scholarly journals Multiphasic growth models for cattle

2011 ◽  
Vol 50 (No. 8) ◽  
pp. 347-354 ◽  
Author(s):  
H. Nešetřilová
Keyword(s):  
Growth Models ◽  
Gompertz Model ◽  
Logistic Function ◽  
Complex Nature ◽  
Model Parameters ◽  
Growth Data ◽  
Unknown Parameters ◽  
Residual Variability ◽  
Low Degree ◽  
Best Fitting

There are several ways of generalizing classical growth models to describe the complex nature of animal growth. One possibility is to construct a model based on a sum of several classical growth functions. In this paper, such multiphasic growth models for breeding bulls of the Czech Pied cattle based on the sum of two logistic functions are studied. The logistic function was chosen as a base for the models due to the relatively low degree of nonlinearity for the growth data. The paper describes three steps of constructing such a multiphasic growth model: in the first step a model with four unknown parameters is considered, in the second step the number of model parameters which are to be estimated is increased to five and in the third step a general model with six parameters is used. In each step, statistical properties of the considered model are checked. The residual variability of the best fitting model is on average approx. 8 times lower than the residual variability of classical Gompertz model which is often used by breeders to model cattle growth.  

scholarly journals Growth Patterns of birds, dinosaurs and reptiles: Are differences real or apparent?

2019 ◽  
Author(s):  
Norbert Brunner ◽  
Manfred Kühleitner ◽  
Werner-Georg Nowak ◽  
Katharina Renner-Martin ◽  
Klaus Scheicher
Keyword(s):  
Differential Equation ◽  
Growth Pattern ◽  
Broiler Chicken ◽  
Growth Models ◽  
Growth Patterns ◽  
Von Bertalanffy ◽  
Growth Data ◽  
Quantitative Studies ◽  
Best Fitting

AbstractSystematics of animals was done on their appearance or genetics. One can also ask about similarities or differences in the growth pattern. Quantitative studies of the growth of dinosaurs have made possible comparisons with modern animals, such as the discovery that dinosaurs grew in relation to their size faster than modern reptiles. However, these studies relied on only a few growth models. If these models are false, what about the conclusions? This paper fits growth data to a more comprehensive class of models, defined by the von Bertalanffy-Pütter differential equation. Applied to data about dinosaurs, reptiles and birds, the best fitting models confirmed that dinosaurs may have grown faster than alligators. However, compared to modern broiler chicken, this difference was small.

scholarly journals Comparison of Two Mechanistic Microbial Growth Models to Estimate Shelf Life of Perishable Food Package under Dynamic Temperature Conditions

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Dong Sun Lee
Keyword(s):  
Shelf Life ◽  
Microbial Growth ◽  
Electronic Device ◽  
Growth Models ◽  
Model Parameters ◽  
Growth Data ◽  
Dynamic Temperature ◽  
Temperature Conditions ◽  
Primary Model ◽  
Set Up

Two mechanistic microbial growth models (Huang’s model and model of Baranyi and Roberts) given in differential and integrated equation forms were compared in predicting the microbial growth and shelf life under dynamic temperature storage and distribution conditions. Literatures consistently reporting the microbial growth data under constant and changing temperature conditions were selected to obtain the primary model parameters, set up the secondary models, and apply them to predict the microbial growth and shelf life under fluctuating temperatures. When evaluated by general estimation behavior, bias factor, accuracy factor, and root-mean-square error, Huang’s model was comparable to Baranyi and Roberts’ model in the capability to estimate microbial growth under dynamic temperature conditions. Its simple form of single differential equation incorporating directly the growth rate and lag time may work as an advantage to be used in online shelf life estimation by using the electronic device.

scholarly journals Age, Growth and Otolith Microstructure of the Spotted Lanternfish Myctophum punctatum Rafinesque 1810

2021 ◽  
Vol 9 (8) ◽  
pp. 801
Author(s):  
Francesco Longo ◽  
Danilo Malara ◽  
Maria Giulia Stipa ◽  
Pierpaolo Consoli ◽  
Teresa Romeo ◽  
...  
Keyword(s):  
Growth Models ◽  
Central Zone ◽  
Logistic Models ◽  
Gompertz Model ◽  
Growth Patterns ◽  
Age And Growth ◽  
Otolith Microstructure ◽  
Central Mediterranean ◽  
External Zone ◽  
Best Fitting

