scholarly journals A sinusoidal equation as alternative to conventional growth functions to describe the evolution of growth in quail

2019 ◽  
Vol 17 (3) ◽  
pp. e0606
Author(s):  
Hassan Darmani-Kuhi ◽  
James France ◽  
Secundino López ◽  
Navid Ghavi Hossein-Zadeh
Keyword(s):  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Growth Curves ◽  
Growth Dynamics ◽  
Information Criteria ◽  
Growth Patterns ◽  
Growth Data ◽  
Suitable Alternative ◽  
Growth Functions ◽  
Age Related

Aim of study: The aim of the present study was to introduce a sinusoidal equation into poultry science by applying it to temporal growth data from quail.Material and methods: To examine the performance of the sinusoidal equation in describing the growth patterns of quail, four conventional growth functions (Gompertz, logistic, López and Richards) were used as reference in this study. Comparison of models was carried out by analysing model behaviour when fitting the curves using nonlinear regression and assessing statistical performance. Maximum log-likelihood estimation, mean squared error, Akaike and Bayesian information criteria were used to evaluate the general goodness-of-fit of each model to the different data profiles.Main results: The selected sinusoidal equation precisely describes the growth dynamics of quail. Comparison of the growth functions in terms of the goodness-of-fit criteria revealed that the sinusoidal equation was one of the most appropriate functions to describe the age-related changes of bodyweight in quail.Research highlights: To the best of our knowledge there are no studies available on the use of sinusoidal equations to describe the evolution of growth in quail. The sinusoidal equation used in this study represents a suitable alternative to conventional growth functions to describe the growth curves for a range of strains/lines of male and female Japanese quail.

scholarly journals Mathematical Models for Tumor Growth and the Reduction of Overtreatment

2018 ◽  
Vol 80 (01) ◽  
pp. 072-078 ◽  
Author(s):  
Berdine Heesterman ◽  
John-Melle Bokhorst ◽  
Lisa de Pont ◽  
Berit Verbist ◽  
Jean-Pierre Bayley ◽  
...  
Keyword(s):  
Tumor Growth ◽  
Mathematical Models ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Age At Onset ◽  
Growth Curves ◽  
Magnetic Resonance Images ◽  
Coefficient Of Determination ◽  
Growth Data ◽  
Significant Difference

Background To improve our understanding of the natural course of head and neck paragangliomas (HNPGL) and ultimately differentiate between cases that benefit from early treatment and those that are best left untreated, we studied the growth dynamics of 77 HNPGL managed with primary observation. Methods Using digitally available magnetic resonance images, tumor volume was estimated at three time points. Subsequently, nonlinear least squares regression was used to fit seven mathematical models to the observed growth data. Goodness of fit was assessed with the coefficient of determination (R 2) and root-mean-squared error. The models were compared with Kruskal–Wallis one-way analysis of variance and subsequent post-hoc tests. In addition, the credibility of predictions (age at onset of neoplastic growth and estimated volume at age 90) was evaluated. Results Equations generating sigmoidal-shaped growth curves (Gompertz, logistic, Spratt and Bertalanffy) provided a good fit (median R 2: 0.996–1.00) and better described the observed data compared with the linear, exponential, and Mendelsohn equations (p < 0.001). Although there was no statistically significant difference between the sigmoidal-shaped growth curves regarding the goodness of fit, a realistic age at onset and estimated volume at age 90 were most often predicted by the Bertalanffy model. Conclusions Growth of HNPGL is best described by decelerating tumor growth laws, with a preference for the Bertalanffy model. To the best of our knowledge, this is the first time that this often-neglected model has been successfully fitted to clinically obtained growth data.

scholarly journals Persistent growth slowdowns in fast-growing middle-income economies

2017 ◽  
Vol 67 (s1) ◽  
pp. 97-111 ◽  
Author(s):  
Krisztina Sőreg
Keyword(s):  
Global Financial Crisis ◽  
Raw Materials ◽  
Commodity Prices ◽  
Growth Dynamics ◽  
Commodity Price ◽  
Growth Patterns ◽  
Growth Data ◽  
Middle Income ◽  
Fast Growing ◽  
Export Structure

From the second half of the 20th century, a set of emerging economies have undergone a remarkable developing path. During the first years of the global financial crisis of 2007–2008, Brazil, Russia, India, China, and South Africa (BRICS) were only slightly affected by its negative impacts. However, after 2013, a considerable growth slowdown period has evolved in these countries with the exception of the Indian economy. In the current study, we examine whether the growth dynamics of the BRICS economies shows significant correlation with the fluctuation of commodity prices, especially in the case of raw materials. Besides applying a cross correlation model on the quarterly commodity price indices and real GDP growth data, the research also focuses on the export structure of the selected fast-growing countries. As a closing element of our paper, a brief analysis is carried out regarding the correlations of growth patterns within the BRICS economies.

