Molecular Dynamics Studies of Entropic Elasticity of Condensed Lattice Networks Connected with Uniform Functionality f = 4

Soft Matter ◽  
2022 ◽  
Author(s):  
Katsumi Hagita ◽  
Takahiro Murashima
Keyword(s):  
Molecular Dynamics ◽  
Least Squares ◽  
Physical Model ◽  
Linear Region ◽  
Entropic Elasticity ◽  
Least Squares Fit

To study the linear region of entropic elasticity, we considered the simplest physical model possible and extracted the linear entropic regime by using the least squares fit and the minimum...

Changes in total body calcium balance with exercise in the rat

1983 ◽  
Vol 55 (1) ◽  
pp. 201-204 ◽  
Author(s):  
A. D. LeBlanc ◽  
H. J. Evans ◽  
P. C. Johnson ◽  
S. Jhingran
Keyword(s):  
Least Squares ◽  
Voluntary Exercise ◽  
Calcium Balance ◽  
Sprague Dawley ◽  
Total Body Calcium ◽  
Sprague Dawley Rats ◽  
Least Squares Fit ◽  
Significant Change ◽  
Total Body ◽  
And Control

The purpose of this study was to evaluate the effect of deconditioning on the total body calcium in rats. Two separate experiments were performed using female Sprague-Dawley rats, 187-266 days of age. Total body calcium was measured in experimental and control rats during and following several weeks of voluntary exercise. The slope from the least-squares fit of total body calcium with time was used to obtain an average calcium balance for each animal during each study period. In both groups the exercised rats had a significantly decreased calcium balance after cessation of exercise, whereas no significant change was seen in nonexercised controls. In both groups, the exercised animals gained calcium at a significantly greater rate than controls. Our findings indicate that while exercised rats may gain calcium at a faster rate compared with nonexercising controls, the rate of gain following cessation of exercise is less than the controls.

Estimation of the free core nutation period by the sliding-window complex least-squares fit method

2016 ◽  
Vol 57 (10) ◽  
pp. 2136-2140 ◽  
Author(s):  
Yonghong Zhou ◽  
Qiang Zhu ◽  
David A. Salstein ◽  
Xueqing Xu ◽  
Si Shi ◽  
...  
Keyword(s):  
Least Squares ◽  
Sliding Window ◽  
Free Core Nutation ◽  
Least Squares Fit

scholarly journals A Robust Regression by Using Huber Estimator and Tukey Bisquare Estimator for Predicting Availability of Corn in Karanganyar Regency, Indonesia

2018 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Hasih Pratiwi ◽  
Yuliana Susanti ◽  
Sri Sulistijowati Handajani
Keyword(s):  
Least Squares ◽  
Robust Regression ◽  
Likelihood Estimation ◽  
Corn Production ◽  
Least Squares Fit ◽  
Class Of Estimators ◽  
M Estimation ◽  
Heavy Tailed ◽  
Least Squares Estimates ◽  
Huber Estimator

Linear least-squares estimates can behave badly when the error distribution is not normal, particularly when the errors are heavy-tailed. One remedy is to remove influential observations from the least-squares fit. Another approach, robust regression, is to use a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation. This class of estimators can be regarded as a generalization of maximum-likelihood estimation. In this paper we discuss robust regression model for corn production by using two popular estimators; i.e. Huber estimator and Tukey bisquare estimator.<br />Keywords : robust regression, M-estimation, Huber estimator, Tukey bisquare estimator

A Wind Wave Co-Cumulative Spectral Model

1970 ◽  
Vol 14 (04) ◽  
pp. 277-295
Author(s):  
Carl F. Kottler
Keyword(s):  
Standard Deviation ◽  
Least Squares ◽  
Wind Velocity ◽  
Wind Wave ◽  
Surface Wind ◽  
Systematic Investigation ◽  
Spectral Model ◽  
Least Squares Fit ◽  
Wind Sea ◽  
Cumulative Model

A systematic investigation was made of the parameters chosen to define the Pierson-Moskowitz wind sea spectral model. The model was generalized and the form was extended to give a better fit of the data. Using the same sets of data as those selected by Pierson and Moskowitz for building their model, a least-squares fit of each set of the co-cumulative data gave a corresponding optimum set of parameters. These unique optimum sets of parameters yielded an eightfold decrease in the standard deviation. From this family of parameter sets, a co-cumulative spectral model was. developed to fix some of the parameters and relate the others to surface wind velocity. This modification and extension show that at least a twofold improvement in accuracy over the associated Pierson-Moskowitz co-cumulative model can be achieved.

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