Generalised growth models for aquatic species with an application to blacklip abalone (Haliotis rubra)

This paper presents a maximum likelihood method for estimating growth parameters for an aquatic species that incorporates growth covariates, and takes into consideration multiple tag-recapture data. Individual variability in asymptotic length, age-at-tagging, and measurement error are also considered in the model structure. Using distribution theory, the log-likelihood function is derived under a generalised framework for the von Bertalanffy and Gompertz growth models. Due to the generality of the derivation, covariate effects can be included for both models with seasonality and tagging effects investigated. Method robustness is established via comparison with the Fabens, improved Fabens, James and a non-linear mixed-effects growth models, with the maximum likelihood method performing the best. The method is illustrated further with an application to blacklip abalone (Haliotis rubra) for which a strong growth-retarding tagging effect that persisted for several months was detected

Stochastic growth models for analyzing crustacean data

The contemporary methodology for growth models of organisms is based on continuous trajectories and thus it hinders us from modelling stepwise growth in crustacean populations. Growth models for fish are normally assumed to follow a continuous function, but a different type of model is needed for crustacean growth. Crustaceans must moult in order for them to grow. The growth of crustaceans is a discontinuous process due to the periodical shedding of the exoskeleton in moulting. The stepwise growth of crustaceans through the moulting process makes the growth estimation more complex. Stochastic approaches can be used to model discontinuous growth or what are commonly known as "jumps" (Figure 1). However, in stochastic growth model we need to ensure that the stochastic growth model results in only positive jumps. In view of this, we will introduce a subordinator that is a special case of a Levy process. A subordinator is a non-decreasing Levy process, that will assist in modelling crustacean growth for better understanding of the individual variability and stochasticity in moulting periods and increments. We develop the estimation methods for parameter estimation and illustrate them with the help of a dataset from laboratory experiments. The motivational dataset is from the ornate rock lobster, Panulirus ornatus, which can be found between Australia and Papua New Guinea. Due to the presence of sex effects on the growth (Munday et al., 2004), we estimate the growth parameters separately for each sex. Since all hard parts are shed too often, the exact age determination of a lobster can be challenging. However, the growth parameters for the aforementioned moult processes from tank data being able to estimate through: (i) inter-moult periods, and (ii) moult increment. We will attempt to derive a joint density, which is made up of two functions: one for moult increments and the other for time intervals between moults. We claim these functions are conditionally independent given pre-moult length and the inter-moult periods. The variables moult increments and inter-moult periods are said to be independent because of the Markov property or conditional probability. Hence, the parameters in each function can be estimated separately. Subsequently, we integrate both of the functions through a Monte Carlo method. We can therefore obtain a population mean for crustacean growth (e. g. red curve in Figure 1). [GRAPHICS]

Estimating equations for parameters in stochastic growth models from tag-recapture data

James (1991, Biometrics 47, 1519-1530) constructed unbiased estimating functions for estimating the two parameters in the von Bertalanffy growth curve from tag-recapture data. This paper provides unbiased estimating functions for a class of growth models that incorporate stochastic components and explanatory variables. a simulation study using seasonal growth models indicates that the proposed method works well while the least-squares methods that are commonly used in the literature may produce substantially biased estimates. The proposed model and method are also applied to real data from tagged rack lobsters to assess the possible seasonal effect on growth.

Bayesian models for longitudinal data

Longitudinal data, where data are repeatedly observed or measured on a temporal basis of time or age provides the foundation of the analysis of processes which evolve over time, and these can be referred to as growth or trajectory models. One of the traditional ways of looking at growth models is to employ either linear or polynomial functional forms to model trajectory shape, and account for variation around an overall mean trend with the inclusion of random eects or individual variation on the functional shape parameters. The identification of distinct subgroups or sub-classes (latent classes) within these trajectory models which are not based on some pre-existing individual classification provides an important methodology with substantive implications. The identification of subgroups or classes has a wide application in the medical arena where responder/non-responder identification based on distinctly diering trajectories delivers further information for clinical processes. This thesis develops Bayesian statistical models and techniques for the identification of subgroups in the analysis of longitudinal data where the number of time intervals is limited. These models are then applied to a single case study which investigates the neuropsychological cognition for early stage breast cancer patients undergoing adjuvant chemotherapy treatment from the Cognition in Breast Cancer Study undertaken by the Wesley Research Institute of Brisbane, Queensland. Alternative formulations to the linear or polynomial approach are taken which use piecewise linear models with a single turning point, change-point or knot at a known time point and latent basis models for the non-linear trajectories found for the verbal memory domain of cognitive function before and after chemotherapy treatment. Hierarchical Bayesian random eects models are used as a starting point for the latent class modelling process and are extended with the incorporation of covariates in the trajectory profiles and as predictors of class membership. The Bayesian latent basis models enable the degree of recovery post-chemotherapy to be estimated for short and long-term followup occasions, and the distinct class trajectories assist in the identification of breast cancer patients who maybe at risk of long-term verbal memory impairment.

