Empirical evaluation of public transport travel time variability

Although transit travel time variability is essential for understanding the deterioration of reliability, optimising transit schedule and route choice; it has not attracted enough attention from the literature. This paper proposes public transport-oriented definitions of travel time variability and explores the distributions of public transport travel time using the Transit Signal Priority data. First, definitions of public transport travel time variability are established by extending the common definitions of variability in the literature and by using route and services data of public transport vehicles. Second, the paper explores the distribution of public transport travel time. A new approach for analysing the distributions involving all transit vehicles as well as vehicles from a specific route is proposed. The Lognormal distribution is revealed as the descriptors for public transport travel time from the same route and service. The methods described in this study could be of interest for both traffic managers and transit operators for planning and managing the transit systems.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Cost-aware business process management: A research agenda

Organisations are constantly seeking cost-effective improvements for their business processes. Business process management (BPM) provides organisations with a range of methods, techniques and tools for analysing, managing, and optimising their business operations. However, BPM initiatives within organisations tend to focus on investigating time and resource utilisation inefficiencies, rather than directly on cost inefficiencies. As a result, high-level cost-based managerial decisions are still being made separately from process related decisions. This position paper describes a research agenda that envisages a holistic approach to managing the cost of business operations in a structured manner, by making an explicit link between cost and processes in all phases of the business process management life cycle. We discuss a number of research challenges that need to be addressed in order to realise such an approach as well as findings from some of the initial research outcomes. It is envisioned that the research outcomes will enable organisations to make operational and strategic decisions with confidence based on accurate and real-time cost information about their operations.

Speech interference on residential balconies with road traffic noise

Residential balcony design influences speech interference levels caused by road traffic noise and a simplified design methodology is needed for optimising balcony acoustic treatments. This research comprehensively assesses speech interference levels and benefits of nine different balcony designs situated in urban street canyons through the use of a combined direct, specular reflection and diffuse reflection path theoretical model. This thesis outlines the theory, analysis and results that lead up to the presentation of a practical design guide which can be used to predict the acoustic effects of balcony geometry and acoustic treatments in streets with variable geometry and acoustic characteristics.

Can vasculature changes be characterised morphologically after closed soft tissue trauma?

INTRODUCTION Closed soft tissue trauma (CSTT) can be the result of a blunt impact, or a prolonged crush injury and involves damage to the skin, muscles and the neurovascular system. It causes a variety of symptoms such as haematoma and in severe cases may result in hypoxia and necrosis. There is evidence that early vasculature changes following the injury delays the tissue healing [1]. However, a precise qualitative and quantitative morphological assessment of vasculature changes after trauma and the effect of this on CSTT healing is currently missing. Research aims: Developing an experimental rat model to characterise the structural changes to the vasculature after trauma qualitatively and quantitatively using micro CT. MATERIAL AND METHODS An impact device was developed to apply a controlled reproducible CSTT to the left thigh (Biceps Femoris) of anaesthetised rats [3]. After euthanizing the animals at 6 hours after trauma, CSTT was qualitatively evaluated by macroscopic observations of the skin and muscles. For vasculature visualisation, the blood vessels of sacrificed rats were flushed with heparinised saline and then perfused with a radio-opaque contrast agent (Microfil) using an infusion pump (Figure 4). The overall changes to the vasculature as a result of impact trauma were characterised qualitatively based on the 3D reconstructed images of the vasculature (Figure 5). For a smaller region of interest, the morphological parameters such as vessel thickness (diameter), spacing, and average number per volume were quantified using the scanner’s software. RESULTS AND DISCUSSION Visual observation of CSTT has revealed a haematoma in some animals (Figure 3). Micro CT images indicate good perfusion of the vasculature with contrast agent, allowing the major vessels to be identified (Figure 5). Qualitatively and quantitatively, no differences between injured and non-injured legs were observed at 6 h after trauma. Further time points of 12h, 24h, 3 days and 14 days after trauma will be characterised for identifying temporal changes of the vasculature during healing. Histomorphometical studies are required for validation of the results derived from the micro CT imaging. CONCLUSION AND FUTURE DIRECTION Findings of this research may contribute towards the establishment of a fundamental basis for the quantitative assessment and monitoring of CSTT based on microvasculature changes after trauma, which will ultimately allow for optimising the clinical treatment and improve patient outcomes.

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.

