Reintroduction for recovery of New Holland mouse in Victoria, Australia

The New Holland Mouse (Pseudomys novaehollandiae) has a highly fragmented distribution in SE Australia. The abundance of the species is correlated with habitat succession. Optimal habitat has been identified as 2-3 years after fire, with population densities declining, sometimes to extinction, as vegetation ages. The species has become extinct at many locations in Victoria and, in 1999, was known to be extant at only four localities. When a remnant population at one locality (Anglesea) was considered at high risk of extinction, objectives identified to recover the species included determination of suitable habitat, development of ecological burning regimes, captive breeding and reintroductions. A GIS-based predictive model of habitat capability was consequently produced, areas of potentially suitable habitat for reintroductions identified and ecological burning regimes implemented. Experimental releases began in 2001 when predator-proof acclimatisation enclosures were constructed at two sites, selected on the basis of their habitat suitability. Small groups of animals have been released into, and subsequently out of, these enclosures. Movements and activity have been monitored by live-trapping, fluorescent dye and radio-tracking techniques. The results of trials have been assessed. Un-collared animals dispersed from the enclosures into surrounding areas, and gained weight, while initial releases of collared animals were less successful. Techniques and planning to improve future releases have been formulated. The future of the species in Victoria may be reliant upon the success of captive breeding and reintroductions.

Reliability of building embodied energy modelling: an analysis of 30 Melbourne case studies

Building design decisions are commonly based on issues pertaining to construction cost, and consideration of energy performance is made only within the context of the initial project budget. Even where energy is elevated to more importance, operating energy is seen as the focus and embodied energy is nearly always ignored. For the first time, a large sample of buildings has been assembled and analysed in a single study to improve the understanding of the relationship between energy and cost performance over their full life cycle. Thirty recently completed buildings in Melbourne, Australia have been studied to explore the accuracy of initial embodied energy prediction based on capital cost at various levels of model detail. The embodied energy of projects, elemental groups, elements and selected items of work are correlated against capital cost and the strength of the relationship is computed. The relationship between initial embodied energy and capital cost generally declines as the predictive model assumes more detail, although elemental modelling may provide the best solution on balance.

The development of biological objectives for streams in a single catchment: a case study on the catchment of western port bay, Victoria, Australia

Government environment protection policies for waterways have traditionally relied on water quality indicators and their objectives. In this paper we describe the development of biological objectives based on invertebrate indicators for inclusion in a government policy for the catchment of Western Port Bay, Victoria. The first step of defining segments (areas with streams in which the same objectives are applied) was problematic, requiring two different approaches, as follows. Site groups initially based on invertebrate community composition derived using multivariate techniques (ordination and classification) proved to be unsuitable for policy segments. Segment boundaries were subsequently defined using topographical (e.g. boundary of foothills and lowland plains), climate (e.g. rainfall) and land-use (e.g. urban) features. We used information and data from reference sites inside as well outside the catchment to derive specific biological objectives based on aquatic invertebrates for these segments. Objectives were specified for the following four indicators – number of invertebrate families, the SIGNAL index, the AUSRIVAS predictive model and the number of key families.

