine ratio, obtained from linear regression models adjusted for age, sex, and body mass index, were regressed onto SNP dosage using variance component models in family data and linear regression in unrelated individuals. Estimates were then combined across centers. Four diabetic loci were associated with eGFR and one locus with urine albumin-to-creatinine ratio. A SNP in the WFS1 gene (rs10010131) was associated with higher eGFR in younger individuals and with increased albuminuria. SNPs in the FTO, KCNJ11, and TCF7L2 genes were associated with lower eGFR, but not albuminuria, and were not significant in prospective analyses. Our findings suggest a shared genetic risk for type 2 diabetes and its kidney complications, and a potential role for WFS1 in early-onset diabetic nephropathy in American Indian populations.

S{sub 2}SA preconditioning for the S{sub n} equations with strictly non negative spatial discretization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bruss, D. E.; Morel, J. E.; Ragusa, J. C.

2013-07-01

Preconditioners based upon sweeps and diffusion-synthetic acceleration have been constructed and applied to the zeroth and first spatial moments of the 1-D S{sub n} transport equation using a strictly non negative nonlinear spatial closure. Linear and nonlinear preconditioners have been analyzed. The effectiveness of various combinations of these preconditioners are compared. In one dimension, nonlinear sweep preconditioning is shown to be superior to linear sweep preconditioning, and DSA preconditioning using nonlinear sweeps in conjunction with a linear diffusion equation is found to be essentially equivalent to nonlinear sweeps in conjunction with a nonlinear diffusion equation. The ability to use amore » linear diffusion equation has important implications for preconditioning the S{sub n} equations with a strictly non negative spatial discretization in multiple dimensions. (authors)« less

Time and frequency domain analysis of sampled data controllers via mixed operation equations

NASA Technical Reports Server (NTRS)

Frisch, H. P.

1981-01-01

Specification of the mathematical equations required to define the dynamic response of a linear continuous plant, subject to sampled data control, is complicated by the fact that the digital components of the control system cannot be modeled via linear ordinary differential equations. This complication can be overcome by introducing two new mathematical operations; namely, the operation of zero order hold and digial delay. It is shown that by direct utilization of these operations, a set of linear mixed operation equations can be written and used to define the dynamic response characteristics of the controlled system. It also is shown how these linear mixed operation equations lead, in an automatable manner, directly to a set of finite difference equations which are in a format compatible with follow on time and frequency domain analysis methods.

Competing regression models for longitudinal data.

PubMed

Alencar, Airlane P; Singer, Julio M; Rocha, Francisco Marcelo M

2012-03-01

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretest-posttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Linear Equating for the NEAT Design: Parameter Substitution Models and Chained Linear Relationship Models

ERIC Educational Resources Information Center

Kane, Michael T.; Mroch, Andrew A.; Suh, Youngsuk; Ripkey, Douglas R.

2009-01-01

This paper analyzes five linear equating models for the "nonequivalent groups with anchor test" (NEAT) design with internal anchors (i.e., the anchor test is part of the full test). The analysis employs a two-dimensional framework. The first dimension contrasts two general approaches to developing the equating relationship. Under a "parameter…

Supporting Students' Understanding of Linear Equations with One Variable Using Algebra Tiles

ERIC Educational Resources Information Center

Saraswati, Sari; Putri, Ratu Ilma Indra; Somakim

2016-01-01

This research aimed to describe how algebra tiles can support students' understanding of linear equations with one variable. This article is a part of a larger research on learning design of linear equations with one variable using algebra tiles combined with balancing method. Therefore, it will merely discuss one activity focused on how students…

Regression Simulation Model. Appendix X. Users Manual,

DTIC Science & Technology

1981-03-01

change as the prediction equations become refined. Whereas no notice will be provided when the changes are made, the programs will be modified such that...NATIONAL BUREAU Of STANDARDS 1963 A ___,_ __ _ __ _ . APPENDIX X ( R4/ EGRESSION IMULATION ’jDEL. Ape’A ’) 7 USERS MANUA submitted to The Great River...regression analysis and to establish a prediction equation (model). The prediction equation contains the partial regression coefficients (B-weights) which

Effects of acceleration in the Gz axis on human cardiopulmonary responses to exercise.

PubMed

Bonjour, Julien; Bringard, Aurélien; Antonutto, Guglielmo; Capelli, Carlo; Linnarsson, Dag; Pendergast, David R; Ferretti, Guido

2011-12-01

The aim of this paper was to develop a model from experimental data allowing a prediction of the cardiopulmonary responses to steady-state submaximal exercise in varying gravitational environments, with acceleration in the G(z) axis (a (g)) ranging from 0 to 3 g. To this aim, we combined data from three different experiments, carried out at Buffalo, at Stockholm and inside the Mir Station. Oxygen consumption, as expected, increased linearly with a (g). In contrast, heart rate increased non-linearly with a (g), whereas stroke volume decreased non-linearly: both were described by quadratic functions. Thus, the relationship between cardiac output and a (g) was described by a fourth power regression equation. Mean arterial pressure increased with a (g) non linearly, a relation that we interpolated again with a quadratic function. Thus, total peripheral resistance varied linearly with a (g). These data led to predict that maximal oxygen consumption would decrease drastically as a (g) is increased. Maximal oxygen consumption would become equal to resting oxygen consumption when a (g) is around 4.5 g, thus indicating the practical impossibility for humans to stay and work on the biggest Planets of the Solar System.

Prediction equation for calculating fat mass in young Indian adults.

PubMed

Sandhu, Jaspal Singh; Gupta, Giniya; Shenoy, Shweta

2010-06-01

Accurate measurement or prediction of fat mass is useful in physiology, nutrition and clinical medicine. Most predictive equations currently used to assess percentage of body fat or fat mass, using simple anthropometric measurements were derived from people in western societies and they may not be appropriate for individuals with other genotypic and phenotypic characteristics. We developed equations to predict fat mass from anthropometric measurements in young Indian adults. Fat mass was measured in 60 females and 58 males, aged 20 to 29 yrs by using hydrostatic weighing and by simultaneous measurement of residual lung volume. Anthropometric measure included weight (kg), height (m) and 4 skinfold thickness [STs (mm)]. Sex specific linear regression model was developed with fat mass as the dependent variable and all anthropometric measures as independent variables. The prediction equation obtained for fat mass (kg) for males was 8.46+0.32 (weight) - 15.16 (height) + 9.54 (log of sum of 4 STs) (R2= 0. 53, SEE=3.42 kg) and - 20.22 + 0.33 (weight) + 3.44 (height) + 7.66 (log of sum of 4 STs) (R2=0.72, SEE=3.01kg) for females. A new prediction equation for the measurement of fat mass was derived and internally validated in young Indian adults using simple anthropometric measurements.

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

PubMed

Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

2011-06-01

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

PubMed Central

2011-01-01

Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems. PMID:21627852

Hydrometer test for estimation of immunoglobulin concentration in bovine colostrum.

PubMed

Fleenor, W A; Stott, G H

1980-06-01

A practical field method for measuring immunoglobulin concentration in bovine colostrum has been developed from the linear relationship between colostral specific gravity and immunoglobulin concentration. Fourteen colostrums were collected within 24 h postpartum from nursed and unnursed cows and were assayed for specific gravity and major colostral constituents. Additionally, 15 colostrums were collected immediately postpartum prior to suckling and assayed for specific gravity and immunoglobulin concentration. Regression analysis provided an equation to estimate colostral immunoglobulin concentration from the specific gravity of fresh whole colostrum. From this, a colostrometer was developed for practical field use.

A parameter estimation subroutine package

NASA Technical Reports Server (NTRS)

Bierman, G. J.; Nead, M. W.

1978-01-01

Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. A library of FORTRAN subroutines were developed to facilitate analyses of a variety of estimation problems. An easy to use, multi-purpose set of algorithms that are reasonably efficient and which use a minimal amount of computer storage are presented. Subroutine inputs, outputs, usage and listings are given, along with examples of how these routines can be used. The routines are compact and efficient and are far superior to the normal equation and Kalman filter data processing algorithms that are often used for least squares analyses.

A long lifetime chemical sensor: study on fluorescence property of fluorescein isothiocyanate and preparation of pH chemical sensor.

PubMed

Ma, Li Ying; Wang, Huai You; Xie, Hui; Xu, Li Xiao

2004-07-01

The fluorescence property of fluorescein isothiocyanate (FITC) in acid-alkaline medium was studied by spectrofluorimetry. The characteristic of FITC response to hydrogen ion has been examined in acid-alkaline solution. A novel pH chemical sensor was prepared based on the relationship between the relative fluorescence intensity of FITC and pH. The measurement of relative fluorescence intensity was carried out at 362 nm with excitation at 250 nm. The excellent linear relationship was obtained between relative fluorescence intensity and pH in the range of pH 1-5. The linear regression equation of the calibration graph is F = 66.871 + 6.605 pH (F is relative fluorescence intensity), with a correlation coefficient of linear regression of 0.9995. Effects of temperature, concentration of FITC on the response to hydrogen ion had been examined. It was important that this chemical sensor was long lifetime, and the property of response to hydrogen ion was stable for at least 70 days. This pH sensor can be used for measuring pH value in water solution. The accuracy is 0.01 pH unit. The results obtained by the pH sensor agreed with those by the pH meter. Obviously, this pH sensor is potential for determining pH change real time in biological system.

A long lifetime chemical sensor: study on fluorescence property of fluorescein isothiocyanate and preparation of pH chemical sensor

NASA Astrophysics Data System (ADS)

Ma, Li Ying; Wang, Huai You; Xie, Hui; Xu, Li Xiao

2004-07-01

The fluorescence property of fluorescein isothiocyanate (FITC) in acid-alkaline medium was studied by spectrofluorimetry. The characteristic of FITC response to hydrogen ion has been examined in acid-alkaline solution. A novel pH chemical sensor was prepared based on the relationship between the relative fluorescence intensity of FITC and pH. The measurement of relative fluorescence intensity was carried out at 362 nm with excitation at 250 nm. The excellent linear relationship was obtained between relative fluorescence intensity and pH in the range of pH 1-5. The linear regression equation of the calibration graph is F=66.871+6.605 pH ( F is relative fluorescence intensity), with a correlation coefficient of linear regression of 0.9995. Effects of temperature, concentration of FITC on the response to hydrogen ion had been examined. It was important that this chemical sensor was long lifetime, and the property of response to hydrogen ion was stable for at least 70 days. This pH sensor can be used for measuring pH value in water solution. The accuracy is 0.01 pH unit. The results obtained by the pH sensor agreed with those by the pH meter. Obviously, this pH sensor is potential for determining pH change real time in biological system.

LINEAR - DERIVATION AND DEFINITION OF A LINEAR AIRCRAFT MODEL

NASA Technical Reports Server (NTRS)

Duke, E. L.

1994-01-01

The Derivation and Definition of a Linear Model program, LINEAR, provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models. LINEAR was developed to provide a standard, documented, and verified tool to derive linear models for aircraft stability analysis and control law design. Linear system models define the aircraft system in the neighborhood of an analysis point and are determined by the linearization of the nonlinear equations defining vehicle dynamics and sensors. LINEAR numerically determines a linear system model using nonlinear equations of motion and a user supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. LINEAR is capable of extracting both linearized engine effects, such as net thrust, torque, and gyroscopic effects and including these effects in the linear system model. The point at which this linear model is defined is determined either by completely specifying the state and control variables, or by specifying an analysis point on a trajectory and directing the program to determine the control variables and the remaining state variables. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to provide easy selection of state, control, and observation variables to be used in a particular model. Thus, the order of the system model is completely under user control. Further, the program provides the flexibility of allowing alternate formulations of both the state and observation equations. Data describing the aircraft and the test case is input to the program through a terminal or formatted data files. All data can be modified interactively from case to case. The aerodynamic model can be defined in two ways: a set of nondimensional stability and control derivatives for the flight point of interest, or a full non-linear aerodynamic model as used in simulations. LINEAR is written in FORTRAN and has been implemented on a DEC VAX computer operating under VMS with a virtual memory requirement of approximately 296K of 8 bit bytes. Both an interactive and batch version are included. LINEAR was developed in 1988.

Optical soliton solutions of the cubic-quintic non-linear Schrödinger's equation including an anti-cubic term

NASA Astrophysics Data System (ADS)

Kaplan, Melike; Hosseini, Kamyar; Samadani, Farzan; Raza, Nauman

2018-07-01

A wide range of problems in different fields of the applied sciences especially non-linear optics is described by non-linear Schrödinger's equations (NLSEs). In the present paper, a specific type of NLSEs known as the cubic-quintic non-linear Schrödinger's equation including an anti-cubic term has been studied. The generalized Kudryashov method along with symbolic computation package has been exerted to carry out this objective. As a consequence, a series of optical soliton solutions have formally been retrieved. It is corroborated that the generalized form of Kudryashov method is a direct, effectual, and reliable technique to deal with various types of non-linear Schrödinger's equations.

Prediction of Undsteady Flows in Turbomachinery Using the Linearized Euler Equations on Deforming Grids

NASA Technical Reports Server (NTRS)

Clark, William S.; Hall, Kenneth C.

1994-01-01

A linearized Euler solver for calculating unsteady flows in turbomachinery blade rows due to both incident gusts and blade motion is presented. The model accounts for blade loading, blade geometry, shock motion, and wake motion. Assuming that the unsteadiness in the flow is small relative to the nonlinear mean solution, the unsteady Euler equations can be linearized about the mean flow. This yields a set of linear variable coefficient equations that describe the small amplitude harmonic motion of the fluid. These linear equations are then discretized on a computational grid and solved using standard numerical techniques. For transonic flows, however, one must use a linear discretization which is a conservative linearization of the non-linear discretized Euler equations to ensure that shock impulse loads are accurately captured. Other important features of this analysis include a continuously deforming grid which eliminates extrapolation errors and hence, increases accuracy, and a new numerically exact, nonreflecting far-field boundary condition treatment based on an eigenanalysis of the discretized equations. Computational results are presented which demonstrate the computational accuracy and efficiency of the method and demonstrate the effectiveness of the deforming grid, far-field nonreflecting boundary conditions, and shock capturing techniques. A comparison of the present unsteady flow predictions to other numerical, semi-analytical, and experimental methods shows excellent agreement. In addition, the linearized Euler method presented requires one or two orders-of-magnitude less computational time than traditional time marching techniques making the present method a viable design tool for aeroelastic analyses.

Discrete integration of continuous Kalman filtering equations for time invariant second-order structural systems

NASA Technical Reports Server (NTRS)

Park, K. C.; Belvin, W. Keith

1990-01-01

A general form for the first-order representation of the continuous second-order linear structural-dynamics equations is introduced to derive a corresponding form of first-order continuous Kalman filtering equations. Time integration of the resulting equations is carried out via a set of linear multistep integration formulas. It is shown that a judicious combined selection of computational paths and the undetermined matrices introduced in the general form of the first-order linear structural systems leads to a class of second-order discrete Kalman filtering equations involving only symmetric sparse N x N solution matrices.

Methods for estimating selected low-flow frequency statistics and harmonic mean flows for streams in Iowa

USGS Publications Warehouse

Eash, David A.; Barnes, Kimberlee K.

2017-01-01

A statewide study was conducted to develop regression equations for estimating six selected low-flow frequency statistics and harmonic mean flows for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include: the annual 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years, the annual 30-day mean low flow for a recurrence interval of 5 years, and the seasonal (October 1 through December 31) 1- and 7-day mean low flows for a recurrence interval of 10 years. Estimation equations also were developed for the harmonic-mean-flow statistic. Estimates of these seven selected statistics are provided for 208 U.S. Geological Survey continuous-record streamgages using data through September 30, 2006. The study area comprises streamgages located within Iowa and 50 miles beyond the State's borders. Because trend analyses indicated statistically significant positive trends when considering the entire period of record for the majority of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. The median number of years of record used to compute each of these seven selected statistics was 35. Geographic information system software was used to measure 54 selected basin characteristics for each streamgage. Following the removal of two streamgages from the initial data set, data collected for 206 streamgages were compiled to investigate three approaches for regionalization of the seven selected statistics. Regionalization, a process using statistical regression analysis, provides a relation for efficiently transferring information from a group of streamgages in a region to ungaged sites in the region. The three regionalization approaches tested included statewide, regional, and region-of-influence regressions. For the regional regression, the study area was divided into three low-flow regions on the basis of hydrologic characteristics, landform regions, and soil regions. A comparison of root mean square errors and average standard errors of prediction for the statewide, regional, and region-of-influence regressions determined that the regional regression provided the best estimates of the seven selected statistics at ungaged sites in Iowa. Because a significant number of streams in Iowa reach zero flow as their minimum flow during low-flow years, four different types of regression analyses were used: left-censored, logistic, generalized-least-squares, and weighted-least-squares regression. A total of 192 streamgages were included in the development of 27 regression equations for the three low-flow regions. For the northeast and northwest regions, a censoring threshold was used to develop 12 left-censored regression equations to estimate the 6 low-flow frequency statistics for each region. For the southern region a total of 12 regression equations were developed; 6 logistic regression equations were developed to estimate the probability of zero flow for the 6 low-flow frequency statistics and 6 generalized least-squares regression equations were developed to estimate the 6 low-flow frequency statistics, if nonzero flow is estimated first by use of the logistic equations. A weighted-least-squares regression equation was developed for each region to estimate the harmonic-mean-flow statistic. Average standard errors of estimate for the left-censored equations for the northeast region range from 64.7 to 88.1 percent and for the northwest region range from 85.8 to 111.8 percent. Misclassification percentages for the logistic equations for the southern region range from 5.6 to 14.0 percent. Average standard errors of prediction for generalized least-squares equations for the southern region range from 71.7 to 98.9 percent and pseudo coefficients of determination for the generalized-least-squares equations range from 87.7 to 91.8 percent. Average standard errors of prediction for weighted-least-squares equations developed for estimating the harmonic-mean-flow statistic for each of the three regions range from 66.4 to 80.4 percent. The regression equations are applicable only to stream sites in Iowa with low flows not significantly affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. If the equations are used at ungaged sites on regulated streams, or on streams affected by water-supply and agricultural withdrawals, then the estimates will need to be adjusted by the amount of regulation or withdrawal to estimate the actual flow conditions if that is of interest. Caution is advised when applying the equations for basins with characteristics near the applicable limits of the equations and for basins located in karst topography. A test of two drainage-area ratio methods using 31 pairs of streamgages, for the annual 7-day mean low-flow statistic for a recurrence interval of 10 years, indicates a weighted drainage-area ratio method provides better estimates than regional regression equations for an ungaged site on a gaged stream in Iowa when the drainage-area ratio is between 0.5 and 1.4. These regression equations will be implemented within the U.S. Geological Survey StreamStats web-based geographic-information-system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the seven selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these seven selected statistics are provided for the streamgage.

