Forest tree species diversity: Assessment, analysis and interpretation in the presence of non-identification and non-observance

Abstract not available

Species diversity structure analysis at two sites in the tropical rain forest of Sumatra

Data from a hilly forest study site at Batang Ule, Sumatra, are organized into 30 100-m × 10-m subplots lying perpendicular to the line of maximal topographic gradient, from the valley to the plateau/ridge. The following methodological question is addressed: what species diversity measures are best used in order to reveal the ecologically distinct regions in the site. The main tool used to answer this question is the α-diversity curve (Hα). Graphical examination of tree and species densities, and α-diversity curves identifies an anomalous species diversity behaviour of the ‘ridge above the slope’ subplots which may have implications on land-facet class definitions. Factor analysis of the α-diversity curves indicates that the diversity space is two-dimensional: i.e. two diversity measures are sufficient to characterize the site; the species density (H0), and the Berger-Parker index (H[infty infinity]). In the two-dimensional diversity-space three distinct species diversity groups are found which relate to the topographic gradient at the Batang Ule site. The results are compared with those for a flat homogeneous site at Pasirmayang, Sumatra. The implications of the results on land-classifications in species-diversity mapping and conservation strategy are discussed.

Event models for tumor classification with SAGE gene expression data

Serial Analysis of Gene Expression (SAGE) is a relatively new method for monitoring gene expression levels and is expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. A promising application of SAGE gene expression data is classification of tumors. In this paper, we build three event models (the multivariate Bernoulli model, the multinomial model and the normalized multinomial model) for SAGE data classification. Both binary classification and multicategory classification are investigated. Experiments on two SAGE datasets show that the multivariate Bernoulli model performs well with small feature sizes, but the multinomial performs better at large feature sizes, while the normalized multinomial performs well with medium feature sizes. The multinomial achieves the highest overall accuracy.

An analysis of the passenger to cabin crew ratio and exit reliability based on past survivable aviation accidents

A hotly debated issue in the area of aviation safety is the number of cabin crew members required to evacuate an aircraft in the event of an emergency. Most countries regulate the minimum number required for the safe operation of an aircraft, but these rulings are based on little if any scientific evidence. Another issue of concern is the failure rate of exits and slides. This paper examines these issues using the latest version of Aircraft Accident Statistics and Knowledge database AASK V4.0, which contains information from 105 survivable crashes and more than 2,000 survivors, including accounts from 155 cabin crew members.