What is an expert? A systems perspective on expertise

1. Expert knowledge continues to gain recognition as a valuable source of information in a wide range of research applications. Despite recent advances in defining expert knowledge, comparatively little attention has been given to how to view expertise as a system of interacting contributory factors, and thereby, to quantify an individual’s expertise. 2. We present a systems approach to describing expertise that accounts for many contributing factors and their interrelationships, and allows quantification of an individual’s expertise. A Bayesian network (BN) was chosen for this purpose. For the purpose of illustration, we focused on taxonomic expertise. The model structure was developed in consultation with professional taxonomists. The relative importance of the factors within the network were determined by a second set of senior taxonomists. This second set of experts (i.e. supra-experts) also provided validation of the model structure. Model performance was then assessed by applying the model to hypothetical career states in the discipline of taxonomy. Hypothetical career states were used to incorporate the greatest possible differences in career states and provide an opportunity to test the model against known inputs. 3. The resulting BN model consisted of 18 primary nodes feeding through one to three higher-order nodes before converging on the target node (Taxonomic Expert). There was strong consistency among node weights provided by the supra-experts for some nodes, but not others. The higher order nodes, “Quality of work” and “Total productivity”, had the greatest weights. Sensitivity analysis indicated that although some factors had stronger influence in the outer nodes of the network, there was relatively equal influence of the factors leading directly into the target node. Despite differences in the node weights provided by our supra-experts, there was remarkably good agreement among assessments of our hypothetical experts that accurately reflected differences we had built into them. 4. This systems approach provides a novel way of assessing the overall level of expertise of individuals, accounting for multiple contributory factors, and their interactions. Our approach is adaptable to other situations where it is desirable to understand components of expertise.

Perspectives on Reconceptualizing Early Mathematics Learning

This introductory section provides an overview of the different perspectives on reconceptualizing early mathematics learning. The chapters provide a broad scope in their topics and approaches to advancing young children’s mathematical learning. They incorporate studies that highlight the importance of pattern and structure across the curriculum, studies that target particular content such as statistics, early algebra, and beginning number, and studies that consider how technology and other tools can facilitate early mathematical development. Reconceptualizing the professional learning of teachers in promoting young children’s mathematics, including a consideration of the role of play, is also addressed. Although these themes are diffused throughout the chapters, we restrict our introduction to the core focus of each of the chapters.

The ECOS Project. An interactive simulation to promote an ECO-friendly universe.

Custom designed for display on the Cube Installation situated in the new Science and Engineering Centre (SEC) at QUT, the ECOS project is a playful interface that uses real-time weather data to simulate how a five-star energy building operates in climates all over the world. In collaboration with the SEC building managers, the ECOS Project incorporates energy consumption and generation data of the building into an interactive simulation, which is both engaging to users and highly informative, and which invites play and reflection on the roles of green buildings. ECOS focuses on the principle that humans can have both a positive and negative impact on ecosystems with both local and global consequence. The ECOS project draws on the practice of Eco-Visualisation, a term used to encapsulate the important merging of environmental data visualization with the philosophy of sustainability. Holmes (2007) uses the term Eco-Visualisation (EV) to refer to data visualisations that ‘display the real time consumption statistics of key environmental resources for the goal of promoting ecological literacy’. EVs are commonly artifacts of interaction design, information design, interface design and industrial design, but are informed by various intellectual disciplines that have shared interests in sustainability. As a result of surveying a number of projects, Pierce, Odom and Blevis (2008) outline strategies for designing and evaluating effective EVs, including ‘connecting behavior to material impacts of consumption, encouraging playful engagement and exploration with energy, raising public awareness and facilitating discussion, and stimulating critical reflection.’ Consequently, Froehlich (2010) and his colleagues also use the term ‘Eco-feedback technology’ to describe the same field. ‘Green IT’ is another variation which Tomlinson (2010) describes as a ‘field at the juncture of two trends… the growing concern over environmental issues’ and ‘the use of digital tools and techniques for manipulating information.’ The ECOS Project team is guided by these principles, but more importantly, propose an example for how these principles may be achieved. The ECOS Project presents a simplified interface to the very complex domain of thermodynamic and climate modeling. From a mathematical perspective, the simulation can be divided into two models, which interact and compete for balance – the comfort of ECOS’ virtual denizens and the ecological and environmental health of the virtual world. The comfort model is based on the study of psychometrics, and specifically those relating to human comfort. This provides baseline micro-climatic values for what constitutes a comfortable working environment within the QUT SEC buildings. The difference between the ambient outside temperature (as determined by polling the Google Weather API for live weather data) and the internal thermostat of the building (as set by the user) allows us to estimate the energy required to either heat or cool the building. Once the energy requirements can be ascertained, this is then balanced with the ability of the building to produce enough power from green energy sources (solar, wind and gas) to cover its energy requirements. Calculating the relative amount of energy produced by wind and solar can be done by, in the case of solar for example, considering the size of panel and the amount of solar radiation it is receiving at any given time, which in turn can be estimated based on the temperature and conditions returned by the live weather API. Some of these variables can be altered by the user, allowing them to attempt to optimize the health of the building. The variables that can be changed are the budget allocated to green energy sources such as the Solar Panels, Wind Generator and the Air conditioning to control the internal building temperature. These variables influence the energy input and output variables, modeled on the real energy usage statistics drawn from the SEC data provided by the building managers.