Four models of the creative industries

Les activités et industries culturelles sont aujourd’hui englobées dans une nouvelle approche, celle d’industries créatives. Dans ce contexte, les interrogations sur les contributions de la culture au développement économique peuvent être repensées de manière élargie. La contribution examine les différentes réponses possibles à cette question, et quatre modèles sont ainsi distingués: l’approche du bien être; l’approche concurrentielle; l’approche de la croissance; l’approche de l’innovation. A chacun de ces modèles correspond une interprétation du lien entre activités créatives et économie. Ce sont ces interprétations dont la pertinence est appréciée à l’aide de données statistiques simples.

Essays on technology adoption under uncertainty, globalization and economic growth

This thesis examines how the initial institutional and technological aspects of the economy and the reforms that alter these aspects influence long run growth and development. These issues are addressed in the framework of stochastic endogenous growth models and an empirical framework. The thesis is able to explain why developing nations exhibit diverse growth and inequality patterns. Consequently, the thesis raises a number of policy implications regarding how these nations can improve their economic outcomes.

Growth curves with time-dependent explanatory variables

A class of growth models incorporating time-dependent factors and stochastic perturbations are introduced. The proposed model includes the existing growth models used in fisheries as special cases. Particular attention is given to growth of a population (in average weight or length) from which observations are taken randomly each time and the analysis of tag-recapture data. Two real data sets are used for illustration: (a) to estimate the seasonal effect and population density effect on growth of farmed prawn (Penaeus monodon) from weight data and (b) to assess the effect of tagging on growth of barramundi (Lates calcarifer)

Sustainable urban and regional infrastructure development : technologies, applications and management

Sustainable Urban and Regional Infrastructure Development: Technologies, Applications and Management, bridges the gap in the current literature by addressing the overall problems present in society's major infrastructures, and the technologies that may be applied to overcome these problems. It focuses on ways in which energy intensive but 'invisible' (to the general public) facilities can become green or greener. The studies presented re lessons to be learnt from our neighbors and from our own backyard, and provide an excellent general overview of the issues facing us all.

Preface : sustainable urban and regional infrastructure development : technologies, applications and management

The concept of ‘sustainability’ has been pushed to the forefront of policy-making and politics as the world wakes up to the impacts of climate change and the effects of the modern urban lifestyle. Climate change has emerged to be one of the biggest challenges faced by our planet today, threatening both built and natural systems with long term consequences which may be irreversible. While there is a vast literature in the market on sustainable cities and urban development, there is currently none that bring together the vital issues of urban and regional development, and the planning, management and implementation of sustainable infrastructure. Large scale infrastructure plays an important part in modern society by not only promoting economic growth, but also by acting as a key indicator for it. More importantly, it supplies municipal/local amenity and services: water, electricity, social and communication facilities, waste removal, transport of people and goods, as well as numerous other services. For the most part, infrastructure has been built by teams lead by engineers who are more concerned about functionality than the concept of sustainability. However, it has been widely stated that current practices and lifestyle cannot continue if we are to leave a healthy living planet to not only the next generation, but also to the generations beyond. Therefore, in order to be sustainable, there are drastic measures that need to be taken. Current single purpose and design infrastructures that are open looped are not sustainable; they are too resource intensive, consume too much energy and support the consumption of natural resources at a rate that will exhaust their supply. Because of this, it is vital that modern society, policy-makers, developers, engineers and planners become pioneers in introducing and incorporating sustainable features into urban and regional infrastructure.

