Stability and convergence of implicit numerical methods for a class of fractional advection-dispersion models

In this paper, a class of fractional advection-dispersion models (FADM) is investigated. These models include five fractional advection-dispersion models: the immobile, mobile/immobile time FADM with a temporal fractional derivative 0 < γ < 1, the space FADM with skewness, both the time and space FADM and the time fractional advection-diffusion-wave model with damping with index 1 < γ < 2. They describe nonlocal dependence on either time or space, or both, to explain the development of anomalous dispersion. These equations can be used to simulate regional-scale anomalous dispersion with heavy tails, for example, the solute transport in watershed catchments and rivers. We propose computationally effective implicit numerical methods for these FADM. The stability and convergence of the implicit numerical methods are analyzed and compared systematically. Finally, some results are given to demonstrate the effectiveness of our theoretical analysis.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

Using process models to support design of airport terminals

The complex design process of airport terminal needs to support a wide range of changes in operational facilities for both usual and unusual/emergency events. Process model describes how activities within a process are connected and also states logical information flow of the various activities. The traditional design process overlooks the necessity of information flow from the process model to the actual building design, which needs to be considered as a integral part of building design. The current research introduced a generic method to obtain design related information from process model to incorporate with the design process. Appropriate integration of the process model prior to the design process uncovers the relationship exist between spaces and their relevant functions, which could be missed in the traditional design approach. The current paper examines the available Business Process Model (BPM) and generates modified Business Process Model(mBPM) of check-in facilities of Brisbane International airport. The information adopted from mBPM then transform into possible physical layout utilizing graph theory.

ACE research briefing paper 005: Global Entrepreneurship Monitor (GEM) - Australia 2011 National Report

This report maps the current state of entrepreneurship in Australia using data from the Global Entrepreneurship Monitor (GEM) for the year 2011. Entrepreneurship is regarded as a crucial driver for economic well-being. Entrepreneurial activity in new and established firms drives innovation and creates jobs. Entrepreneurs also fuel competition thereby contributing indirectly to market and productivity growth along with improving competitiveness of the national economy. Given the economic landscape that exists as a result of the global financial crisis (GFC), it is probably more important than ever for us to understand the effects and drivers of entrepreneurial activity and attitudes in Australia. The central finding of this report is that entrepreneurship is certainly alive and well in Australia. With 10.5 per cent of the adult population involved in setting up a new business or owning a newly founded business as measured by the total entrepreneurial activity rate (TEA) in 2011, Australia ranks second only to the United States among the innovation-driven (developed) economies. Compared with 2010 the TEA rate has increased by 2.7 percentage points. Furthermore, in regard to employee entrepreneurial activity (EEA) rate in established firms, Australia ranks above average. According to GEM data, 5 per cent of the adult population is engaged in developing or launching new products, a new business unit or subsidiary for their employer. Further analysis of the GEM data also clearly shows that Australia compares well with other major economies in terms of the ‘quality’ of entrepreneurial activities being pursued. Indeed, it is not only the quantity of entrepreneurs but also the level of their aspirations and business goals that are important drivers for economic growth. On average, for each business started in Australia driven by the lack of alternatives for the founder to generate income from any other source, there are five other businesses started where the founders specifically want to take advantage of a business opportunity that they believe will increase their personal income or independence. With respect to innovativeness, 31 per cent of Australian new businesses offer products or services which they consider to be new to customers or where very few, or in some cases no, other businesses offer the same product or service. Both these indicators are higher than the average for innovation-driven economies. Somewhat below average is the international orientation of Australian entrepreneurs whereby only 12 per cent aim at having a substantial share of customers from international markets. So what drives this high quantity and quality of entrepreneurship in Australia? The analysis of the data suggests it is a combination of both business opportunities and entrepreneurial skills. It seems that around 50 per cent of the Australian population identify opportunities for a start-up venture and believe that they have the necessary skills to start a business. Furthermore, a large majority of the Australian population report that high media attention for entrepreneurship provides successful role models for prospective entrepreneurs. As a result, 12 per cent of our respondents have expressed the intention to start a business within the next three years. These numbers are all well above average when compared to the other major economies. With regard to gender, the GEM survey shows a high proportion of female entrepreneurs. Approximately 8.4 per cent of adult females are actually involved in setting up a business or have recently done so. Although this female TEA rate is slightly down from 2010, Australia ranks second among the innovation-driven economies. This paints a healthy picture of access to entrepreneurial opportunities for Australian women.