Ignition delay of bio-fuels in a common-rail compression ignition engine

The utility of a novel technique for determining the ignition delay in a compression ignition engine has been shown. This method utilises statistical modelling in the Bayesian paradigm to accurately resolve the start of combustion from a band-pass in-cylinder pressure signal. Applied to neat diesel and six biofuels, including four fractionations of palm oil of varying carbon chain length and degree of unsaturation, the relationships between ignition delay, cetane number and oxygen content have been explored. It is noted that the expected negative relationship between ignition delay and cetane number held, as did the positive relationship between ignition delay and oxygen content. The degree of unsaturation was also identified as a potential factor influencing the ignition delay.

Creating a Sustainability Scorecard as a predictive tool for measuring the complex social, economic and environmental impacts of industries, a case study : assessing the viability and sustainability of the dairy industry

Sustainability is a key driver for decisions in the management and future development of industries. The World Commission on Environment and Development (WCED, 1987) outlined imperatives which need to be met for environmental, economic and social sustainability. Development of strategies for measuring and improving sustainability in and across these domains, however, has been hindered by intense debate between advocates for one approach fearing that efforts by those who advocate for another could have unintended adverse impacts. Studies attempting to compare the sustainability performance of countries and industries have also found ratings of performance quite variable depending on the sustainability indices used. Quantifying and comparing the sustainability of industries across the triple bottom line of economy, environment and social impact continues to be problematic. Using the Australian dairy industry as a case study, a Sustainability Scorecard, developed as a Bayesian network model, is proposed as an adaptable tool to enable informed assessment, dialogue and negotiation of strategies at a global level as well as being suitable for developing local solutions.

The impact of spatial scales and spatial smoothing on the outcome of Bayesian spatial model

Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such as occurrence of a disease), we study the geographical variation of residual disease risk using regular grid cells. The individual disease risk is modelled using a logistic model with the inclusion of spatially unstructured and/or spatially structured random effects. Three spatial smoothness priors for the spatially structured component are employed in modelling, namely an intrinsic Gaussian Markov random field, a second-order random walk on a lattice, and a Gaussian field with Matern correlation function. We investigate how changes in grid cell size affect model outcomes under different spatial structures and different smoothness priors for the spatial component. A realistic example (the Humberside data) is analyzed and a simulation study is described. Bayesian computation is carried out using an integrated nested Laplace approximation. The results suggest that the performance and predictive capacity of the spatial models improve as the grid cell size decreases for certain spatial structures. It also appears that different spatial smoothness priors should be applied for different patterns of point data.

Mathematical modelling of counter flow v-grove solar air collector

Double-pass counter flow v-grove collector is considered one of the most efficient solar air-collectors. In this design of the collector, the inlet air initially flows at the top part of the collector and changes direction once it reaches the end of the collector and flows below the collector to the outlet. A mathematical model is developed for this type of collector and simulation is carried out using MATLAB programme. The simulation results were verified with three distinguished research results and it was found that the simulation has the ability to predict the performance of the air collector accurately as proven by the comparison of experimental data with simulation. The difference between the predicted and experimental results is, at maximum, approximately 7% which is within the acceptable limit considering some uncertainties in the input parameter values to allow comparison. A parametric study was performed and it was found that solar radiation, inlet air temperature, flow rate and length have a significant effect on the efficiency of the air collector. Additionally, the results are compared with single flow V-groove collector.

Rich and purposeful mathematical knowledge of mothers and children in a Torres Strait Islander community

This paper focuses on a pilot study that explored the situated mathematical knowledge of mothers and children in one Torres Strait Islander community in Australia. The community encouraged parental involvement in their children’s learning and schooling. The study explored parents’ understandings of mathematics and how their children came to learn about it on the island. A funds of knowledge approach was used in the study. This approach is based on the premise that people are competent and have knowledge that has been historically and culturally accumulated into a body of knowledge and skills essential for their functioning and well-being (Moll, 1992). The participants, three adults and one child are featured in this paper. Three separate events are described with epiphanic or illuminative moments analysed to ascertain the features that enabled an understanding of the nature of the mathematical events. The study found that Indigenous ways of knowing of mathematics were deeply embedded in rich cultural practices that were tied to the community. This finding has implications for teachers of children in the early years. Where school mathematics is often presented as disembodied and isolated facts with children seeing little relevance, learning a different perspective of mathematics that is tied to the resources and practices of children’s lives and facilitated through social relationships, may go a long way to improving the engagement of children and their parents in learning and schooling.

Modelling survival data to account for model uncertainty: a single model or model averaging?