Application of variational and Galerkin equations to linear and nonlinear finite element analysis

NASA Technical Reports Server (NTRS)

Yu, Y.-Y.

1974-01-01

The paper discusses the application of the variational equation to nonlinear finite element analysis. The problem of beam vibration with large deflection is considered. The variational equation is shown to be flexible in both the solution of a general problem and in the finite element formulation. Difficulties are shown to arise when Galerkin's equations are used in the consideration of the finite element formulation of two-dimensional linear elasticity and of the linear classical beam.

A Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics. Part 1. Analysis Development

DTIC Science & Technology

1980-06-01

sufficient. Dropping the time lag terms, the equations for Xu, Xx’, and X reduce to linear algebraic equations.Y Hence in the quasistatic case the...quasistatic variables now are not described by differential equations but rather by linear algebraic equations. The solution for x0 then is simply -365...matrices for two-bladed rotor 414 7. LINEAR SYSTEM ANALYSIS 425 7,1 State Variable Form 425 7.2 Constant Coefficient System 426 7.2. 1 Eigen-analysis 426

A comparative look at sunspot cycles

NASA Technical Reports Server (NTRS)

Wilson, R. M.

1984-01-01

On the basis of cycles 8 through 20, spanning about 143 years, observations of sunspot number, smoothed sunspot number, and their temporal properties were used to compute means, standard deviations, ranges, and frequency of occurrence histograms for a number of sunspot cycle parameters. The resultant schematic sunspot cycle was contrasted with the mean sunspot cycle, obtained by averaging smoothed sunspot number as a function of time, tying all cycles (8 through 20) to their minimum occurence date. A relatively good approximation of the time variation of smoothed sunspot number for a given cycle is possible if sunspot cycles are regarded in terms of being either HIGH- or LOW-R(MAX) cycles or LONG- or SHORT-PERIOD cycles, especially the latter. Linear regression analyses were performed comparing late cycle parameters with early cycle parameters and solar cycle number. The early occurring cycle parameters can be used to estimate later occurring cycle parameters with relatively good success, based on cycle 21 as an example. The sunspot cycle record clearly shows that the trend for both R(MIN) and R(MAX) was toward decreasing value between cycles 8 through 14 and toward increasing value between cycles 14 through 20. Linear regression equations were also obtained for several measures of solar activity.

Constructive Development of the Solutions of Linear Equations in Introductory Ordinary Differential Equations

ERIC Educational Resources Information Center

Mallet, D. G.; McCue, S. W.

2009-01-01

The solution of linear ordinary differential equations (ODEs) is commonly taught in first-year undergraduate mathematics classrooms, but the understanding of the concept of a solution is not always grasped by students until much later. Recognizing what it is to be a solution of a linear ODE and how to postulate such solutions, without resorting to…

Prediction of oxygen consumption in cardiac rehabilitation patients performing leg ergometry

NASA Astrophysics Data System (ADS)

Alvarez, John Gershwin

The purpose of this study was two-fold. First, to determine the validity of the ACSM leg ergometry equation in the prediction of steady-state oxygen consumption (VO2) in a heterogeneous population of cardiac patients. Second, to determine whether a more accurate prediction equation could be developed for use in the cardiac population. Thirty-one cardiac rehabilitation patients participated in the study of which 24 were men and 7 were women. Biometric variables (mean +/- sd) of the participants were as follows: age = 61.9 +/- 9.5 years; height = 172.6 +/- 1.6 cm; and body mass = 82.3 +/- 10.6 kg. Subjects exercised on a MonarchTM cycle ergometer at 0, 180, 360, 540 and 720 kgm ˙ min-1. The length of each stage was five minutes. Heart rate, ECG, and VO2 were continuously monitored. Blood pressure and heart rate were collected at the end of each stage. Steady state VO 2 was calculated for each stage using the average of the last two minutes. Correlation coefficients, standard error of estimate, coefficient of determination, total error, and mean bias were used to determine the accuracy of the ACSM equation (1995). The analysis found the ACSM equation to be a valid means of estimating VO2 in cardiac patients. Simple linear regression was used to develop a new equation. Regression analysis found workload to be a significant predictor of VO2. The following equation is the result: VO2 = (1.6 x kgm ˙ min-1) + 444 ml ˙ min-1. The r of the equation was .78 (p < .05) and the standard error of estimate was 211 ml ˙ min-1. Analysis of variance was used to determine significant differences between means for actual and predicted VO2 values for each equation. The analysis found the ACSM and new equation to significantly (p < .05) under predict VO2 during unloaded pedaling. Furthermore, the ACSM equation was found to significantly (p < .05) under predict VO 2 during the first loaded stage of exercise. When the accuracy of the ACSM and new equations were compared based on correlation coefficients, coefficients of determinations, SEEs, total error, and mean bias the new equation was found to have equal or better accuracy at all workloads. The final form of the new equation is: VO2 (ml ˙ min-1) = (kgm ˙ min-1 x 1.6 ml ˙ kgm-1) + (3.5 ml ˙ kg-1 ˙ min-1 x body mass in kg) + 156 ml ˙ min-1.

Estimating Flow-Duration and Low-Flow Frequency Statistics for Unregulated Streams in Oregon

USGS Publications Warehouse

Risley, John; Stonewall, Adam J.; Haluska, Tana

2008-01-01

Flow statistical datasets, basin-characteristic datasets, and regression equations were developed to provide decision makers with surface-water information needed for activities such as water-quality regulation, water-rights adjudication, biological habitat assessment, infrastructure design, and water-supply planning and management. The flow statistics, which included annual and monthly period of record flow durations (5th, 10th, 25th, 50th, and 95th percent exceedances) and annual and monthly 7-day, 10-year (7Q10) and 7-day, 2-year (7Q2) low flows, were computed at 466 streamflow-gaging stations at sites with unregulated flow conditions throughout Oregon and adjacent areas of neighboring States. Regression equations, created from the flow statistics and basin characteristics of the stations, can be used to estimate flow statistics at ungaged stream sites in Oregon. The study area was divided into 10 regression modeling regions based on ecological, topographic, geologic, hydrologic, and climatic criteria. In total, 910 annual and monthly regression equations were created to predict the 7 flow statistics in the 10 regions. Equations to predict the five flow-duration exceedance percentages and the two low-flow frequency statistics were created with Ordinary Least Squares and Generalized Least Squares regression, respectively. The standard errors of estimate of the equations created to predict the 5th and 95th percent exceedances had medians of 42.4 and 64.4 percent, respectively. The standard errors of prediction of the equations created to predict the 7Q2 and 7Q10 low-flow statistics had medians of 51.7 and 61.2 percent, respectively. Standard errors for regression equations for sites in western Oregon were smaller than those in eastern Oregon partly because of a greater density of available streamflow-gaging stations in western Oregon than eastern Oregon. High-flow regression equations (such as the 5th and 10th percent exceedances) also generally were more accurate than the low-flow regression equations (such as the 95th percent exceedance and 7Q10 low-flow statistic). The regression equations predict unregulated flow conditions in Oregon. Flow estimates need to be adjusted if they are used at ungaged sites that are regulated by reservoirs or affected by water-supply and agricultural withdrawals if actual flow conditions are of interest. The regression equations are installed in the USGS StreamStats Web-based tool (http://water.usgs.gov/osw/streamstats/index.html, accessed July 16, 2008). StreamStats provides users with a set of annual and monthly flow-duration and low-flow frequency estimates for ungaged sites in Oregon in addition to the basin characteristics for the sites. Prediction intervals at the 90-percent confidence level also are automatically computed.

Methods for estimating selected low-flow frequency statistics for unregulated streams in Kentucky

USGS Publications Warehouse

Martin, Gary R.; Arihood, Leslie D.

2010-01-01

This report provides estimates of, and presents methods for estimating, selected low-flow frequency statistics for unregulated streams in Kentucky including the 30-day mean low flows for recurrence intervals of 2 and 5 years (30Q2 and 30Q5) and the 7-day mean low flows for recurrence intervals of 5, 10, and 20 years (7Q2, 7Q10, and 7Q20). Estimates of these statistics are provided for 121 U.S. Geological Survey streamflow-gaging stations with data through the 2006 climate year, which is the 12-month period ending March 31 of each year. Data were screened to identify the periods of homogeneous, unregulated flows for use in the analyses. Logistic-regression equations are presented for estimating the annual probability of the selected low-flow frequency statistics being equal to zero. Weighted-least-squares regression equations were developed for estimating the magnitude of the nonzero 30Q2, 30Q5, 7Q2, 7Q10, and 7Q20 low flows. Three low-flow regions were defined for estimating the 7-day low-flow frequency statistics. The explicit explanatory variables in the regression equations include total drainage area and the mapped streamflow-variability index measured from a revised statewide coverage of this characteristic. The percentage of the station low-flow statistics correctly classified as zero or nonzero by use of the logistic-regression equations ranged from 87.5 to 93.8 percent. The average standard errors of prediction of the weighted-least-squares regression equations ranged from 108 to 226 percent. The 30Q2 regression equations have the smallest standard errors of prediction, and the 7Q20 regression equations have the largest standard errors of prediction. The regression equations are applicable only to stream sites with low flows unaffected by regulation from reservoirs and local diversions of flow and to drainage basins in specified ranges of basin characteristics. Caution is advised when applying the equations for basins with characteristics near the applicable limits and for basins with karst drainage features.

Multigrid Methods for Fully Implicit Oil Reservoir Simulation

NASA Technical Reports Server (NTRS)

Molenaar, J.

1996-01-01

In this paper we consider the simultaneous flow of oil and water in reservoir rock. This displacement process is modeled by two basic equations: the material balance or continuity equations and the equation of motion (Darcy's law). For the numerical solution of this system of nonlinear partial differential equations there are two approaches: the fully implicit or simultaneous solution method and the sequential solution method. In the sequential solution method the system of partial differential equations is manipulated to give an elliptic pressure equation and a hyperbolic (or parabolic) saturation equation. In the IMPES approach the pressure equation is first solved, using values for the saturation from the previous time level. Next the saturations are updated by some explicit time stepping method; this implies that the method is only conditionally stable. For the numerical solution of the linear, elliptic pressure equation multigrid methods have become an accepted technique. On the other hand, the fully implicit method is unconditionally stable, but it has the disadvantage that in every time step a large system of nonlinear algebraic equations has to be solved. The most time-consuming part of any fully implicit reservoir simulator is the solution of this large system of equations. Usually this is done by Newton's method. The resulting systems of linear equations are then either solved by a direct method or by some conjugate gradient type method. In this paper we consider the possibility of applying multigrid methods for the iterative solution of the systems of nonlinear equations. There are two ways of using multigrid for this job: either we use a nonlinear multigrid method or we use a linear multigrid method to deal with the linear systems that arise in Newton's method. So far only a few authors have reported on the use of multigrid methods for fully implicit simulations. Two-level FAS algorithm is presented for the black-oil equations, and linear multigrid for two-phase flow problems with strong heterogeneities and anisotropies is studied. Here we consider both possibilities. Moreover we present a novel way for constructing the coarse grid correction operator in linear multigrid algorithms. This approach has the advantage in that it preserves the sparsity pattern of the fine grid matrix and it can be extended to systems of equations in a straightforward manner. We compare the linear and nonlinear multigrid algorithms by means of a numerical experiment.

Unified Framework for Deriving Simultaneous Equation Algorithms for Water Distribution Networks

EPA Science Inventory

The known formulations for steady state hydraulics within looped water distribution networks are re-derived in terms of linear and non-linear transformations of the original set of partly linear and partly non-linear equations that express conservation of mass and energy. All of ...

Perturbations of linear delay differential equations at the verge of instability.

PubMed

Lingala, N; Namachchivaya, N Sri

2016-06-01

The characteristic equation for a linear delay differential equation (DDE) has countably infinite roots on the complex plane. This paper considers linear DDEs that are on the verge of instability, i.e., a pair of roots of the characteristic equation lies on the imaginary axis of the complex plane and all other roots have negative real parts. It is shown that when small noise perturbations are present, the probability distribution of the dynamics can be approximated by the probability distribution of a certain one-dimensional stochastic differential equation (SDE) without delay. This is advantageous because equations without delay are easier to simulate and one-dimensional SDEs are analytically tractable. When the perturbations are also linear, it is shown that the stability depends on a specific complex number. The theory is applied to study oscillators with delayed feedback. Some errors in other articles that use multiscale approach are pointed out.

How Darcy's equation is linked to the linear reservoir at catchment scale

NASA Astrophysics Data System (ADS)

Savenije, Hubert H. G.

2017-04-01

In groundwater hydrology two simple linear equations exist that describe the relation between groundwater flow and the gradient that drives it: Darcy's equation and the linear reservoir. Both equations are empirical at heart: Darcy's equation at the laboratory scale and the linear reservoir at the watershed scale. Although at first sight they show similarity, without having detailed knowledge of the structure of the underlying aquifers it is not trivial to upscale Darcy's equation to the watershed scale. In this paper, a relatively simple connection is provided between the two, based on the assumption that the groundwater system is organized by an efficient drainage network, a mostly invisible pattern that has evolved over geological time scales. This drainage network provides equally distributed resistance to flow along the streamlines that connect the active groundwater body to the stream, much like a leaf is organized to provide all stomata access to moisture at equal resistance.

Polynomial elimination theory and non-linear stability analysis for the Euler equations

NASA Technical Reports Server (NTRS)

Kennon, S. R.; Dulikravich, G. S.; Jespersen, D. C.

1986-01-01

Numerical methods are presented that exploit the polynomial properties of discretizations of the Euler equations. It is noted that most finite difference or finite volume discretizations of the steady-state Euler equations produce a polynomial system of equations to be solved. These equations are solved using classical polynomial elimination theory, with some innovative modifications. This paper also presents some preliminary results of a new non-linear stability analysis technique. This technique is applicable to determining the stability of polynomial iterative schemes. Results are presented for applying the elimination technique to a one-dimensional test case. For this test case, the exact solution is computed in three iterations. The non-linear stability analysis is applied to determine the optimal time step for solving Burgers' equation using the MacCormack scheme. The estimated optimal time step is very close to the time step that arises from a linear stability analysis.

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

Estimating total body water content in suckling and lactating llamas (Lama glama) by isotope dilution.

PubMed

Riek, Alexander; Gerken, Martina

2010-08-01

Total body water (TBW) in 17 suckling and six lactating llamas was estimated from isotope dilution at three different post natum and lactation stages using both (18)O and deuterium oxide (D(2)O). In total, 69 TBW measurements were undertaken. While TBW in lactating dams, expressed in kilogram, remained stable during the three measurement periods (91.8 +/- 15.0 kg), the body water fraction (TBW expressed in percent of body mass) increased slightly (P = 0.042) from 62.9% to 65.8%. In contrast, TBW (kilogram) in suckling llamas increased significantly (P < 0.001) with age and decreased slightly when expressed as a percentage of body mass (P = 0.016). Relating TBW to body mass across all animals yielded a highly significant regression equation (TBW in kilogram = 2.633 + 0.623 body mass in kilogram, P < 0.001, n = 69) explaining 99.5% of the variation. The water fraction instead decreased in a curve linear fashion with increasing body mass (TBW in percent of body mass = 88.23 body mass in kilogram(-0.064), P < 0.001, R (2) = 0.460). The present results on TBW can serve as reference values for suckling and lactating llamas, e.g., for the evaluation of fluid losses during disease. Additionally, the established regression equations can be used to predict TBW from body mass, providing that the body masses fall inside the range of masses used to derive the equations.

A three operator split-step method covering a larger set of non-linear partial differential equations

NASA Astrophysics Data System (ADS)

Zia, Haider

2017-06-01

This paper describes an updated exponential Fourier based split-step method that can be applied to a greater class of partial differential equations than previous methods would allow. These equations arise in physics and engineering, a notable example being the generalized derivative non-linear Schrödinger equation that arises in non-linear optics with self-steepening terms. These differential equations feature terms that were previously inaccessible to model accurately with low computational resources. The new method maintains a 3rd order error even with these additional terms and models the equation in all three spatial dimensions and time. The class of non-linear differential equations that this method applies to is shown. The method is fully derived and implementation of the method in the split-step architecture is shown. This paper lays the mathematical ground work for an upcoming paper employing this method in white-light generation simulations in bulk material.

Regression Equations for Monthly and Annual Mean and Selected Percentile Streamflows for Ungaged Rivers in Maine

USGS Publications Warehouse

Dudley, Robert W.