The development of ground impact models for the analysis of the risks associated with Unmanned Aircraft Operations over inhabited areas

With the emergence of Unmanned Aircraft Systems (UAS) there is a growing need for safety standards and regulatory frameworks to manage the risks associated with their operations. The primary driver for airworthiness regulations (i.e., those governing the design, manufacture, maintenance and operation of UAS) are the risks presented to people in the regions overflown by the aircraft. Models characterising the nature of these risks are needed to inform the development of airworthiness regulations. The output from these models should include measures of the collective, individual and societal risk. A brief review of these measures is provided. Based on the review, it was determined that the model of the operation of an UAS over inhabited areas must be capable of describing the distribution of possible impact locations, given a failure at a particular point in the flight plan. Existing models either do not take the impact distribution into consideration, or propose complex and computationally expensive methods for its calculation. A computationally efficient approach for estimating the boundary (and in turn area) of the impact distribution for fixed wing unmanned aircraft is proposed. A series of geometric templates that approximate the impact distributions are derived using an empirical analysis of the results obtained from a 6-Degree of Freedom (6DoF) simulation. The impact distributions can be aggregated to provide impact footprint distributions for a range of generic phases of flight and missions. The maximum impact footprint areas obtained from the geometric template are shown to have a relative error of typically less than 1% compared to the areas calculated using the computationally more expensive 6DoF simulation. Computation times for the geometric models are on the order of one second or less, using a standard desktop computer. Future work includes characterising the distribution of impact locations within the footprint boundaries.

Numerical and experimental studies of cold-formed steel floor systems under standard fire conditions

Light gauge cold-formed steel frame (LSF) structures are increasingly used in industrial, commercial and residential buildings because of their non-combustibility, dimensional stability, and ease of installation. A floor-ceiling system is an example of its applications. LSF floor-ceiling systems must be designed to serve as fire compartment boundaries and provide adequate fire resistance. Fire rated floor-ceiling assemblies formed with new materials and construction methodologies have been increasingly used in buildings. However, limited research has been undertaken in the past and hence a thorough understanding of their fire resistance behaviour is not available. Recently a new composite panel in which an external insulation layer is used between two plasterboards has been developed at QUT to provide a higher fire rating to LSF floors under standard fire conditions. But its increased fire rating could not be determined using the currently available design methods. Research on LSF floor systems under fire conditions is relatively recent and the behaviour of floor joists and other components in the systems is not fully understood. The present design methods thus require the use of expensive fire protection materials to protect them from excessive heat increase during a fire. This leads to uneconomical and conservative designs. Fire rating of these floor systems is provided simply by adding more plasterboard sheets to the steel joists and such an approach is totally inefficient. Hence a detailed fire research study was undertaken into the structural and thermal performance of LSF floor systems including those protected by the new composite panel system using full scale fire tests and extensive numerical studies. Experimental study included both the conventional and the new steel floor-ceiling systems under structural and fire loads using a gas furnace designed to deliver heat in accordance with the standard time- temperature curve in AS 1530.4 (SA, 2005). Fire tests included the behavioural and deflection characteristics of LSF floor joists until failure as well as related time-temperature measurements across the section and along the length of all the specimens. Full scale fire tests have shown that the structural and thermal performance of externally insulated LSF floor system was superior than traditional LSF floors with or without cavity insulation. Therefore this research recommends the use of the new composite panel system for cold-formed LSF floor-ceiling systems. The numerical analyses of LSF floor joists were undertaken using the finite element program ABAQUS based on the measured time-temperature profiles obtained from fire tests under both steady state and transient state conditions. Mechanical properties at elevated temperatures were considered based on the equations proposed by Dolamune Kankanamge and Mahendran (2011). Finite element models were calibrated using the full scale test results and used to further provide a detailed understanding of the structural fire behaviour of the LSF floor-ceiling systems. The models also confirmed the superior performance of the new composite panel system. The validated model was then used in a detailed parametric study. Fire tests and the numerical studies showed that plasterboards provided sufficient lateral restraint to LSF floor joists until their failure. Hence only the section moment capacity of LSF floor joists subjected to local buckling effects was considered in this research. To predict the section moment capacity at elevated temperatures, the effective section modulus of joists at ambient temperature is generally considered adequate. However, this research has shown that it leads to considerable over- estimation of the local buckling capacity of joist subject to non-uniform temperature distributions under fire conditions. Therefore new simplified fire design rules were proposed for LSF floor joist to determine the section moment capacity at elevated temperature based on AS/NZS 4600 (SA, 2005), NAS (AISI, 2007) and Eurocode 3 Part 1.3 (ECS, 2006). The accuracy of the proposed fire design rules was verified with finite element analysis results. A spread sheet based design tool was also developed based on these design rules to predict the failure load ratio versus time, moment capacity versus time and temperature for various LSF floor configurations. Idealised time-temperature profiles of LSF floor joists were developed based on fire test measurements. They were used in the detailed parametric study to fully understand the structural and fire behaviour of LSF floor panels. Simple design rules were also proposed to predict both critical average joist temperatures and failure times (fire rating) of LSF floor systems with various floor configurations and structural parameters under any given load ratio. Findings from this research have led to a comprehensive understanding of the structural and fire behaviour of LSF floor systems including those protected by the new composite panel, and simple design methods. These design rules were proposed within the guidelines of the Australian/New Zealand, American and European cold- formed steel structures standard codes of practice. These may also lead to further improvements to fire resistance through suitable modifications to the current composite panel system.

