Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

Weibull model selection for reliability modelling

A large number of models have been derived from the two-parameter Weibull distribution and are referred to as Weibull models. They exhibit a wide range of shapes for the density and hazard functions, which makes them suitable for modelling complex failure data sets. The WPP and IWPP plot allows one to determine in a systematic manner if one or more of these models are suitable for modelling a given data set. This paper deals with this topic.

Variants of temporal defeasible logics for modelling norm modifications

This paper proposes some variants of Temporal Defeasible Logic (TDL) to reason about normative modifications. These variants make it possible to differentiate cases in which, for example, modifications at some time change legal rules but their conclusions persist afterwards from cases where also their conclusions are blocked.

Sampling phylogenetic tree space with the generalized Gibbs sampler

The generalized Gibbs sampler (GGS) is a recently developed Markov chain Monte Carlo (MCMC) technique that enables Gibbs-like sampling of state spaces that lack a convenient representation in terms of a fixed coordinate system. This paper describes a new sampler, called the tree sampler, which uses the GGS to sample from a state space consisting of phylogenetic trees. The tree sampler is useful for a wide range of phylogenetic applications, including Bayesian, maximum likelihood, and maximum parsimony methods. A fast new algorithm to search for a maximum parsimony phylogeny is presented, using the tree sampler in the context of simulated annealing. The mathematics underlying the algorithm is explained and its time complexity is analyzed. The method is tested on two large data sets consisting of 123 sequences and 500 sequences, respectively. The new algorithm is shown to compare very favorably in terms of speed and accuracy to the program DNAPARS from the PHYLIP package.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Air Bubble Entrainment in Hydraulic Jumps: Physical Modelling and Scale Effects

A hydraulic jump is characterised by strong energy dissipation and air entrainment. In the present study, new air-water flow measurements were performed in hydraulic jumps with partially-developed flow conditions in relatively large-size facilities with phase-detection probes. The experiments were conducted with identical Froude numbers, but a range of Reynolds numbers and relative channel widths. The results showed drastic scale effects at small Reynolds numbers in terms of void fraction and bubble count rate distributions. The void fraction distributions implied comparatively greater detrainment at low Reynolds numbers leading to a lesser overall aeration of the jump roller, while dimensionless bubble count rates were drastically lower especially in the mixing layer. The experimental results suggested also that the relative channel width had little effect on the air-water flow properties for identical inflow Froude and Reynolds numbers.

Modelling fungal growth on surfaces

Fungal growth in time and space at the substrate surface was modelled for a simple system mimicking solid-state fermentation, using a polycarbonate Nucleopore membrane laid over a glucose solution. Biomass production depends on both tip density and the diffusion of glucose within the fungal hyphae. The model predicts early increases in both height and concentration, followed by a period in which the biomass profile moves with a constant wavefront. The rate of increase in height increases as tip diffusivity increases or as the Monod saturation constant for glucose decreases.

Erosion/productivity modelling of maize farming in the Philippine uplands part II: Simulation of alternative farming methods

A version of the Agricultural Production Systems Simulator (APSIM) capable of simulating the key agronomic aspects of intercropping maize between legume shrub hedgerows was described and parameterised in the first paper of this series (Nelson et al., this issue). In this paper, APSIM is used to simulate maize yields and soil erosion from traditional open-field farming and hedgerow intercropping in the Philippine uplands. Two variants of open-field farming were simulated using APSIM, continuous and fallow, for comparison with intercropping maize between leguminous shrub hedgerows. Continuous open-field maize farming was predicted to be unsustainable in the long term, while fallow open-field farming was predicted to slow productivity decline by spreading the effect of erosion over a larger cropping area. Hedgerow intercropping was predicted to reduce erosion by maintaining soil surface cover during periods of intense rainfall, contributing to sustainable production of maize in the long term. In the third paper in this series, Nelson et al. (this issue) use cost-benefit analysis to compare the economic viability of hedgerow intercropping relative to traditional open-field farming of maize in relatively inaccessible upland areas. (C) 1998 Elsevier Science Ltd. All rights reserved.

Erosion/productivity modelling of maize farming in the Philippine uplands part III: Economic analysis of alternative farming methods

Two previous papers in this series (Nelson et al., this issue) described the use of the Agricultural Production Systems Simulator (APSIM) to simulate the effect of erosion on maize yields from open-field farming and hedgerow intercropping in the Philippine uplands. In this paper, maize yields simulated with APSIM are used to compare the economic viability of intercropping maize between leguminous shrub hedgerows with that of continuous and fallow open-field farming of maize. The analysis focuses on the economic incentives of upland farmers to adopt hedgerow intercropping, discussing farmers' planning horizons, access to credit and security of land tenure, as well as maize pricing in the Philippines. Insecure land tenure has limited the planning horizons of upland farmers, and high establishment costs reduce the economic viability of hedgerow intercropping relative to continuous and fallow open-field farming in the short term, In the long term, high discount rates and share-tenancy arrangements in which landlords do not contribute to establishment costs reduce the economic viability of hedgerow intercropping relative to fallow open-field farming, (C) 1998 Elsevier Science Ltd. All rights reserved.

Finite element modelling of temperature gradient driven rock alteration and mineralization in porous rock masses

We present finite element simulations of temperature gradient driven rock alteration and mineralization in fluid saturated porous rock masses. In particular, we explore the significance of production/annihilation terms in the mass balance equations and the dependence of the spatial patterns of rock alteration upon the ratio of the roll over time of large scale convection cells to the relaxation time of the chemical reactions. Special concepts such as the gradient reaction criterion or rock alteration index (RAI) are discussed in light of the present, more general theory. In order to validate the finite element simulation, we derive an analytical solution for the rock alteration index of a benchmark problem on a two-dimensional rectangular domain. Since the geometry and boundary conditions of the benchmark problem can be easily and exactly modelled, the analytical solution is also useful for validating other numerical methods, such as the finite difference method and the boundary element method, when they are used to dear with this kind of problem. Finally, the potential of the theory is illustrated by means of finite element studies related to coupled flow problems in materially homogeneous and inhomogeneous porous rock masses. (C) 1998 Elsevier Science S.A. All rights reserved.

Modelling dispersal behaviour on a fractal landscape

We use a spatially explicit population model to explore the population consequences of different habitat selection mechanisms on landscapes with fractal variation in habitat quality. We consider dispersal strategies ranging from random walks to perfect habitat selectors for two species of arboreal marsupial, the greater glider (Petauroides volans) and the mountain brushtail possum (Trichosurus caninus). In this model increasing habitat selection means individuals obtain higher quality territories, but experience increased mortality during dispersal. The net effect is that population sizes are smaller when individuals actively select habitat. We find positive relationships between habitat quality and population size can occur when individuals do not use information about the entire landscape when habitat quality is spatially autocorrelated. We also find that individual behaviour can mitigate the negative effects of spatial variation on population average survival and fecundity. (C) 1998 Elsevier Science Ltd. All rights reserved.