An assessment tool for sub-catchment to catchment scale indicators of sustainability - a case study in south west Victoria, Australia

The research reported in this paper represents an attempt to produce a practical, indicator-based sustainability assessment tool incorporating all these elements is based on relationships between indicators determined considering spatial influences. Through the use of an existing sustainability indicator set and data currently available, relationships will be determined using Arcview Geographic Information Systems (GIS), correlation analysis and Principal Component Analysis (PCA). Indicator interactions will be identified at two spatial scales and compared to determine impacts of changing spatial scale. Further PCA and multiple regression analyses will then be used to reduce the complexity of the indicator set. These findings will be incorporated into a practical indicator-based assessment tool through the adoption of the Analytic Hierarchy Process (AHP) combined with GIS techniques that will then be validated. Once validated the tool can be used to aid in guiding planning and decision-making regarding sustainable development in the Glenelg Hopkins catchment, Victoria; while also moving towards producing a standard set of procedures for assessing sustainability.

Texture recognition by using GLCM and various aggregation functions

We discuss the problem of texture recognition based on the grey level co-occurrence matrix (GLCM). We performed a number of numerical experiments to establish whether the accuracy of classification is optimal when GLCM entries are aggregated into standard metrics like contrast, dissimilarity, homogeneity, entropy, etc., and compared these metrics to several alternative aggregation methods.We conclude that k nearest neighbors classification based on raw GLCM entries typically works better than classification based on the standard metrics for noiseless data, that metrics based on principal component analysis inprove classification, and that a simple change from the arithmetic to quadratic mean in calculating the standard metrics also improves classification.

Ultraviolet plumage colors predict mate preferences in starlings

Avian plumage has long been used to test theories of sexual selection, with humans assessing the colors, However, many birds see in the ultraviolet (<400 nm), to which humans are blind, Consequently, it is important to know whether natural variation in UV reflectance from plumage functions in sexual signaling, We show that female starlings rank males differently when UV wavelengths are present or absent, Principal component analysis of approximate to 1300 reflectance spectra (300-700 nm) taken from sexually dimorphic plumage regions of males predicted preference under the UV+ treatment. Under UV- conditions, females ranked males in a different and nonrandom order, but plumage reflectance in the human visible spectrum did not predict choice, Natural variation in UV reflectance is thus important in avian mate assessment, and the prevailing light environment can have profound effects on observed mating preferences.