2015-12-03

The largest average errors of prediction are associated with regression equations for the lowest streamflows derived for months during which the lowest streamflows of the year occur (such as the 5 and 1 monthly percentiles for August and September). The regression equations have been derived on the basis of streamflow and basin characteristics data for unregulated, rural drainage basins without substantial streamflow or drainage modifications (for example, diversions and (or) regulation by dams or reservoirs, tile drainage, irrigation, channelization, and impervious paved surfaces), therefore using the equations for regulated or urbanized basins with substantial streamflow or drainage modifications will yield results of unknown error. Input basin characteristics derived using techniques or datasets other than those documented in this report or using values outside the ranges used to develop these regression equations also will yield results of unknown error.

An extended harmonic balance method based on incremental nonlinear control parameters

NASA Astrophysics Data System (ADS)

Khodaparast, Hamed Haddad; Madinei, Hadi; Friswell, Michael I.; Adhikari, Sondipon; Coggon, Simon; Cooper, Jonathan E.

2017-02-01

A new formulation for calculating the steady-state responses of multiple-degree-of-freedom (MDOF) non-linear dynamic systems due to harmonic excitation is developed. This is aimed at solving multi-dimensional nonlinear systems using linear equations. Nonlinearity is parameterised by a set of 'non-linear control parameters' such that the dynamic system is effectively linear for zero values of these parameters and nonlinearity increases with increasing values of these parameters. Two sets of linear equations which are formed from a first-order truncated Taylor series expansion are developed. The first set of linear equations provides the summation of sensitivities of linear system responses with respect to non-linear control parameters and the second set are recursive equations that use the previous responses to update the sensitivities. The obtained sensitivities of steady-state responses are then used to calculate the steady state responses of non-linear dynamic systems in an iterative process. The application and verification of the method are illustrated using a non-linear Micro-Electro-Mechanical System (MEMS) subject to a base harmonic excitation. The non-linear control parameters in these examples are the DC voltages that are applied to the electrodes of the MEMS devices.

Comparative evaluation of urban storm water quality models

NASA Astrophysics Data System (ADS)

Vaze, J.; Chiew, Francis H. S.

2003-10-01

The estimation of urban storm water pollutant loads is required for the development of mitigation and management strategies to minimize impacts to receiving environments. Event pollutant loads are typically estimated using either regression equations or "process-based" water quality models. The relative merit of using regression models compared to process-based models is not clear. A modeling study is carried out here to evaluate the comparative ability of the regression equations and process-based water quality models to estimate event diffuse pollutant loads from impervious surfaces. The results indicate that, once calibrated, both the regression equations and the process-based model can estimate event pollutant loads satisfactorily. In fact, the loads estimated using the regression equation as a function of rainfall intensity and runoff rate are better than the loads estimated using the process-based model. Therefore, if only estimates of event loads are required, regression models should be used because they are simpler and require less data compared to process-based models.

Analytical solution of Luedeking-Piret equation for a batch fermentation obeying Monod growth kinetics.

PubMed

Garnier, Alain; Gaillet, Bruno

2015-12-01

Not so many fermentation mathematical models allow analytical solutions of batch process dynamics. The most widely used is the combination of the logistic microbial growth kinetics with Luedeking-Piret bioproduct synthesis relation. However, the logistic equation is principally based on formalistic similarities and only fits a limited range of fermentation types. In this article, we have developed an analytical solution for the combination of Monod growth kinetics with Luedeking-Piret relation, which can be identified by linear regression and used to simulate batch fermentation evolution. Two classical examples are used to show the quality of fit and the simplicity of the method proposed. A solution for the combination of Haldane substrate-limited growth model combined with Luedeking-Piret relation is also provided. These models could prove useful for the analysis of fermentation data in industry as well as academia. © 2015 Wiley Periodicals, Inc.

Molecular relaxation processes of 2-bromopropane in solutions from IR ν(C-Br) band shape analysis

NASA Astrophysics Data System (ADS)

Bratu, I.; Grecu, R.; Constantinescu, R.; Iliescu, T.

1998-03-01

The infrared (C-Br) stretching band profile of 2-bromopropane in pure liquid and in solution was studied. The frequency shifts, described by the Buckingham equation, account for the influence of the polarity and polarizability of the solvents. To evaluate the importance of the last term in the Buckingham equation, which describes the mutual influence of these two effects, a linear multidimensional regression analysis was done. The correlation factor increased when the cross term was considered. The concentration dependence of the FWHH (full width at half height) can be related to the vibrational relaxation processes, among them vibrational dephasing being the most important. More information about mechanisms responsible for the vibrational bandshape can be obtained from the correlation function Φ( t). As a result of modelling the experimental CF with Kubo-Rothschild's model, the modulation of the vibrational frequencies is found to be of intermediate type.

Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression

USGS Publications Warehouse

Kokaly, R.F.; Clark, R.N.

1999-01-01

We develop a new method for estimating the biochemistry of plant material using spectroscopy. Normalized band depths calculated from the continuum-removed reflectance spectra of dried and ground leaves were used to estimate their concentrations of nitrogen, lignin, and cellulose. Stepwise multiple linear regression was used to select wavelengths in the broad absorption features centered at 1.73 ??m, 2.10 ??m, and 2.30 ??m that were highly correlated with the chemistry of samples from eastern U.S. forests. Band depths of absorption features at these wavelengths were found to also be highly correlated with the chemistry of four other sites. A subset of data from the eastern U.S. forest sites was used to derive linear equations that were applied to the remaining data to successfully estimate their nitrogen, lignin, and cellulose concentrations. Correlations were highest for nitrogen (R2 from 0.75 to 0.94). The consistent results indicate the possibility of establishing a single equation capable of estimating the chemical concentrations in a wide variety of species from the reflectance spectra of dried leaves. The extension of this method to remote sensing was investigated. The effects of leaf water content, sensor signal-to-noise and bandpass, atmospheric effects, and background soil exposure were examined. Leaf water was found to be the greatest challenge to extending this empirical method to the analysis of fresh whole leaves and complete vegetation canopies. The influence of leaf water on reflectance spectra must be removed to within 10%. Other effects were reduced by continuum removal and normalization of band depths. If the effects of leaf water can be compensated for, it might be possible to extend this method to remote sensing data acquired by imaging spectrometers to give estimates of nitrogen, lignin, and cellulose concentrations over large areas for use in ecosystem studies.We develop a new method for estimating the biochemistry of plant material using spectroscopy. Normalized band depths calculated from the continuum-removed reflectance spectra of dried and ground leaves were used to estimate their concentrations of nitrogen, lignin, and cellulose. Stepwise multiple linear regression was used to select wavelengths in the broad absorption features centered at 1.73 ??m, 2.10 ??m, and 2.301 ??m that were highly correlated with the chemistry of samples from eastern U.S. forests. Band depths of absorption features at these wavelengths were found to also be highly correlated with the chemistry of four other sites. A subset of data from the eastern U.S. forest sites was used to derive linear equations that were applied to the remaining data to successfully estimate their nitrogen, lignin, and cellulose concentrations. Correlations were highest for nitrogen (R2 from 0.75 to 0.94). The consistent results indicate the possibility of establishing a single equation capable of estimating the chemical concentrations in a wide variety of species from the reflectance spectra of dried leaves. The extension of this method to remote sensing was investigated. The effects of leaf water content, sensor signal-to-noise and bandpass, atmospheric effects, and background soil exposure were examined. Leaf water was found to be the greatest challenge to extending this empirical method to the analysis of fresh whole leaves and complete vegetation canopies. The influence of leaf water on reflectance spectra must be removed to within 10%. Other effects were reduced by continuum removal and normalization of band depths. If the effects of leaf water can be compensated for, it might be possible to extend this method to remote sensing data acquired by imaging spectrometers to give estimates of nitrogen, lignin, and cellulose concentrations over large areas for use in ecosystem studies.

[Comparison of three stand-level biomass estimation methods].

PubMed

Dong, Li Hu; Li, Feng Ri

2016-12-01

At present, the forest biomass methods of regional scale attract most of attention of the researchers, and developing the stand-level biomass model is popular. Based on the forestry inventory data of larch plantation (Larix olgensis) in Jilin Province, we used non-linear seemly unrelated regression (NSUR) to estimate the parameters in two additive system of stand-level biomass equations, i.e., stand-level biomass equations including the stand variables and stand biomass equations including the biomass expansion factor (i.e., Model system 1 and Model system 2), listed the constant biomass expansion factor for larch plantation and compared the prediction accuracy of three stand-level biomass estimation methods. The results indicated that for two additive system of biomass equations, the adjusted coefficient of determination (R a 2 ) of the total and stem equations was more than 0.95, the root mean squared error (RMSE), the mean prediction error (MPE) and the mean absolute error (MAE) were smaller. The branch and foliage biomass equations were worse than total and stem biomass equations, and the adjusted coefficient of determination (R a 2 ) was less than 0.95. The prediction accuracy of a constant biomass expansion factor was relatively lower than the prediction accuracy of Model system 1 and Model system 2. Overall, although stand-level biomass equation including the biomass expansion factor belonged to the volume-derived biomass estimation method, and was different from the stand biomass equations including stand variables in essence, but the obtained prediction accuracy of the two methods was similar. The constant biomass expansion factor had the lower prediction accuracy, and was inappropriate. In addition, in order to make the model parameter estimation more effective, the established stand-level biomass equations should consider the additivity in a system of all tree component biomass and total biomass equations.

Piecewise linear emulator of the nonlinear Schrödinger equation and the resulting analytic solutions for Bose-Einstein condensates.

PubMed

Theodorakis, Stavros

2003-06-01

We emulate the cubic term Psi(3) in the nonlinear Schrödinger equation by a piecewise linear term, thus reducing the problem to a set of uncoupled linear inhomogeneous differential equations. The resulting analytic expressions constitute an excellent approximation to the exact solutions, as is explicitly shown in the case of the kink, the vortex, and a delta function trap. Such a piecewise linear emulation can be used for any differential equation where the only nonlinearity is a Psi(3) one. In particular, it can be used for the nonlinear Schrödinger equation in the presence of harmonic traps, giving analytic Bose-Einstein condensate solutions that reproduce very accurately the numerically calculated ones in one, two, and three dimensions.

Dissipative behavior of some fully non-linear KdV-type equations

NASA Astrophysics Data System (ADS)

Brenier, Yann; Levy, Doron

2000-03-01

The KdV equation can be considered as a special case of the general equation u t+f(u) x-δg(u xx) x=0, δ>0, where f is non-linear and g is linear, namely f( u)= u2/2 and g( v)= v. As the parameter δ tends to 0, the dispersive behavior of the KdV equation has been throughly investigated (see, e.g., [P.G. Drazin, Solitons, London Math. Soc. Lect. Note Ser. 85, Cambridge University Press, Cambridge, 1983; P.D. Lax, C.D. Levermore, The small dispersion limit of the Korteweg-de Vries equation, III, Commun. Pure Appl. Math. 36 (1983) 809-829; G.B. Whitham, Linear and Nonlinear Waves, Wiley/Interscience, New York, 1974] and the references therein). We show through numerical evidence that a completely different, dissipative behavior occurs when g is non-linear, namely when g is an even concave function such as g( v)=-∣ v∣ or g( v)=- v2. In particular, our numerical results hint that as δ→0 the solutions strongly converge to the unique entropy solution of the formal limit equation, in total contrast with the solutions of the KdV equation.

A model for predicting sulcus-to-sulcus diameter in posterior chamber phakic intraocular lens candidates: correlation between ocular biometric parameters.

PubMed

Ghoreishi, Mohammad; Abdi-Shahshahani, Mehdi; Peyman, Alireza; Pourazizi, Mohsen

2018-02-21

The aim of this study was to determine the correlation between ocular biometric parameters and sulcus-to-sulcus (STS) diameter. This was a cross-sectional study of preoperative ocular biometry data of patients who were candidates for phakic intraocular lens (IOL) surgery. Subjects underwent ocular biometry analysis, including refraction error evaluation using an autorefractor and Orbscan topography for white-to-white (WTW) corneal diameter and measurement. Pentacam was used to perform WTW corneal diameter and measurements of minimum and maximum keratometry (K). Measurements of STS and angle-to-angle (ATA) were obtained using a 50-MHz B-mode ultrasound device. Anterior optical coherence tomography was performed for anterior chamber depth measurement. Pearson's correlation test and stepwise linear regression analysis were used to find a model to predict STS. Fifty-eight eyes of 58 patients were enrolled. Mean age ± standard deviation of sample was 28.95 ± 6.04 years. The Pearson's correlation coefficient between STS with WTW, ATA, mean K was 0.383, 0.492, and - 0.353, respectively, which was statistically significant (all P < 0.001). Using stepwise linear regression analysis, there is a statistically significant association between STS with WTW (P = 0.011) and mean K (P = 0.025). The standardized coefficient was 0.323 and - 0.284 for WTW and mean K, respectively. The stepwise linear regression analysis equation was: (STS = 9.549 + 0.518 WTW - 0.083 mean K). Based on our result, given the correlation of STS with WTW and mean K and potential of direct and essay measurement of WTW and mean K, it seems that current IOL sizing protocols could be estimating with WTW and mean K.

Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach.

PubMed

Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David

2017-10-01

Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P < .001). Means of software machine-derived values differed significantly from actual PLT yield, 4.72 × 10 11 vs.6.12 × 10 11 , respectively, (P < .001). The following equation was developed to adjust these values: actual PLT yield= 0.221 + (1.254 × theoretical platelet yield). ROC curve model showed an optimal apheresis device software prediction cut-off of 4.65 × 10 11 to obtain a DP, with a sensitivity of 82.2%, specificity of 93.3%, and an area under the curve (AUC) of 0.909. Trima Accel v6.0 software consistently underestimated PLT yields. Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.

Symbolic computation of recurrence equations for the Chebyshev series solution of linear ODE's. [ordinary differential equations

NASA Technical Reports Server (NTRS)

Geddes, K. O.

1977-01-01

If a linear ordinary differential equation with polynomial coefficients is converted into integrated form then the formal substitution of a Chebyshev series leads to recurrence equations defining the Chebyshev coefficients of the solution function. An explicit formula is presented for the polynomial coefficients of the integrated form in terms of the polynomial coefficients of the differential form. The symmetries arising from multiplication and integration of Chebyshev polynomials are exploited in deriving a general recurrence equation from which can be derived all of the linear equations defining the Chebyshev coefficients. Procedures for deriving the general recurrence equation are specified in a precise algorithmic notation suitable for translation into any of the languages for symbolic computation. The method is algebraic and it can therefore be applied to differential equations containing indeterminates.

A General Linear Method for Equating with Small Samples

ERIC Educational Resources Information Center

Albano, Anthony D.

2015-01-01

Research on equating with small samples has shown that methods with stronger assumptions and fewer statistical estimates can lead to decreased error in the estimated equating function. This article introduces a new approach to linear observed-score equating, one which provides flexible control over how form difficulty is assumed versus estimated…

ESEA Title I Linking Project. Final Report.

ERIC Educational Resources Information Center

Holmes, Susan E.

The Rasch model for test score equating was compared with three other equating procedures as methods for implementing the norm referenced method (RMC Model A) of evaluating ESEA Title I projects. The Rasch model and its theoretical limitations were described. The three other equating methods used were: linear observed score equating, linear true…

A Factorization Approach to the Linear Regulator Quadratic Cost Problem

NASA Technical Reports Server (NTRS)

Milman, M. H.

1985-01-01

A factorization approach to the linear regulator quadratic cost problem is developed. This approach makes some new connections between optimal control, factorization, Riccati equations and certain Wiener-Hopf operator equations. Applications of the theory to systems describable by evolution equations in Hilbert space and differential delay equations in Euclidean space are presented.

Predonation Volume of Future Remnant Cortical Kidney Helps Predict Postdonation Renal Function in Live Kidney Donors.

PubMed

Fananapazir, Ghaneh; Benzl, Robert; Corwin, Michael T; Chen, Ling-Xin; Sageshima, Junichiro; Stewart, Susan L; Troppmann, Christoph

2018-07-01

Purpose To determine whether the predonation computed tomography (CT)-based volume of the future remnant kidney is predictive of postdonation renal function in living kidney donors. Materials and Methods This institutional review board-approved, retrospective, HIPAA-compliant study included 126 live kidney donors who had undergone predonation renal CT between January 2007 and December 2014 as well as 2-year postdonation measurement of estimated glomerular filtration rate (eGFR). The whole kidney volume and cortical volume of the future remnant kidney were measured and standardized for body surface area (BSA). Bivariate linear associations between the ratios of whole kidney volume to BSA and cortical volume to BSA were obtained. A linear regression model for 2-year postdonation eGFR that incorporated donor age, sex, and either whole kidney volume-to-BSA ratio or cortical volume-to-BSA ratio was created, and the coefficient of determination (R 2 ) for the model was calculated. Factors not statistically additive in assessing 2-year eGFR were removed by using backward elimination, and the coefficient of determination for this parsimonious model was calculated. Results Correlation was slightly better for cortical volume-to-BSA ratio than for whole kidney volume-to-BSA ratio (r = 0.48 vs r = 0.44, respectively). The linear regression model incorporating all donor factors had an R 2 of 0.66. The only factors that were significantly additive to the equation were cortical volume-to-BSA ratio and predonation eGFR (P = .01 and P < .01, respectively), and the final parsimonious linear regression model incorporating these two variables explained almost the same amount of variance (R 2 = 0.65) as did the full model. Conclusion The cortical volume of the future remnant kidney helped predict postdonation eGFR at 2 years. The cortical volume-to-BSA ratio should thus be considered for addition as an important variable to living kidney donor evaluation and selection guidelines. © RSNA, 2018.

