AR Model Based on Time Series Modeling for Predicting Egg Market Price in 2021

Eggs,as a meat consumer product in China,are closely related to the vegetable basket project.Exploring and predicting the future trend of egg market price is of great significance for stabilizing egg price and market supply.In this study,the time series AR model was used for fitting the egg market prices in the 66 d from January 1 to March 7,2021,and the delay operator nlag18 was used for white noise test,giving pr>probability of chisq<0.005.The time series was not a white noise series,and then the stationary series was used for modeling.The optimal model was selected as the AR series(BIC(3,0)),and finally,the egg market price model AM was obtained as X_(t)=9.0556+(1+0.8926)ε_(t),which was the optimal model.The model showed that the egg price fluctuations in 2021 will be clustered,and the later price will be significantly affected by external factors in the previous period.The dynamic prediction results of the model showed that the egg price would stop falling in March 2020,and the egg price would continue to slow down in March.

Parameter Sensitivity and Qualitative Analysis of Dynamics of Ovarian Tumor Growth Model with Treatment Strategy

In this paper, we are interested to find the most sensitive parameter, local and global stability of ovarian tumor growth model. For sensitivity analysis, we use Latin Hypercube Sampling (LHS) method to generate sample points and Partial Rank Correlation Coefficient (PRCC) method, uses those sample points to find out which parameters are important for the model. Based on our findings, we suggest some treatment strategies. We investigate the sensitivity of the parameters for tumor volume, y, cell nutrient density, Q and maximum tumor size, ymax. We also use Scatter Plot method using LHS samples to show the consistency of the results obtained by using PRCC. Moreover, we discuss the qualitative analysis of ovarian tumor growth model investigating the local and global stability.

Global sensitivity analysis of a single-cell HBV model for viral dynamics in the liver

The predictive accuracy of mathematical models representing anything ranging from the meteorological to the biological system profoundly depends on the quality of model parameters derived from experimental data.Hence,robust sensitivity analysis(SA)of these critical model parameters aids in sifting the influential from the negligible out of typically vast parameter regimes,thus illuminating key components of the system under study.We here move beyond traditional local sensitivity analysis to the adoption of global SA techniques.Partial rank correlation coefficient(PRCC)based on Latin hypercube sampling is compared with the variance-based Sobol method.We selected for this SA investigation an infection model for the hepatitis-B virus(HBV)that describes infection dynamics and clearance of HBV in the liver[Murray&Goyal,2015].The model tracks viral particles such as the tenacious and nearly ineradicable covalently closed circular DNA(cccDNA)embedded in infected nuclei and an HBV protein known as p36.Our application of these SA methods to the HBV model illuminates,especially over time,the quantitative relationships between cccDNA synthesis rate and p36 synthesis and export.Our results reinforce previous observations that the viral protein,p36,is by far the most influential factor for cccDNA replication.Moreover,both methods are capable of finding crucial parameters of the model.Though the Sobol method is independent of model structure(e.g.,linearity and monotonicity)and well suited for SA,our results ensure that LHS-PRCC suffices for SA of a non-linear model if it is monotonic.