我国生猪年度供给量预测研究

在运用协整检验、成本分析等方法对生猪供给量的影响因素进行分析的基础上,综合运用了回归分析、专家经验分析、指数平滑、情景预测等预测和决策方法,对我国2010-2012年的生猪年度供给量进行了预测.预测结果表明,在不发生影响生猪供给的重大突发事件的情况下,2010-2012年我国生猪产量将呈逐年上升的趋势.     

β-Expectation tolerance regions for a generalized multivariate model with normal error variables

In this paper a procedure of construction of β-expectation tolerance regions in the framework of the structural method of inference has been developed. The procedure has been applied to the generalized multivariate model and the β-expectation tolerance region for this case has been constructed assuming the normal distribution for the error variables of the model.     

On timing Jitter in wavelength-division multiplexed soliton systems

Collision-induced timing shifts in a wavelength-division multiplexed soliton system are computed when damping, amplification, filtering and positive dispersion management following the loss profile are included. A statistical analysis is presented which takes into account the resulting effect of the large number of collisions occurring in the fiber. Analytic expressions are derived for the root mean square timing jitter and the maximum length of error-free transmission with an arbitrary number of channels. An extensive analysis of system performance corresponding to situations with and without filters and/or dispersion management is carried out.     

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.