Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

Catalytic activity evaluation of industrial Pd/C catalyst via gray-box dynamic modeling and simulation of hydropurification reactor

In this paper, dynamic modeling and simulation of the hydropurification reactor in a purified terephthalic acid production plant has been investigated by gray-box technique to evaluate the catalytic activity of palladium supported on carbon (0.5 wt.% Pd/C) catalyst. The reaction kinetics and catalyst deactivation trend have been modeled by employing artificial neural network (ANN). The network output has been incorporated with the reactor first principle model (FPM). The simulation results reveal that the gray-box model (FPM and ANN) is about 32 percent more accurate than FPM. The model demonstrates that the catalyst is deactivated after eleven months. Moreover, the catalyst lifetime decreases about two and half months in case of 7 percent increase of reactor feed flowrate. It is predicted that 10 percent enhancement of hydrogen flowrate promotes catalyst lifetime at the amount of one month. Additionally, the enhancement of 4-carboxybenzaldehyde concentration in the reactor feed improves CO and benzoic acid synthesis. CO is a poison to the catalyst, and benzoic acid might affect the product quality. The model can be applied into actual working plants to analyze the Pd/C catalyst efficient functioning and the catalytic reactor performance.

Socio-cultural influences on vehicle defects in the Omani heavy vehicle industry

With recent economic growth in Oman there is increased use of heavy vehicles, presenting an increase in heavy vehicle crashes, associated fatalities and injuries. Vehicle defects cause a significant number of heavy vehicle crashes in Oman and increase the likelihood of fatalities. The aim of this study is to explore factors contributing to driving with vehicle defects in the Omani heavy vehicle industry. A series of qualitative participants observations were conducted in Oman with 49 drivers. These observations also involved discussion and interviews with drivers. The observations occurred at two road-side locations where heavy vehicle drivers gather for eating, resting, vehicle check-up, etc. Data collection was conducted over a three week period. The data was analysed using thematic analysis. A broad number of factors were identified as contributing to the driving of vehicles with defects. Participants indicated that tyres and vehicle mechanical faults were a common issue in the heavy vehicle industry. Participants regularly reported that their companies use cheap, poor quality standards parts and conducted minimal maintenance. Drivers also indicated that they felt powerless to resist company pressure to drive vehicles with known faults. In addition, drivers reported that traffic police were generally in effective and lacked skill to appropriately conduct roadside inspection on trucks. Further, participants stated that it was possible for companies to avoid being fined during annual or roadside vehicle inspections if members of the company knew the traffic police officer conducting the inspection. Moreover, fines issued by police are generally directed to the individual driver rather than being applied to the company, thus providing no incentive for companies to address vehicle faults. The implications of the findings are discussed.

Extending Alexander’s ecological dominance-social competition (EDSC) evolutional model

Alexander’s Ecological Dominance and Social Competition (EDSC) model currently provides the most comprehensive overview of human traits in the development of a theory of human evolution and sociality (Alexander, 1990; Finn, Geary & Ward, 2005; Irons, 2005). His model provides a basis for explaining the evolution of human socio-cognitive abilities. Our paper examines the extension of Alexander’s model to incorporate the human trait of information behavior in synergy with ecological dominance and social competition as a human socio-cognitive competence. This paper discusses the various interdisciplinary perspectives exploring how evolution has shaped information behavior and why information behavior is emerging as an important human socio-cognitive competence. This paper outlines these issues, including the extension of Spink and Currier’s (2006a,b) evolution of information behavior model towards a more integrated understanding of how information behaviors have evolved (Spink & Cole, 2006).

Extending Alexander’s ecological dominance-social competition model : information behavior adaptation

Alexander’s Ecological Dominance and Social Competition (EDSC) model currently provides the most comprehensive overview of human traits in the development of a theory of human evolution and sociality (Alexander, 1990; Finn, Geary & Ward, 2005; Irons, 2005). His model provides a basis for explaining the evolution of human socio-cognitive abilities. Our paper examines the extension of Alexander’s model to incorporate the human trait of information behavior in synergy with ecological dominance and social competition as a human socio-cognitive competence. This paper discusses the various interdisciplinary perspectives exploring how evolution has shaped information behavior and why information behavior is emerging as an important human socio-cognitive competence. This paper outlines these issues, including the extension of Spink and Currier’s (2006a,b) evolution of information behavior model towards a more integrated understanding of how information behaviors have evolved (Spink & Cole, 2006).