This study investigated, for the first time, the age and growth of the spotted lanternfish Myctophum punctatum through an analysis of otolith microstructure. A total of 377 individuals were collected from the Strait of Messina (central Mediterranean Sea), ranging between 20.3 and 73.7 mm of standard length. Their length–weight relationship was estimated, and these outputs indicated an isometric growth, for all specimens and when males and females were analysed separately. The sagittal otoliths were removed from 185 fish, although the microincrement readings were considered valid for only 173 otoliths. Microincrement counts ranged from 32 to 48 (average = 37.6) in the otolith central zone, 30 to 56 (average = 44.3) in the middle zone, and 36 to 384 (average = 165.5) in the external zone. Overall, total microincrements ranged between 106 and 469. Different growth models (Gompertz, von Bertalanffy and logistic models) were considered, to understand which one fit best in describing the growth patterns in M. punctatum. The Gompertz model was then selected as the best-fitting model and its parameters for all individuals were L∞ = 74.79, k = 0.0084 and I = 139.60.

scholarly journals A Modified Triple-Diode Model Parameters Identification for Perovskite Solar Cells via Nature-Inspired Search Optimization Algorithms

2021 ◽  
Vol 13 (23) ◽  
pp. 12969
Author(s):  
Alaa A. Zaky ◽  
Ahmed Fathy ◽  
Hegazy Rezk ◽  
Konstantina Gkini ◽  
Polycarpos Falaras ◽  
...  
Keyword(s):  
Solar Cells ◽  
Perovskite Solar Cells ◽  
Optimization Techniques ◽  
Bald Eagle ◽  
Complex Nature ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Search Optimization ◽  
Decision Variables ◽  
Efficient Alternative

Recently, perovskite solar cells (PSCs) have been widely investigated as an efficient alternative for silicon solar cells. In this work, a proposed modified triple-diode model (MTDM) for PSCs modeling and simulation was used. The Bald Eagle Search (BES) algorithm, which is a novel nature-inspired search optimizer, was suggested for solving the model and estimating the PSCs device parameters because of the complex nature of determining the model parameters. Two PSC architectures, namely control and modified devices, were experimentally fabricated, characterized and tested in the lab. The I–V datasets of the fabricated devices were recorded at standard conditions. The decision variables in the proposed optimization process are the nine and ten unknown parameters of triple-diode model (TDM) and MTDM, respectively. The direct comparison with a number of modern optimization techniques including grey wolf (GWO), particle swarm (PSO) and moth flame (MFO) optimizers, as well as sine cosine (SCA) and slap swarm (SSA) algorithms, confirmed the superiority of the proposed BES approach, where the Root Mean Square Error (RMSE) objective function between the experimental data and estimated characteristics achieves the least value.

Describing and predicting potential growth in the pig

2004 ◽  
Vol 78 (3) ◽  
pp. 379-388 ◽  
Author(s):  
I. J. Wellock ◽  
G. C. Emmans ◽  
I. Kyriazakis
Keyword(s):  
Growth Rate ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Growth Function ◽  
Logistic Function ◽  
Growth Data ◽  
Gompertz Function ◽  
Potential Growth ◽  
Nutritional Conditions ◽  
Wide Range

AbstractMost animal growth models contain an explicit growth function. It determines the pattern of growth over the lifetime of the animal and defines an upper limit to growth rate (the potential). The criterion of the ‘goodness-of-fit’ to one or more sets of data is frequently used to select a suitable growth function. Alternative criteria are described here that can be used to choose between forms that describe potential growth. Of the functions reviewed only a few fulfilled all of the proposed criteria. Of these the Logistic and Gompertz functions were favoured because of an economy of parameters and their ability to describe relative growth rate as a simple function of size. The Logistic function was rejected on the grounds of its numerical consequences for growth in pigs over a wide range of degrees of maturity, leaving the Gompertz function to be tested for its ability to make sensible predictions of potential growth. Pre-natal growth data, assumed to occur under non-limiting conditions as long as the mother is not subjected to extremely adverse nutritional conditions or incidence of infection, were used to estimate the values of the two Gompertz function parameters-the growth coefficient and the initial condition-given an estimate of mature size. The values were comparable with literature estimates based on post-natal growth and predictions of growth rate over a wide range of degree of maturity were thus sensible. On these grounds, and because it uses few parameters all with biological meaning, the Gompertz function is proposed as a suitable descriptor of potential growth.

scholarly journals Using Sigmoid Growth Curves to Establish Growth Models of Tomato and Eggplant Stems Suitable for Grafting in Subtropical Countries

Horticulturae ◽  
2021 ◽  
Vol 7 (12) ◽  
pp. 537
Author(s):  
Chih-Yu Hsieh ◽  
Shih-Lun Fang ◽  
Yea-Fang Wu ◽  
Yung-Chu Chu ◽  
Bo-Jein Kuo
Keyword(s):  
Seedling Growth ◽  
Growth Models ◽  
Solanum Melongena ◽  
Growth Curves ◽  
Model Parameters ◽  
Growth Data ◽  
Growing Seasons ◽  
Different Seasons ◽  
Prime Objective ◽  
Autumn And Winter

