Implementing the Outcomes Star

State and Federal governments are exploring the use and implementation of outcomes measures in the human services sector in Australia, and pilot studies are being conducted at a number of sites using the Outcomes Star, one such system for measuring outcomes.Outcomes Star is both a case-management and an outcomes-measurement tool developed in the UK which acknowledges the significance of personal motivation and agency for a service user in achieving sustainable change in their journey towards independence and choice in critical areas of their lives.Dr Lisa Harris and Dr Sharon Andrews are lecturers in social policy at RMIT University. Lisa worked as a caseworker and in management for many years in the social and community sector before moving into higher education. Sharon has worked in government at both a state and federal level, and also served on the board for a number of community sector organisations. Lisa and Sharon’s joint research interests are exploring community sector driven research, social policy development and implementation, practice innovation, and the implementation and use of outcomes-measurement tools in the human services sector.Lisa and Sharon have been commissioned by the Salvation Army Melbourne Central Division Research and Advocacy portfolio to undertake two action-research projects. The aim of this first project was to provide a robust implementation strategy for the Outcomes StarTM that would be of use to social service networks within the Salvation Army and other community service organisations. The second project will explore, over time, the use of data from the Outcomes StarTM in professional practice, individual and team supervision, program design, organisational accountability and structural advocacy

Surface modification of a proton exchange membrane and hydrogen storage in a metal hydride for fuel cells

Interest in fuel cell technology is rising as a result of the need for more affordable and available fuel sources. Proton exchange membrane fuel cells involve the catalysis of a fuel to release protons and electrons. It requires the use of a polymer electrolyte membrane to transfer protons through the cell, while the electrons pass through an external circuit, producing electricity. The surface modification of the polymer, Nafion®, commonly researched as a proton exchange membrane, may improve efficiency of a fuel cell. Surface modification can change the chemistry of the surface of a polymer while maintaining bulk properties. Plasma modification techniques such as microwave discharge of an argon and oxygen gas mixture as well as vacuum-ultraviolet (VUV) photolysis may cause favorable chemical and physical changes on the surface of Nafion for improved fuel cell function. A possible increase in hydrophilicity as a result of microwave discharge experiments may increase proton conductivity. Grafting of acrylic acid from the surface of modified Nafion may decrease the permeation of methanol in a direct methanol fuel cell, a process which can decrease efficiency. Modification of the surface of Nafion samples were carried out using: 1) An indirect Ar/O2 gas mixture plasma investigating the reaction of oxygen radicals with the surface, 2) A direct Ar/O2 gas mixture plasma investigating the reaction of oxygen radicals and VUV radiation with the surface and, 3) VUV photolysis investigating exclusively the interaction of VUV radiation with the surface and any possible oxidation upon exposure to air. Acrylic acid was grafted from the VUV photolysed Nafion samples. All treated surfaces were analyzed using X-ray photoelectron spectroscopy (XPS). Fourier transform infrared spectroscopy (FTIR) was used to analyze the grafted Nafion samples. Scanning electron microscopy (SEM) and contact angle measurements were used to analyze experiments 2 and 3. Using hydrogen as fuel is a promising option. Effective hydrogen storage methods must be used as sources of available hydrogen. One possibility is to use hydrogen stored in a solid chemical compound such as magnesium hydride. The kinetics of hydrogen release from the hydrolysis of magnesium hydride with 2 wt% acetic acid was examined. The hydrogen produced was supplied to a fuel cell and the amount of hydrogen consumed by the fuel cell was determined. Carbon nanotubes also can play a role in energy sources and as components in fuel cells. VUV photo-oxidized single walled carbon nanotubes (SWNT) paper was grafted with polyacrylic acid and analyzed using XPS

Bomb radiocarbon and tag-recapture dating of sandbar shark (Carcharhinus plumbeus)

The sandbar shark (Carcharhinus plumbeus) was the cornerstone species of western North Atlantic and Gulf of Mexico large coastal shark fisheries until 2008 when they were allocated to a research-only fishery. Despite decades of fishing on this species, important life history parameters, such as age and growth, have not been well known. Some validated age and growth information exists for sandbar shark, but more comprehensive life history information is needed. The complementary application of bomb radiocarbon and tag-recapture dating was used in this study to determine valid age-estimation criteria and longevity estimates for this species. These two methods indicated that current age interpretations based on counts of growth bands in vertebrae are accurate to 10 or 12 years. Beyond these years, we could not determine with certainty when such an underestimation of age begins; however, bomb radiocarbon and tag-recapture data indicated that large adult sharks were considerably older than the estimates derived from counts of growth bands. Three adult sandbar sharks were 20 to 26 years old based on bomb radiocarbon results and were a 5- to 11-year increase over the previous age estimates for these sharks. In support of these findings, the tag-recapture data provided results that were consistent with bomb radiocarbon dating and further supported a longevity that exceeds 30 years for this species