Applying the ecological model of behavior change to a physical activity trial in retirement communities : description of the study protocol

Objectives To describe the intervention protocol for the first multilevel ecological intervention for physical activity in retirement communities that addresses individual, interpersonal and community influences on behavior change. Design A cluster randomized controlled trial design was employed with two study arms: a physical activity intervention and an attention control successful aging condition. Setting Sixteen continuing care retirement communities in San Diego County. Participants Three hundred twenty older adults, aged 65 years and older, are being recruited to participate in the trial. In addition, peer leaders are being recruited to lead some study activities, especially to sustain the intervention after study activities ceased. Intervention Participants in the physical activity trial receive individual, interpersonal and community intervention components. The individual level components include pedometers, goal setting and individual phone counseling. The interpersonal level components include group education sessions and peer-led activities. The community level components include resource audits and enumeration, tailored walking maps, and community improvement projects. The successful aging group receives individual and group attention about successful aging topics. Measurements The main outcome is light to moderate physical activity, measured objectively by accelerometry. Other objective outcomes included physical functioning, blood pressure, physical fitness, and cognitive functioning. Self report measures include depressive symptoms and health related quality of life. Results The intervention is being delivered successfully in the communities and compliance rates are high. Conclusion Ecological Models call for interventions that address multiple levels of the model. Previous studies have not included components at each level and retirement communities provide a model environment to demonstrate how to implement such an intervention.

A multi-level ecological model of psychotropic prescribing to adults with intellectual disability

Purpose – Simple linear accounts of prescribing do not adequately address reasons “why” doctors prescribe psychotropic medication to people with intellectual disability (ID). Greater understanding of the complex array of factors that influence decisions to prescribe is needed. Design/methodology/approach – After consideration of a number of conceptual frameworks that have potential to better understand prescribing of psychotropic medication to adults with ID, an ecological model of prescribing was developed. A case study is used to outline how the model can provide greater understanding of prescribing processes. Findings – The model presented aims to consider the complexity and multi-dimensional nature of community-based psychotropic prescribing to adults with ID. The utility of the model is illustrated through a consideration of the case study. Research limitations/implications – The model presented is conceptual and is as yet untested. Practical implications – The model presented aims to capture the complexity and multi-dimensional nature of community-based psychotropic prescribing to adults with ID. The model may provide utility for clinicians and researchers as they seek clarification of prescribing decisions. Originality/value – The paper adds valuable insight into factors influencing psychotropic prescribing to adults with ID. The ecological model of prescribing extends traditional analysis that focuses on patient characteristics and introduces multi-level perspectives that may provide utility for clinicians and researchers.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Bayesian model averaging for harmful algal bloom prediction

Harmful Algal Blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian Model Averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with Temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate average monthly minimum temperature showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilised the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.

The development of decision making skill in sport : An ecological dynamics perspective

In this chapter we introduce a theoretical framework for studying decision making in sport: the ecological dynamics approach, which we integrate with key ideas from the literature on learning complex motor skills. Our analysis will include insights from Berstein (1967) on the coordination of degrees of freedom and Newell's (1985) model of motor learning. We particularly focus on the role of perceptual degrees of freedom advocated in an ecological approach to learning. In introducing this framework to readers we contrast this perspective with more traditional models of decision-making. Finally, we propose some implications to the training of decision-making skill in sport.

Interpersonal pattern dynamics and adaptive behavior in multiagent neurobiological systems : conceptual model and data

Ecological dynamics characterizes adaptive behavior as an emergent, self-organizing property of interpersonal interactions in complex social systems. The authors conceptualize and investigate constraints on dynamics of decisions and actions in the multiagent system of team sports. They studied coadaptive interpersonal dynamics in rugby union to model potential control parameter and collective variable relations in attacker–defender dyads. A videogrammetry analysis revealed how some agents generated fluctuations by adapting displacement velocity to create phase transitions and destabilize dyadic subsystems near the try line. Agent interpersonal dynamics exhibited characteristics of chaotic attractors and informational constraints of rugby union boxed dyadic systems into a low dimensional attractor. Data suggests that decisions and actions of agents in sports teams may be characterized as emergent, self-organizing properties, governed by laws of dynamical systems at the ecological scale. Further research needs to generalize this conceptual model of adaptive behavior in performance to other multiagent populations.