Retrospective application of remote sensing to the Tasmanian lakeland

This thesis describes the research undertaken for a degree of Master of Science in a retrospective study of airborne remotely sensed data registered in 1990 and 1993, and field captured data of aquatic humus concentrations for ~ 45 lakes in Tasmania. The aim was to investigate and describe the relationship between the remotely sensed data and the field data and to test the hypothesis that the remotely sensed data would establish further evidence of a limnological corridor of change running north-west to south- east. The airborne remotely sensed data consisted of data captured by the CSIRO Ocean Colour Scanner (OCS) and a newly developed Canadian scanner, a compact airborne spectrographic imager (CASI). The thesis investigates the relationship between the two kinds of data sources. The remotely sensed data was collected with the OCS scanner in 1990 (during one day) and with both the OCS and the CASI in 1993 (during three days). The OCS scanner registers data in 9 wavelength bands between 380 nm and 960 nm with a 10-20 nm bandwidth, and the CASI in 288 wavelength bands between 379.57 nm and 893.5 nm (ie. spectral mode) with a spectral resolution of 2.5 nm. The remotely sensed data were extracted from the original tapes with the help of the CSIRO and supplied software and digital sample areas (band value means) for each lake were subsequently extracted for data manipulation and statistical analysis. Field data was captured concurrently with the remotely sensed data in 1993 by lake hopping using a light aircraft with floats. The field data used for analysis with the remotely sensed data were the laboratory determined g440 values from the 1993 water samples collated with g440 values determined from earlier years. No spectro-radiometric data of the lakes, data of incoming irradiance or ancillary climatic data were captured during the remote sensing missions. The sections of the background chapter in the thesis provide a background to the research both in regards to remote sensing of water quality and the relationship between remotely sensed spectral data and water quality parameters, as well as a description of the Tasmanian lakes flown. The lakes were divided into four groups based on results from previous studies and optical parameters, especially aquatic humus concentrations as measured from field captured data. The four groups consist of the ‘green” clear water lakes mostly situated on the Central Plateau, the ‘brown” highly dystrophic lakes in western Tasmania, the ‘corridor” lakes situated along a corridor of change lying approximately between the two lines denoting the Jurassic edge and 1200 mm isohyet, and the ‘eastern, turbid” lakes make up the fourth group. The analytical part of the research work was mostly concerned with manipulating and analysing the CASI data because of its higher spectral resolution. The research explores methods to apply corrections to this data to reduce the disturbing effects of varying illumination and atmospheric conditions. Three different methods were attempted. In the first method two different standardisation formulas are applied to the data as well as ‘day correction” factors calculated from data from one of the lakes, Lake Rolleston, which had data captured for all three days of the remote sensing operations. The standardisation formulas were also applied to the OCS data. In second method an attempt to reduce the effects of the atmosphere was performed using spectro-radiometric captured in 1988 for one of the lakes flown, Great Lake. All the lake sample data were time normalised using general irradiance data obtained from the University of Tasmania and the sky portion as calculated from Great Lake upwelling irradiance data was then subtracted. The last method involved using two different band ratios to eliminate atmospheric effects. Statistical analysis was applied to the data resulting from the three methods to try to describe the relationship between the remotely sensed data and the field captured data. Discriminant analysis, cluster analysis and factor analysis using principal component analysis (pea) were applied to the remotely sensed data and the field data. The factor scores resulting from the pca were regressed against the field collated data of g440 as were the values resulting from last method. The results from the statistical analysis of the data from the first method show that the lakes group well (100%) against the predetermined groups using discriminant analysis applied to the remotely sensed CASI data. Most variance in the data are contained in the first factor resulting from pca regardless of data manipulation method. Regression of the factor scores against g440 field data show a strong non- linear relationship and a one-sided linear regression test is therefore considered an inappropriate analysis method to describe the dataset relationships. The research has shown that with the available data, correction and analysis methods, and within the scope of the Masters study, it was not possible to establish the relationships between the remotely sensed data and the field measured parameters as hoped. The main reason for this was the failure to retrieve remotely sensed lake signatures adequately corrected for atmospheric noise for comparison with the field data. This in turn is a result of the lack of detailed ancillary information needed to apply available established methods for noise reduction - to apply these methods we require field spectroradiometric measurements and environmental information of the varying conditions both within the study area and within the time frame of capture of the remotely sensed data.

Impact of stresses imposed on macroinvertebrate communities in two suburban streams

The aim of the project was to determine factors which explain the distribution of macroinvertebrates in two Melbourne streams both drastically affected by urbanisation. A detailed description is given of Dandenong Creek, flowing through the south-eastern suburbs, and Darebin Creek, in the northern suburbs, emphasising stream features likely, or known, to influence the drift and benthic fauna. Faunal sampling was carried out in Dandenong Creek from June 1992 until July 1993, and in Darebin Creek from February 1995 until March 1998. Physicochemical parameters were also recorded. The collected data, together with previously existing data, were analysed using multivariate analyses: non-metric multi-dimensional scaling (NMDS); analysis of similarities (ANOSIM); matching biotic and abiotic variables using BIOENV, and principal component analysis (PCA). Various biotic and diversity indices were calculated in an attempt to identify the major factors responsible for the failure of the fauna to recover from previously more seriously degraded water quality. The contribution of drift to the colonisation potential in Dandenong Creek appeared to be impacted by retarding basins, underground barrel-draining and channelization. Results also indicated that increased conductivity adversely affected the fauna in the lower reaches of Dandenong Creek. It was concluded that in Darebin Creek, high nutrient levels, as well as other pollutants, had resulted in low macroinvertebrate diversity in both the drift and benthos. If, as this study suggests, faunal diversity is a valid measure of stream health, the following factors need to be addressed for catchment-wide, stream management: lack of riparian zone vegetation (increasing bank erosion and making the benthic habitat unstable, with greater temperature variability); control of stormwater runoff (flow variability, increased conductivity, nutrient levels, sediment loads, sewage effluent, industrial discharges and heavy metals), and to modify retarding basins to increase stream continuity.