Variations in the Solution of Linear First-Order Differential Equations. Classroom Notes

ERIC Educational Resources Information Center

Seaman, Brian; Osler, Thomas J.

2004-01-01

A special project which can be given to students of ordinary differential equations is described in detail. Students create new differential equations by changing the dependent variable in the familiar linear first-order equation (dv/dx)+p(x)v=q(x) by means of a substitution v=f(y). The student then creates a table of the new equations and…

The Equation, the Whole Equation, and Nothing but the Equation! One Approach to the Teaching of Linear Equations.

ERIC Educational Resources Information Center

Pirie, Susan E. B.; Martin, Lyndon

1997-01-01

Presents the results of a case study which looked at the mathematics classroom of one teacher trying to teach mathematics with meaning to pupils or lower ability at the secondary level. Contrasts methods of teaching linear equations to a variety of ability levels and uses the Pirie-Kieren model to account for the successful growth in understanding…

Arterial blood gas reference values for sea level and an altitude of 1,400 meters.

PubMed

Crapo, R O; Jensen, R L; Hegewald, M; Tashkin, D P

1999-11-01

Blood gas measurements were collected on healthy lifetime nonsmokers at sea level (n = 96) and at an altitude of 1,400 meters (n = 243) to establish reference equations. At each study site, arterial blood samples were analyzed in duplicate on two separate blood gas analyzers and CO-oximeters. Arterial blood gas variables included Pa(O(2)), Pa(CO(2)), pH, and calculated alveolar-arterial PO(2) difference (AaPO(2)). CO-oximeter variables were Hb, COHb, MetHb, and Sa(O(2)). Subjects were 18 to 81 yr of age with 166 male and 173 female. Outlier data were excluded from multiple regression analysis, and reference equations were fitted to the data in two ways: (1) best fit using linear, squared, and cross-product terms; (2) simple equations, including only the variables that explained at least 3% of the variance. Two sets of equations were created: (1) using only the sea level data and (2) using the combined data with barometric pressure as an independent variable. Comparisons with earlier studies revealed small but significant differences; the decline in Pa(O(2)) with age at each altitude was consistent with most previous studies. At sea level, the equation that included barometric pressure predicted Pa(O(2)) slightly better than the sea level specific equation. The inclusion of barometric pressure in the equations allows better prediction of blood gas reference values at sea level and at altitudes as high as 1,400 meters.

Estimating energy expenditure from heart rate in older adults: a case for calibration.

PubMed

Schrack, Jennifer A; Zipunnikov, Vadim; Goldsmith, Jeff; Bandeen-Roche, Karen; Crainiceanu, Ciprian M; Ferrucci, Luigi

2014-01-01

Accurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear. To assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration. Heart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance. Heart rate and energy expenditure were highly correlated (r=0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. =0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min). These findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.

Modeling the relationships between quality and biochemical composition of fatty liver in mule ducks.

PubMed

Theron, L; Cullere, M; Bouillier-Oudot, M; Manse, H; Dalle Zotte, A; Molette, C; Fernandez, X; Vitezica, Z G

2012-09-01

The fatty liver of mule ducks (i.e., French "foie gras") is the most valuable product in duck production systems. Its quality is measured by the technological yield, which is the opposite of the fat loss during cooking. The purpose of this study was to determine whether biochemical measures of fatty liver could be used to accurately predict the technological yield (TY). Ninety-one male mule ducks were bred, overfed, and slaughtered under commercial conditions. Fatty liver weight (FLW) and biochemical variables, such as DM, lipid (LIP), and protein content (PROT), were collected. To evaluate evidence for nonlinear fat loss during cooking, we compared regression models describing linear and nonlinear relations between biochemical measures and TY. We detected significantly greater (P = 0.02) linear relation between DM and TY. Our results indicate that LIP and PROT follow a different pattern (linear) than DM and showed that LIP and PROT are nonexclusive contributing factors to TY. Other components, such as carbohydrates, other than those measured in this study, could contribute to DM. Stepwise regression for TY was performed. The traditional model with FLW was tested. The results showed that the weight of the liver is of limited value in the determination of fat loss during cooking (R(2) = 0.14). The most accurate TY prediction equation included DM (in linear and quadratic terms), FLW, and PROT (R(2) = 0.43). Biochemical measures in the fatty liver were more accurate predictors of TY than FLW. The model is useful in commercial conditions because DM, PROT, and FLW are noninvasive measures.

Least-Squares Data Adjustment with Rank-Deficient Data Covariance Matrices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, J.G.

2011-07-01

A derivation of the linear least-squares adjustment formulae is required that avoids the assumption that the covariance matrix of prior parameters can be inverted. Possible proofs are of several kinds, including: (i) extension of standard results for the linear regression formulae, and (ii) minimization by differentiation of a quadratic form of the deviations in parameters and responses. In this paper, the least-squares adjustment equations are derived in both these ways, while explicitly assuming that the covariance matrix of prior parameters is singular. It will be proved that the solutions are unique and that, contrary to statements that have appeared inmore » the literature, the least-squares adjustment problem is not ill-posed. No modification is required to the adjustment formulae that have been used in the past in the case of a singular covariance matrix for the priors. In conclusion: The linear least-squares adjustment formula that has been used in the past is valid in the case of a singular covariance matrix for the covariance matrix of prior parameters. Furthermore, it provides a unique solution. Statements in the literature, to the effect that the problem is ill-posed are wrong. No regularization of the problem is required. This has been proved in the present paper by two methods, while explicitly assuming that the covariance matrix of prior parameters is singular: i) extension of standard results for the linear regression formulae, and (ii) minimization by differentiation of a quadratic form of the deviations in parameters and responses. No modification is needed to the adjustment formulae that have been used in the past. (author)« less

Statistical summary of selected physical, chemical, and toxicity characteristics and estimates of annual constituent loads in urban stormwater, Maricopa County, Arizona

USGS Publications Warehouse

Fossum, Kenneth D.; O'Day, Christie M.; Wilson, Barbara J.; Monical, Jim E.

2001-01-01

Stormwater and streamflow in Maricopa County were monitored to (1) describe the physical, chemical, and toxicity characteristics of stormwater from areas having different land uses, (2) describe the physical, chemical, and toxicity characteristics of streamflow from areas that receive urban stormwater, and (3) estimate constituent loads in stormwater. Urban stormwater and streamflow had similar ranges in most constituent concentrations. The mean concentration of dissolved solids in urban stormwater was lower than in streamflow from the Salt River and Indian Bend Wash. Urban stormwater, however, had a greater chemical oxygen demand and higher concentrations of most nutrients. Mean seasonal loads and mean annual loads of 11 constituents and volumes of runoff were estimated for municipalities in the metropolitan Phoenix area, Arizona, by adjusting regional regression equations of loads. This adjustment procedure uses the original regional regression equation and additional explanatory variables that were not included in the original equation. The adjusted equations had standard errors that ranged from 161 to 196 percent. The large standard errors of the prediction result from the large variability of the constituent concentration data used in the regression analysis. Adjustment procedures produced unsatisfactory results for nine of the regressions?suspended solids, dissolved solids, total phosphorus, dissolved phosphorus, total recoverable cadmium, total recoverable copper, total recoverable lead, total recoverable zinc, and storm runoff. These equations had no consistent direction of bias and no other additional explanatory variables correlated with the observed loads. A stepwise-multiple regression or a three-variable regression (total storm rainfall, drainage area, and impervious area) and local data were used to develop local regression equations for these nine constituents. These equations had standard errors from 15 to 183 percent.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Non-linear analysis of wave progagation using transform methods and plates and shells using integral equations

NASA Astrophysics Data System (ADS)

Pipkins, Daniel Scott

Two diverse topics of relevance in modern computational mechanics are treated. The first involves the modeling of linear and non-linear wave propagation in flexible, lattice structures. The technique used combines the Laplace Transform with the Finite Element Method (FEM). The procedure is to transform the governing differential equations and boundary conditions into the transform domain where the FEM formulation is carried out. For linear problems, the transformed differential equations can be solved exactly, hence the method is exact. As a result, each member of the lattice structure is modeled using only one element. In the non-linear problem, the method is no longer exact. The approximation introduced is a spatial discretization of the transformed non-linear terms. The non-linear terms are represented in the transform domain by making use of the complex convolution theorem. A weak formulation of the resulting transformed non-linear equations yields a set of element level matrix equations. The trial and test functions used in the weak formulation correspond to the exact solution of the linear part of the transformed governing differential equation. Numerical results are presented for both linear and non-linear systems. The linear systems modeled are longitudinal and torsional rods and Bernoulli-Euler and Timoshenko beams. For non-linear systems, a viscoelastic rod and Von Karman type beam are modeled. The second topic is the analysis of plates and shallow shells under-going finite deflections by the Field/Boundary Element Method. Numerical results are presented for two plate problems. The first is the bifurcation problem associated with a square plate having free boundaries which is loaded by four, self equilibrating corner forces. The results are compared to two existing numerical solutions of the problem which differ substantially.

A Novel Blast-mitigation Concept for Light Tactical Vehicles

DTIC Science & Technology

2013-01-01

analysis which utilizes the mass and energy (but not linear momentum ) conservation equations is provided. It should be noted that the identical final...results could be obtained using an analogous analysis which combines the mass and the linear momentum conservation equations. For a calorically...governing mass, linear momentum and energy conservation and heat conduction equations are solved within ABAQUS/ Explicit with a second-order accurate

Mortality rates in OECD countries converged during the period 1990-2010.

PubMed

Bremberg, Sven G

2017-06-01

Since the scientific revolution of the 18th century, human health has gradually improved, but there is no unifying theory that explains this improvement in health. Studies of macrodeterminants have produced conflicting results. Most studies have analysed health at a given point in time as the outcome; however, the rate of improvement in health might be a more appropriate outcome. Twenty-eight OECD member countries were selected for analysis in the period 1990-2010. The main outcomes studied, in six age groups, were the national rates of decrease in mortality in the period 1990-2010. The effects of seven potential determinants on the rates of decrease in mortality were analysed in linear multiple regression models using least squares, controlling for country-specific history constants, which represent the mortality rate in 1990. The multiple regression analyses started with models that only included mortality rates in 1990 as determinants. These models explained 87% of the intercountry variation in the children aged 1-4 years and 51% in adults aged 55-74 years. When added to the regression equations, the seven determinants did not seem to significantly increase the explanatory power of the equations. The analyses indicated a decrease in mortality in all nations and in all age groups. The development of mortality rates in the different nations demonstrated significant catch-up effects. Therefore an important objective of the national public health sector seems to be to reduce the delay between international research findings and the universal implementation of relevant innovations.

[Contents of vitreous humor of dead body with different postmortem intervals].

PubMed

Tao, Tao; Xu, Jing; Luo, Tong-Xing; Liao, Zhi-Gang; Pan, Hong-Fu

2006-11-01

To establish regression correlations between postmortem interval (PMI) and contents of human vitreous humor of dead bodies for forensic purposes. The human vitreous humor were taken from 126 dead bodies between 0.5 to 216 hours after death, and 11 chemical elements were detected by the OLYMPUS AU400 auto-biochemistry instrument. (1) The glucose, natrium and chlorine in human vitreous humor decreased, while the urea, creatinine, uric acid, potassium, calcium, magnesium, phosphorus, and micro-protein increased after death. The change of glucose, potassium and phosphorus were well correlated with the PMI (r = 0.824, 0.967, 0.880). But the uric acid and micro-protein did not have a good correlation with the PMI(r = 0.350, 0.153). (2) The stepwise regression analysis established the following equations for the PMI (Y): Y = -35. 15+6.05X, R2 = 0.957 (X = potassium); Y = -27.83+ 5.49X(1) - 1.35X(2), R2 = 0.960 (X(1) = potassium, X(2) = glucose); Y = -6.37+3.93X(1) -2.29X(2) + 5.36X(3), R2 = 0.966 (X(1) = potassium, X(2) = glucose, X(3) = phosphorus). (1) Eleven chemical components in human vitreous humor change after death, among which postassium has the best linear correlation with the PMI within 72 hours after death. (2) The accuracy of the estimation of PMI could be improved by establishing a multi-variable equation through stepwise regression.

Estimating forest crown area removed by selection cutting: a linked regression-GIS approach based on stump diameters

USGS Publications Warehouse

Anderson, S.C.; Kupfer, J.A.; Wilson, R.R.; Cooper, R.J.

2000-01-01

The purpose of this research was to develop a model that could be used to provide a spatial representation of uneven-aged silvicultural treatments on forest crown area. We began by developing species-specific linear regression equations relating tree DBH to crown area for eight bottomland tree species at White River National Wildlife Refuge, Arkansas, USA. The relationships were highly significant for all species, with coefficients of determination (r(2)) ranging from 0.37 for Ulmus crassifolia to nearly 0.80 for Quercus nuttalliii and Taxodium distichum. We next located and measured the diameters of more than 4000 stumps from a single tree-group selection timber harvest. Stump locations were recorded with respect to an established gl id point system and entered into a Geographic Information System (ARC/INFO). The area occupied by the crown of each logged individual was then estimated by using the stump dimensions (adjusted to DBHs) and the regression equations relating tree DBH to crown area. Our model projected that the selection cuts removed roughly 300 m(2) of basal area from the logged sites resulting in the loss of approximate to 55 000 m(2) of crown area. The model developed in this research represents a tool that can be used in conjunction with remote sensing applications to assist in forest inventory and management, as well as to estimate the impacts of selective timber harvest on wildlife.

A panning DLT procedure for three-dimensional videography.

PubMed

Yu, B; Koh, T J; Hay, J G

1993-06-01

The direct linear transformation (DLT) method [Abdel-Aziz and Karara, APS Symposium on Photogrammetry. American Society of Photogrammetry, Falls Church, VA (1971)] is widely used in biomechanics to obtain three-dimensional space coordinates from film and video records. This method has some major shortcomings when used to analyze events which take place over large areas. To overcome these shortcomings, a three-dimensional data collection method based on the DLT method, and making use of panning cameras, was developed. Several small single control volumes were combined to construct a large total control volume. For each single control volume, a regression equation (calibration equation) is developed to express each of the 11 DLT parameters as a function of camera orientation, so that the DLT parameters can then be estimated from arbitrary camera orientations. Once the DLT parameters are known for at least two cameras, and the associated two-dimensional film or video coordinates of the event are obtained, the desired three-dimensional space coordinates can be computed. In a laboratory test, five single control volumes (in a total control volume of 24.40 x 2.44 x 2.44 m3) were used to test the effect of the position of the single control volume on the accuracy of the computed three dimensional space coordinates. Linear and quadratic calibration equations were used to test the effect of the order of the equation on the accuracy of the computed three dimensional space coordinates. For four of the five single control volumes tested, the mean resultant errors associated with the use of the linear calibration equation were significantly larger than those associated with the use of the quadratic calibration equation. The position of the single control volume had no significant effect on the mean resultant errors in computed three dimensional coordinates when the quadratic calibration equation was used. Under the same data collection conditions, the mean resultant errors in the computed three dimensional coordinates associated with the panning and stationary DLT methods were 17 and 22 mm, respectively. The major advantages of the panning DLT method lie in the large image sizes obtained and in the ease with which the data can be collected. The method also has potential for use in a wide variety of contexts. The major shortcoming of the method is the large amount of digitizing necessary to calibrate the total control volume. Adaptations of the method to reduce the amount of digitizing required are being explored.

Graphical Calculation of Estimated Energy Expenditure in Burn Patients.

PubMed

Egro, Francesco M; Manders, Ernest C; Manders, Ernest K

2018-03-01

Historically, estimated energy expenditure (EEE) has been related to the percent of body surface area burned. Subsequent evaluations of these estimates have indicated that the earlier formulas may overestimate the amount of caloric support necessary for burn-injured patients. Ireton-Jones et al derived 2 equations for determining the EEE required to support burn patients, 1 for ventilator-dependent patients and 1 for spontaneously breathing patients. Evidence has proved their reliability, but they remain challenging to apply in a clinical setting given the difficult and cumbersome mathematics involved. This study aims to introduce a graphical calculation of EEE in burn patients that can be easily used in the clinical setting. The multivariant linear regression analysis from Ireton-Jones et al yielded equations that were rearranged into the form of a simple linear equation of the type y = mx + b. By choosing an energy expenditure and the age of the subject, the weight was calculated. The endpoints were then calculated, and a graph was mapped by means of Adobe FrameMaker. A graphical representation of Ireton-Jones et al's equations was obtained by plotting the weight (kg) on the y axis, the age (years) on the x axis, and a series of parallel lines representing the EEE in burn patients. The EEE has been displayed graphically on a grid to allow rapid determination of the EEE needed for a given patient of a designated weight and age. Two graphs were plotted: 1 for ventilator-dependent patients and 1 for spontaneously breathing patients. Correction factors for sex, the presence of additional trauma, and obesity are indicated on the graphical calculators. We propose a graphical tool to calculate caloric requirements in a fast, easy, and portable manner.

