Variation in length, fecundity and survival of pink wax scale, Ceroplastes rubens Maskell (Hemiptera: Coccidae), on umbrella trees

We investigated some of the factors that may lead to outbreaks of pink wax scale, Ceroplastes rubens Maskell, on umbrella trees, Schefflera actinophylla (Endl.). Estimates of birth and death rates of pink wax scale were high and variable within and among trees; variation in these rates was not related to scale density. Adult fecundity correlated significantly but weakly with adult test length; mean fecundity was 292 eggs per female with a range of 5-1178. Adult test length and its variance decreased weakly with increasing density. Field experiments showed that mortality of C. rubens is greatest during the first 24 hours after hatching when approximately half disappear. The rate of loss decreases over time with 0.3% of initial motile first-instar nymphs surviving to maturity. Rates of loss varied significantly between trees, indicating that some trees are more suitable for scale colonisation and survival.

Estimating Investment Risk in Small-Scale Plantation Rainforest Cabinet Timbers and Eucalypts

‘This book is a landmark opening and first attempt at such a process for defining farm forestry, as well as making a contribution to small-scale forestry.’

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.