Novel methods and the maximum likelihood estimation technique for estimating traffic critical gap

Most unsignalised intersection capacity calculation procedures are based on gap acceptance models. Accuracy of critical gap estimation affects accuracy of capacity and delay estimation. Several methods have been published to estimate drivers’ sample mean critical gap, the Maximum Likelihood Estimation (MLE) technique regarded as the most accurate. This study assesses three novel methods; Average Central Gap (ACG) method, Strength Weighted Central Gap method (SWCG), and Mode Central Gap method (MCG), against MLE for their fidelity in rendering true sample mean critical gaps. A Monte Carlo event based simulation model was used to draw the maximum rejected gap and accepted gap for each of a sample of 300 drivers across 32 simulation runs. Simulation mean critical gap is varied between 3s and 8s, while offered gap rate is varied between 0.05veh/s and 0.55veh/s. This study affirms that MLE provides a close to perfect fit to simulation mean critical gaps across a broad range of conditions. The MCG method also provides an almost perfect fit and has superior computational simplicity and efficiency to the MLE. The SWCG method performs robustly under high flows; however, poorly under low to moderate flows. Further research is recommended using field traffic data, under a variety of minor stream and major stream flow conditions for a variety of minor stream movement types, to compare critical gap estimates using MLE against MCG. Should the MCG method prove as robust as MLE, serious consideration should be given to its adoption to estimate critical gap parameters in guidelines.

An authorization framework using building information models

A building information model (BIM) is an electronic repository of structured, three-dimensional data that captures both the physical and dynamic functional characteristics of a facility. In addition to its more traditional function as a tool to aid design and construction, a BIM can be used throughout the life cycle of a facility, functioning as a living database that places resources contained within the building in their spatial and temporal context. Through its comprehension of spatial relationships, a BIM can meaningfully represent and integrate previously isolated control and management systems and processes, and thereby provide a more intuitive interface to users. By placing processes in a spatial context, decision-making can be improved, with positive flow-on effects for security and efficiency. In this article, we systematically analyse the authorization requirements involved in the use of BIMs. We introduce the concept of using a BIM as a graphical tool to support spatial access control configuration and management (including physical access control). We also consider authorization requirements for regulating access to the structured data that exists within a BIM as well as to external systems and data repositories that can be accessed via the BIM interface. With a view to addressing these requirements we present a survey of relevant spatiotemporal access control models, focusing on features applicable to BIMs and highlighting capability gaps. Finally, we present a conceptual authorization framework that utilizes BIMs.

