After sigmoid activation function is replaced with piecewise linear activation function, the adding decaying self-feedback continuous Hopfield neural network (ADSCHNN) searching space changes to hyper-cube space, i.e. the simplified ADSCHNN is obtained. Then, convergence analysis is given for the simplified ADSCHNN in hyper-cube space. It is proved through convergence analysis that the ADSCHNN outperforms the continuous Hopfield neural network (CHNN), when they are applied to solve optimization problem. It is also proved that when extra self-feedback is negative, the ADSCHNN is more effective than the extra self-feedback is positive, when the ADSCHNN is applied to solve TSP.
To investigate the effect of glutamine-enriched total parenteral nutrition (TPM) on the protein synthesis and morphology of jejunal mucosa in non-hypercatabolic stress, sixty-two male Sprague-Dawley rats were subjected to surgical stress by femoral fracture. The rats were divided into 3 groups and received TPM for 8 days. One group received a standard amino acid solution without glutamine, the second group a standard solution enriched with glycine and glutamic acid, and the third group a standard solution enriched with glycyl-glutamine. All regimens were isocaloric and isonitrogenous-nitrogen (2.2 g/kg.day), glucose (150 Kcal/kg.day), and lipids (150 Kcal/kg.day). There were no statistically significant differences in jejunal mucosal thickness, DMA content, protein content, fractional synthesis rate or absolute protein synthesis among the groups after eight days of parenteral nutrition. In conclusion, the addition of glutamine to TPM did not influence either protein metabolism or morphology of the jejunal mucosa in non-hypercatabolic surgical stress.
In a case-cohort design, covariate histories are measured only on cases and a subcohort that is randomly selected from the entire cohort. This design has been widely used in large epidemiologic studies, especially when the exposures of interest are expensive to assemble for all the subjects. In this paper, we propose statistical procedures for analyzing case-cohort sampled current status data under the additive hazards model. Asymptotical properties of the proposed estimator are described and we suggest a resampling method to estimate the variances. Simulation studies show that the proposed method works well for finite sample sizes, and one data set is analyzed for illustrative purposes.