Physiology and biochemistry of budburst in Vitis vinifera

Both the physiological and biochemical control of budburst in the grapevine, Vitis Vinifera L. were investigated. It was found that the accuracy of a predictive model for grapevine budburst based on ambient temperature was limited under the experimental conditions. There was a significant correlation of 4.7 ± 0.3 days between the days of maximal xylem exudation and budburst over the 3 years of investigation. The co-relationships between daily xylem exudate volume and a range of environmental parameters were considered. It was found that soil temperature was highly correlated against daily xylem exudation. Ambient temperature and soil moisture were significantly correlated with xylem exudation, however the coefficients of correlation were much lower than that of soil temperature. Rainfall showed only a very limited correlation with daily xylem exudate flow. Seasonal variations in the pH and the carbohydrate and inorganic nutrient concentrations of xylem exudate were investigated. Exudate carbohydrate concentrations fell from 660 µM before the day of maximal xylem exudation to zero levels within 4 weeks. Xylem exudate pH was found to consistently fall to a minimum at the time of maximal exudate flow. Exudate concentrations of the metallic cofactors Ca, K, Mg, Mn and Zn varied directly with daily exudate flow, suggesting some sort of flow-dependent mobilisation of these nutrients. A growth promontory oligosaccharide fraction was prepared by partial acid hydrolysis of grapevine primary cell wall material. This fraction significantly increased control growth of the Lemna minor L. bioassay over a limited ‘window’ of bioactivity. A growth inhibitory oligosaccharide fraction, similar in activity to abscisic acid was isolated from grapevine xylem exudate prior to budburst. The exudate concentration or efficacy of this substance declined after budburst such that there was no apparent growth inhibition. A model is proposed for grapevine budburst whereby an oligosaccharide growth inhibitor is gradually removed from the xylematic stream under the effects of soil temperature, allowing the surge of metabolic activity and vegetative growth that constitute budburst.

Controlling and predicting the pilling propensity of knitted wool fabrics

This thesis tackles an important quality issue in the wool industry - the pilling of wool knitwear. Through artificial neural network modelling, the important fibre, yarn and fabric attributes that affect fabric pilling have been identified. A predictive model on fabric pilling has been developed, which will assist the wool industry in the management and control of fabric pilling.

Forecasting the onset of the grass pollen season in Melbourne (Australia)

In Melbourne, a southern hemisphere city with a cool temperate climate, the grass pollen season has been monitored using a Burkard spore trap for 12 years (11 pollen seasons, which extend from October through January). The onset of the grass pollen season (OGPS) has been defined in various ways using both arbitrary cumulative scores (Sum 75, Sum 100) and percentages (10% Pollen Fly). OGPS, based on the forecast model of pollen season devised by Lejoly-Gabriel (Acta Geogr. Lovan., 13 (1978) 1–260) has been most widely used in efforts to forecast the beginning of the pollen season. OGPS occurred in Melbourne between 20 October to 24 November (average 6 November), a difference of 35 days. Duration of the pollen season ranged from 46 to 81 days, with a mean of 55 days, one of the longest reported. The relationships between onset and various weather parameters for July have enabled us to modify a model, using linear regression analysis, to predict onset. The prediction model is based on a negative correlation between date of onset and the sum of rainfall for July (a winter month). The error of prediction (Ep) is 24% and predicted day of OGPS was precisely predicted on 2 occasions, and on others with a range of accuracy of 3 to 14 days.

Predicting management development and learning behaviour in New Zealand SMEs

Despite concern on the part of policy makers to raise managerial capability in SMEs, there is little evidence on the key drivers of owner-manager participation in management development programmes. The authors argue that such participation is poorly understood. The paper develops a predictive model of the drivers of participation in sources of learning by owner-managers. It tests a theoretical model, based on the small firm as a learning organization, which posits that participation is driven by owner-managers' learning orientation and the extent of their belief in self-improvement. The implications of the results are discussed in light of the provision of management development programmes.

Entry-level college mathematics success predictors : developmental performance, 'stopping-out' and demographics

Institutionally collected data identifying student demographics, course performance in both the precollege mathematics course and the college-level mathematics course, and 'stopping-out' time between the pre-college course and the college-level course were used to create a predictive model of academic success for 'high risk' college-level mathematics students. The two most significant factors were the pre-college mathematics course grade and the student's over-all college GPA.

Optimising errors in signaling corporate collapse using MCCCRA

Purpose – The purpose of this paper is to put forward an innovative approach for reducing the variation between Type I and Type II errors in the context of ratio-based modeling of corporate collapse, without compromising the accuracy of the predictive model. Its contribution to the literature lies in resolving the problematic trade-off between predictive accuracy and variations between the two types of errors.