Understanding hydrochemical processes using 3D models and hydrochemical facies in Wairau Plain groundwater, Marlborough

Three dimensional models and groundwater quality are combined to better understand and conceptualise groundwater systems in complex geological settings in the Wairau Plain, Marlborough. Hydrochemical facies, which are characteristic of distinct evolutionary pathways and a common hydrologic history of groundwaters, are identified within geological formations to assess natural water-rock interactions, redox potential and human agricultural impact on groundwater quality in the Wairau Plain.

Development of zonal cartilage constructs : effects of chondrocyte subpopulation, compressive stimulation, and culture models

Articular cartilage is a highly resilient tissue located at the ends of long bones. It has a zonal structure, which has functional significance in load-bearing. Cartilage does not spontaneously heal itself when damaged, and untreated cartilage lesions or age-related wear often lead to osteoarthritis (OA). OA is a degenerative condition that is highly prevalent, age-associated, and significantly affects patient mobility and quality of life. There is no cure for OA, and patients usually resort to replacing the biological joint with an artificial prosthesis. An alternative approach is to dynamically regenerate damaged or diseased cartilage through cartilage tissue engineering, where cells, materials, and stimuli are combined to form new cartilage. However, despite extensive research, major limitations remain that have prevented the wide-spread application of tissue-engineered cartilage. Critically, there is a dearth of information on whether autologous chondrocytes obtained from OA patients can be used to successfully generate cartilage tissues with structural hierarchy typically found in normal articular cartilage. I aim to address these limitations in this thesis by showing that chondrocyte subpopulations isolated from macroscopically normal areas of the cartilage can be used to engineer stratified cartilage tissues and that compressive loading plays an important role in zone-dependent biosynthesis of these chondrocytes. I first demonstrate that chondrocyte subpopulations from the superficial (S) and middle/deep (MD) zones of OA cartilage are responsive to compressive stimulation in vitro, and that the effect of compression on construct quality is zone-dependent. I also show that compressive stimulation can influence pericelluar matrix production, matrix metalloproteinase secretion, and cytokine expression in zonal chondrocytes in an alginate hydrogel model. Subsequently, I focus on recreating the zonal structure by forming layered constructs using the alginate-released chondrocyte (ARC) method either with or without polymeric scaffolds. Resulting zonal ARC constructs had hyaline morphology, and expressed cartilage matrix molecules such as proteoglycans and collagen type II in both scaffold-free and scaffold-based approaches. Overall, my findings demonstrate that chondrocyte subpopulations obtained from OA joints respond sensitively to compressive stimulation, and are able to form cartilaginous constructs with stratified organization similar to native cartilage using the scaffold-free and scaffold-based ARC technique. The ultimate goal in tissue engineering is to help provide improved treatment options for patients suffering from debilitating conditions such as OA. Further investigations in developing functional cartilage replacement tissues using autologous chondrocytes will bring us a step closer to improving the quality of life for millions of OA patients worldwide.

Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease

In this paper, the goal of identifying disease subgroups based on differences in observed symptom profile is considered. Commonly referred to as phenotype identification, solutions to this task often involve the application of unsupervised clustering techniques. In this paper, we investigate the application of a Dirichlet Process mixture (DPM) model for this task. This model is defined by the placement of the Dirichlet Process (DP) on the unknown components of a mixture model, allowing for the expression of uncertainty about the partitioning of observed data into homogeneous subgroups. To exemplify this approach, an application to phenotype identification in Parkinson’s disease (PD) is considered, with symptom profiles collected using the Unified Parkinson’s Disease Rating Scale (UPDRS). Clustering, Dirichlet Process mixture, Parkinson’s disease, UPDRS.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