An indexing model for sustainable urban environmental management : the case of Gold Coast, Australia

Improving urban ecosystems and the quality of life of citizens have become a central issue in the global effort of creating sustainable built environments. As human beings our lives completely depend on the sustainability of the nature and we need to protect and manage natural resources in a more sustainable way in order to sustain our existence. As a result of population growth and rapid urbanisation, increasing demand of productivity depletes and degrades natural resources. However, the increasing activities and rapid development require more resources, and therefore, ecological planning becomes an essential vehicle in preserving scarce natural resources. This paper aims to indentify the interation between urban ecosystems and human activities in the context of urban sustainability and explores the degrading environmental impacts of this interaction and the necessity and benefits of using sustainability indicators as a tool in sustainable urban evnironmental management. Additionally, the paper also introduces an environmental sustainability indexing model (ASSURE) as an innovative approach to evaluate the environmental conditions of built environment.

Sustainable urban futures : an ecological approach to sustainable urban development

In recent years, cities show increasing signs of environmental problems due to the negative impacts of urban activities. The degradation and depletion of natural resources, climate change and development pressure on green areas have become major concerns for cities. In response to these problems, urban planning policies have shifted to a sustainable focus and authorities have begun to develop new strategies for improving the quality of urban ecosystems. An extremely important function of an urban ecosystem is to provide healthy and sustainable environments for both natural systems and communities. Therefore, ecological planning is a functional requirement in the establishment of sustainable built environment. With ecological planning human needs are supplied while natural resources are used in the most effective and sustainable manner. And the maintenance of ecological balance is sustained. Protecting human and environmental health, having healthy ecosystems, reducing environmental pollution and providing green spaces are just a few of the many benefits of ecological planning. In this context, the paper briefly presents a short overview of the importance of the implementation of ecological planning into sustainable urban development. Furthermore, the paper defines the conceptual framework of a new method for developing sustainable urban ecosystems through ecological planning approach. In the future of the research, this model will be developed as a guideline for the assessment of the ecological sustainability in built environments.

An ecological assessment of secondary poisoning risk in the Australian sugarcane industry

Rodenticide use in agriculture can lead to the secondary poisoning of avian predators. Currently the Australian sugarcane industry has two rodenticides, Racumin® and Rattoff®, available for in-crop use but, like many agricultural industries, it lacks an ecologically-based method of determining the potential secondary poisoning risk the use of these rodenticides poses to avian predators. The material presented in this thesis addresses this by: a. determining where predator/prey interactions take place in sugar producing districts; b. quantifying the amount of rodenticide available to avian predators and the probability of encounter; and c. developing a stochastic model that allows secondary poisoning risk under various rodenticide application scenarios to be investigated. Results demonstrate that predator/prey interactions are highly constrained by environmental structure. Rodents used crops that provided high levels of canopy cover and therefore predator protection and poorly utilised open canopy areas. In contrast, raptors over-utilised areas with low canopy cover and low rodent densities, but which provided high accessibility to prey. Given this pattern of habitat use, and that industry baiting protocols preclude rodenticide application in open canopy crops, these results indicate that secondary poisoning can only occur if poisoned rodents leave closed canopy crops and become available for predation in open canopy areas. Results further demonstrate that after in-crop rodenticide application, only a small proportion of rodents available in open areas are poisoned and that these rodents carry low levels of toxicant. Coupled with the low level of rodenticide use in the sugar industry, the high toxic threshold raptors have to these toxicants and the low probability of encountering poisoned rodents, results indicate that the risk of secondary poisoning events occurring is minimal. A stochastic model was developed to investigate the effect of manipulating factors that might influence secondary poisoning hazard in a sugarcane agro-ecosystem. These simulations further suggest that in all but extreme scenarios, the risk of secondary poisoning is also minimal. Collectively, these studies demonstrate that secondary poisoning of avian predators associated with the use of the currently available rodenticides in Australian sugar producing districts is minimal. Further, the ecologically-based method of assessing secondary poisoning risk developed in this thesis has broader applications in other agricultural systems where rodenticide use may pose risks to avian predators.