Design/methodology/approach – The methodological approach in this paper – called MCCCRA – utilizes a novel multi-classification matrix based on a combination of correlation and regression analysis, with the former being subject to optimisation criteria. In order to ascertain its accuracy in signaling collapse, MCCCRA is empirically tested against multiple discriminant analysis (MDA).

Findings – Based on a data sample of 899 US publicly listed companies, the empirical results indicate that in addition to a high level of accuracy in signaling collapse, MCCCRA generates lower variability between Type I and Type II errors when compared to MDA.

Originality/value – Although correlation and regression analysis are long-standing statistical tools, the optimisation constraints that are applied to the correlations are unique. Moreover, the multi-classification matrix is a first in signaling collapse. By providing economic insight into more stable financial modeling, these innovations make an original contribution to the literature.

A weighted version of the Melbourne low-vision ADL index : a measure of disability impact

Objective. To develop a version of the Melbourne Low-Vision ADL Index that measures the personal impact of disability in activities of daily living (ADL's). Also, to determine the relationship between clinical measures of vision impairment and disability impact.

Methods. The Melbourne Low-Vision ADL Index (MLVAI) is a desk-based clinical assessment of disability in ADL's. Ability to perform each item is rated on a five-level descriptive scale from zero to four. In this study, the original version of the MLVAI was modified to measure disability impact. The simple modification involved weighting each item by the importance of that item to the person being tested. Importance was also rated on a five-level scale from zero to four. The validity and reliability of the Weighted Melbourne Low-Vision ADL Index (MLVAIW) was determined for 97 vision-impaired subjects in a cross-sectional study.

Results. Cronbach's alpha coefficient indicated an internal reliability of 0.94, and an intraclass correlation coefficient indicated an overall reliability of 0.88. The standard error of measurement was 24.7 points (out of a possible score of 400). There was a statistically significant difference in test scores between normal subjects and vision-impaired subjects. All vision measures had a high, statistically significant correlation with MLVAIW score. Near-word acuity had the strongest correlation (rs = 0.78, p < 0.001), followed by Melbourne Edge Test contrast sensitivity (rs = -0.72, p < 0.001). Visual field had the weakest correlation (rs = -0.52, p < 0.001). The best predictive model of MLVAIW score incorporated the variables age, near-word acuity, and visual field. Together, these variables accounted for 65.1% of the variance in MLVAIW score.

Conclusions. The MLVAI is highly valid and reliable when weighted by a scale that reflects the personal importance of ADL's. The MLVAIW can provide information over and above that obtained with the usual clinical vision measures and may be used to assess low-vision patients and to measure low-vision rehabilitation outcomes. It is suggested that the assessment of disability using the original MLVAI and the assessment of the impact of disability using the MLVAIW should be kept separate to facilitate the clear interpretation of the outcomes of low-vision rehabilitation.

Application of an Adaptive Neural Network to Medical Decision Support

Medical diagnostic and prognostic problems are prime examples of decision making in the face of uncertainty. In this paper, we investigate the applicability of the Fuzzy ARTMAP neural network as an intelligent decision support system in clinical medicine. In particular, Fuzzy ARTMAP is employed as a predictive model for prognosis of complications in patients admitted to the Coronary Care Units. A number of off-line and on-line experiments have been conducted with various network parameter settings, training methods, and learning rules. The results are compared with those from other systems including the logistic regression model. In addition, the outcomes of a set of on-line learning experiments revealed the potential of employing Fuzzy ARTMAP as an autono-mously learning system that is able to learn perpetually and, at the same time, to provide useful decision support.