A potential HIV epidemic among male street laborers in urban Vietnam

Background: Mass migration to Asian cities is a defining phenomenon of the present age, as hundreds of millions of people move from rural areas or between cities in search of economic prosperity. Although many do prosper, large numbers of people experience significant social disadvantage. This is especially the case among poorly educated, migrant unskilled unregistered male laborers who do much of the manual work throughout the cities. These men are at significant risk for many health problems, including HIV infection. However, to date there has been little research in developing countries to explain the determinants of this risk, and thereby to suggest feasible preventive strategies. Objectives and Methodology: Using combined qualitative and quantitative methods, the aim of this study was to explore the social contexts that affect health vulnerabilities and to develop conceptual models to predict risk behaviors for HIV [illicit drug use, unsafe sex, and non-testing for HIV] among male street laborers in Hanoi, Vietnam. Qualitative Research: Sixteen qualitative interviews revealed a complex variety of life experiences, beliefs and knowledge deficits that render these mostly poor and minimally educated men vulnerable to health problems including HIV infection. This study formed a conceptual model of numerous stressors related to migrants’ life experiences in urban space, including physical, financial and social factors. A wide range of coping strategies were adopted to deal with stressors – including problem-focused coping (PFC) and emotion-focused coping (EFC), pro-social and anti-social, active and passive. These men reported difficulty in coping with stressors because they had weak social networks and lacked support from formal systems. A second conceptual model emerged that highlighted equivalent influences of individual psychological factors, social integration, social barriers, and accessibility regarding drug use and sexual risk behavior. Psychological dimensions such as tedium, distress, fatalism and revenge, were important. There were strong effects of collective decision-making and fear of social isolation on shaping risk behaviors. These exploratory qualitative interviews helped to develop a culturally appropriate instrument for the quantitative survey and informed theoretical models of the factors that affect risk behaviors for HIV infection. Quantitative Research: The Information-Motivation-Behavioral Skills (IMB) model was adopted as the theoretical framework for a large-scale survey. It was modified to suit the contexts of these Vietnamese men. By doing a social mapping technique, 450 male street laborers were interviewed in Hanoi, Vietnam. The survey revealed that the risk of acquiring and transmitting HIV was high among these men. One in every 12 men reported homosexual or bisexual behavior. These men on average had 3 partners within the preceding year, and condom use was inconsistent. One third had had sex with commercial sex workers (CSW) and only 30% of them reported condom use; 17% used illicit drugs sometimes, with 66.7% of them frequently sharing injecting equipment with peers. Despite the risks, only 19.8% of men had been tested for HIV during the previous 12 months. These men have limited HIV knowledge and only moderate motivation and perceived behavioral skills for protective behavior. Although rural-to-urban migration was not associated with sexual risk behavior, three elements of the IMB model and depression associated with the process of mobility were significant determinants of sexual behavior. A modified model that incorporated IMB elements and psychosocial stress was found to be a better fit than the original IMB model alone in predicting protected sex behavior among the men. Men who were less psychologically and socially stressed, better informed and motivated for HIV prevention were more likely to demonstrate behavioral skills, and in turn were more likely to engage in safer sexual behavior. With regard to drug use, although the conventional model accounted for slightly less variance than the modified IMB model, data were of better fit for the conventional model. Multivariate analyses revealed that men who originated from urban areas, those who were homo- or bi-sexually identified and had better knowledge and skills for HIV prevention were more likely to access HIV testing, while men who had more sexual partners and those who did not use a condom for sex with CSW were least likely to take a test. The modified IMB model provided a better fit than the conventional model, as it explained a greater variance in HIV testing. Conclusions and Implications: This research helps to highlight a potential hidden HIV epidemic among street male, unskilled, unregistered laborers. This group has multiple vulnerabilities to HIV infection through both their partners and peers. However, most do not know their HIV status and have limited knowledge about preventing infection. This is the first application of a modified IMB model of risk behaviors for HIV such as drug use, condom use, and uptake of HIV testing to research with male street laborers in urban settings. The study demonstrated that while the extended IMB model had better fit than the conventional version in explaining the behaviors of safe sex and HIV testing, it was not so for drug use. The results provide interesting directions for future research and suggest ways to effectively design intervention strategies. The findings should shed light on culturally appropriate HIV preventive education and support programs for these men. As Vietnam has much in common with other developing countries in Southeast Asia, this research provides evidence for policy and practice that may be useful for public health systems in similar countries.

Quantum models of cognition and decision

Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, "contextuality", is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, "entanglement", allows cognitive phenomena to be modelled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light...