In subtropical regions, tomato (Solanum lycopersicum) is mainly produced in autumn and winter. To enhance the off-season production of tomato, summer cultivation has become a prime objective. Grafting tomato scions onto eggplant (Solanum melongena) rootstocks is a key method to overcome the difficulties of tomato cultivation in summer. In this study, we collected seedling growth data over six growing seasons in Taiwan and established growth models by employing three commonly used sigmoid growth curves, namely the Gompertz, Richards, and Logistic curves. Cumulative temperature was introduced as an independent variable and its relationship with plant stem diameter determined. The R2 values of the growth models were 0.74–0.85 and 0.72–0.80 in calibration and validation, respectively. Performance did not differ markedly among models in the same growing season, but notable differences were observed among models for different growing seasons. In addition, the estimates of several model parameters differed significantly among the seasons; hence, separate models should be established for different seasons. The results of this study can be used in prediction of tomato and eggplant seedling growth and arrangement of the grafting schedule to improve the efficiency of seedling production in subtropical countries.

scholarly journals Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts

2021 ◽  
Vol 11 (15) ◽  
pp. 6998
Author(s):  
Qiuying Li ◽  
Hoang Pham
Keyword(s):  
Time Delay ◽  
Fault Detection ◽  
Software Reliability ◽  
Growth Models ◽  
Predictive Performance ◽  
Real Data ◽  
Reliability Estimation ◽  
Model Parameters ◽  
Dual Processes ◽  
Correction Process

Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.

scholarly journals Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 109 ◽  
Author(s):  
Iman Rahimi ◽  
Amir H. Gandomi ◽  
Panagiotis G. Asteris ◽  
Fang Chen
Keyword(s):  
Machine Learning ◽  
Logistic Function ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Model Parameters ◽  
The Novel ◽  
Chinese City ◽  
Limited Memory ◽  
Increasing Trend ◽  
Novel Coronavirus

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

scholarly journals Comparison of growth models to describe growth from birth to 6 years in a Beninese cohort of children with repeated measurements

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e035785
Author(s):  
Shukrullah Ahmadi ◽  
Florence Bodeau-Livinec ◽  
Roméo Zoumenou ◽  
André Garcia ◽  
David Courtin ◽  
...  
Keyword(s):  
Growth Model ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Information Criterion ◽  
Height Growth ◽  
Sub Saharan Africa ◽  
Individual Growth ◽  
Secondary Outcome ◽  
Growth Trajectories ◽  
Best Fitting

ObjectiveTo select a growth model that best describes individual growth trajectories of children and to present some growth characteristics of this population.SettingsParticipants were selected from a prospective cohort conducted in three health centres (Allada, Sekou and Attogon) in a semirural region of Benin, sub-Saharan Africa.ParticipantsChildren aged 0 to 6 years were recruited in a cohort study with at least two valid height and weight measurements included (n=961).Primary and secondary outcome measuresThis study compared the goodness-of-fit of three structural growth models (Jenss-Bayley, Reed and a newly adapted version of the Gompertz growth model) on longitudinal weight and height growth data of boys and girls. The goodness-of-fit of the models was assessed using residual distribution over age and compared with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-fitting model allowed estimating mean weight and height growth trajectories, individual growth and growth velocities. Underweight, stunting and wasting were also estimated at age 6 years.ResultsThe three models were able to fit well both weight and height data. The Jenss-Bayley model presented the best fit for weight and height, both in boys and girls. Mean height growth trajectories were identical in shape and direction for boys and girls while the mean weight growth curve of girls fell slightly below the curve of boys after neonatal life. Finally, 35%, 27.7% and 8% of boys; and 34%, 38.4% and 4% of girls were estimated to be underweight, wasted and stunted at age 6 years, respectively.ConclusionThe growth parameters of the best-fitting Jenss-Bayley model can be used to describe growth trajectories and study their determinants.

Identification of Viscously Damped Distributed Structural Dynamic Systems

1991 ◽  
Author(s):  
R. Chander ◽  
M. Meyyappa ◽  
S. Hanagud
Keyword(s):  
Dynamic Systems ◽  
Beam Theory ◽  
Continuous Functions ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Structural Dynamic ◽  
Domain Identification ◽  
Forcing Function ◽  
Cardinal Splines ◽  
Stiffness And Damping

Abstract A frequency domain identification technique applicable to damped distributed structural dynamic systems is presented. The technique is developed for beams whose behavior can be modeled using the Euler-Bernoulli beam theory. External damping of the system is included by means of a linear viscous damping model. Parameters to be identified, mass, stiffness and damping distributions are assumed to be continuous functions over the beam. The response at a discrete number of points along the length of the beam for a given forcing function is used as the data for identification. The identification scheme involves approximating the infinite dimensional response and parameter spaces by using quintic B-splines and cubic cardinal splines, respectively. A Galerkin type weighted residual procedure, in conjunction with the least squares technique, is employed to determine the unknown parameters. Numerically simulated response data for an applied impulse load are utilized to validate the developed technique. Estimated values for the mass, stiffness and damping distributions are discussed.

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