Seeds of many species of plants may survive for a long time in the soil and germinate when brought to the surface, but
whether they are subsequently eaten by seed predators is unknown. We examined the preferences of three species of carabids
(Coleoptera: Carabidae) for 25 species of seeds and determined the difference in palatability between freshly dispersed and those
buried for six years. The stability of their preferences was tested using a collection of seeds of different species, each of which was
offered fresh or after being buried. Carabid beetles readily accepted previously buried seeds as food. In total, Pseudoophonus
rufi pes and Amara littorea ate more fresh seeds than previously buried seeds, while the opposite was true for Harpalus affi nis. The
seeds of some species were even more attractive to carabids after burial than in the fresh state. For all the species of carabids
tested, the diet breadth was similar when the beetles were fed fresh or buried seeds, but the preferences for fresh and buried seed
of particular species were correlated only in P. rufi pes and A. littorea. We measured the seed characteristics (mass and viability)
likely to be associated with the loss of attractiveness to carabids during burial. The change in carabid consumption was not related
to changes in any of these characteristics. This fi nding indicates that factors responsible for variation in seed acceptability are
complex. This study provides the fi rst conclusive evidence that invertebrate seed predators will feed on seeds from seed banks,
although they prefer fresh seeds.
In a cultured network of rat embryonic hypothalamic cells, synaptic interaction is through GABAA-receptors, that mediate inhibition by an increase in Cl' conductance, and AMPA-receptors, that mediate excitation by an increase in monovalent cationic conductance. Changes in the balance of inhibition and excitation towards a predominance of excitation lead to phasic synchronous activity of the cells. Synaptic interaction through these receptors is thus capable of modulating neurosecretion rapidly.
Considering the correlations of the input indexes and the deficiency of calibrating kernel function parameters when support vector machine (SVM) is applied, a forecasting method based on principal component analysis-genetic algorithm-support vector machine (PCA-GA-SVM) is proposed to improve the precision of bus arrival time prediction. And the No. 232 bus in Shenyang City of China is taken as an example. The traditional SVM and Kalman Filtering model and GA-SVM are also employed to make comparative analysis on the prediction rate, respectively. The result indicates that PCA-GA-SVM obtains more accurate prediction results of bus arrival time prediction.
Accurate prediction of bus arrival time is of great significance to improve passenger satisfaction and bus attraction. This paper presents the prediction model of bus arrival time based on support vector machine with genetic algorithm (GA-SVM). The character of the time period, the length of road, the weather, the bus speed and the rate of road usage are adopted as input vectors in Support Vector Machine (SVM), and the genetic algorithm search algorithm is combined to find the best parameters. Finally, the data from Bus No.249 in Shenyang, china are used to check the model. The experimental results show that the forecasting model is superior to the traditional SVM model and the Artificial Neural Network (ANN) model in terms of the same data, and is of higher accuracy, which verified the feasibility of the model to predict the bus arrival time.