A Simplified Evaluation Procedure of the Scale of Fluctuation Combined Recurrence Averaging and Weighted Linear Regression

The divergence of properties from one location to another within a soil mass is termed spatial variability, which traditionally includes three parameters the mean, the standard deviation, and the scale of fluctuation, in order to stochastically describe a soil property. Among them, determining the scale of fluctuation in the evaluation of spatial variability of soil profiles is not easy due to soil condition complexity. A simplified procedure is presented in the paper to determine the scale of fluctuation combined recurrence averaging and weighted linear regression. The alternative approach utilizes widely usable spreadsheet to solve the problem more directly and efficiently.

The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.

Sleeping sites, sleeping trees, and sleep-related behaviors of black crested gibbons (Nomascus concolor jingdongensis) at Mt. Wuliang, central Yunnan, China

Data on sleep-related behaviors were collected for a group of central Yunnan black crested gibbons (Nomascus concolor jingdongensis) at Mt. Wuliang, Yunnan, China from March 2005 to April 2006. Members of the group usually formed four sleeping units (adult male and juvenile, adult female with one semi-dependent black infant, adult female with one dependent yellow infant, and subadult male) spread over different sleeping trees. Individuals or units preferred specific areas to sleep; all sleeping sites were situated in primary forest, mostly (77%) between 2,200 and 2,400 m in elevation. They tended to sleep in the tallest and thickest trees with large crowns on steep slopes and near important food patches. Factors influencing sleeping site selection were (1) tree characteristics, (2) accessibility, and (3) easy escape. Few sleeping trees were used repeatedly by the same or other members of the group. The gibbons entered the sleeping trees on average 128 min before sunset and left the sleeping trees on average 33 min after sunrise. The lag between the first and last individual entering the trees was on average 17.8 min. We suggest that sleep-related behaviors are primarily adaptations to minimize the risk of being detected by predators. Sleeping trees may be chosen to make approach and attack difficult for the predator, and to provide an easy escape route in the dark. In response to cold temperatures in a higher habitat, gibbons usually sit and huddle together during the night, and in the cold season they tend to sleep on ferns and/or orchids.