Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

Genetic algorithms in statistical tolerancing

An important aspect in manufacturing design is the distribution of geometrical tolerances so that an assembly functions with given probability, while minimising the manufacturing cost. This requires a complex search over a multidimensional domain, much of which leads to infeasible solutions and which can have many local minima. As well, Monte-Carlo methods are often required to determine the probability that the assembly functions as designed. This paper describes a genetic algorithm for carrying out this search and successfully applies it to two specific mechanical designs, enabling comparisons of a new statistical tolerancing design method with existing methods. (C) 2003 Elsevier Ltd. All rights reserved.

The distribution of Tannerella forsythia in an adolescent and adult population

Background: The fact that Tannerella forsythia, an important periopathogen, is difficult to cultivate from mixed infections has impeded precise estimates of its distribution within a given population. In order to discern T. forsythia alone from the mixed infection of plaque, the use of sensitive 16S ribosomal RNA based polymerase chain reaction (PCR) detection is necessary. Objectives: The aim of the present study was to determine the distribution of T. forsythia in an adult and in an adolescent population. Materials and methods: Subgingival plaque samples were obtained from 498 Australian adults and from 228 adolescent subjects from Manchester, UK. Tannerella forsythia was detected using PCR and confirmed by restriction analysis. Semi-quantitation of the organisms was carried out using two specific primers of differing sensitivities. Results: In the adolescent population, 25% were found to carry T. forsythia, albeit in relatively low numbers. In the adult population, a total of 37.8% and 11% were found to carry the organism with primer 2 and primer 1, respectively, suggesting that around 27% had between 10(3) and 10(7) organisms. Although there was an apparent increased proportion of T. forsythia positive subjects in those aged >= 50 years, this was not statistical significant. However, T. forsythia positive male smokers showed increased disease severity compared with T. forsythia negative subjects. Conclusion: This study has shown that at least 25% of the adolescent population carry low numbers of T. forsythia, whereas at least 37% of adults carry the organism, with some 11% having relatively high numbers. The relationship between T. forsythia and disease progression in these populations, however, remains to be determined.

Rank test of location optimal for hyperbolic secant distribution

There are at least two reasons for a symmetric, unimodal, diffuse tailed hyperbolic secant distribution to be interesting in real-life applications. It displays one of the common types of non normality in natural data and is closely related to the logistic and Cauchy distributions that often arise in practice. To test the difference in location between two hyperbolic secant distributions, we develop a simple linear rank test with trigonometric scores. We investigate the small-sample and asymptotic properties of the test statistic and provide tables of the exact null distribution for small sample sizes. We compare the test to the Wilcoxon two-sample test and show that, although the asymptotic powers of the tests are comparable, the present test has certain practical advantages over the Wilcoxon test.

R-estimator of location of the generalized secant hyperbolic distribution

The generalized secant hyperbolic distribution (GSHD) proposed in Vaughan (2002) includes a wide range of unimodal symmetric distributions, with the Cauchy and uniform distributions being the limiting cases, and the logistic and hyperbolic secant distributions being special cases. The current article derives an asymptotically efficient rank estimator of the location parameter of the GSHD and suggests the corresponding one- and two-sample optimal rank tests. The rank estimator derived is compared to the modified MLE of location proposed in Vaughan (2002). By combining these two estimators, a computationally attractive method for constructing an exact confidence interval of the location parameter is developed. The statistical procedures introduced in the current article are illustrated by examples.

Novel approach to tailoring molecular weight distribution and structure with a difunctional RAFT agent

This work has demonstrated that for the first time a single RAFT agent (i. e., difunctional) can be used in conjunction with a radical initiator to obtain a desired M-n and PDI with controlled rates of polymerization. Simulations were used not only to verify the model but also to provide us with a predictive tool to generate other MWDs. It was also shown that all the MWDs prepared in this work could be translated to higher molecular weights through chain extension experiments with little or no compromise in the control of end group functionality. The ratio of monofunctional to difunctional SdC(CH2Ph)S- end groups, XPX and XP (where X) S=C(CH2Ph) S-), can be controlled by simply changing the concentration of initiator, AIBN. Importantly, the amount of dead polymer is extremely low and fulfils the criterion as suggested by Szwarc (Nature 1956) that to meet living requirements nonfunctional polymeric species formed by side reactions in the process should be undetectable by analytical techniques. In addition, this novel methodology will allow the synthesis of AB, ABA, and statistical multiblock copolymers with predetermined ratios to be produced in a one-pot reaction.

3D statistical shape models to embed spatial relationship information

This paper presents the creation of 3D statistical shape models of the knee bones and their use to embed information into a segmentation system for MRIs of the knee. We propose utilising the strong spatial relationship between the cartilages and the bones in the knee by embedding this information into the created models. This information can then be used to automate the initialisation of segmentation algorithms for the cartilages. The approach used to automatically generate the 3D statistical shape models of the bones is based on the point distribution model optimisation framework of Davies. Our implementation of this scheme uses a parameterized surface extraction algorithm, which is used as the basis for the optimisation scheme that automatically creates the 3D statistical shape models. The current approach is illustrated by generating 3D statistical shape models of the patella, tibia and femoral bones from a segmented database of the knee. The use of these models to embed spatial relationship information to aid in the automation of segmentation algorithms for the cartilages is then illustrated.