A broad-scale analysis of links between coastal fisheries production and mangrove extent : a case-study for northeastern Australia

The paradigm that mangroves are critical for sustaining production in coastal fisheries is widely accepted, but empirical evidence has been tenuous. This study showed that links between mangrove extent and coastal fisheries production could be detected for some species at a broad regional scale (1000s of kilometres) on the east coast of Queensland, Australia. The relationships between catch-per-unit-effort for different commercially caught species in four fisheries (trawl, line, net and pot fisheries) and mangrove characteristics, estimated from Landsat images were examined using multiple regression analyses. The species were categorised into three groups based on information on their life history characteristics, namely mangrove-related species (banana prawns Penaeus merguiensis, mud crabs Scylla serrata and barramundi Lates calcarifer), estuarine species (tiger prawns Penaeus esculentus and Penaeus semisulcatus, blue swimmer crabs Portunus pelagicus and blue threadfin Eleutheronema tetradactylum) and offshore species (coral trout Plectropomus spp.). For the mangrove-related species, mangrove characteristics such as area and perimeter accounted for most of the variation in the model; for the non-mangrove estuarine species, latitude was the dominant parameter but some mangrove characteristics (e.g. mangrove perimeter) also made significant contributions to the models. In contrast, for the offshore species, latitude was the dominant variable, with no contribution from mangrove characteristics. This study also identified that finer scale spatial data for the fisheries, to enable catch information to be attributed to a particular catchment, would help to improve our understanding of relationships between mangroves and fisheries production.

Accurately defining the reference condition for summary biotic metrics: A comparison of four approaches

Protocols for bioassessment often relate changes in summary metrics that describe aspects of biotic assemblage structure and function to environmental stress. Biotic assessment using multimetric indices now forms the basis for setting regulatory standards for stream quality and a range of other goals related to water resource management in the USA and elsewhere. Biotic metrics are typically interpreted with reference to the expected natural state to evaluate whether a site is degraded. It is critical that natural variation in biotic metrics along environmental gradients is adequately accounted for, in order to quantify human disturbance-induced change. A common approach used in the IBI is to examine scatter plots of variation in a given metric along a single stream size surrogate and a fit a line (drawn by eye) to form the upper bound, and hence define the maximum likely value of a given metric in a site of a given environmental characteristic (termed the 'maximum species richness line' - MSRL). In this paper we examine whether the use of a single environmental descriptor and the MSRL is appropriate for defining the reference condition for a biotic metric (fish species richness) and for detecting human disturbance gradients in rivers of south-eastern Queensland, Australia. We compare the accuracy and precision of the MSRL approach based on single environmental predictors, with three regression-based prediction methods (Simple Linear Regression, Generalised Linear Modelling and Regression Tree modelling) that use (either singly or in combination) a set of landscape and local scale environmental variables as predictors of species richness. We compared the frequency of classification errors from each method against set biocriteria and contrast the ability of each method to accurately reflect human disturbance gradients at a large set of test sites. The results of this study suggest that the MSRL based upon variation in a single environmental descriptor could not accurately predict species richness at minimally disturbed sites when compared with SLR's based on equivalent environmental variables. Regression-based modelling incorporating multiple environmental variables as predictors more accurately explained natural variation in species richness than did simple models using single environmental predictors. Prediction error arising from the MSRL was substantially higher than for the regression methods and led to an increased frequency of Type I errors (incorrectly classing a site as disturbed). We suggest that problems with the MSRL arise from the inherent scoring procedure used and that it is limited to predicting variation in the dependent variable along a single environmental gradient.

The impacts of variable speed limit on speed variation and headway distribution

This thesis investigates the impacts of variable speed limit on motorway speed variation and headway distribution. Initiative techniques of traffic flow categorisation study contribute in analysing the effects of variable speed limit on various traffic states. The project focuses on the speed harmonisation impacts within and across lanes as well as the uniformity of headway spread in the application of variable speed limit.

Semiparametric estimation of count regression models

This paper develops a semiparametric estimation approach for mixed count regression models based on series expansion for the unknown density of the unobserved heterogeneity. We use the generalized Laguerre series expansion around a gamma baseline density to model unobserved heterogeneity in a Poisson mixture model. We establish the consistency of the estimator and present a computational strategy to implement the proposed estimation techniques in the standard count model as well as in truncated, censored, and zero-inflated count regression models. Monte Carlo evidence shows that the finite sample behavior of the estimator is quite good. The paper applies the method to a model of individual shopping behavior. © 1999 Elsevier Science S.A. All rights reserved.

Imputing a continuous income variable from a bracketed income variable with special attention to missing observations

This paper proposes a method, based on polychotomous discrete choice methods, to impute a continuous measure of income when only a bracketed measure of income is available and for only a subset of the obsevations. The method is shown to perform well with CP5 data. © 1991.

The eight variable homogeneous degree three bent functions

We determine the affine equivalence classes of the eight variable degree three homogeneous bent functions using a new algorithm. Our algorithm applies to general bent functions and can systematically determine the automorphism groups. We provide a partial verification of the enumeration of eight variable degree three homogeneous bent functions obtained by Meng et al. We determine the affine equivalence classes of these functions.

Evolution of Langmuir waves in a plasma contaminated by variable-charge impurities

The propagation of Langmuir waves in nonisothermal plasmas contaminated by fine dust particles with variable charge is investigated for a self-consistent closed system. Dust charge relaxation, ionization, recombination, and collisional dissipation are taken into account. It is shown that the otherwise unstable coupling of the Langmuir and dust-charge relaxation modes becomes stable and the Langmuir waves are frequency down-shifted.