Efficient basic event orderings for binary decision diagrams

Over the last five years significant advances have been made in methodologies to analyse the fault tree diagram. The most successful of these developments has been the Binary Decision Diagram (BDD) approach. The Binary Decision Diagram approach has been shown to improve both the efficiency of determining the minimal cut sets of the fault tree ancl also the accuracy of the calculation procedure used to determine the top event parameters. The BDD technique povides a potential alternative to the traditional approaches based on Kinetic Tree Theory. To utilise the Binary Decision Diagram approach the fault tree structure is first converted to the BDD format. This conversion can be accomplished efficiently but requires the basic events in the fault tree to be placed in an ordering. A poor ordering can result in a Binary Decision Diagram which is not an efficient representation of the fault tree logic structure. The advantages to be gained by utilising the BDD technique rely on the efficiency of the ordering scheme. Alternative ordering schemes have been investigated and no one scheme is appropriate for every tree structure. Research to date has not found any rule based means of determining the best way of ordering basic events for a given fault tree structure. The work presented in this paper takes a machine learning approach based on Genetic Algorithms to select the most appropriate ordering scheme. Features which describe a fault tree structure have been identified and these provide the inputs to the machine learning algorithm. A set of possible ordering schemes has been selected based on previous heuristic work. The objective of the work detailed in the pap:r is to predict the most efficient of the possible ordering alternatives from parameters which describe a fault tree structure

A branching search approach to safety system design optimisation

Safety systems are designed to prevent or mitigate the consequences of potentially hazardous events. In many industries the failure of such systems can result in fatalities. Current design practice is usually to produce a safety system which meets a target level of performance that is deemed acceptable by the regulators. However, when the system failure will result in fatalities it is desirable for the system to achieve an optimal rather than adequate level of performance given the limitations placed on available resources. The unavailability of safety systems can be predicted using fault tree analysis methods. Formulating an optimisation problem for the system design has features which make standard mathematical optimisation techniques inappropriate. The form of the objective function is itself a function of the design variables, the design variables are mainly integers and the constraint forms can be implicit or non-linear. This paper presents a Branching Search algorithm which exploits characteristics common to many safety systems to explore the potential design space and deliver an optimal design. Efficiency in the method is maintained by performing the system unavailability evaluations using the Binary Decision Diagram method of fault tree solution. Limitations are placed on resources such as cost, maintenance down-time and spurious trip frequency. Its application is demonstrated on a High Integrity Protection System

Using statistically designed experiments for safety system optimization

This paper describes the method of statistically designed experiments (SDE's), used as a structured method to investigate the best setting for a number of decision variables in a system design problem. Traditionally, in the design of safety critical systems, a trial and error type approach is undertaken to achieve a final system that meets the design objectives. This approach can be time consuming and often only an adequate design is found rather than the optimal design for the available resources. Optimal use of resources should be imperative when possible lives are at risk. To demonstrate the practicality of this new structured approach for optimising a safety system design, a high integrity safety system has been used. Each design is analysed using the Binary Decision Diagram analysis technique to establish the system unavailability, which is penalised if the system constraints are exceeded. System constraints indicate the limitations on the resources which can be utilised. The SDE approach highlights good and bad settings for possible design variables. This knowledge can then be used by more sophisticated search techniques. The latter part of this paper analyses the results from the best design generated using the SDE, for further optimisation using localised optimisation approaches

Choosing a heuristic for the “fault tree to binary decision diagram” conversion, using neural networks

Fault-tree analysis is commonly used for risk assessment of industrial systems. Several computer packages are available to carry out the analysis. Despite its common usage there are associated limitations of the technique in terms of accuracy and efficiency when dealing with large fault-tree structures. The most recent approach to aid the analysis of the fault-tree diagram is the BDD (binary decision diagram). To use the BDD, the fault-tree structure needs to be converted into the BDD format. Converting the fault tree is relatively straightforward but requires that the basic events of the tree be ordered. This ordering is critical to the resulting size of the BDD, and ultimately affects the qualitative and quantitative performance and benefits of this technique. Several heuristic approaches were developed to produce an optimal ordering permutation for a specific tree. These heuristic approaches do not always yield a minimal BDD structure for all trees. There is no single heuristic that guarantees a minimal BDD for any fault-tree structure. This paper looks at a selection approach using a neural network to choose the best heuristic from a set of alternatives that will yield the smallest BDD and promote an efficient analysis. The set of possible selection choices are 6 alternative heuristics, and the prediction capacity produced was a 70% chance of the neural network choosing the best ordering heuristic from the set of 6 for the test set of given fault trees