Structural equation model of factors related to quality of life for community-dwelling schizophrenic patients in Japan

PubMed Central

2014-01-01

Background This study aimed to clarify how community mental healthcare systems can be improved. Methods We included 79 schizophrenic patients, aged 20 to 80 years, residing in the Tokyo metropolitan area who regularly visited rehabilitation facilities offering assistance to psychiatric patients and were receiving treatment on an outpatient basis. No subjects had severe cognitive disorders or were taking medication with side effects that could prevent the completion of questionnaires. Questionnaires included items related to quality of life, self-efficacy, self-esteem, psychosis based on the Behavior and Symptom Identification Scale, health locus of control, and socio-demographic factors. We performed multiple linear regression analysis with quality of life as the dependent variable and, based on covariance structural analysis, evaluated the goodness of fit of the resulting structural equations models. Results Self-efficacy, self-esteem, and degree of psychosis significantly impacted quality of life. Marital status, age, and types of medications also influenced quality of life. Multiple linear regression analysis revealed psychiatric symptoms (Behavior and Symptom Identification Scale-32 [daily living and role functioning] (Beta = −0.537, p < 0.001) and self-efficacy (Beta = 0.249, p < 0.05) to be predictors of total quality of life score. Based on covariance structural analysis, the resulting model was found to exhibit reasonable goodness of fit. Conclusions Self-efficacy had an especially strong and direct impact on QOL. Therefore, it is important to provide more positive feedback to patients, provide social skills training based on cognitive behavioral therapy, and engage patients in role playing to improve self-efficacy and self-concept. PMID:25101143

Upper arm elevation and repetitive shoulder movements: a general population job exposure matrix based on expert ratings and technical measurements.

PubMed

Dalbøge, Annett; Hansson, Gert-Åke; Frost, Poul; Andersen, Johan Hviid; Heilskov-Hansen, Thomas; Svendsen, Susanne Wulff

2016-08-01

We recently constructed a general population job exposure matrix (JEM), The Shoulder JEM, based on expert ratings. The overall aim of this study was to convert expert-rated job exposures for upper arm elevation and repetitive shoulder movements to measurement scales. The Shoulder JEM covers all Danish occupational titles, divided into 172 job groups. For 36 of these job groups, we obtained technical measurements (inclinometry) of upper arm elevation and repetitive shoulder movements. To validate the expert-rated job exposures against the measured job exposures, we used Spearman rank correlations and the explained variance[Formula: see text] according to linear regression analyses (36 job groups). We used the linear regression equations to convert the expert-rated job exposures for all 172 job groups into predicted measured job exposures. Bland-Altman analyses were used to assess the agreement between the predicted and measured job exposures. The Spearman rank correlations were 0.63 for upper arm elevation and 0.64 for repetitive shoulder movements. The expert-rated job exposures explained 64% and 41% of the variance of the measured job exposures, respectively. The corresponding calibration equations were y=0.5%time+0.16×expert rating and y=27°/s+0.47×expert rating. The mean differences between predicted and measured job exposures were zero due to calibration; the 95% limits of agreement were ±2.9% time for upper arm elevation >90° and ±33°/s for repetitive shoulder movements. The updated Shoulder JEM can be used to present exposure-response relationships on measurement scales. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Abdominal girth and vertebral column length can adjust spinal anesthesia for lower limb surgery, a prospective, observational study.

PubMed

Zhou, Qing-he; Zhu, Bo; Wei, Chang-na; Yan, Min

2016-03-24

Studies have shown that abdominal girth and vertebral column length have high predictive value for spinal spread after administering a dose of plain bupivacaine. we designed a study to identify the specific correlations between abdominal girth, vertebral column length and a 0.5% dosage of plain bupivacaine, which should provide a minimum upper block level (T12) and a suitable upper block level (T10) for lower limb surgeries. A suitable dose of 0.5% plain bupivacaine was administered intrathecally between the L3 and L4 vertebrae for lower limb surgeries. If the upper cephalad spread of the patient by loss of pinprick discrimination was T12 or T10, the patient was enrolled in this study. Five patient variables and intrathecal plain bupivacaine dose were recorded. Linear regression and multiple regression analyses were performed. Totals of 111 patients and 121 patients who lost pinprick discrimination at T12 and T10, respectively, were analyzed in this study. Linear regression analysis showed that only abdominal girth and plain bupivacaine dose were strongly correlated (r =-0.827 for T12, r = -0.806 for T10; both p < 0.0001). Multiple linear regression analysis showed that both abdominal girth and vertebral column length were the key determinants of plain bupivacaine dose (both p < 0.0001). R(2) was 0.874 and 0.860 for the loss of pinprick discrimination at T12 and T10, respectively. Our data indicated that vertebral column length and abdominal girth were strongly correlated with the dosage of intrathecal plain bupivacaine for the loss of pinprick discrimination at T12 and T10. The two regression equations were YT12 = 3.547 + 0.045X1-0.044X2 and YT10 = 3.848 + 0.047X1- 0.046X2 (Y, 0.5% plain bupivacaine volume; X1, vertebral column length;and X 2, abdominal girth), which can accurately predict the minimum and suitable intrathecal bupivacaine dose for lower limb surgery to a great extent, separately.

Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

ERIC Educational Resources Information Center

Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

2013-01-01

In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…

Linear-time reconstruction of zero-recombinant Mendelian inheritance on pedigrees without mating loops.

PubMed

Liu, Lan; Jiang, Tao

2007-01-01

With the launch of the international HapMap project, the haplotype inference problem has attracted a great deal of attention in the computational biology community recently. In this paper, we study the question of how to efficiently infer haplotypes from genotypes of individuals related by a pedigree without mating loops, assuming that the hereditary process was free of mutations (i.e. the Mendelian law of inheritance) and recombinants. We model the haplotype inference problem as a system of linear equations as in [10] and present an (optimal) linear-time (i.e. O(mn) time) algorithm to generate a particular solution (A particular solution of any linear system is an assignment of numerical values to the variables in the system which satisfies the equations in the system.) to the haplotype inference problem, where m is the number of loci (or markers) in a genotype and n is the number of individuals in the pedigree. Moreover, the algorithm also provides a general solution (A general solution of any linear system is denoted by the span of a basis in the solution space to its associated homogeneous system, offset from the origin by a vector, namely by any particular solution. A general solution for ZRHC is very useful in practice because it allows the end user to efficiently enumerate all solutions for ZRHC and performs tasks such as random sampling.) in O(mn2) time, which is optimal because the size of a general solution could be as large as Theta(mn2). The key ingredients of our construction are (i) a fast consistency checking procedure for the system of linear equations introduced in [10] based on a careful investigation of the relationship between the equations (ii) a novel linear-time method for solving linear equations without invoking the Gaussian elimination method. Although such a fast method for solving equations is not known for general systems of linear equations, we take advantage of the underlying loop-free pedigree graph and some special properties of the linear equations.

Semigroup theory and numerical approximation for equations in linear viscoelasticity

NASA Technical Reports Server (NTRS)

Fabiano, R. H.; Ito, K.

1990-01-01

A class of abstract integrodifferential equations used to model linear viscoelastic beams is investigated analytically, applying a Hilbert-space approach. The basic equation is rewritten as a Cauchy problem, and its well-posedness is demonstrated. Finite-dimensional subspaces of the state space and an estimate of the state operator are obtained; approximation schemes for the equations are constructed; and the convergence is proved using the Trotter-Kato theorem of linear semigroup theory. The actual convergence behavior of different approximations is demonstrated in numerical computations, and the results are presented in tables.

Internal null controllability of a linear Schrödinger-KdV system on a bounded interval

NASA Astrophysics Data System (ADS)

Araruna, Fágner D.; Cerpa, Eduardo; Mercado, Alberto; Santos, Maurício C.

2016-01-01

The control of a linear dispersive system coupling a Schrödinger and a linear Korteweg-de Vries equation is studied in this paper. The system can be viewed as three coupled real-valued equations by taking real and imaginary parts in the Schrödinger equation. The internal null controllability is proven by using either one complex-valued control on the Schrödinger equation or two real-valued controls, one on each equation. Notice that the single Schrödinger equation is not known to be controllable with a real-valued control. The standard duality method is used to reduce the controllability property to an observability inequality, which is obtained by means of a Carleman estimates approach.

A new analysis of the Fornberg-Whitham equation pertaining to a fractional derivative with Mittag-Leffler-type kernel

NASA Astrophysics Data System (ADS)

Kumar, Devendra; Singh, Jagdev; Baleanu, Dumitru

2018-02-01

The mathematical model of breaking of non-linear dispersive water waves with memory effect is very important in mathematical physics. In the present article, we examine a novel fractional extension of the non-linear Fornberg-Whitham equation occurring in wave breaking. We consider the most recent theory of differentiation involving the non-singular kernel based on the extended Mittag-Leffler-type function to modify the Fornberg-Whitham equation. We examine the existence of the solution of the non-linear Fornberg-Whitham equation of fractional order. Further, we show the uniqueness of the solution. We obtain the numerical solution of the new arbitrary order model of the non-linear Fornberg-Whitham equation with the aid of the Laplace decomposition technique. The numerical outcomes are displayed in the form of graphs and tables. The results indicate that the Laplace decomposition algorithm is a very user-friendly and reliable scheme for handling such type of non-linear problems of fractional order.

Nonlinear (time domain) and linearized (time and frequency domain) solutions to the compressible Euler equations in conservation law form

NASA Technical Reports Server (NTRS)

Sreenivas, Kidambi; Whitfield, David L.

1995-01-01

Two linearized solvers (time and frequency domain) based on a high resolution numerical scheme are presented. The basic approach is to linearize the flux vector by expressing it as a sum of a mean and a perturbation. This allows the governing equations to be maintained in conservation law form. A key difference between the time and frequency domain computations is that the frequency domain computations require only one grid block irrespective of the interblade phase angle for which the flow is being computed. As a result of this and due to the fact that the governing equations for this case are steady, frequency domain computations are substantially faster than the corresponding time domain computations. The linearized equations are used to compute flows in turbomachinery blade rows (cascades) arising due to blade vibrations. Numerical solutions are compared to linear theory (where available) and to numerical solutions of the nonlinear Euler equations.

The use of experimental design in the development of an HPLC-ECD method for the analysis of captopril.

PubMed

Khamanga, Sandile M; Walker, Roderick B

2011-01-15

An accurate, sensitive and specific high performance liquid chromatography-electrochemical detection (HPLC-ECD) method that was developed and validated for captopril (CPT) is presented. Separation was achieved using a Phenomenex(®) Luna 5 μm (C(18)) column and a mobile phase comprised of phosphate buffer (adjusted to pH 3.0): acetonitrile in a ratio of 70:30 (v/v). Detection was accomplished using a full scan multi channel ESA Coulometric detector in the "oxidative-screen" mode with the upstream electrode (E(1)) set at +600 mV and the downstream (analytical) electrode (E(2)) set at +950 mV, while the potential of the guard cell was maintained at +1050 mV. The detector gain was set at 300. Experimental design using central composite design (CCD) was used to facilitate method development. Mobile phase pH, molarity and concentration of acetonitrile (ACN) were considered the critical factors to be studied to establish the retention time of CPT and cyclizine (CYC) that was used as the internal standard. Twenty experiments including centre points were undertaken and a quadratic model was derived for the retention time for CPT using the experimental data. The method was validated for linearity, accuracy, precision, limits of quantitation and detection, as per the ICH guidelines. The system was found to produce sharp and well-resolved peaks for CPT and CYC with retention times of 3.08 and 7.56 min, respectively. Linear regression analysis for the calibration curve showed a good linear relationship with a regression coefficient of 0.978 in the concentration range of 2-70 μg/mL. The linear regression equation was y=0.0131x+0.0275. The limits of detection (LOQ) and quantitation (LOD) were found to be 2.27 and 0.6 μg/mL, respectively. The method was used to analyze CPT in tablets. The wide range for linearity, accuracy, sensitivity, short retention time and composition of the mobile phase indicated that this method is better for the quantification of CPT than the pharmacopoeial methods. Copyright © 2010 Elsevier B.V. All rights reserved.

[Correlation of retinol binding protein 4 with 
metabolic indexes of glucose and 
lipid, bile cholesterol saturation index].

PubMed

Wang, Wen; Li, Nianfeng

2015-06-01

To measure retinol binding protein 4 (RBP4) levels in serum and bile and to analyze their relationship with insulin resistance, dyslipidemia or cholesterol saturation index (CSI).
 A total of 60 patients with gallstone were divided into a diabetes group (n=30) and a control group (n=30). The concentrations of RBP4 in serum and bile were detected by enzyme-linked immunosorbent assay (ELISA). Enzyme colorimetric method was used to measure the concentration of biliary cholesterol, bile acid and phospholipid. Biliary CSI was calculated by Carey table. Partial correlation and multiple linear regression analysis were used to evaluate the correlation between the RBP4 levels in serum or bile and the above indexes.
 The RBP4 concentrations in serum and bile in the diabetes group were significantly elevated compared with those in the control group (both P<0.01). There was no significant difference in the serum total bile acid (TBA), serum triglyceride (TG), serum high-density lipoprotein (HDL), bile TBA, bile total cholesterol (TC) , bile phospholipids and bile CSI between the 2 groups (all P>0.05); but the serum TC, low density lipoprotein (LDL), fasting blood glucose (FBG), fasting insulin (FINS), and homeostasis model assessment for insulin resistance (HOMA-IR) in the diabetes group were significantly increased compared to those in the control group (all P<0.05). The partial correlation analysis, which was adjusted by age, showed that the bile RBP4 was positively correlated with body mass index (BMI), waist circumference (WC), FINS, FBG, TC, LDL and HOMA-IR (r=0.283, 0.405, 0.685, 0.667, 0.553, 0.424 and 0.735, respectively), and the serum RBP4 was also positively correlated with the WC, FINS, FBG, TC, LDL and HOMA-IR (r=0.317, 0.734, 0.609, 0.528, 0.386 and 0.751, respectively). Stepwise multivariate linear regression analysis suggested that the HOMA-IR, BMI and WC were independently correlated with the level of bile RBP4 (multiple regression equation: Ybile RBP4=2.372XHOMA-IR+0.420XBMI+0.178XWC-26.813), and the serum RBP4 level was correlated with the HOMA-IR and WC independently (multiple regression equation: Yserum RBP4=2.832XHOMA-IR +0.235XWC-20.128). Multiple regression equations showed that HOMA-IR was the strongest correlation factor with RBP4.
 RBP4 concentrations in serum and bile in the diabetes group are significantly higher than those in the control group. HOMA-IR, BMI and WC are independently correlated with the level of bile RBP4. HOMA-IR and WC are independently correlated with the serum RBP4 level. HOMA-IR is the strongest correlation factor with RBP4. RBP4 might play an important role in the course of gallstone formation in Type 2 diabetes mellitus.

Meteorological adjustment of yearly mean values for air pollutant concentration comparison

NASA Technical Reports Server (NTRS)

Sidik, S. M.; Neustadter, H. E.

1976-01-01

Using multiple linear regression analysis, models which estimate mean concentrations of Total Suspended Particulate (TSP), sulfur dioxide, and nitrogen dioxide as a function of several meteorologic variables, two rough economic indicators, and a simple trend in time are studied. Meteorologic data were obtained and do not include inversion heights. The goodness of fit of the estimated models is partially reflected by the squared coefficient of multiple correlation which indicates that, at the various sampling stations, the models accounted for about 23 to 47 percent of the total variance of the observed TSP concentrations. If the resulting model equations are used in place of simple overall means of the observed concentrations, there is about a 20 percent improvement in either: (1) predicting mean concentrations for specified meteorological conditions; or (2) adjusting successive yearly averages to allow for comparisons devoid of meteorological effects. An application to source identification is presented using regression coefficients of wind velocity predictor variables.

Least square regularized regression in sum space.

PubMed

Xu, Yong-Li; Chen, Di-Rong; Li, Han-Xiong; Liu, Lu

2013-04-01

This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed. We measure the complexity of the sum space by its covering number and demonstrate that the covering number can be bounded by the product of the covering numbers of basic RKHSs. For sum space of RKHSs with Gaussian kernels, by choosing appropriate parameters, we tradeoff the sample error and regularization error, and obtain a polynomial learning rate, which is better than that in any single RKHS. The utility of this method is illustrated with two simulated data sets and five real-life databases.

Estimation of sex and stature using anthropometry of the upper extremity in an Australian population.

PubMed

Howley, Donna; Howley, Peter; Oxenham, Marc F

2018-06-01

Stature and a further 8 anthropometric dimensions were recorded from the arms and hands of a sample of 96 staff and students from the Australian National University and The University of Newcastle, Australia. These dimensions were used to create simple and multiple logistic regression models for sex estimation and simple and multiple linear regression equations for stature estimation of a contemporary Australian population. Overall sex classification accuracies using the models created were comparable to similar studies. The stature estimation models achieved standard errors of estimates (SEE) which were comparable to and in many cases lower than those achieved in similar research. Generic, non sex-specific models achieved similar SEEs and R 2 values to the sex-specific models indicating stature may be accurately estimated when sex is unknown. Copyright © 2018 Elsevier B.V. All rights reserved.

A Model for Oil-Gas Pipelines Cost Prediction Based on a Data Mining Process

NASA Astrophysics Data System (ADS)

Batzias, Fragiskos A.; Spanidis, Phillip-Mark P.

2009-08-01

This paper addresses the problems associated with the cost estimation of oil/gas pipelines during the elaboration of feasibility assessments. Techno-economic parameters, i.e., cost, length and diameter, are critical for such studies at the preliminary design stage. A methodology for the development of a cost prediction model based on Data Mining (DM) process is proposed. The design and implementation of a Knowledge Base (KB), maintaining data collected from various disciplines of the pipeline industry, are presented. The formulation of a cost prediction equation is demonstrated by applying multiple regression analysis using data sets extracted from the KB. Following the methodology proposed, a learning context is inductively developed as background pipeline data are acquired, grouped and stored in the KB, and through a linear regression model provide statistically substantial results, useful for project managers or decision makers.

Regression analysis of sparse asynchronous longitudinal data.

PubMed

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.

Symbolic Solution of Linear Differential Equations

NASA Technical Reports Server (NTRS)

Feinberg, R. B.; Grooms, R. G.

