scholarly journals Growth model of the pineapple guava fruit as a function of thermal time and altitude

2016 ◽  
Vol 36 (3) ◽  
pp. 6 ◽  
Author(s):  
Alfonso Parra-Coronado ◽  
Gerhard Fischer ◽  
Gerhard Fischer ◽  
Jesus Hernan Camacho-Tamayo ◽  
Jesus Hernan Camacho-Tamayo
Keyword(s):  
Regression Analysis ◽  
San Francisco ◽  
Growth Model ◽  
Cross Validation ◽  
Thermal Time ◽  
Logistic Growth ◽  
Guava Fruit ◽  
Pineapple Guava ◽  
Production Area ◽  
Logistic Growth Model

The growth of the pineapple guava fruit is primarily stimulated by temperature but is also influenced by other climactic factors, such as altitude. The goal of this study was to develop a growth model for the pineapple guava fruit as a function of thermal time (GDD, growing-degree day) and altitude (H) of the production area. Twenty trees per farm were marked in two sites in the Cundinamarca department (Colombia) during the 2012 and 2014 seasons. The measurements were performed every seven days after day 96 and 99 post-anthesis until harvest in the sites of Tenjo (2,580 m.a.s.l.) and San Francisco de Sales (1,800 m.a.s.l.), respectively. A growth model was produced for weight as a function of fruit length and diameter as well as for the weight of the fruit as a function of GDD and H, with this last measure adjusted to a sigmoidal logistic growth model. The parameters for the regression analysis showed that the models satisfactorily predicted fruit growth for both of the sites, with a high determination coefficient. The cross-validation showed good statistical fit between the predicted and observed models; the intercept was not significantly different than zero, and the slope was statistically equal to one.

scholarly journals Analysis and Prediction for Confirmed COVID-19 Cases in Czech Republic with Uncertain Logistic Growth Model

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2264
Author(s):  
Chunxiao Ding ◽  
Wenjian Liu
Keyword(s):  
Regression Analysis ◽  
Czech Republic ◽  
Growth Model ◽  
Hypothesis Test ◽  
Logistic Growth ◽  
Cumulative Number ◽  
Uncertain Variables ◽  
Stochastic Regression ◽  
Disturbance Term ◽  
Logistic Growth Model

This paper presents an uncertain logistic growth model to analyse and predict the evolution of the cumulative number of COVID-19 infection in Czech Republic. Some fundamental knowledge about the uncertain regression analysis are reviewed firstly. Stochastic regression analysis is invalid to model cumulative number of confirmed COVID-19 cases in Czech Republic, by considering the disturbance term as random variables, because that the normality test and the identical distribution test of residuals are not passed, and the residual plot does not look like a null plot in the sense of probability theory. In this case, the uncertain logistic growth model is applied by characterizing the disturbance term as uncertain variables. Then parameter estimation, residual analysis, the forecast value and confidence interval are studied. Additionally, the uncertain hypothesis test is proposed to evaluate the appropriateness of the fitted logistic growth model and estimated disturbance term. The analysis and prediction for the cumulative number of COVID-19 infection in Czech Republic can propose theoretical support for the disease control and prevention. Due to the symmetry and similarity of epidemic transmission, other regions of COVID-19 infections, or other diseases can be disposed in a similar theory and method.

Regression Analysis of Observed Foundation Settlement by Modified Logistic Growth Model

2011 ◽  
Vol 250-253 ◽  
pp. 2583-2587
Author(s):  
Yu Qi Li ◽  
Huan Zhang ◽  
Yi Ran Liu
Keyword(s):  
Regression Analysis ◽  
Growth Model ◽  
Nonlinear Regression ◽  
Logistic Growth ◽  
Model Parameters ◽  
Foundation Settlement ◽  
Geological Condition ◽  
Nonlinear Regression Analysis ◽  
Geological Conditions ◽  
Logistic Growth Model

Logistic model is modified through introducing the pseudo construction settlement. Based on the observed settlement data of foundation in Yangshan deepwater port project, Logistic growth model and modified Logistic growth model are used for nonlinear regression analysis of foundation settlement respectively. It is indicated that the fitting curves by using modified Logistic growth model agree better with the observed settlement values than those by using Logistic growth model and that the correlation coefficients by using modified Logistic growth model are also bigger. Model parameters of different geological conditions obtained by nonlinear regression analysis can be used for significant reference to foundation settlement prediction of similar geological condition in other deepwater port.

scholarly journals Extended logistic growth model for heterogeneous populations

2017 ◽  
Author(s):  
Wang Jin ◽  
Scott W McCue ◽  
Matthew J Simpson
Keyword(s):  
Cell Proliferation ◽  
Growth Model ◽  
Numerical Solutions ◽  
Cellular Level ◽  
Logistic Growth ◽  
Logistic Growth Model ◽  
Living Tissues ◽  
Discrete Mathematical Model ◽  
The Continuum

AbstractCell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability as heterogeneity. Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.

A Neoclassical Growth Model for Population Dynamics in a Homogeneous Habitat

2001 ◽  
Author(s):  
Peter Vadasz ◽  
Alisa S. Vadasz
Keyword(s):  
Experimental Data ◽  
Growth Model ◽  
Logistic Growth ◽  
Lag Phase ◽  
Growth Stages ◽  
Initial Growth ◽  
Neoclassical Model ◽  
Wide Range ◽  
Logistic Growth Model ◽  
Special Case

Abstract A neoclassical model is proposed for the growth of cell and other populations in a homogeneous habitat. The model extends on the Logistic Growth Model (LGM) in a non-trivial way in order to address the cases where the Logistic Growth Model (LGM) fails short in recovering qualitative as well as quantitative features that appear in experimental data. These features include in some cases overshooting and oscillations, in others the existence of a “Lag Phase” at the initial growth stages, as well as an inflection point in the “In curve” of the population size. The proposed neoclassical model recovers also the Logistic Growth Curve as a special case. Comparisons of the solutions obtained from the proposed neoclassical model with experimental data confirm its quantitative validity, as well as its ability to recover a wide range of qualitative features captured in experiments.

scholarly journals Outbreak analysis with a logistic growth model shows COVID-19 suppression dynamics in China

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0235247 ◽  
Author(s):  
Yi Zou ◽  
Stephen Pan ◽  
Peng Zhao ◽  
Lei Han ◽  
Xiaoxiang Wang ◽  
...  
Keyword(s):  
Growth Model ◽  
Logistic Growth ◽  
Logistic Growth Model

scholarly journals Extended logistic growth model for heterogeneous populations

2018 ◽  
Vol 445 ◽  
pp. 51-61 ◽  
Author(s):  
Wang Jin ◽  
Scott W. McCue ◽  
Matthew J. Simpson
Keyword(s):  
Growth Model ◽  
Logistic Growth ◽  
Heterogeneous Populations ◽  
Logistic Growth Model
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