Low-frequency dispersion properties of plasmas with variable-charge impurities

A theory of low-frequency dust-acoustic waves in low-temperature collisional plasmas containing variable-charge impurities is presented. Physical processes such as dust-charge relaxation, ionization-recombination of the electrons and ions, electron and ion elastic collisions with neutrals and dusts, as well as charging collisions with the dusts, are taken into account. Inclusion of these processes allows a balance of the plasma particles and thus a self-consistent determination of the stationary state of the unperturbed plasma. The generalized dispersion relation describing the propagation and damping of the dust acoustic waves is derived and analyzed. © 2000 American Institute of Physics.

Rectifier systems for variable speed wind generation : a review

The drive towards high efficiency wind energy conversion systems has resulted in almost all the modern wind turbines to operate in the variable speed mode which inevitably requires back-to-back power electronic converters to decouple generator dynamics from the grid. The aim of this paper is to present an analysis on suitable topologies for the generator-side converter (rectifier) of the back-to-back converter arrangement. Performance of the two most popular rectifier systems, namely, the passive diode bridge rectifier and the active six-switch two-level rectifier are taken as two extremes to evaluate other topologies presented in this paper. The other rectifier systems considered in this study include combinations of a diode bridge rectifier and electronic reactance(s), a combination of a rectifier and a dc-dc converter and a half controlled rectifier. Diode-clamped and capacitor-clamped three-level active rectifier topologies and their possible switch reductions are also discussed in relation to the requirements of modern high power wind energy conversion systems (WECSs). Simulation results are presented to support conclusion derived from this analysis.

Controller design for variable-speed permanent magnet wind turbine generators interfaced with Z-source inverter

Increased awareness of environmental concerns has caused greater interest in developing power sources based on renewable technologies, such as wind. Due to the intermittent nature of the wind speed, output voltage and frequency of the direct driven permanent magnet synchronous generators (PMSG) are normally unsteady. Recently proposed Z-source inverter has been considered as a potential solution for grid interfacing wind power generators, thanks to buck-boost function that the single stage Z-source inverter can offer. Two control methodologies, namely unified controller for isolated operation and a multi-loop controller for grid interfaced operation are investigated in this paper. Theoretical analysis of these two control schemes is presented and experimental results to verify the effectiveness of the control method are also included.

Z-source converter based grid-interface for variable-speed permanent magnet wind turbine generators

A Z-source inverter based grid-interface for a variable-speed wind turbine connected to a permanent magnet synchronous generator is proposed. A control system is designed to harvest maximum wind energy under varied wind conditions with the use of the permanent magnet synchronous generator, diode-rectifier and Z-source inverter. Control systems for speed regulation of the generator and for DC- and AC- sides of the Z-source inverter are investigated using computer simulations and laboratory experiments. Simulation and experimental results verify the efficacy of the proposed approach.

Coping strategies used by ballet dancers : effects of individual differences in competitive trait anxiety

The world of classical ballet exerts considerable physical and psychological stress upon those who participate, and yet the process of coping with such stressors is not well understood. The purpose of the present investigation was to examine relationships between coping strategies and competitive trait anxiety among ballet dancers. Participants were 104 classical dancers (81 females and 23 males) ranging in age from 15 to 35 years (M = 19.4 yr., SD = 3.8 yr.) from three professional ballet companies, two private dance schools, and two full-time, university dance courses in Australia. Participants had a mean of 11.5 years of classical dance training (SD = 5.2 yr.), having started dance training at 6.6 years of age (SD = 3.4 yr.). Coping strategies were assessed using the Modified COPE scale (MCOPE: Crocker & Graham, 1995), a 48-item measure comprising 12 coping subscales (Seeking Social Support for Instrumental Reasons, Seeking Social Support for Emotional Reasons, Behavioral Disengagement, Planning, Suppression of Competing Activities, Venting of Emotions, Humor, Active Coping, Denial, Self-Blame, Effort, and Wishful Thinking). Competitive trait anxiety was assessed using the Sport Anxiety Scale (SAS: Smith, Smoll, & Schutz, 1990), a 21-item measure comprising three anxiety subscales (Somatic Anxiety, Worry, Concentration Disruption). Standard multiple regression analyses showed that trait anxiety scores, in particular for Somatic Anxiety and Worry, were significant predictors of seven of the 12 coping strategies (Suppression of Competing Activities: R2 = 27.1%; Venting of Emotions: R2 = 23.2%; Active Coping: R2 = 14.3%; Denial: R2 = 17.7%; Self-Blame: R2 = 35.7%; Effort: R2 = 16.6%; Wishful Thinking: R2 = 42.3%). High trait anxious dancers reported more frequent use of all categories of coping strategies. A separate two-way MANOVA showed no significant main effect for gender nor status (professional versus students) and no significant interaction effect. The present findings are generally consistent with previous research in the sport psychology domain (Crocker & Graham, 1995; Giacobbi & Weinberg, 2000) which has shown that high trait anxious athletes tend, in particular, to use more maladaptive, emotion-focused coping strategies when compared to low trait anxious athletes; a tendency which has been proposed to lead to negative performance effects. The present results emphasize the need for the effectiveness of specific coping strategies to be considered during the process of preparing young classical dancers for a career in professional ballet. In particular, the results suggest that dancers who are, by nature, anxious about performance may need special attention to help them to learn to cope with performance-related stress. Given the absence of differences in coping strategies between student and professional dancers and between males and females, it appears that such educational efforts should begin at an early career stage for all dancers.

An evaluation of crowd counting methods, features and regression models

Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression

A national satellite-based land-use regression model for air pollution exposure assessment in Australia

Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research.