The process of remembering with The Forgotten Australians : digital storytelling and marginalized groups

Digital storytelling projects have proliferated in Australia since the early 2000s, and have been theorized as a means to disseminate the stories and voices of “ordinary” people. In this paper I examine through the case study of a 2009 digital storytelling project between the Australasian Centre for Interactive Design and a group identifying as Forgotten Australian whether digital storytelling in its predominant workshop-based format is able to meet the needs of profoundly marginalized and traumatized individuals and groups. For digital storytelling to be of use to marginalized groups as a means of communication or reflection a significant re-examination of the current approaches to its format, and its function needs to undertaken. This paper posits new ways of utilizing digital storytelling when dealing with trauma narratives.

Editorial : Learning, teaching and disseminating knowledge in business process management

Process-oriented thinking has become the major paradigm for managing companies and other organizations. The push for better processes has been even more intense due to rapidly evolving client needs, borderless global markets and innovations swiftly penetrating the market. Thus, education is decisive for successfully introducing and implementing Business Process Management (BPM) initiatives. However, BPM education has been an area of challenge. This special issue aims to provide current research on various aspects of BPM education. It is an initial effort for consolidating better practices, experiences and pedagogical outcomes founded with empirical evidence to contribute towards the three pillars of education: learning, teaching, and disseminating knowledge in BPM.

Chemical vapor deposited boron carbide : growth by a vapor-liquid-solid process

Detailed analytical electron microscope (AEM) studies of yellow whiskers produced by chemical vapor deposition (CVD)1 show that two basic types of whiskers are produced at low temperatures (between 1200°C and 1400°C) and low boron to carbon gas ratios. Both whisker types show planar microstructures such as twin planes and stacking faults oriented parallel to, or at a rhombohedral angle to, the growth direction. For both whisker types, the presence of droplet-like terminations containing both Si and Ni indicate that the growth process during CVD is via a vapor-liquid-solid (VLS) mechanism.

Collaborative 3D BPMN process modeller

A video detailing our new virtual world BPMN process modelling tool developed by Erik Poppe. Enables better situational awareness via use of remotely connected avatars and a shared 3D process diagram.

3D process visualisation configurations

Video detailing three process model visualisation configurations integrated into an agent driven virtual world simulation.

The Citizen’s Round Table process : canvassing public opinion on energy technologies to mitigate climate change

This study draws on communication accommodation theory, social identity theory and cognitive dissonance theory to drive a ‘Citizen’s Round Table’ process that engages community audiences on energy technologies and strategies that potentially mitigate climate change. The study examines the effectiveness of the process in determining the strategies that engage people in discussion. The process is designed to canvas participants’ perspectives and potential reactions to the array of renewable and non-renewable energy sources, in particular, underground storage of CO2. Ninety-five people (12 groups) participated in the process. Questionnaires were administered three times to identify changes in attitudes over time, and analysis of video, audio-transcripts and observer notes enabled an evaluation of level of engagement and communication among participants. The key findings of this study indicate that the public can be meaningfully engaged in discussion on the politically sensitive issue of CO2 capture and storage (CCS) and other low emission technologies. The round table process was critical to participants’ engagement and led to attitude change towards some methods of energy production. This study identifies a process that can be used successfully to explore community attitudes on politically-sensitive topics and encourages an examination of attitudes and potential attitude change.

A Comprehensive Benchmarking Framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM

Process mining encompasses the research area which is concerned with knowledge discovery from information system event logs. Within the process mining research area, two prominent tasks can be discerned. First of all, process discovery deals with the automatic construction of a process model out of an event log. Secondly, conformance checking focuses on the assessment of the quality of a discovered or designed process model in respect to the actual behavior as captured in event logs. Hereto, multiple techniques and metrics have been developed and described in the literature. However, the process mining domain still lacks a comprehensive framework for assessing the goodness of a process model from a quantitative perspective. In this study, we describe the architecture of an extensible framework within ProM, allowing for the consistent, comparative and repeatable calculation of conformance metrics. For the development and assessment of both process discovery as well as conformance techniques, such a framework is considered greatly valuable.

Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focused recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the "best" choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy.

Numerical simulation of stochastic ordinary differential equations in biomathematical modelling

In this work we discuss the effects of white and coloured noise perturbations on the parameters of a mathematical model of bacteriophage infection introduced by Beretta and Kuang in [Math. Biosc. 149 (1998) 57]. We numerically simulate the strong solutions of the resulting systems of stochastic ordinary differential equations (SDEs), with respect to the global error, by means of numerical methods of both Euler-Taylor expansion and stochastic Runge-Kutta type.

Numerical methods for strong solutions of stochastic differential equations : an overview

This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications, and give the necessary analytical tools for understanding some of the important concepts associated with stochastic processes. We present the stochastic Taylor series expansion as the fundamental mechanism for constructing effective numerical methods, give general results that relate local and global order of convergence and mention the Magnus expansion as a mechanism for designing methods that preserve the underlying structure of the problem. We also present various classes of explicit and implicit methods for strong solutions, based on the underlying structure of the problem. Finally, we discuss implementation issues relating to maintaining the Brownian path, efficient simulation of stochastic integrals and variable-step-size implementations based on various types of control.

High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations

The pioneering work of Runge and Kutta a hundred years ago has ultimately led to suites of sophisticated numerical methods suitable for solving complex systems of deterministic ordinary differential equations. However, in many modelling situations, the appropriate representation is a stochastic differential equation and here numerical methods are much less sophisticated. In this paper a very general class of stochastic Runge-Kutta methods is presented and much more efficient classes of explicit methods than previous extant methods are constructed. In particular, a method of strong order 2 with a deterministic component based on the classical Runge-Kutta method is constructed and some numerical results are presented to demonstrate the efficacy of this approach.

Order conditions of stochastic Runge-Kutta methods by B-series

In this paper, general order conditions and a global convergence proof are given for stochastic Runge Kutta methods applied to stochastic ordinary differential equations ( SODEs) of Stratonovich type. This work generalizes the ideas of B-series as applied to deterministic ordinary differential equations (ODEs) to the stochastic case and allows a completely general formalism for constructing high order stochastic methods, either explicit or implicit. Some numerical results will be given to illustrate this theory.

Numerical solutions of stochastic differential equations – implementation and stability issues

Stochastic differential equations (SDEs) arise fi om physical systems where the parameters describing the system can only be estimated or are subject to noise. There has been much work done recently on developing numerical methods for solving SDEs. This paper will focus on stability issues and variable stepsize implementation techniques for numerically solving SDEs effectively.

A variable stepsize implementation for stochastic differential equations

Stochastic differential equations (SDEs) arise from physical systems where the parameters describing the system can only be estimated or are subject to noise. Much work has been done recently on developing higher order Runge-Kutta methods for solving SDEs numerically. Fixed stepsize implementations of numerical methods have limitations when, for example, the SDE being solved is stiff as this forces the stepsize to be very small. This paper presents a completely general variable stepsize implementation of an embedded Runge Kutta pair for solving SDEs numerically; in this implementation, there is no restriction on the value used for the stepsize, and it is demonstrated that the integration remains on the correct Brownian path.