A Comparison of von Bertalanffy and Polynomial Functions in Modelling Fish Growth Data

1992 ◽  
Vol 49 (6) ◽  
pp. 1228-1235 ◽  
Author(s):  
Y. Chen ◽  
D. A. Jackson ◽  
H. H. Harvey
Keyword(s):  
Goodness Of Fit ◽  
Growth Function ◽  
Fish Growth ◽  
Analysis Of Covariance ◽  
Polynomial Functions ◽  
Von Bertalanffy ◽  
Growth Data ◽  
Data Set ◽  
Growth Functions ◽  
Von Bertalanffy Growth Function

We compared the von Bertalanffy growth function (VBGF) and five polynomial functions (PF) in modelling fish growth for 16 populations comprising six species of freshwater fishes. Ranked results of the variance explained by each growth function indicated that VBGF described growth data better than three- and four-parameter polynomial functions. Log-transforming length and age greatly improved the goodness-of-fit of the three-parameter polynomial function. Statistical comparison of growth between populations or sexes was done using a general linear model for polynomial functions. An analysis of residual sum of squares was proposed to compare the resultant VBGFs because the nonlinear formulation of the VBGF prevented traditional analysis of covariance procedures. Fitting of different growth functions to the same growth data set yielded the same result in the intra-species growth comparisons for three species (eight populations) but different results for two species (seven populations). Where ages of the fish were less than the maximum age in the samples, dL/dt were similar for all growth functions except the parabola based on the log-transformation of length alone. The VBGF proved to be the best growth model for all 16 populations.

Growth and Modeling of Staphylococcus aureus in Flour Products under Isothermal and Nonisothermal Conditions

2017 ◽  
Vol 80 (3) ◽  
pp. 523-531
Author(s):  
Hui Cao ◽  
Tingting Wang ◽  
Min Yuan ◽  
Jingsong Yu ◽  
Fei Xu
Keyword(s):  
Staphylococcus Aureus ◽  
Goodness Of Fit ◽  
Storage Temperature ◽  
Growth Curves ◽  
Gompertz Model ◽  
Growth Data ◽  
Safety Standards ◽  
Bias Factor ◽  
Steamed Bread ◽  
Nonisothermal Conditions

ABSTRACT This study was conducted to investigate the growth of Staphylococcus aureus in traditional Chinese flour products under isothermal (10, 15, 20, 25, 30, and 37°C) and nonisothermal (10 to 20, 20 to 30, and 25 to 37°C) conditions. Then, models for the growth of S. aureus in flour products as a function of storage temperature, pH, and water activity (aw) were developed, and the goodness of fit of models was evaluated using the determination coefficient (R2), root mean square error (RMSE), bias factor (Bf), and accuracy factor (Af). Based on the above information, S. aureus growth in steamed bread under nonisothermal conditions was predicted from experiments performed under isothermal conditions. It was shown that different combinations of temperature and aw in flour products have a strong influence on the growth of S. aureus. The modified Gompertz model was found to be more suitable for describing the growth data of S. aureus in flour products, with an R2 of &gt;0.99 and an RMSE of &lt;0.37. The newly developed secondary models were validated, and for the specific growth rate and the lag time, the R2 values were 0.96 and 0.97, Af was 1.12 and 1.06, and Bf was 1.13 and 1.05, respectively. The predicted nonisothermal growth curves of S. aureus were in agreement with the reported experimental ones, with RMSE &lt;0.29, Af value 1.02 to 1.09, and Bf value 0.92 to 0.99. These results indicated that the predictive models provided useful information for the establishment of safety standards and a risk assessment for S. aureus in flour products.

scholarly journals Phenomenological Modelling of COVID-19 epidemics in Sri Lanka, Italy and Hebei Province of China

2020 ◽  
Author(s):  
A.M.C.H. Attanayake ◽  
S.S.N. Perera ◽  
S. Jayasinghe
Keyword(s):  
Sri Lanka ◽  
Specific Treatment ◽  
Growth Curves ◽  
Information Criteria ◽  
Hebei Province ◽  
Disease Spread ◽  
Peak Time ◽  
Cumulative Number ◽  
Growth Data ◽  
Daily Growth

AbstractThe COVID-19 pandemic has resulted in increasing number of infections and deaths on a daily basis. There is no specific treatment or vaccine identified and the focus has been preventive measures based on statistical and mathematical models. These have relied on analyzing the behavior of populations and characteristics of the infection and applying modelling techniques. The analysis of epidemiological curve fitting on number of daily infections across affected countries could give useful insights on the characteristics of the epidemic. A variety of phenomenological models are available to capture dynamics of disease spread and growth. Data for this study used the number of daily new infections and cumulative number of infections in COVID-19 in three selected countries, Sri Lanka, Italy and Hebei province of China, from the first day of appearance of cases to 20th April 2020. In this study Gompertz, Logistic and Exponential growth curves were fitted on cumulative number of infections across countries. Akaike’s information criteria (AIC) was used in determining the best fitting curve for each country. Results revealed that the most appropriate growth curves for Sri Lanka, Italy and China-Hebei are Exponential, Gompertz and Logistic curves respectively. The overall growth rate and final epidemic size evaluated from best models for the three countries and short-term forecasts were also generated. Log incidences over time in each country were regressed before and after the identified peak time of the respective outbreaks of countries. Hence, doubling time/halving time together with daily growth rates and predictions were estimated. Findings altogether demonstrate that outbreak seems extinct in Hebei-China whereas further transmissions are possible in Sri Lanka. In Italy, current outbreak transmits in a decreasing rate.

