A New General Allometric Biomass Model

To implement monitoring and assessment of national forest biomass, it is becoming the trend to develop generalized single-tree biomass models suitable for large scale forest biomass estimation. Considering that the theoretical biomass allometric model developed by West et al. [1,2] was statistically different from the empirical one, the two parameters in the most commonly used biomass equation M=aDb were analyzed in this paper. Firstly, based on the knowledge of geometry, the theoretical value of parameter b was deduced, i.e., b=7/3(~2.33), and the comparison with many empirical studies conducted throughout the globe indicated that the theoretical parameter could describe soundly the average allometric relationship between aboveground biomass M and D (diameter on breast height). Secondly, using five datasets of aboveground biomass which consisted of 1441 M-D pairs of sample trees, the new general biomass allometric model was validated. Finally, the relationship between parameter a and wood density p was analyzed, and the linear regression was developed. The new model, which is not only simple but also species-specific, offers a feasible approach on establishment of generalized biomass models for regional and national forest biomass estimation

Bias Correction in Logarithmic Regression and Comparison with Weighted Regression for Nonlinear Models

Non-linear models with heteroscedasticity are commonly used in ecological and forestry modeling, and logarithmic regression and weighted regression are usually employed to estimate the parameters. Using the single-tree biomass data of three large samples, the bias correction in logarithmic regression for non-linear models was studied and comparison between logarithmic regression and weighted regression was discussed in this paper. Firstly, the imminent cause producing bias in logarithmic regression was analyzed, and a new correction factor was presented with which three commonly used bias correction factors were examined together, and the results showed that the correction factors presented here and derived by Baskerville (1972) should be recommended, which could insure the corrected model to be asymptotically consistent with that fitted by weighted regression. Secondly, the fitting results of weighted regression for non-linear models, using the weight function based on residual errors of the model estimated by ordinary least squares (OLS) and the general weight function (w=1/ƒ(x)2) presented by Zeng (1998) respectively, were compared with each other that showed two weight functions worked well and the general function was more applicable. It was suggested that the best way to fit non-linear models with heteroscedasticity would be using weighted regression, and if the total relative error of the estimates from the model fitted by the general weight function was more than a special allowance such as ±3%, a better weight function based on residual errors of the model fitted by OLS should be used in weighted regression

Test of CPT symmetry in cascade decays

Cascade mixing provides an elegant place to study the $B^0-\bar{B^0}$ mixing. We use this idea to study the CPT violation caused by $B^0-\bar{B^0}$ mixing. An approximation method is adopted to treat the two complex $B^0-\bar{B^0}$ mixing parameters $\theta$ and $\phi$. A procedure to extract the parameters $\theta$ and $\phi$ is suggested. The feasibility of exploring the CPT violation and determining of $\theta$ and $\phi$ in the future B-factories and LHC-B is discussed.Comment: Latex, 17 pages, some errors modifie