1981-01-01

An algorithm for solving linear constant-coefficient ordinary differential equations is presented. The computational complexity of the algorithm is discussed and its implementation in the FORMAC system is described. A comparison is made between the algorithm and some classical algorithms for solving differential equations.

Optimum pelvic incidence minus lumbar lordosis value can be determined by individual pelvic incidence.

PubMed

Inami, Satoshi; Moridaira, Hiroshi; Takeuchi, Daisaku; Shiba, Yo; Nohara, Yutaka; Taneichi, Hiroshi

2016-11-01

Adult spinal deformity (ASD) classification showing that ideal pelvic incidence minus lumbar lordosis (PI-LL) value is within 10° has been received widely. But no study has focused on the optimum level of PI-LL value that reflects wide variety in PI among patients. This study was conducted to determine the optimum PI-LL value specific to an individual's PI in postoperative ASD patients. 48 postoperative ASD patients were recruited. Spino-pelvic parameters and Oswestry Disability Index (ODI) were measured at the final follow-up. Factors associated with good clinical results were determined by stepwise multiple regression model using the ODI. The patients with ODI under the 75th percentile cutoff were designated into the "good" health related quality of life (HRQOL) group. In this group, the relationship between the PI-LL and PI was assessed by regression analysis. Multiple regression analysis revealed PI-LL as significant parameters associated with ODI. Thirty-six patients with an ODI <22 points (75th percentile cutoff) were categorized into a good HRQOL group, and linear regression models demonstrated the following equation: PI-LL = 0.41PI-11.12 (r = 0.45, P = 0.0059). On the basis of this equation, in the patients with a PI = 50°, the PI-LL is 9°. Whereas in those with a PI = 30°, the optimum PI-LL is calculated to be as low as 1°. In those with a PI = 80°, PI-LL is estimated at 22°. Consequently, an optimum PI-LL is inconsistent in that it depends on the individual PI.

Comparative study of three methods of estimation of creatinine clearance in critically ill patients.

PubMed

Blasco, V; Antonini, F; Zieleskiewicz, L; Hammad, E; Albanèse, J; Martin, C; Leone, M

2014-05-01

At the bedside, the reference method for creatinine clearance determination is based on the measurement of creatinine concentrations in urine and serum (mCrCl). Several models are available to calculate the creatinine clearance from the serum creatinine concentration. This observational survey aimed at testing the hypothesis that the proposed equations are unreliable to determine accurate creatinine clearance in patients admitted to intensive care unit (ICU). Creatinine clearance was determined by the use of mCrCl. Then, we compared three equations: Cockcroft-Gault (CG), Simplified Modification of Diet in Renal Disease (MDRDs), and Chronic Kidney Disease Epidemiology (CKD-EPI) in 156 consecutive patients within the first 24hours after ICU admission. We tested the hypothesis that the three equations were equivalent. The agreement between the three equations was evaluated by linear regression and Bland and Altman analysis. Bland and Altman analysis showed similar agreement between the three equations. The biases and precisions were -4.8±51, -1.3±50, and 8.2±44 for CG, MDRDs, and CKD-EPI equations, respectively (P>0.05). The precisions were similar for the three equations (P>0.05). The percentages of outliers at ±30% were 44%, 45%, and 49% for CG, MDRDs, and CKD-EPI, respectively (P>0.05). Regarding the high percentage of outliers, the use of these equations cannot be recommended in ICU patients. Copyright © 2014 Société française d’anesthésie et de réanimation (Sfar). Published by Elsevier SAS. All rights reserved.

Estimating selected low-flow frequency statistics and harmonic-mean flows for ungaged, unregulated streams in Indiana

USGS Publications Warehouse

Martin, Gary R.; Fowler, Kathleen K.; Arihood, Leslie D.

2016-09-06

Information on low-flow characteristics of streams is essential for the management of water resources. This report provides equations for estimating the 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and the harmonic-mean flow at ungaged, unregulated stream sites in Indiana. These equations were developed using the low-flow statistics and basin characteristics for 108 continuous-record streamgages in Indiana with at least 10 years of daily mean streamflow data through the 2011 climate year (April 1 through March 31). The equations were developed in cooperation with the Indiana Department of Environmental Management.Regression techniques were used to develop the equations for estimating low-flow frequency statistics and the harmonic-mean flows on the basis of drainage-basin characteristics. A geographic information system was used to measure basin characteristics for selected streamgages. A final set of 25 basin characteristics measured at all the streamgages were evaluated to choose the best predictors of the low-flow statistics.Logistic-regression equations applicable statewide are presented for estimating the probability that selected low-flow frequency statistics equal zero. These equations use the explanatory variables total drainage area, average transmissivity of the full thickness of the unconsolidated deposits within 1,000 feet of the stream network, and latitude of the basin outlet. The percentage of the streamgage low-flow statistics correctly classified as zero or nonzero using the logistic-regression equations ranged from 86.1 to 88.9 percent.Generalized-least-squares regression equations applicable statewide for estimating nonzero low-flow frequency statistics use total drainage area, the average hydraulic conductivity of the top 70 feet of unconsolidated deposits, the slope of the basin, and the index of permeability and thickness of the Quaternary surficial sediments as explanatory variables. The average standard error of prediction of these regression equations ranges from 55.7 to 61.5 percent.Regional weighted-least-squares regression equations were developed for estimating the harmonic-mean flows by dividing the State into three low-flow regions. The Northern region uses total drainage area and the average transmissivity of the entire thickness of unconsolidated deposits as explanatory variables. The Central region uses total drainage area, the average hydraulic conductivity of the entire thickness of unconsolidated deposits, and the index of permeability and thickness of the Quaternary surficial sediments. The Southern region uses total drainage area and the percent of the basin covered by forest. The average standard error of prediction for these equations ranges from 39.3 to 66.7 percent.The regional regression equations are applicable only to stream sites with low flows unaffected by regulation and to stream sites with drainage basin characteristic values within specified limits. Caution is advised when applying the equations for basins with characteristics near the applicable limits and for basins with karst drainage features and for urbanized basins. Extrapolations near and beyond the applicable basin characteristic limits will have unknown errors that may be large. Equations are presented for use in estimating the 90-percent prediction interval of the low-flow statistics estimated by use of the regression equations at a given stream site.The regression equations are to be incorporated into the U.S. Geological Survey StreamStats Web-based application for Indiana. StreamStats allows users to select a stream site on a map and automatically measure the needed basin characteristics and compute the estimated low-flow statistics and associated prediction intervals.

Technique for estimating the 2- to 500-year flood discharges on unregulated streams in rural Missouri

USGS Publications Warehouse

Alexander, Terry W.; Wilson, Gary L.

1995-01-01

A generalized least-squares regression technique was used to relate the 2- to 500-year flood discharges from 278 selected streamflow-gaging stations to statistically significant basin characteristics. The regression relations (estimating equations) were defined for three hydrologic regions (I, II, and III) in rural Missouri. Ordinary least-squares regression analyses indicate that drainage area (Regions I, II, and III) and main-channel slope (Regions I and II) are the only basin characteristics needed for computing the 2- to 500-year design-flood discharges at gaged or ungaged stream locations. The resulting generalized least-squares regression equations provide a technique for estimating the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year flood discharges on unregulated streams in rural Missouri. The regression equations for Regions I and II were developed from stream-flow-gaging stations with drainage areas ranging from 0.13 to 11,500 square miles and 0.13 to 14,000 square miles, and main-channel slopes ranging from 1.35 to 150 feet per mile and 1.20 to 279 feet per mile. The regression equations for Region III were developed from streamflow-gaging stations with drainage areas ranging from 0.48 to 1,040 square miles. Standard errors of estimate for the generalized least-squares regression equations in Regions I, II, and m ranged from 30 to 49 percent.

An investigation to improve the Menhaden fishery prediction and detection model through the application of ERTS-A data

NASA Technical Reports Server (NTRS)

Maughan, P. M. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Linear regression of secchi disc visibility against number of sets yielded significant results in a number of instances. The variability seen in the slope of the regression lines is due to the nonuniformity of sample size. The longer the period sampled, the larger the total number of attempts. Further, there is no reason to expect either the influence of transparency or of other variables to remain constant throughout the season. However, the fact that the data for the entire season, variable as it is, was significant at the 5% level, suggests its potential utility for predictive modeling. Thus, this regression equation will be considered representative and will be utilized for the first numerical model. Secchi disc visibility was also regressed against number of sets for the three day period September 27-September 29, 1972 to determine if surface truth data supported the intense relationship between ERTS-1 identified turbidity and fishing effort previously discussed. A very negative correlation was found. These relationship lend additional credence to the hypothesis that ERTS imagery, when utilized as a source of visibility (turbidity) data, may be useful as a predictive tool.

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

Estimation of peak-discharge frequency of urban streams in Jefferson County, Kentucky

USGS Publications Warehouse

Martin, Gary R.; Ruhl, Kevin J.; Moore, Brian L.; Rose, Martin F.

1997-01-01

An investigation of flood-hydrograph characteristics for streams in urban Jefferson County, Kentucky, was made to obtain hydrologic information needed for waterresources management. Equations for estimating peak-discharge frequencies for ungaged streams in the county were developed by combining (1) long-term annual peakdischarge data and rainfall-runoff data collected from 1991 to 1995 in 13 urban basins and (2) long-term annual peak-discharge data in four rural basins located in hydrologically similar areas of neighboring counties. The basins ranged in size from 1.36 to 64.0 square miles. The U.S. Geological Survey Rainfall- Runoff Model (RRM) was calibrated for each of the urban basins. The calibrated models were used with long-term, historical rainfall and pan-evaporation data to simulate 79 years of annual peak-discharge data. Peak-discharge frequencies were estimated by fitting the logarithms of the annual peak discharges to a Pearson-Type III frequency distribution. The simulated peak-discharge frequencies were adjusted for improved reliability by application of bias-correction factors derived from peakdischarge frequencies based on local, observed annual peak discharges. The three-parameter and the preferred seven-parameter nationwide urban-peak-discharge regression equations previously developed by USGS investigators provided biased (high) estimates for the urban basins studied. Generalized-least-square regression procedures were used to relate peakdischarge frequency to selected basin characteristics. Regression equations were developed to estimate peak-discharge frequency by adjusting peak-dischargefrequency estimates made by use of the threeparameter nationwide urban regression equations. The regression equations are presented in equivalent forms as functions of contributing drainage area, main-channel slope, and basin development factor, which is an index for measuring the efficiency of the basin drainage system. Estimates of peak discharges for streams in the county can be made for the 2-, 5-, 10-, 25-, 50-, and 100-year recurrence intervals by use of the regression equations. The average standard errors of prediction of the regression equations ranges from ? 34 to ? 45 percent. The regression equations are applicable to ungaged streams in the county having a specific range of basin characteristics.

Efficient Craig Interpolation for Linear Diophantine (Dis)Equations and Linear Modular Equations

DTIC Science & Technology

2008-02-01

Craig interpolants has enabled the development of powerful hardware and software model checking techniques. Efficient algorithms are known for computing...interpolants in rational and real linear arithmetic. We focus on subsets of integer linear arithmetic. Our main results are polynomial time algorithms ...congruences), and linear diophantine disequations. We show the utility of the proposed interpolation algorithms for discovering modular/divisibility predicates

Designing pinhole vacancies in graphene towards functionalization: Effects on critical buckling load

NASA Astrophysics Data System (ADS)

Georgantzinos, S. K.; Markolefas, S.; Giannopoulos, G. I.; Katsareas, D. E.; Anifantis, N. K.

2017-03-01

The effect of size and placement of pinhole-type atom vacancies on Euler's critical load on free-standing, monolayer graphene, is investigated. The graphene is modeled by a structural spring-based finite element approach, in which every interatomic interaction is approached as a linear spring. The geometry of graphene and the pinhole size lead to the assembly of the stiffness matrix of the nanostructure. Definition of the boundary conditions of the problem leads to the solution of the eigenvalue problem and consequently to the critical buckling load. Comparison to results found in the literature illustrates the validity and accuracy of the proposed method. Parametric analysis regarding the placement and size of the pinhole-type vacancy, as well as the graphene geometry, depicts the effects on critical buckling load. Non-linear regression analysis leads to empirical-analytical equations for predicting the buckling behavior of graphene, with engineered pinhole-type atom vacancies.

Assimilation of GOES-Derived Cloud Fields Into MM5

NASA Astrophysics Data System (ADS)

Biazar, A. P.; Doty, K. G.; McNider, R.

2007-12-01

This approach for the assimilation of GOES-derived cloud data into an atmospheric model (the Fifth-Generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model, or MM5) was performed in two steps. In the first step, multiple linear regression equations were developed using a control MM5 simulation to develop relationships for several dependent variables in model columns that had one or more layers of clouds. In the second step, the regression equations were applied during an MM5 simulation with assimilation in which the hourly GOES satellite data were used to determine the cloud locations and some of the cloud properties, but with all the other variables being determined by the model data. The satellite-derived fields used were shortwave cloud albedo and cloud top pressure. Ten multiple linear regression equations were developed for the following dependent variables: total cloud depth, number of cloud layers, depth of the layer that contains the maximum vertical velocity, the maximum vertical velocity, the height of the maximum vertical velocity, the estimated 1-h stable (i.e., grid scale) precipitation rate, the estimated 1-h convective precipitation rate, the height of the level with the maximum positive diabatic heating, the magnitude of the maximum positive diabatic heating, and the largest continuous layer of upward motion. The horizontal components of the divergent wind were adjusted to be consistent with the regression estimate of the maximum vertical velocity. The new total horizontal wind field with these new divergent components was then used to nudge an ongoing MM5 model simulation towards the target vertical velocity. Other adjustments included diabatic heating and moistening at specified levels. Where the model simulation had clouds when the satellite data indicated clear conditions, procedures were taken to remove or diminish the errant clouds. The results for the period of 0000 UTC 28 June - 0000 UTC 16 July 1999 for both a continental 32-km grid and an 8-km grid over the Southeastern United States indicate a significant improvement in the cloud bias statistics. The main improvement was the reduction of high bias values that indicated times and locations in the control run when there were model clouds but when the satellite indicated clear conditions. The importance of this technique is that it has been able to assimilate the observed clouds in the model in a dynamically sustainable manner. Acknowledgments. This work was partially funded by the following grants: a GEWEX grant from NASA , the Cooperative Agreement between the University of Alabama in Huntsville and the Minerals Management Service on Gulf of Mexico Issues, a NASA applications grant, and a NSF grant.

Stress stiffening and approximate equations in flexible multibody dynamics

NASA Technical Reports Server (NTRS)

Padilla, Carlos E.; Vonflotow, Andreas H.

1993-01-01

A useful model for open chains of flexible bodies undergoing large rigid body motions, but small elastic deformations, is one in which the equations of motion are linearized in the small elastic deformations and deformation rates. For slow rigid body motions, the correctly linearized, or consistent, set of equations can be compared to prematurely linearized, or inconsistent, equations and to 'oversimplified,' or ruthless, equations through the use of open loop dynamic simulations. It has been shown that the inconsistent model should never be used, while the ruthless model should be used whenever possible. The consistent and inconsistent models differ by stress stiffening terms. These are due to zeroth-order stresses effecting virtual work via nonlinear strain-displacement terms. In this paper we examine in detail the nature of these stress stiffening terms and conclude that they are significant only when the associated zeroth-order stresses approach 'buckling' stresses. Finally it is emphasized that when the stress stiffening terms are negligible the ruthlessly linearized equations should be used.

A formulation of rotor-airframe coupling for design analysis of vibrations of helicopter airframes

NASA Technical Reports Server (NTRS)

Kvaternik, R. G.; Walton, W. C., Jr.

1982-01-01

A linear formulation of rotor airframe coupling intended for vibration analysis in airframe structural design is presented. The airframe is represented by a finite element analysis model; the rotor is represented by a general set of linear differential equations with periodic coefficients; and the connections between the rotor and airframe are specified through general linear equations of constraint. Coupling equations are applied to the rotor and airframe equations to produce one set of linear differential equations governing vibrations of the combined rotor airframe system. These equations are solved by the harmonic balance method for the system steady state vibrations. A feature of the solution process is the representation of the airframe in terms of forced responses calculated at the rotor harmonics of interest. A method based on matrix partitioning is worked out for quick recalculations of vibrations in design studies when only relatively few airframe members are varied. All relations are presented in forms suitable for direct computer implementation.

Techniques for estimating flood-peak discharges of rural, unregulated streams in Ohio

USGS Publications Warehouse

Koltun, G.F.

2003-01-01

Regional equations for estimating 2-, 5-, 10-, 25-, 50-, 100-, and 500-year flood-peak discharges at ungaged sites on rural, unregulated streams in Ohio were developed by means of ordinary and generalized least-squares (GLS) regression techniques. One-variable, simple equations and three-variable, full-model equations were developed on the basis of selected basin characteristics and flood-frequency estimates determined for 305 streamflow-gaging stations in Ohio and adjacent states. The average standard errors of prediction ranged from about 39 to 49 percent for the simple equations, and from about 34 to 41 percent for the full-model equations. Flood-frequency estimates determined by means of log-Pearson Type III analyses are reported along with weighted flood-frequency estimates, computed as a function of the log-Pearson Type III estimates and the regression estimates. Values of explanatory variables used in the regression models were determined from digital spatial data sets by means of a geographic information system (GIS), with the exception of drainage area, which was determined by digitizing the area within basin boundaries manually delineated on topographic maps. Use of GIS-based explanatory variables represents a major departure in methodology from that described in previous reports on estimating flood-frequency characteristics of Ohio streams. Examples are presented illustrating application of the regression equations to ungaged sites on ungaged and gaged streams. A method is provided to adjust regression estimates for ungaged sites by use of weighted and regression estimates for a gaged site on the same stream. A region-of-influence method, which employs a computer program to estimate flood-frequency characteristics for ungaged sites based on data from gaged sites with similar characteristics, was also tested and compared to the GLS full-model equations. For all recurrence intervals, the GLS full-model equations had superior prediction accuracy relative to the simple equations and therefore are recommended for use.

