Nonlinear Scatchard plots are often found for agonist binding to G-protein-coupled receptors. Because there is clear evidence of receptor dimerization, these nonlinear Scatchard plots can reflect cooperativity on agonist binding to the two binding sites in the dimer. According to this, the "two-state dimer receptor model" has been recently derived. In this article, the performance of the model ...

an analysis of the stages of crop growth presents an important step in the improvement of production management. through growth analysis, planning for planting systems, fertilization, pruning operations, harvest time as well as obtaining economical yield can be more accessible. therefore, a development of mathematical models simulating plant growth would be helpful in improving crop management,...

The problem of recovering the shape of objects from three-dimensional data is important to many areas of computer graphics and vision. We present here a method for the recovery of single-part objects from unstructured 3D points sets, based on the fitting of deformable superquadric models. The limitations of least-squares minimisation as a technique for fitting superquadric models are discussed....

This work presents the use of Harmonic Balance to simulate the nonlinear behavior of HTS transmission lines. Good agreement with theoretical cross-checks is found We also show the use of this algorithm to fit the model of HTS lines from experimental measurements. We illustrate this by fitting several types of experimental data, and discuss how to avoid ambiguity in this fitting. Non-linear elem...

Optical power spectra obtained from experiments or from numerical simulations are probabilistic in nature. When fitting such spectra to few-parameter analytical line shapes, the results from a weighted nonlinear least squares fit are significantly better than those from an unweighted fit. This is demonstrated by fitting a Lorentzian to directly simulated power spectra and to power spectra obtai...

In this paper, we discuss the efficiency of noise reduction for curve fitting in growth curve models. We use singular spectrum analysis as a nonlinear–nonparametric denoising method. A set of longitudinal measurements is used in considering the performance of the method. The results show that noise reduction is important for curve fitting in growth curve model and also, that the singular spectr...

This article describes a simple computational method for obtaining the maximum likelihood estimates (MLE) in nonlinear mixed-effects models when the random effects are assumed to have a nonnormal distribution. Many computer programs for fitting nonlinear mixed-effects models, such as PROC NLMIXED in SAS, require that the random effects have a normal distribution. However, there is often interes...

There are many programs for fitting nonlinear models to experimental data, and the use of this type of software is now widespread. After fitting a model or sequence of models, these programs usually calculate x2, run, sign, F and I statistics as an aid to model discrimination and parameter estimation. The distribution of such statistics from linear regression is well known, but these random var...

This paper compares a family of methods for characterizing neural feature selectivity with natural stimuli in the framework of the linear-nonlinear model. In this model, the neural firing rate is a nonlinear function of a small number of relevant stimulus components. The relevant stimulus dimensions can be found by maximizing one of the family of objective functions, Rényi divergences of differ...