Seed dormancy and germination of Ficus lundellii and tropical forest restoration

We investigated seed dormancy and germination in Ficus lundellii Standl. (Moraceae), a native species of Mexico's Los Tuxtlas tropical rain forest. In an 8-h photoperiod at an alternating diurnal (16/8 h) temperature of 20/30 degrees C, germination was essentially complete (96%) within 28 days, whereas in darkness, all seeds remained dormant. Neither potassium nitrate (0.05-0.2%) applied continuously nor gibberellic acid applied either continuously (10-200 ppm) or as a 24 hour pretreatment (2000 ppm) induced germination in the dark. Germination in the light was not reduced by a 24-h hydrochloric acid (0.1-1%) pretreatment, but it was reduced both by a 24-h pretreatment with either H2O2 (0. 1-5 M) or 5% HCl, or by more than 5 days of storage at 40 degrees C (4.5% seed water content). In a study with a 2-dimensional temperature gradient plate, seeds germinated fully and rapidly in the light at a constant temperature of 30 degrees C, and fully but less rapidly in the light at alternating temperatures with low amplitudes (< 12 degrees C) about the optimal constant temperature. The base, optimal and ceiling temperatures for rate of germination were estimated as 13.8, 30.1 and 41.1 degrees C, respectively. In all temperature regimes, light was essential for the germination of F lundellii seeds.

Gold mining in Ghana's forest reserves: a report on the current debate

This paper examines the debate surrounding a recent decision made by the Ghanaian government to permit gold exploration - and potentially, mining - in 'protected' forest reserves. In 2001, four mining companies were awarded mineral exploration concessions in forested regions of the country, and have since put forward applications to mine for gold. Notwithstanding the sharp divide in opinion on the issue, the continued uncertainty surrounding the implications of the proposed activities makes further research on the ground imperative in the short term. Work aiming to elicit indigenous perspectives on the projects, as well as research that facilitates dialogue between and/or among stakeholder parties, should be prioritized.

Intra- and interspecific variations in vacuolar flavonoids among Ficus species from the Budongo Forest, Uganda

Leaves of 14 species of Ficus growing in the Budongo Forest, Uganda, were analysed for vacuolar flavonoids. Three to six accessions were studied for each species to see whether there was intraspecific chemical variation. Thirty-nine phenolic compounds were identified or characterised, including 14 flavonol O-glycosides, six flavone O-glycosides and 15 flavone C-glycosides. In some species the flavonoid glycosides were acylated. Ficus thonningii contained in addition four stilbenes including glycosides. Most of the species could be distinguished from each other on the basis of their flavonoid profiles, apart from Ficus sansibarica and Ficus saussureana, which showed a very strong intraspecific variation. However, on the whole flavonoid profiles were sufficiently distinct to help in future identifications.

Design and evaluation of intelligent classifier based intrusion detection system

We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

A data forest: multi-dimensional visualization

As Terabyte datasets become the norm, the focus has shifted away from our ability to produce and store ever larger amounts of data, onto its utilization. It is becoming increasingly difficult to gain meaningful insights into the data produced. Also many forms of the data we are currently producing cannot easily fit into traditional visualization methods. This paper presents a new and novel visualization technique based on the concept of a Data Forest. Our Data Forest has been designed to be used with vir tual reality (VR) as its presentation method. VR is a natural medium for investigating large datasets. Our approach can easily be adapted to be used in a variety of different ways, from a stand alone single user environment to large multi-user collaborative environments. A test application is presented using multi-dimensional data to demonstrate the concepts involved.

Data forest: a collaborative version

As we increase our ability to produce and store ever larger amounts of data, it is becoming increasingly difficult to understand what the data is trying to tell us. Not all the data we are currently producing can easily fit into traditional visualization methods. This paper presents a new and novel visualization technique based on the concept of a Data Forest. Our Data Forest has been developed to be utilised by virtual reality (VR) systems. VR is a natural information medium. This approach can easily be adapted to be used in collaborative environments. A test application has been developed to demonstrate the concepts involved and a collaborative version tested.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.