Genetic and environmental influences on variation in balance performance among female twin pairs aged 21-82 years

Genetic and environmental influences on variation in balance performance were measured in 93 monozygous and 83 dizygous female twin pairs aged 21–82 years (mean age, 50.5 years) in Melbourne, Australia, between 1999 and 2003. The authors administered clinical (Lord's Balance Test and Step Test) and laboratory tests of static and dynamic balance from the Chattecx Balance System with and without distractor tasks. The authors conducted factor analysis and estimated genetic and environmental variance components and heritability (defined as additive genetic variance as a proportion of all variance, after adjustment for age) using a multivariate normal model with the statistical package FISHER. Three factors were identified and adjusted for age. Heritability was 46% (standard error (SE), 9) for the "sensory balance tests" factor and 30% (SE, 9) for the "static and dynamic perturbations" factor. For both factors, the remaining variance was attributed to unique environmental effects. There was no evidence that genetic factors influenced variation in the "dynamic weight shift tests" factor, with environmental effects shared by twins accounting for 38% (SE, 7) of variance. Neither genetic nor environmental proportions of variance differed significantly between twin subgroups by age (≤50/>50 years). An age-related decline in performance measures was found across the whole sample. These results imply that balance impairments may have a heritable element.

Feature extraction for animal fiber identification

Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones.

Impact of board composition and ownership on internationalisation strategy amongst small New Zealand firms

Internationalisation strategies are important for company expansion because New Zealand, with its four million people, has such a small market. Nonetheless, there mayor may not exist ;"agency costs" in the use of Outside Directors. Ownership patterns may also influence Internationalization Strategy. Using Binary Correlation, N-Way Cross-Tabulation, and Principal Component Analysis, we find evidence that Outside Directors have less influence on Internationalisation Strategy than Inside Directors. Family ownership also seems to have a greater association than non-family owned companies. Despite substantial limitations, the methods and models proposed seem to have some utility in examining the association of Internationalisation Strategy with Board Composition and Ownership Patterns.

From rural beginnings to statewide roll-out : evaluation of facilitator training for a group-based diabetes prevention program

Objective: To evaluate the approach used to train facilitators for a large-scale group-based diabetes prevention program developed from a rural implementation research project.

Participants: Orientation day was attended by 224 health professionals; 188 submitted the self-learning task; 175 achieved the satisfactory standard for the self-learning task and attended the workshop; 156 completed the pre- and post-training questionnaires.

Main outcome measures: Two pre- and post-training scales were developed to assess knowledge and confidence in group-based diabetes prevention program facilitation. Principal component analysis found four factors for measuring training effectiveness: knowledge of diabetes prevention, knowledge of group facilitation, confidence to facilitate a group to improve health literacy and confidence in diabetes prevention program facilitation. Self-learning task scores, training discontinuation rates and satisfaction scores were also assessed.

Results: There was significant improvement in all four knowledge and confidence factors from pre- to post-training (P < 0.001). The self-learning task mean test score was 88.7/100 (SD = 7.7), and mean assignment score was 72.8/100 (SD = 16.1). Satisfaction with training scores were positive and 'previous training' interacted with 'change in knowledge of diabetes prevention program facilitation' but not with change in 'confidence to facilitate.'

Conclusions: The training program was effective when analysed by change in facilitator knowledge and confidence and the positive mean satisfaction score. Learning task scores suggest tasks were manageable and the requirement contributed to facilitator self-selection. Improvement in confidence scores in facilitating a group-based diabetes prevention program, irrespective of previous training and experience, show that program-specific skill development activities are necessary in curriculum design.