Stages of knowledge management systems in policing financial crime

A stage model for knowledge management systems in policing financial crime is developed in this paper. Stages of growth models enable identification of organizational maturity and direction. Information technology to support knowledge work of police officers is improving. For example, new information systems supporting police investigations are evolving. Police investigation is an information-rich and knowledge-intensive practice. Its success depends on turning information into evidence. This paper presents an organizing framework for knowledge management systems in policing financial crime. Future case studies will empirically have to illustrate and validate the stage hypothesis developed in this paper.

Analysis of productive performance of crop and animal production systems : an integrated analytical framework

This article presents a two-stage analytical framework that integrates ecological crop (animal) growth and economic frontier production models to analyse the productive efficiency of crop (animal) production systems. The ecological crop (animal) growth model estimates "potential" output levels given the genetic characteristics of crops (animals) and the physical conditions of locations where the crops (animals) are grown (reared). The economic frontier production model estimates "best practice" production levels, taking into account economic, institutional and social factors that cause farm and spatial heterogeneity. In the first stage, both ecological crop growth and economic frontier production models are estimated to calculate three measures of productive efficiency: (1) technical efficiency, as the ratio of actual to "best practice" output levels; (2) agronomic efficiency, as the ratio of actual to "potential" output levels; and (3) agro-economic efficiency, as the ratio of "best practice" to "potential" output levels. Also in the first stage, the economic frontier production model identifies factors that determine technical efficiency. In the second stage, agro-economic efficiency is analysed econometrically in relation to economic, institutional and social factors that cause farm and spatial heterogeneity. The proposed framework has several important advantages in comparison with existing proposals. Firstly, it allows the systematic incorporation of all physical, economic, institutional and social factors that cause farm and spatial heterogeneity in analysing the productive performance of crop and animal production systems. Secondly, the location-specific physical factors are not modelled symmetrically as other economic inputs of production. Thirdly, climate change and technological advancements in crop and animal sciences can be modelled in a "forward-looking" manner. Fourthly, knowledge in agronomy and data from experimental studies can be utilised for socio-economic policy analysis. The proposed framework can be easily applied in empirical studies due to the current availability of ecological crop (animal) growth models, farm or secondary data, and econometric software packages. The article highlights several directions of empirical studies that researchers may pursue in the future.

Energy dependence of methyl-radical adsorption on diamond (001)-(2×1) surface

The deposition of hyperthermal CH3 on diamond (001)-(2×1) surface at room temperature has been studied by means of molecular dynamics simulation using the many-body hydrocarbon potential. The energy threshold effect has been observed. That is, with fixed collision geometry, chemisorption can occur only when the incident energy of CH3 is above a critical value (Eth). Increasing the incident energy, dissociation of hydrogen atoms from the incident molecule was observed. The chemisorption probability of CH3 as a function of its incident energy was calculated and compared with that of C2H2. We found that below 10 eV, the chemisorption probability of C2H2 is much lower than that of CH3 on the same surface. The interesting thing is that it is even lower than that of CH3 on a hydrogen covered surface at the same impact energy. It indicates that the reactive CH3 molecule is the more important species than C2H2 in diamond synthesis at low energy, which is in good agreement with the experimental observation.

Improved estimation of size-transition matrices using tag-recapture data

We derive a new method for determining size-transition matrices (STMs) that eliminates probabilities of negative growth and accounts for individual variability. STMs are an important part of size-structured models, which are used in the stock assessment of aquatic species. The elements of STMs represent the probability of growth from one size class to another, given a time step. The growth increment over this time step can be modelled with a variety of methods, but when a population construct is assumed for the underlying growth model, the resulting STM may contain entries that predict negative growth. To solve this problem, we use a maximum likelihood method that incorporates individual variability in the asymptotic length, relative age at tagging, and measurement error to obtain von Bertalanffy growth model parameter estimates. The statistical moments for the future length given an individual's previous length measurement and time at liberty are then derived. We moment match the true conditional distributions with skewed-normal distributions and use these to accurately estimate the elements of the STMs. The method is investigated with simulated tag-recapture data and tag-recapture data gathered from the Australian eastern king prawn (Melicertus plebejus).

A review of models of spatial innovation infrastructures in support of high growth SMEs

This Paper first provides a review and analysis of the recent trends on innovation infrastructures developed in industrialised countries to promote innovation and competitiveness for high growth SMEs. It specifically aims to examine various spatial models developed to support provision of innovation infrastructure for high growth sector.