scholarly journals Isotonic regression analysis of Guzerá cattle growth curves

Revista CERES ◽  
2018 ◽  
Vol 65 (1) ◽  
pp. 24-27 ◽  
Author(s):  
Adriano Rodrigues ◽  
Lucas Monteiro Chaves ◽  
Fabyano Fonseca Silva ◽  
Idalmo Pereira Garcia ◽  
Darlene Ana Souza Duarte ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Least Squares Method ◽  
Age Groups ◽  
Growth Curves ◽  
Generalized Least Squares ◽  
Isotonic Regression ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Generalized Least Squares Method

ABSTRACT The objective of this study was to apply data transformation via isotonic regression in growth curves studies of Guzerá cattle whose data presented disturbances characterized by decreased body weight in certain age groups. Weight-age data were collected on newly weaned Guzerá males according to the methodology of weight gain tests (WGT) defined by the Brazilian Association of Zebu Breeders (ABCZ). The Logistic, Von Bertalanffy and Gompertz models were fitted to weight-age data using the generalized least squares method for non-linear regression models through the Gauss-Newton algorithm. The proposed transformation based on isotonic regression theory proved to be efficient; and the Logistic model was the best to describe the growth of animals, with a high percentage of convergence (100%) and goodness of fit assessed by the mean squared error (MSE) and the coefficient of determination (R2).

LENGTH AND CROSS-SECTION GROWTH PATTERNS IN THE HUMAN TRACHEA

PEDIATRICS ◽  
1968 ◽  
Vol 42 (2) ◽  
pp. 336-341
Author(s):  
Ralph O. Butz
Keyword(s):  
Cross Section ◽  
Growth Curves ◽  
Growth Patterns ◽  
Endotracheal Tubes ◽  
Age Related ◽  
Cross Section Area ◽  
Section Area ◽  
Human Trachea ◽  
Selection Of ◽  
Made In

Twenty-four tracheas taken from autopsies of children ranging in age from premature to 14 years and in weight from 1½ pounds to 155 pounds, were studied. Details of the growth patterns in length and cross section are discussed. Length and cross-section area are found to have different growth curves. "Flattening" of the trachea is also found to be age related. Application of the data to the selection of proper endotracheal tubes for anesthesia is made in this study, including special attention to length in the newborn infant.

Empirical comparison of skewed t-copula models for insurance and financial data

2020 ◽  
Vol 15 (4) ◽  
pp. 351-361
Author(s):  
Liwei Huang ◽  
Arkady Shemyakin
Keyword(s):  
Bayesian Estimation ◽  
Goodness Of Fit ◽  
Model Performance ◽  
Information Criteria ◽  
Stock Index ◽  
Likelihood Method ◽  
Special Importance ◽  
Copula Model ◽  
Technical Problems ◽  
T Copula

Skewed t-copulas recently became popular as a modeling tool of non-linear dependence in statistics. In this paper we consider three different versions of skewed t-copulas introduced by Demarta and McNeill; Smith, Gan and Kohn; and Azzalini and Capitanio. Each of these versions represents a generalization of the symmetric t-copula model, allowing for a different treatment of lower and upper tails. Each of them has certain advantages in mathematical construction, inferential tools and interpretability. Our objective is to apply models based on different types of skewed t-copulas to the same financial and insurance applications. We consider comovements of stock index returns and times-to-failure of related vehicle parts under the warranty period. In both cases the treatment of both lower and upper tails of the joint distributions is of a special importance. Skewed t-copula model performance is compared to the benchmark cases of Gaussian and symmetric Student t-copulas. Instruments of comparison include information criteria, goodness-of-fit and tail dependence. A special attention is paid to methods of estimation of copula parameters. Some technical problems with the implementation of maximum likelihood method and the method of moments suggest the use of Bayesian estimation. We discuss the accuracy and computational efficiency of Bayesian estimation versus MLE. Metropolis-Hastings algorithm with block updates was suggested to deal with the problem of intractability of conditionals.

scholarly journals Box–Jenkins Black-Box Modeling of a Lithium-Ion Battery Cell Based on Automotive Drive Cycle Data

2021 ◽  
Vol 12 (3) ◽  
pp. 102
Author(s):  
Jaouad Khalfi ◽  
Najib Boumaaz ◽  
Abdallah Soulmani ◽  
El Mehdi Laadissi
Keyword(s):  
Lithium Ion Battery ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Lithium Ion ◽  
Drive Cycle ◽  
Cell Dynamics ◽  
Battery Cell ◽  
Parameter Identifications

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

scholarly journals Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 53
Author(s):  
Yves Staudt ◽  
Joël Wagner
Keyword(s):  
Random Forests ◽  
Goodness Of Fit ◽  
Generalized Additive Model ◽  
Mean Squared Error ◽  
Additive Model ◽  
Continuous Variables ◽  
Insurance Portfolio ◽  
Normal Transformation ◽  
Car Insurance ◽  
Log Normal

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance.

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