scholarly journals Growth curve of Nile tilapia from different families of the AquaAmérica variety

2022 ◽  
Vol 82 ◽  
Author(s):  
J. C. Carvalho ◽  
R. A. C. Corrêa Filho ◽  
C. A. L. Oliveira ◽  
R. P. Ribeiro ◽  
G. N. Seraphim ◽  
...  
Keyword(s):  
Growth Curve ◽  
Nile Tilapia ◽  
Specific Growth ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Breeding Programs ◽  
Weighted Least Squares Method ◽  
Fish Breeding ◽  
Asymptotic Values ◽  
Abstract Selection

Abstract Selection can affect growth, changing performance and asymptotic values. However, there is little information about the growth of families in fish breeding programs. The aim of this study was to evaluate the performance and growth of families of Nile tilapia AquaAmérica. Twenty AquaAmérica families cultivated in a net cage (13.5 m3) for 181 days were evaluated. The nonlinear Gompertz regression model was fitted to the data by the weighted least squares method, taking the inverse of the variance of weight in different families and at different ages as the weighting variable. The model was adjusted to describe the growth in weight and morphometric characteristics. Two families showed highest (P<0.05) weights at both 133 days (family AA10: 743.2 g; family AA16: 741.2 g) and 181 days (family AA10: 1,422.1 g; family AA16: 1,393.4 g) of the experiment. In both experimental periods, the males showed a heavier weight, with the greatest contrast between the sexes occurring at 181 days. The analysis of the three most contrasting families (AA1, AA9 and AA14) showed that the asymptotic value for weight was higher (P<0.05) in family AA9 (3,926.3 g) than in family AA14 (3,251.6 g), but specific growth rate and age at the inflection point did not differ significantly between families. In conclusion, two of the 20 families were superior; males exhibited a greater growth, mainly in the period of 181 days; and the growth curve differed between the families, especially for asymptotic weight.

scholarly journals Novel Optimization-Based Parameter Estimation Method for the Bass Diffusion Model

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110269
Author(s):  
Lang Liang
Keyword(s):  
Parameter Estimation ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Estimation Method ◽  
Reference Value ◽  
Diffusion Problem ◽  
Bass Model ◽  
Weighted Least Squares Method ◽  
Estimated Parameters ◽  
Bass Diffusion Model

The Bass model is the most popular model for forecasting the diffusion process of a new product. However, the controlling parameters in it are unknown in practice and need to be determined in advance. Currently, the estimation of the controlling parameters has been approached by various techniques. In this case, a novel optimization-based parameter estimation (OPE) method for the Bass model is proposed in the theoretical framework of system dynamics ( SD). To do this, the SD model of the Bass differential equation is first established and then the corresponding optimization mathematical model is formulated by introducing the controlling parameters as design variable and the discrepancy of the adopter function to the reference value as objective function. Using the VENSIM software, the present SD optimization model is solved, and its effectiveness and accuracy are demonstrated by two examples: one involves the exact solution and another is related to the actual user diffusion problem from Chinese Mobile. The results show that the present OPE method can produce higher predicting accuracy of the controlling parameters than the nonlinear weighted least squares method and the genetic algorithms. Moreover, the reliability interval of the estimated parameters and the goodness of fitting of the optimal results are given as well to further demonstrate the accuracy of the present OPE method.

scholarly journals Spatial interpolation of daily rainfall at catchment scale: a case study of the Ourthe and Ambleve catchments, Belgium

2010 ◽  
Vol 7 (5) ◽  
pp. 7383-7416 ◽  
Author(s):  
S. Ly ◽  
C. Charles ◽  
A. Degré
Keyword(s):  
Spatial Interpolation ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Daily Rainfall ◽  
Daily Basis ◽  
Catchment Scale ◽  
Geostatistical Methods ◽  
Weighted Least Squares Method ◽  
Thiessen Polygon ◽  
Single Moment

Abstract. Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (krigings) are widely used in spatial interpolation from point measurement to continuous surfaces. However, the majority of existing geostatistical algorithms are available only for single-moment data. The first step in kriging computation is the semi-variogram modelling which usually uses only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. In this study, we used daily rainfall data from 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, Cressie's Approximate Weighted Least Squares method was used to fit seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) to daily sample semi-variogram on a daily basis. Seven selected raingages were used to compare the interpolation performance of these algorithms applied to many degenerated-raingage cases. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably interpolation with the Thiessen polygon that is commonly used in various hydrological models. Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) presented the highest Root Mean Square Error (RMSE) between the geostatistical and IDW methods. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases.

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