Based on a practical research test, the statistic analysis method for the experimental data in the split-split plot design was introduced in detail, especially in- troduced the significant test method of three-factor ...Based on a practical research test, the statistic analysis method for the experimental data in the split-split plot design was introduced in detail, especially in- troduced the significant test method of three-factor interaction and the calculation of test value, which solved the problem of how to make statistical analysis on the data in split-split plot design.展开更多
Vegetative filter strip (VFS) is a main kind of Best Management Practices for the control of non-point source pollution. The goal of this paper is to evaluate the effectiveness of VFS in Chinese northwest regions. Thr...Vegetative filter strip (VFS) is a main kind of Best Management Practices for the control of non-point source pollution. The goal of this paper is to evaluate the effectiveness of VFS in Chinese northwest regions. Three VFSs with natural grass and Hippophae rhamnoides/grass patterns have been constructed in the bank slope of Xiaohuashan reservoir, Huaxian County, Shannxi Province. The removal effects of VFS and influencing factors have been analyzed based on field experiment data. The result reveals a positive effect on reducing the transportation of suspended solids, phosphorus and nitrogen in surface runoff, and it is more efficient on suspended solids removal. The experiment also shows that most of the suspended particles and pollutants bound to them were entrapped in the first 10 m of VFS. The main factors influencing effectiveness of VFS include vegetation patterns and inflow rate. In addition, inflow pollutant concentration has a larger impact on reducing total nitrogen and total phosphorus by VFS, but the reduction effect on SS has no significant difference.展开更多
This paper discussed Bayesian variable selection methods for models from split-plot mixture designs using samples from Metropolis-Hastings within the Gibbs sampling algorithm. Bayesian variable selection is easy to im...This paper discussed Bayesian variable selection methods for models from split-plot mixture designs using samples from Metropolis-Hastings within the Gibbs sampling algorithm. Bayesian variable selection is easy to implement due to the improvement in computing via MCMC sampling. We described the Bayesian methodology by introducing the Bayesian framework, and explaining Markov Chain Monte Carlo (MCMC) sampling. The Metropolis-Hastings within Gibbs sampling was used to draw dependent samples from the full conditional distributions which were explained. In mixture experiments with process variables, the response depends not only on the proportions of the mixture components but also on the effects of the process variables. In many such mixture-process variable experiments, constraints such as time or cost prohibit the selection of treatments completely at random. In these situations, restrictions on the randomisation force the level combinations of one group of factors to be fixed and the combinations of the other group of factors are run. Then a new level of the first-factor group is set and combinations of the other factors are run. We discussed the computational algorithm for the Stochastic Search Variable Selection (SSVS) in linear mixed models. We extended the computational algorithm of SSVS to fit models from split-plot mixture design by introducing the algorithm of the Stochastic Search Variable Selection for Split-plot Design (SSVS-SPD). The motivation of this extension is that we have two different levels of the experimental units, one for the whole plots and the other for subplots in the split-plot mixture design.展开更多
文摘Based on a practical research test, the statistic analysis method for the experimental data in the split-split plot design was introduced in detail, especially in- troduced the significant test method of three-factor interaction and the calculation of test value, which solved the problem of how to make statistical analysis on the data in split-split plot design.
文摘Vegetative filter strip (VFS) is a main kind of Best Management Practices for the control of non-point source pollution. The goal of this paper is to evaluate the effectiveness of VFS in Chinese northwest regions. Three VFSs with natural grass and Hippophae rhamnoides/grass patterns have been constructed in the bank slope of Xiaohuashan reservoir, Huaxian County, Shannxi Province. The removal effects of VFS and influencing factors have been analyzed based on field experiment data. The result reveals a positive effect on reducing the transportation of suspended solids, phosphorus and nitrogen in surface runoff, and it is more efficient on suspended solids removal. The experiment also shows that most of the suspended particles and pollutants bound to them were entrapped in the first 10 m of VFS. The main factors influencing effectiveness of VFS include vegetation patterns and inflow rate. In addition, inflow pollutant concentration has a larger impact on reducing total nitrogen and total phosphorus by VFS, but the reduction effect on SS has no significant difference.
文摘This paper discussed Bayesian variable selection methods for models from split-plot mixture designs using samples from Metropolis-Hastings within the Gibbs sampling algorithm. Bayesian variable selection is easy to implement due to the improvement in computing via MCMC sampling. We described the Bayesian methodology by introducing the Bayesian framework, and explaining Markov Chain Monte Carlo (MCMC) sampling. The Metropolis-Hastings within Gibbs sampling was used to draw dependent samples from the full conditional distributions which were explained. In mixture experiments with process variables, the response depends not only on the proportions of the mixture components but also on the effects of the process variables. In many such mixture-process variable experiments, constraints such as time or cost prohibit the selection of treatments completely at random. In these situations, restrictions on the randomisation force the level combinations of one group of factors to be fixed and the combinations of the other group of factors are run. Then a new level of the first-factor group is set and combinations of the other factors are run. We discussed the computational algorithm for the Stochastic Search Variable Selection (SSVS) in linear mixed models. We extended the computational algorithm of SSVS to fit models from split-plot mixture design by introducing the algorithm of the Stochastic Search Variable Selection for Split-plot Design (SSVS-SPD). The motivation of this extension is that we have two different levels of the experimental units, one for the whole plots and the other for subplots in the split-plot mixture design.