Common variants of the resistance mechanism in the Smith Machine: analysis of mechanical loading characteristics and application to strength-oriented and hypertrophy-oriented training

The Smith machine is a pervasive weight-training apparatus, used extensively by a wide population of weight trainers, from novices to high-level athletes. The advantages of using a Smith machine over free-weight resistance are disputed, with conflicting findings reported in the literature. In this study, we are interested in practical differences between 3 types of loading mechanisms found in modern Smith machines. In addition to the basic design comprising a constrained weighted barbell, alterations with a counterweight and a viscous resistance component are examined. The approach taken is that of employing a recently proposed representation of force characteristics that may be exhibited by a trainee and a predictive model of thus effected adaptation. A computer simulation is used to predict the effects of the 3 linear Smith machine designs in the framework of different exercise protocols. Our results demonstrate that each resistance component, vertically constrained load, counterweight, and viscous, can be matched with a particular training context, in which it should be preferred. Thus, a number of practical guidelines for weight-training practitioners are recommended. In summary, (a) at low intensities (55–75% of 1 repetition maximum [1RM]) used in strength-endurance training, a viscous resistance containing the Smith machine was found to offer advantages over both a constrained load only and counterweighted designs; (b) at medium intensities (75–85% of 1RM) typically employed in hypertrophy-specific training, the counterweighted Smith machine design was found to offer the best choice in terms of high-force development and total external work performed; finally, (c) at high training intensity (90–100% of 1RM), the optimal prescription was found to be more dependent on the specific athlete’s weaknesses, highlighting the need for continual monitoring of the athlete’s force production capabilities. To ensure that appropriate adjustments are made to the athlete’s training regimen, the practitioner should consider the full set of findings of this article and the accompanying discussion.

Stabilized sparse ordinal regression for medical risk stratification

The recent wide adoption of electronic medical records (EMRs) presents great opportunities and challenges for data mining. The EMR data are largely temporal, often noisy, irregular and high dimensional. This paper constructs a novel ordinal regression framework for predicting medical risk stratification from EMR. First, a conceptual view of EMR as a temporal image is constructed to extract a diverse set of features. Second, ordinal modeling is applied for predicting cumulative or progressive risk. The challenges are building a transparent predictive model that works with a large number of weakly predictive features, and at the same time, is stable against resampling variations. Our solution employs sparsity methods that are stabilized through domain-specific feature interaction networks. We introduces two indices that measure the model stability against data resampling. Feature networks are used to generate two multivariate Gaussian priors with sparse precision matrices (the Laplacian and Random Walk). We apply the framework on a large short-term suicide risk prediction problem and demonstrate that our methods outperform clinicians to a large margin, discover suicide risk factors that conform with mental health knowledge, and produce models with enhanced stability. © 2014 Springer-Verlag London.

Demographic risk factors for alcohol-related aggression in and around licensed venues

AIMS: Few studies have examined the role of gender and both area-level and individual socio-economic status (SES) as independent predictors of alcohol-related aggression (ARA) in and around licensed venues. METHODS: The aim of the present study was to investigate the relationship between gender, area-level SES and individual SES (operationalised as occupational category) and ARA in and around licensed venues. The sample comprised 697 men and 649 women aged 16-47, who completed a patron intercept survey as part of a larger study assessing trends in harm and stakeholders' views surrounding local community level interventions in dealing with alcohol-related problems in the night-time economy. RESULTS: Binary logistic regression analyses showed that age, gender, occupational category, area-level SES and level of intoxication at time of interview were all significant predictors of involvement in ARA. Being male doubled the odds of involvement in ARA, while age was a protective factor. Blue collar workers had more than double the odds of ARA involvement of professionals, while those living in the most socio-economically disadvantaged areas were over twice as likely to report experiencing ARA compared to those living in the most advantaged areas. However, assessment of the predictive model by gender revealed that effects of age, occupational category and area-level SES were restricted to male participants, with greater intoxication no longer predictive. CONCLUSIONS: ARA among patrons was significantly more likely to occur among men, those in blue collar occupations, and individuals living in low SES areas, suggesting both individual and area-level disadvantage may play a role in ARA.