A neural network based human identification framework using ear images

This paper presents a framework that uses ear images for human identification. The framework makes use of Principal Component Analysis (PCA) for ear image feature extraction and Multilayer Feed Forward Neural Network for classification. Framework are proposed to improve recognition accuracy of human identification. The framework was tested on an ear image database to evaluate its reliability and recognition accuracy. The experimental results showed that our framework achieved higher stable recognition accuracy and over-performed other existing methods. The recognition accuracy stability and computation time with respect to different image sizes and factors were investigated thoroughly as well in the experiments.

The relationship between building adaptation and property attributes

Using a database of building adaptation and property attributes this research examined every adaptation event in Melbourne's CBD between 1998 and 2008. The importance of property attributes was derived using a principal component analysis and a weighted index of optimal decision making attributes was proposed; the Preliminary Assessment Adaptation Model (PAAM). The findings indicate the relationship between property attributes is more complex than hitherto held. Overall physical attributes were found to be more important than others such as economic, environmental, legal and social attributes; however physical attributes alone are not important and are closely related to other attributes.

Field evaluation of a suite of biomarkers in an Australian Tropical Reef Species, Stripey Seaperch (Lutjanus carponotatus) : Assessment of Produced Formation water from the Harriet A Platform

There is paucity of data regarding hydrocarbon exposure of tropical fish species inhabiting the waters near oil and gas platforms on the Northwest Shelf of Australia. A comprehensive field study assessed the exposure and potential effects associated with the produced water (PW) plume from the Harriet A production platform on the northwest shelf in a local reef species, Stripey seaperch (Lutjanus carponotatus). This field study was a continuation of an earlier pilot study which concluded that there were “warning signs” of potential biological effects on fish populations exposed to PW. A 10-day field caging study was conducted deploying 15 individual fish into 6 separate steel cages set 1-m subsurface at 3 stations in a concentration gradient moving away from the platform. A battery of biomarkers were evaluated including hepatosomatic index (HSI), total cytochrome P450, bile metabolites, CYP1A-, CYP2K- and CYP2M-like proteins, cholinesterase (ChE) activity, and histopathology of liver and gill tissues. Water column and PW effluent samples was also collected. Results confirmed that PAH metabolites in bile, CYP1A-, CYP2K-, and CYP2M-like proteins and liver histopathology provided evidence of significant exposure and effects after 10 days at the near-field site (~200 m off the Harriet A platform). Hepatosomatic index, total cytochrome P450, and ChE did not provide site-specific differences by day 10 of exposure to PW. CYP proteins were shown by principal component analysis (PCA) to be the best diagnostic tool for determining exposure and associated biological effects of PW on L. carponotatus. Using a suite of biomarkers has been widely advocated as a vital component in environmental risk assessments worldwide. This study demonstrates the usefulness of biomarkers for assessing the Harriet A PW discharge into Australian waters with broader applications for other PW discharges. This approach has merit as a valuable addition to environmental management strategies for protecting Australia’s tropical environment and its rich biodiversity.

Automatic parameters selection for eigenfaces

In this paper, we investigate the parameters selection for Eigenfaces. Our focus is on the eigenvectors and threshold selection issues. We will propose a systematic approach in selecting the eigenvectors based on relative errors of the eigenvalues for the covariance matrix. In addition, we have proposed a method for selecting the classification threshold that utilizes the information obtained from the training data set. Experimentation was conducted on two benchmark face databases, ORL and AMP, with results indicating that the proposed automatic eigenvectors and threshold selection methods produce better recognition performance in terms of precision and recall rates. Furthermore, we show that the eigenvector selection method outperforms energy and stretching dimension methods in terms of selected number of eigenvectors and computation cost.

Recognising faces in unseen modes : a tensor based approach

This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. This basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost.

Robust learning of discriminative projection for multicategory classification on the Stiefel manifold

Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.