This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single “best” model, where “best” is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as “best”, suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival. Keywords: Bayesian modelling; Bayesian model averaging; Cure model; Markov Chain Monte Carlo; Mixture model; Survival analysis; Weibull distribution

Sampling designs on stream networks using the pseudo-Bayesian approach

Monitoring stream networks through time provides important ecological information. The sampling design problem is to choose locations where measurements are taken so as to maximise information gathered about physicochemical and biological variables on the stream network. This paper uses a pseudo-Bayesian approach, averaging a utility function over a prior distribution, in finding a design which maximizes the average utility. We use models for correlations of observations on the stream network that are based on stream network distances and described by moving average error models. Utility functions used reflect the needs of the experimenter, such as prediction of location values or estimation of parameters. We propose an algorithmic approach to design with the mean utility of a design estimated using Monte Carlo techniques and an exchange algorithm to search for optimal sampling designs. In particular we focus on the problem of finding an optimal design from a set of fixed designs and finding an optimal subset of a given set of sampling locations. As there are many different variables to measure, such as chemical, physical and biological measurements at each location, designs are derived from models based on different types of response variables: continuous, counts and proportions. We apply the methodology to a synthetic example and the Lake Eacham stream network on the Atherton Tablelands in Queensland, Australia. We show that the optimal designs depend very much on the choice of utility function, varying from space filling to clustered designs and mixtures of these, but given the utility function, designs are relatively robust to the type of response variable.

Bayesian networks in environmental and resource management

This overview article for the special series “Bayesian Networks in Environmental and Resource Management” reviews 7 case study articles with the aim to compare Bayesian network (BN) applications to different environmental and resource management problems from around the world. The article discusses advances in the last decade in the use of BNs as applied to environmental and resource management. We highlight progress in computational methods, best-practices for model design and model communication. We review several research challenges to the use of BNs in environmental and resource management that we think may find a solution in the near future with further research attention.

Mathematical investigation of the interactions between the inflammatory response and mechanical aspects of dermal wound repair

Dermal wound repair involves complex interactions between cells, cytokines and mechanics to close injuries to the skin. In particular, we investigate the contribution of fibroblasts, myofibroblasts, TGFβ, collagen and local tissue mechanics to wound repair in the human dermis. We develop a morphoelastic model where a realistic representation of tissue mechanics is key, and a fibrocontractive model that involves a reasonable approximation to the true kinetics of the important bioactive species. We use each of these descriptions to elucidate the mechanisms that generate pathologies such as hypertrophic scars, contractures and keloids. We find that for hypertrophic scar and contracture development, factors regulating the myofibroblast phenotype are critical, with heightened myofibroblast activation, reduced myofibroblast apoptosis or prolonged inflammation all predicted as mediators for scar hypertrophy and contractures. Prevention of these pathologies is predicted when myofibroblast apoptosis is induced, myofibroblast activation is blocked or TGFβ is neutralised. To investigate keloid invasion, we develop a caricature representation of the fibrocontractive model and find that TGFβ spread is the driving factor behind keloid growth. Blocking activation of TGFβ is found to cause keloid regression. Thus, we recommend myofibroblasts and TGFβ as targets for clinicians when developing intervention strategies for prevention and cure of fibrotic scars.

A multiscale road map of cancer spheroids : incorporating experimental and mathematical modelling to understand cancer progression

Computational models represent a highly suitable framework, not only for testing biological hypotheses and generating new ones but also for optimising experimental strategies. As one surveys the literature devoted to cancer modelling, it is obvious that immense progress has been made in applying simulation techniques to the study of cancer biology, although the full impact has yet to be realised. For example, there are excellent models to describe cancer incidence rates or factors for early disease detection, but these predictions are unable to explain the functional and molecular changes that are associated with tumour progression. In addition, it is crucial that interactions between mechanical effects, and intracellular and intercellular signalling are incorporated in order to understand cancer growth, its interaction with the extracellular microenvironment and invasion of secondary sites. There is a compelling need to tailor new, physiologically relevant in silico models that are specialised for particular types of cancer, such as ovarian cancer owing to its unique route of metastasis, which are capable of investigating anti-cancer therapies, and generating both qualitative and quantitative predictions. This Commentary will focus on how computational simulation approaches can advance our understanding of ovarian cancer progression and treatment, in particular, with the help of multicellular cancer spheroids, and thus, can inform biological hypothesis and experimental design.

Mathematical relationships for metal build-up on urban road surfaces based on traffic and land use characteristics

The study investigated the influence of traffic and land use parameters on metal build-up on urban road surfaces. Mathematical relationships were developed to predict metals originating from fuel combustion and vehicle wear. The analysis undertaken found that nickel and chromium originate from exhaust emissions, lead, copper and zinc from vehicle wear, cadmium from both exhaust and wear and manganese from geogenic sources. Land use does not demonstrate a clear pattern in relation to the metal build-up process, though its inherent characteristics such as traffic activities exert influence. The equation derived for fuel related metal load has high cross-validated coefficient of determination (Q2) and low Standard Error of Cross-Validation (SECV) values indicates that the model is reliable, while the equation derived for wear-related metal load has low Q2 and high SECV values suggesting its use only in preliminary investigations. Relative Prediction Error values for both equations are considered to be well within the error limits for a complex system such as an urban road surface. These equations will be beneficial for developing reliable stormwater treatment strategies in urban areas which specifically focus on mitigation of metal pollution.