The behavior of the homozygous and heterozygous sub-types of rats which are genetically-selected for diabetes insipidus: A comparison with Long Evans and Wistar stocks

Several aspects of spontaneous and conditioned behavior (food and water intake, locomotion and emotionality, passive and active avoidance acquisition and retention) of standard (albino and pigmented) rats, and rats heterozygous (HEDI) and homozygous (HODI) for diabetes insipidus, are reviewed. As would be expected, HODI rats have been repeatedly found to consume far more fluid than either HEDI or control rats. Pigmented rats appear to be more active than albinos. HODI rats exhibit less marked emotional responses than do control rats, among which the pigmented ones exhibit the highest emotionality. Light aversion is more evident in albino than in pigmented rats. No differences are found among HEDI, HODI and normal Long Evans rats. It is quite difficult to provide a clear-cut statement concerning inter-strain differences in passive avoidance behavior, possibly because of the variety of techniques employed. In any case, HODI rats do not perform worse than normal controls do. In one-way active avoidance paradigms, pigmented rats perform better than albinos, and the performance of HODI rats does not differ from that of controls. In two-way avoidance paradigms, albinos appear to outperform pigmented rats. Once again, there are no obvious differences between HODI and control animals.In addition to indicating that HODI rats may actually be less emotional than the other groups of rats reviewed here, the studies described once again fail to confirm the previously alleged functions of vasopressin in memory consolidation.     

乙烷—丙烷混合裂解模型及产率预计

本文研究了乙烷和丙烷的混合裂解,并与乙烷、丙烷单独裂解进行了比较。解决了微分方程组的刚性问题,提出了混合裂解反应动力学模型。明确了三种选择性的概念,即;真实选择性、迭加选择性和总选择性,从而澄清了国际上对于选择性有正偏差和负偏差的不同观点。为乙烯生产提供了有效利用乙烷和丙烷的分析评价方法。     

Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks

Abstract: After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks isintroduced, an intelligent PID controller is adopted to correct the errors including identified model errors and accumulatederrors produced in the recursive process. Characterized by predictive control, this method can achieve a good controlaccuracy and has good robustness. A simulation study shows that this control algorithm is very effective.     

Dynamic Characteristics of Inter-Satellite Links in LEO Networks

To establish an efficient inter-satellite link (ISL) in an LEO network, the effect of geometric characteristics of ISL on the ISLs and the devices on the LEO satellite should be examined. Because of the continuous movement of the LEO satellite, the time-varying behaviours of the ISL's geometric charactersistics continuously change with the changes of the satellite's position on the orbit. These dynamic geometric characteristics of the ISLs are important for ISL's performance analyzing and the design of the devices on the LEO satellite. This paper describes dynamic geometric characteristics of ISL, analyzes the impact of these regulations on the tracking system of the satellite's antenna and the power adjusting system of the satellite's transmitter, with the Iridium system as an example.     

Radar Imaging Based on Iterative Algorithms

It has long been realized that the problem of radar imaging is a special case of image reconstruction in which the data are incomplete and noisy. In other fields, iterative reconstruction algorithms have been used successfully to improve the image quality. This paper studies the application of iterative algorithms in radar imaging. A discrete model is first derived, and the iterative algorithms are then adapted to radar imaging. Although such algorithms are usually time consuming, this paper shows that, if the algorithms are appropriately simplified, it is possible to realize them even in real time. The efficiency of iterative algorithms is shown through computer simulations.     

A small GTP-binding protein dissociates from synaptic vesicles during exocytosis.

Low-molecular-weight GTP-binding proteins are strong candidates for regulators of membrane traffic. In yeast, mutations in the sec4 or ypt1 genes encoding small GTP-binding proteins inhibit constitutive membrane flow at the plasma membrane or Golgi complex, respectively. It has been suggested that membrane fusion-fission events are regulated by cycling of small GTP-binding proteins between a membrane-bound and free state, but although most of these small proteins are found in both soluble and tightly membrane-bound forms, there is no direct evidence to support such cycling. In rat brain a small GTP-binding protein, rab3A, is exclusively associated with synaptic vesicles, the secretory organelles of nerve terminals. Here we use isolated nerve terminals to study the fate of rab3A during synaptic vesicle exocytosis. We find that rab3A dissociates quantitatively from the vesicle membrane after Ca2(+)-dependent exocytosis and that this dissociation is partially reversible during recovery after stimulation. These results are direct evidence for an association-dissociation cycle of a small GTP-binding protein during traffic of its host membrane.     

Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier

Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-     

Subset threshold autoregression

We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright © 2003 John Wiley & Sons, Ltd.