Symmetry groups of integro-differential equations for linear thermoviscoelastic materials with memory

NASA Astrophysics Data System (ADS)

Zhou, L.-Q.; Meleshko, S. V.

2017-07-01

The group analysis method is applied to a system of integro-differential equations corresponding to a linear thermoviscoelastic model. A recently developed approach for calculating the symmetry groups of such equations is used. The general solution of the determining equations for the system is obtained. Using subalgebras of the admitted Lie algebra, two classes of partially invariant solutions of the considered system of integro-differential equations are studied.

Chandrasekhar equations for infinite dimensional systems

NASA Technical Reports Server (NTRS)

Ito, K.; Powers, R.

1985-01-01

The existence of Chandrasekhar equations for linear time-invariant systems defined on Hilbert spaces is investigated. An important consequence is that the solution to the evolutional Riccati equation is strongly differentiable in time, and that a strong solution of the Riccati differential equation can be defined. A discussion of the linear-quadratic optimal-control problem for hereditary differential systems is also included.

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Estimation of abbreviated mycophenolic acid area under the concentration-time curve during early posttransplant period by limited sampling strategy.

PubMed

Mohammadpour, A-H; Nazemian, F; Abtahi, B; Naghibi, M; Gholami, K; Rezaee, S; Nazari, M-R A; Rajabi, O

2008-12-01

Area under the concentration curve (AUC) of mycophenolic acid (MPA) could help to optimize therapeutic drug monitoring during the early post-renal transplant period. The aim of this study was to develop a limited sampling strategy to estimate an abbreviated MPA AUC within the first month after renal transplantation. In this study we selected 19 patients in the early posttransplant period with normal renal graft function (glomerular filtration rate > 70 mL/min). Plasma MPA concentrations were measured using reverse-phase high-performance liquid chromatography. MPA AUC(0-12h) was calculated using the linear trapezoidal rule. Multiple stepwise regression analysis was used to determine the minimal and convenient time points of MPA levels that could be used to derive model equations best fitted to MPA AUC(0-12h). The regression equation for AUC estimation that gave the best performance was AUC = 14.46 C(10) + 15.547 (r(2) = .882). The validation of the method was performed using the jackknife method. Mean prediction error of this model was not different from zero (P > .05) and had a high root mean square prediction error (8.06). In conclusion, this limited sampling strategy provided an effective approach for therapeutic drug monitoring during the early posttransplant period.

Whitham modulation theory for the Kadomtsev- Petviashvili equation.

PubMed

Ablowitz, Mark J; Biondini, Gino; Wang, Qiao

2017-08-01

The genus-1 Kadomtsev-Petviashvili (KP)-Whitham system is derived for both variants of the KP equation; namely the KPI and KPII equations. The basic properties of the KP-Whitham system, including symmetries, exact reductions and its possible complete integrability, together with the appropriate generalization of the one-dimensional Riemann problem for the Korteweg-de Vries equation are discussed. Finally, the KP-Whitham system is used to study the linear stability properties of the genus-1 solutions of the KPI and KPII equations; it is shown that all genus-1 solutions of KPI are linearly unstable, while all genus-1 solutions of KPII are linearly stable within the context of Whitham theory.

Whitham modulation theory for the Kadomtsev- Petviashvili equation

NASA Astrophysics Data System (ADS)

Ablowitz, Mark J.; Biondini, Gino; Wang, Qiao

2017-08-01

The genus-1 Kadomtsev-Petviashvili (KP)-Whitham system is derived for both variants of the KP equation; namely the KPI and KPII equations. The basic properties of the KP-Whitham system, including symmetries, exact reductions and its possible complete integrability, together with the appropriate generalization of the one-dimensional Riemann problem for the Korteweg-de Vries equation are discussed. Finally, the KP-Whitham system is used to study the linear stability properties of the genus-1 solutions of the KPI and KPII equations; it is shown that all genus-1 solutions of KPI are linearly unstable, while all genus-1 solutions of KPII are linearly stable within the context of Whitham theory.

Flood-Frequency Estimates for Streams on Kaua`i, O`ahu, Moloka`i, Maui, and Hawai`i, State of Hawai`i

USGS Publications Warehouse

Oki, Delwyn S.; Rosa, Sarah N.; Yeung, Chiu W.

2010-01-01

This study provides an updated analysis of the magnitude and frequency of peak stream discharges in Hawai`i. Annual peak-discharge data collected by the U.S. Geological Survey during and before water year 2008 (ending September 30, 2008) at stream-gaging stations were analyzed. The existing generalized-skew value for the State of Hawai`i was retained, although three methods were used to evaluate whether an update was needed. Regional regression equations were developed for peak discharges with 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals for unregulated streams (those for which peak discharges are not affected to a large extent by upstream reservoirs, dams, diversions, or other structures) in areas with less than 20 percent combined medium- and high-intensity development on Kaua`i, O`ahu, Moloka`i, Maui, and Hawai`i. The generalized-least-squares (GLS) regression equations relate peak stream discharge to quantified basin characteristics (for example, drainage-basin area and mean annual rainfall) that were determined using geographic information system (GIS) methods. Each of the islands of Kaua`i,O`ahu, Moloka`i, Maui, and Hawai`i was divided into two regions, generally corresponding to a wet region and a dry region. Unique peak-discharge regression equations were developed for each region. The regression equations developed for this study have standard errors of prediction ranging from 16 to 620 percent. Standard errors of prediction are greatest for regression equations developed for leeward Moloka`i and southern Hawai`i. In general, estimated 100-year peak discharges from this study are lower than those from previous studies, which may reflect the longer periods of record used in this study. Each regression equation is valid within the range of values of the explanatory variables used to develop the equation. The regression equations were developed using peak-discharge data from streams that are mainly unregulated, and they should not be used to estimate peak discharges in regulated streams. Use of a regression equation beyond its limits will produce peak-discharge estimates with unknown error and should therefore be avoided. Improved estimates of the magnitude and frequency of peak discharges in Hawai`i will require continued operation of existing stream-gaging stations and operation of additional gaging stations for areas such as Moloka`i and Hawai`i, where limited stream-gaging data are available.

The Fundamental Solution of the Linearized Navier Stokes Equations for Spinning Bodies in Three Spatial Dimensions Time Dependent Case

NASA Astrophysics Data System (ADS)

Thomann, Enrique A.; Guenther, Ronald B.

2006-02-01

Explicit formulae for the fundamental solution of the linearized time dependent Navier Stokes equations in three spatial dimensions are obtained. The linear equations considered in this paper include those used to model rigid bodies that are translating and rotating at a constant velocity. Estimates extending those obtained by Solonnikov in [23] for the fundamental solution of the time dependent Stokes equations, corresponding to zero translational and angular velocity, are established. Existence and uniqueness of solutions of these linearized problems is obtained for a class of functions that includes the classical Lebesgue spaces L p (R 3), 1 < p < ∞. Finally, the asymptotic behavior and semigroup properties of the fundamental solution are established.

A new formulation for anisotropic radiative transfer problems. I - Solution with a variational technique

NASA Technical Reports Server (NTRS)

Cheyney, H., III; Arking, A.

1976-01-01

The equations of radiative transfer in anisotropically scattering media are reformulated as linear operator equations in a single independent variable. The resulting equations are suitable for solution by a variety of standard mathematical techniques. The operators appearing in the resulting equations are in general nonsymmetric; however, it is shown that every bounded linear operator equation can be embedded in a symmetric linear operator equation and a variational solution can be obtained in a straightforward way. For purposes of demonstration, a Rayleigh-Ritz variational method is applied to three problems involving simple phase functions. It is to be noted that the variational technique demonstrated is of general applicability and permits simple solutions for a wide range of otherwise difficult mathematical problems in physics.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

PubMed

Ding, A Adam; Wu, Hulin

2014-10-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression

PubMed Central

Ding, A. Adam; Wu, Hulin

2015-01-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method. PMID:26401093

Oscillation criteria for half-linear dynamic equations on time scales

NASA Astrophysics Data System (ADS)

Hassan, Taher S.

2008-09-01

This paper is concerned with oscillation of the second-order half-linear dynamic equation(r(t)(x[Delta])[gamma])[Delta]+p(t)x[gamma](t)=0, on a time scale where [gamma] is the quotient of odd positive integers, r(t) and p(t) are positive rd-continuous functions on . Our results solve a problem posed by [R.P. Agarwal, D. O'Regan, S.H. Saker, Philos-type oscillation criteria for second-order half linear dynamic equations, Rocky Mountain J. Math. 37 (2007) 1085-1104; S.H. Saker, Oscillation criteria of second order half-linear dynamic equations on time scales, J. Comput. Appl. Math. 177 (2005) 375-387] and our results in the special cases when and involve and improve some oscillation results for second-order differential and difference equations; and when , and , etc., our oscillation results are essentially newE Some examples illustrating the importance of our results are also included.

Mathematical Modeling of Chemical Stoichiometry

ERIC Educational Resources Information Center

Croteau, Joshua; Fox, William P.; Varazo, Kristofoland

2007-01-01

In beginning chemistry classes, students are taught a variety of techniques for balancing chemical equations. The most common method is inspection. This paper addresses using a system of linear mathematical equations to solve for the stoichiometric coefficients. Many linear algebra books carry the standard balancing of chemical equations as an…

Generalized Multilevel Structural Equation Modeling

ERIC Educational Resources Information Center

Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew

2004-01-01

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent…

Comparison of stability and control parameters for a light, single-engine, high-winged aircraft using different flight test and parameter estimation techniques

NASA Technical Reports Server (NTRS)

Suit, W. T.; Cannaday, R. L.

1979-01-01

The longitudinal and lateral stability and control parameters for a high wing, general aviation, airplane are examined. Estimations using flight data obtained at various flight conditions within the normal range of the aircraft are presented. The estimations techniques, an output error technique (maximum likelihood) and an equation error technique (linear regression), are presented. The longitudinal static parameters are estimated from climbing, descending, and quasi steady state flight data. The lateral excitations involve a combination of rudder and ailerons. The sensitivity of the aircraft modes of motion to variations in the parameter estimates are discussed.

Improved Linear Algebra Methods for Redshift Computation from Limited Spectrum Data - II

NASA Technical Reports Server (NTRS)

Foster, Leslie; Waagen, Alex; Aijaz, Nabella; Hurley, Michael; Luis, Apolo; Rinsky, Joel; Satyavolu, Chandrika; Gazis, Paul; Srivastava, Ashok; Way, Michael

2008-01-01

Given photometric broadband measurements of a galaxy, Gaussian processes may be used with a training set to solve the regression problem of approximating the redshift of this galaxy. However, in practice solving the traditional Gaussian processes equation is too slow and requires too much memory. We employed several methods to avoid this difficulty using algebraic manipulation and low-rank approximation, and were able to quickly approximate the redshifts in our testing data within 17 percent of the known true values using limited computational resources. The accuracy of one method, the V Formulation, is comparable to the accuracy of the best methods currently used for this problem.

Mapping of fluoride endemic areas and correlation studies of fluoride with other quality parameters of drinking water of Veppanapalli block of Dharmapuri district in Tamil Nadu.

PubMed

Karthikeyan, G; Sundarraj, A Shunmuga; Elango, K P

2003-10-01

193 drinking water samples from water sources of 27 panchayats of Veppanapalli block of Dharmapuri district of Tamil Nadu were analysed for chemical quality parameters. Based on the fluoride content of the water sources, fluoride maps differentiating regions with high / low fluoride levels were prepared using Isopleth mapping technique. The interdependence among the important chemical quality parameters were assessed using correlation studies. The experimental results of the application of linear and multiple regression equations on the influence of hardness, alkalinity, total dissolved solids and pH on fluoride are discussed.

Analysis of the thermal comfort model in an environment of metal mechanical branch.

PubMed

Pinto, N M; Xavier, A A P; do Amaral, Regiane T

2012-01-01

This study aims to identify the correlation between the Predicted Mean Vote (PMV) with the thermal sensation (S) of 55 employees, establishing a linear multiple regression equation. The measurement of environmental variables followed established standards. The survey was conducted in a metal industry located in Ponta Grossa of the State of Parana in Brazil. It was applied the physical model of thermal comfort to the environmental variables and also to the subjective data on the thermal sensations of employees. The survey was conducted from May to November, 2010, with 48 measurements. This study will serve as the basis for a dissertation consisting of 72 measurements.

Time-resolved perfusion imaging at the angiography suite: preclinical comparison of a new flat-detector application to computed tomography perfusion.

PubMed

Jürgens, Julian H W; Schulz, Nadine; Wybranski, Christian; Seidensticker, Max; Streit, Sebastian; Brauner, Jan; Wohlgemuth, Walter A; Deuerling-Zheng, Yu; Ricke, Jens; Dudeck, Oliver

2015-02-01

The objective of this study was to compare the parameter maps of a new flat-panel detector application for time-resolved perfusion imaging in the angiography room (FD-CTP) with computed tomography perfusion (CTP) in an experimental tumor model. Twenty-four VX2 tumors were implanted into the hind legs of 12 rabbits. Three weeks later, FD-CTP (Artis zeego; Siemens) and CTP (SOMATOM Definition AS +; Siemens) were performed. The parameter maps for the FD-CTP were calculated using a prototype software, and those for the CTP were calculated with VPCT-body software on a dedicated syngo MultiModality Workplace. The parameters were compared using Pearson product-moment correlation coefficient and linear regression analysis. The Pearson product-moment correlation coefficient showed good correlation values for both the intratumoral blood volume of 0.848 (P < 0.01) and the blood flow of 0.698 (P < 0.01). The linear regression analysis of the perfusion between FD-CTP and CTP showed for the blood volume a regression equation y = 4.44x + 36.72 (P < 0.01) and for the blood flow y = 0.75x + 14.61 (P < 0.01). This preclinical study provides evidence that FD-CTP allows a time-resolved (dynamic) perfusion imaging of tumors similar to CTP, which provides the basis for clinical applications such as the assessment of tumor response to locoregional therapies directly in the angiography suite.

Inflammation, homocysteine and carotid intima-media thickness.

PubMed

Baptista, Alexandre P; Cacdocar, Sanjiva; Palmeiro, Hugo; Faísca, Marília; Carrasqueira, Herménio; Morgado, Elsa; Sampaio, Sandra; Cabrita, Ana; Silva, Ana Paula; Bernardo, Idalécio; Gome, Veloso; Neves, Pedro L

2008-01-01

Cardiovascular disease is the main cause of morbidity and mortality in chronic renal patients. Carotid intima-media thickness (CIMT) is one of the most accurate markers of atherosclerosis risk. In this study, the authors set out to evaluate a population of chronic renal patients to determine which factors are associated with an increase in intima-media thickness. We included 56 patients (F=22, M=34), with a mean age of 68.6 years, and an estimated glomerular filtration rate of 15.8 ml/min (calculated by the MDRD equation). Various laboratory and inflammatory parameters (hsCRP, IL-6 and TNF-alpha) were evaluated. All subjects underwent measurement of internal carotid artery intima-media thickness by high-resolution real-time B-mode ultrasonography using a 10 MHz linear transducer. Intima-media thickness was used as a dependent variable in a simple linear regression model, with the various laboratory parameters as independent variables. Only parameters showing a significant correlation with CIMT were evaluated in a multiple regression model: age (p=0.001), hemoglobin (p=00.3), logCRP (p=0.042), logIL-6 (p=0.004) and homocysteine (p=0.002). In the multiple regression model we found that age (p=0.001) and homocysteine (p=0.027) were independently correlated with CIMT. LogIL-6 did not reach statistical significance (p=0.057), probably due to the small population size. The authors conclude that age and homocysteine correlate with carotid intima-media thickness, and thus can be considered as markers/risk factors in chronic renal patients.

Construction of reduced order models for the non-linear Navier-Stokes equations using the proper orthogonal fecomposition (POD)/Galerkin method.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fike, Jeffrey A.

2013-08-01

The construction of stable reduced order models using Galerkin projection for the Euler or Navier-Stokes equations requires a suitable choice for the inner product. The standard L2 inner product is expected to produce unstable ROMs. For the non-linear Navier-Stokes equations this means the use of an energy inner product. In this report, Galerkin projection for the non-linear Navier-Stokes equations using the L2 inner product is implemented as a first step toward constructing stable ROMs for this set of physics.

A Semi-linear Backward Parabolic Cauchy Problem with Unbounded Coefficients of Hamilton–Jacobi–Bellman Type and Applications to Optimal Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Addona, Davide, E-mail: d.addona@campus.unimib.it

2015-08-15

We obtain weighted uniform estimates for the gradient of the solutions to a class of linear parabolic Cauchy problems with unbounded coefficients. Such estimates are then used to prove existence and uniqueness of the mild solution to a semi-linear backward parabolic Cauchy problem, where the differential equation is the Hamilton–Jacobi–Bellman equation of a suitable optimal control problem. Via backward stochastic differential equations, we show that the mild solution is indeed the value function of the controlled equation and that the feedback law is verified.

Program for the solution of multipoint boundary value problems of quasilinear differential equations

NASA Technical Reports Server (NTRS)

1973-01-01

Linear equations are solved by a method of superposition of solutions of a sequence of initial value problems. For nonlinear equations and/or boundary conditions, the solution is iterative and in each iteration a problem like the linear case is solved. A simple Taylor series expansion is used for the linearization of both nonlinear equations and nonlinear boundary conditions. The perturbation method of solution is used in preference to quasilinearization because of programming ease, and smaller storage requirements; and experiments indicate that the desired convergence properties exist although no proof or convergence is given.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

PubMed Central

Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan

2016-01-01

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing. PMID:27035946

Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

PubMed

Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2016-04-12

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.

Prediction Equation for Calculating Fat Mass in Young Indian Adults

PubMed Central

Sandhu, Jaspal Singh; Gupta, Giniya; Shenoy, Shweta

2010-01-01

Purpose Accurate measurement or prediction of fat mass is useful in physiology, nutrition and clinical medicine. Most predictive equations currently used to assess percentage of body fat or fat mass, using simple anthropometric measurements were derived from people in western societies and they may not be appropriate for individuals with other genotypic and phenotypic characteristics. We developed equations to predict fat mass from anthropometric measurements in young Indian adults. Methods Fat mass was measured in 60 females and 58 males, aged 20 to 29 yrs by using hydrostatic weighing and by simultaneous measurement of residual lung volume. Anthropometric measure included weight (kg), height (m) and 4 skinfold thickness [STs (mm)]. Sex specific linear regression model was developed with fat mass as the dependent variable and all anthropometric measures as independent variables. Results The prediction equation obtained for fat mass (kg) for males was 8.46+0.32 (weight) − 15.16 (height) + 9.54 (log of sum of 4 STs) (R2= 0. 53, SEE=3.42 kg) and − 20.22 + 0.33 (weight) + 3.44 (height) + 7.66 (log of sum of 4 STs) (R2=0.72, SEE=3.01kg) for females. Conclusion A new prediction equation for the measurement of fat mass was derived and internally validated in young Indian adults using simple anthropometric measurements. PMID:22375197

Improving estimates of streamflow characteristics by using Landsat-1 imagery

USGS Publications Warehouse

Hollyday, Este F.

1976-01-01

Imagery from the first Earth Resources Technology Satellite (renamed Landsat-1) was used to discriminate physical features of drainage basins in an effort to improve equations used to estimate streamflow characteristics at gaged and ungaged sites. Records of 20 gaged basins in the Delmarva Peninsula of Maryland, Delaware, and Virginia were analyzed for 40 statistical streamflow characteristics. Equations relating these characteristics to basin characteristics were obtained by a technique of multiple linear regression. A control group of equations contains basin characteristics derived from maps. An experimental group of equations contains basin characteristics derived from maps and imagery. Characteristics from imagery were forest, riparian (streambank) vegetation, water, and combined agricultural and urban land use. These basin characteristics were isolated photographically by techniques of film-density discrimination. The area of each characteristic in each basin was measured photometrically. Comparison of equations in the control group with corresponding equations in the experimental group reveals that for 12 out of 40 equations the standard error of estimate was reduced by more than 10 percent. As an example, the standard error of estimate of the equation for the 5-year recurrence-interval flood peak was reduced from 46 to 32 percent. Similarly, the standard error of the equation for the mean monthly flow for September was reduced from 32 to 24 percent, the standard error for the 7-day, 2-year recurrence low flow was reduced from 136 to 102 percent, and the standard error for the 3-day, 2-year flood volume was reduced from 30 to 12 percent. It is concluded that data from Landsat imagery can substantially improve the accuracy of estimates of some streamflow characteristics at sites in the Delmarva Peninsula.

The development and validation of new equations for estimating body fat percentage among Chinese men and women.

PubMed

Liu, Xin; Sun, Qi; Sun, Liang; Zong, Geng; Lu, Ling; Liu, Gang; Rosner, Bernard; Ye, Xingwang; Li, Huaixing; Lin, Xu

2015-05-14

Equations based on simple anthropometric measurements to predict body fat percentage (BF%) are lacking in Chinese population with increasing prevalence of obesity and related abnormalities. We aimed to develop and validate BF% equations in two independent population-based samples of Chinese men and women. The equations were developed among 960 Chinese Hans living in Shanghai (age 46.2 (SD 5.3) years; 36.7% male) using a stepwise linear regression and were subsequently validated in 1150 Shanghai residents (58.7 (SD 6.0) years; 41.7% male; 99% Chinese Hans, 1% Chinese minorities). The associations of equation-derived BF% with changes of 6-year cardiometabolic outcomes and incident type 2 diabetes (T2D) were evaluated in a sub-cohort of 780 Chinese, compared with BF% measured by dual-energy X-ray absorptiometry (DXA; BF%-DXA). Sex-specific equations were established with age, BMI and waist circumference as independent variables. The BF% calculated using new sex-specific equations (BF%-CSS) were in reasonable agreement with BF%-DXA (mean difference: 0.08 (2 SD 6.64) %, P= 0.606 in men; 0.45 (2 SD 6.88) %, P< 0.001 in women). In multivariate-adjusted models, the BF%-CSS and BF%-DXA showed comparable associations with 6-year changes in TAG, HDL-cholesterol, diastolic blood pressure, C-reactive protein and uric acid (P for comparisons ≥ 0.05). Meanwhile, the BF%-CSS and BF%-DXA had comparable areas under the receiver operating characteristic curves for associations with incident T2D (men P= 0.327; women P= 0.159). The BF% equations might be used as surrogates for DXA to estimate BF% among adult Chinese. More studies are needed to evaluate the application of our equations in different populations.

Regression equations for disinfection by-products for the Mississippi, Ohio and Missouri rivers

USGS Publications Warehouse

Rathbun, R.E.

1996-01-01

Trihalomethane and nonpurgeable total organic-halide formation potentials were determined for the chlorination of water samples from the Mississippi, Ohio and Missouri Rivers. Samples were collected during the summer and fall of 1991 and the spring of 1992 at twelve locations on the Mississippi from New Orleans to Minneapolis, and on the Ohio and Missouri 1.6 km upstream from their confluences with the Mississippi. Formation potentials were determined as a function of pH, initial free-chlorine concentration, and reaction time. Multiple linear regression analysis of the data indicated that pH, reaction time, and the dissolved organic carbon concentration and/or the ultraviolet absorbance of the water were the most significant variables. The initial free-chlorine concentration had less significance and bromide concentration had little or no significance. Analysis of combinations of the dissolved organic carbon concentration and the ultraviolet absorbance indicated that use of the ultraviolet absorbance alone provided the best prediction of the experimental data. Regression coefficients for the variables were generally comparable to coefficients previously presented in the literature for waters from other parts of the United States.

Methods for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations on streams in North Georgia

USGS Publications Warehouse

Gotvald, Anthony J.

2017-01-13

The U.S. Geological Survey, in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division, developed regional regression equations for estimating selected low-flow frequency and mean annual flow statistics for ungaged streams in north Georgia that are not substantially affected by regulation, diversions, or urbanization. Selected low-flow frequency statistics and basin characteristics for 56 streamgage locations within north Georgia and 75 miles beyond the State’s borders in Alabama, Tennessee, North Carolina, and South Carolina were combined to form the final dataset used in the regional regression analysis. Because some of the streamgages in the study recorded zero flow, the final regression equations were developed using weighted left-censored regression analysis to analyze the flow data in an unbiased manner, with weights based on the number of years of record. The set of equations includes the annual minimum 1- and 7-day average streamflow with the 10-year recurrence interval (referred to as 1Q10 and 7Q10), monthly 7Q10, and mean annual flow. The final regional regression equations are functions of drainage area, mean annual precipitation, and relief ratio for the selected low-flow frequency statistics and drainage area and mean annual precipitation for mean annual flow. The average standard error of estimate was 13.7 percent for the mean annual flow regression equation and ranged from 26.1 to 91.6 percent for the selected low-flow frequency equations.The equations, which are based on data from streams with little to no flow alterations, can be used to provide estimates of the natural flows for selected ungaged stream locations in the area of Georgia north of the Fall Line. The regression equations are not to be used to estimate flows for streams that have been altered by the effects of major dams, surface-water withdrawals, groundwater withdrawals (pumping wells), diversions, or wastewater discharges. The regression equations should be used only for ungaged sites with drainage areas between 1.67 and 576 square miles, mean annual precipitation between 47.6 and 81.6 inches, and relief ratios between 0.146 and 0.607; these are the ranges of the explanatory variables used to develop the equations. An attempt was made to develop regional regression equations for the area of Georgia south of the Fall Line by using the same approach used during this study for north Georgia; however, the equations resulted with high average standard errors of estimates and poorly predicted flows below 0.5 cubic foot per second, which may be attributed to the karst topography common in that area.The final regression equations developed from this study are planned to be incorporated into the U.S. Geological Survey StreamStats program. StreamStats is a Web-based geographic information system that provides users with access to an assortment of analytical tools useful for water-resources planning and management, and for engineering design applications, such as the design of bridges. The StreamStats program provides streamflow statistics and basin characteristics for U.S. Geological Survey streamgage locations and ungaged sites of interest. StreamStats also can compute basin characteristics and provide estimates of streamflow statistics for ungaged sites when users select the location of a site along any stream in Georgia.

Corrected Implicit Monte Carlo

DOE PAGES

Cleveland, Mathew Allen; Wollaber, Allan Benton

2018-01-02

Here in this work we develop a set of nonlinear correction equations to enforce a consistent time-implicit emission temperature for the original semi-implicit IMC equations. We present two possible forms of correction equations: one results in a set of non-linear, zero-dimensional, non-negative, explicit correction equations, and the other results in a non-linear, non-negative, Boltzman transport correction equation. The zero-dimensional correction equations adheres to the maximum principle for the material temperature, regardless of frequency-dependence, but does not prevent maximum principle violation in the photon intensity, eventually leading to material overheating. The Boltzman transport correction guarantees adherence to the maximum principle formore » frequency-independent simulations, at the cost of evaluating a reduced source non-linear Boltzman equation. Finally, we present numerical evidence suggesting that the Boltzman transport correction, in its current form, significantly improves time step limitations but does not guarantee adherence to the maximum principle for frequency-dependent simulations.« less

Local energy decay for linear wave equations with variable coefficients

NASA Astrophysics Data System (ADS)

Ikehata, Ryo

2005-06-01

A uniform local energy decay result is derived to the linear wave equation with spatial variable coefficients. We deal with this equation in an exterior domain with a star-shaped complement. Our advantage is that we do not assume any compactness of the support on the initial data, and its proof is quite simple. This generalizes a previous famous result due to Morawetz [The decay of solutions of the exterior initial-boundary value problem for the wave equation, Comm. Pure Appl. Math. 14 (1961) 561-568]. In order to prove local energy decay, we mainly apply two types of ideas due to Ikehata-Matsuyama [L2-behaviour of solutions to the linear heat and wave equations in exterior domains, Sci. Math. Japon. 55 (2002) 33-42] and Todorova-Yordanov [Critical exponent for a nonlinear wave equation with damping, J. Differential Equations 174 (2001) 464-489].

Corrected implicit Monte Carlo

NASA Astrophysics Data System (ADS)

Cleveland, M. A.; Wollaber, A. B.

2018-04-01

In this work we develop a set of nonlinear correction equations to enforce a consistent time-implicit emission temperature for the original semi-implicit IMC equations. We present two possible forms of correction equations: one results in a set of non-linear, zero-dimensional, non-negative, explicit correction equations, and the other results in a non-linear, non-negative, Boltzman transport correction equation. The zero-dimensional correction equations adheres to the maximum principle for the material temperature, regardless of frequency-dependence, but does not prevent maximum principle violation in the photon intensity, eventually leading to material overheating. The Boltzman transport correction guarantees adherence to the maximum principle for frequency-independent simulations, at the cost of evaluating a reduced source non-linear Boltzman equation. We present numerical evidence suggesting that the Boltzman transport correction, in its current form, significantly improves time step limitations but does not guarantee adherence to the maximum principle for frequency-dependent simulations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cleveland, Mathew Allen; Wollaber, Allan Benton

Here in this work we develop a set of nonlinear correction equations to enforce a consistent time-implicit emission temperature for the original semi-implicit IMC equations. We present two possible forms of correction equations: one results in a set of non-linear, zero-dimensional, non-negative, explicit correction equations, and the other results in a non-linear, non-negative, Boltzman transport correction equation. The zero-dimensional correction equations adheres to the maximum principle for the material temperature, regardless of frequency-dependence, but does not prevent maximum principle violation in the photon intensity, eventually leading to material overheating. The Boltzman transport correction guarantees adherence to the maximum principle formore » frequency-independent simulations, at the cost of evaluating a reduced source non-linear Boltzman equation. Finally, we present numerical evidence suggesting that the Boltzman transport correction, in its current form, significantly improves time step limitations but does not guarantee adherence to the maximum principle for frequency-dependent simulations.« less

Lyapunov stability and its application to systems of ordinary differential equations

NASA Technical Reports Server (NTRS)

Kennedy, E. W.

1979-01-01

An outline and a brief introduction to some of the concepts and implications of Lyapunov stability theory are presented. Various aspects of the theory are illustrated by the inclusion of eight examples, including the Cartesian coordinate equations of the two-body problem, linear and nonlinear (Van der Pol's equation) oscillatory systems, and the linearized Kustaanheimo-Stiefel element equations for the unperturbed two-body problem.

Chandrasekhar equations for infinite dimensional systems

NASA Technical Reports Server (NTRS)

Ito, K.; Powers, R. K.

1985-01-01

Chandrasekhar equations are derived for linear time invariant systems defined on Hilbert spaces using a functional analytic technique. An important consequence of this is that the solution to the evolutional Riccati equation is strongly differentiable in time and one can define a strong solution of the Riccati differential equation. A detailed discussion on the linear quadratic optimal control problem for hereditary differential systems is also included.

Approximating a nonlinear advanced-delayed equation from acoustics

NASA Astrophysics Data System (ADS)

Teodoro, M. Filomena

2016-10-01

We approximate the solution of a particular non-linear mixed type functional differential equation from physiology, the mucosal wave model of the vocal oscillation during phonation. The mathematical equation models a superficial wave propagating through the tissues. The numerical scheme is adapted from the work presented in [1, 2, 3], using homotopy analysis method (HAM) to solve the non linear mixed type equation under study.

On a new class of completely integrable nonlinear wave equations. I. Infinitely many conservation laws

NASA Astrophysics Data System (ADS)

Nutku, Y.

1985-06-01

We point out a class of nonlinear wave equations which admit infinitely many conserved quantities. These equations are characterized by a pair of exact one-forms. The implication that they are closed gives rise to equations, the characteristics and Riemann invariants of which are readily obtained. The construction of the conservation laws requires the solution of a linear second-order equation which can be reduced to canonical form using the Riemann invariants. The hodograph transformation results in a similar linear equation. We discuss also the symplectic structure and Bäcklund transformations associated with these equations.

Algebraic methods for the solution of some linear matrix equations

NASA Technical Reports Server (NTRS)

Djaferis, T. E.; Mitter, S. K.

1979-01-01

The characterization of polynomials whose zeros lie in certain algebraic domains (and the unification of the ideas of Hermite and Lyapunov) is the basis for developing finite algorithms for the solution of linear matrix equations. Particular attention is given to equations PA + A'P = Q (the Lyapunov equation) and P - A'PA = Q the (discrete Lyapunov equation). The Lyapunov equation appears in several areas of control theory such as stability theory, optimal control (evaluation of quadratic integrals), stochastic control (evaluation of covariance matrices) and in the solution of the algebraic Riccati equation using Newton's method.

Using MathCAD to Teach One-Dimensional Graphs

ERIC Educational Resources Information Center

Yushau, B.

2004-01-01

Topics such as linear and nonlinear equations and inequalities, compound inequalities, linear and nonlinear absolute value equations and inequalities, rational equations and inequality are commonly found in college algebra and precalculus textbooks. What is common about these topics is the fact that their solutions and graphs lie in the real line…

A Bohmian approach to the non-Markovian non-linear Schrödinger–Langevin equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vargas, Andrés F.; Morales-Durán, Nicolás; Bargueño, Pedro, E-mail: p.bargueno@uniandes.edu.co

2015-05-15

In this work, a non-Markovian non-linear Schrödinger–Langevin equation is derived from the system-plus-bath approach. After analyzing in detail previous Markovian cases, Bohmian mechanics is shown to be a powerful tool for obtaining the desired generalized equation.

Universal equation for estimating ideal body weight and body weight at any BMI1

PubMed Central

Peterson, Courtney M; Thomas, Diana M; Blackburn, George L; Heymsfield, Steven B

2016-01-01

Background: Ideal body weight (IBW) equations and body mass index (BMI) ranges have both been used to delineate healthy or normal weight ranges, although these 2 different approaches are at odds with each other. In particular, past IBW equations are misaligned with BMI values, and unlike BMI, the equations have failed to recognize that there is a range of ideal or target body weights. Objective: For the first time, to our knowledge, we merged the concepts of a linear IBW equation and of defining target body weights in terms of BMI. Design: With the use of calculus and approximations, we derived an easy-to-use linear equation that clinicians can use to calculate both IBW and body weight at any target BMI value. We measured the empirical accuracy of the equation with the use of NHANES data and performed a comparative analysis with past IBW equations. Results: Our linear equation allowed us to calculate body weights for any BMI and height with a mean empirical accuracy of 0.5–0.7% on the basis of NHANES data. Moreover, we showed that our body weight equation directly aligns with BMI values for both men and women, which avoids the overestimation and underestimation problems at the upper and lower ends of the height spectrum that have plagued past IBW equations. Conclusions: Our linear equation increases the sophistication of IBW equations by replacing them with a single universal equation that calculates both IBW and body weight at any target BMI and height. Therefore, our equation is compatible with BMI and can be applied with the use of mental math or a calculator without the need for an app, which makes it a useful tool for both health practitioners and the general public. PMID:27030535