Innovation for Equity in Rural Education : Proceedings of the International Symposium for Innovation in Rural Education

PREFACE The concept of an international symposium on rural education arose from a meeting between members of the SiMERR National Centre, Australia and the NURI Teacher Education Innovation Centre (NURI-TEIC) at Kongju National University, Korea in 2007. Despite the very different national contexts, the teams were struck by the similarities of the challenges facing rural schools in the two countries, and curious about the degree to which these challenges were shared by other countries. At a subsequent meeting in Australia in December 2007, the two centre Directors - Professor John Pegg (SiMERR) and Professor Youn-Kee Im (NURI-TEIC) - agreed on a framework for the first International Symposium for Innovation in Rural Education (ISFIRE). This volume consists of the keynotes and refereed papers presented at ISFIRE 2009. The papers provide insights into rural education in Australia, Bhutan, Canada, Korea, Norway, South Africa and the United States along the following themes: 1. Promoting rural policy initiatives; 2. Nurturing the rural teacher experience; 3. Enhancing rural student experience and growth; 4. Optimising the curriculum; 5. Improving resources in rural schools; and 6. Addressing special issues in rural education. The authors and titles of a further 23 presentations based on refereed abstracts only are listed at the end of this volume. The abstracts for these presentations can be found in the symposium Program. The academics and practitioners who came together for this symposium are passionate about rural education and have dedicated their time and capacities to working for the benefit of rural teachers, students and communities. The papers in this volume offer direction not only for Australia and South Korea, who jointly hosted this symposium, but for all countries in which rural education is contested ground. The keynotes in particular contribute rich perspectives on rural education trends, policies and practices. We hope that this volume generates a greater appreciation of the advantages of rural education and the many innovative approaches being implemented to meet its challenges.

Defining expected cost for the air conditioning to avoid a price spike of electricity market under DSR model

This work presents a demand side response model (DSR) which assists small electricity consumers, through an aggregator, exposed to the market price to proactively mitigate price and peak impact on the electrical system. The proposed model allows consumers to manage air-conditioning when as a function of possible price spikes. The main contribution of this research is to demonstrate how consumers can minimise the total expected cost by optimising air-conditioning to account for occurrences of a price spike in the electricity market. This model investigates how pre-cooling method can be used to minimise energy costs when there is a substantial risk of an electricity price spike. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics during hot days on weekdays in the period 2011 to 2012.

Improved molecular tools for sugar cane biotechnology

Sugar cane is a major source of food and fuel worldwide. Biotechnology has the potential to improve economically-important traits in sugar cane as well as diversify sugar cane beyond traditional applications such as sucrose production. High levels of transgene expression are key to the success of improving crops through biotechnology. Here we describe new molecular tools that both expand and improve gene expression capabilities in sugar cane. We have identified promoters that can be used to drive high levels of gene expression in the leaf and stem of transgenic sugar cane. One of these promoters, derived from the Cestrum yellow leaf curling virus, drives levels of constitutive transgene expression that are significantly higher than those achieved by the historical benchmark maize polyubiquitin-1 (Zm-Ubi1) promoter. A second promoter, the maize phosphonenolpyruvate carboxylate promoter, was found to be a strong, leaf-preferred promoter that enables levels of expression comparable to Zm-Ubi1 in this organ. Transgene expression was increased approximately 50-fold by gene modification, which included optimising the codon usage of the coding sequence to better suit sugar cane. We also describe a novel dual transcriptional enhancer that increased gene expression from different promoters, boosting expression from Zm-Ubi1 over eightfold. These molecular tools will be extremely valuable for the improvement of sugar cane through biotechnology.

Security metrics for Java bytecode programs

Although there are many approaches for developing secure programs, they are not necessarily helpful for evaluating the security of a pre-existing program. Software metrics promise an easy way of comparing the relative security of two programs or assessing the security impact of modifications to an existing one. Most studies in this area focus on high level source code but this approach fails to take compiler-specific code generation into account. In this work we describe a set of object-oriented Java bytecode security metrics which are capable of assessing the security of a compiled program from the point of view of potential information flow. These metrics can be used to compare the security of programs or assess the effect of program modifications on security using a tool which we have developed to automatically measure the security of a given Java bytecode program in terms of the accessibility of distinguished ‘classified’ attributes.

Polycaprolactone scaffold and reduced rhBMP-7 dose for the regeneration of critical-sized defects in sheep tibiae

The transplantation of autologous bone graft as a treatment for large bone defects has the limitation of harvesting co-morbidity and limited availability. This drives the orthopaedic research community to develop bone graft substitutes. Routinely, supra-physiological doses of bone morphogenetic proteins (BMPs) are applied perpetuating concerns over undesired side effects and cost of BMPs. We therefore aimed to design a composite scaffold that allows maintenance of protein bioactivity and enhances growth factor retention at the implantation site. Critical-sized defects in sheep tibiae were treated with the autograft and with two dosages of rhBMP-7, 3.5 mg and 1.75 mg, embedded in a slowly degradable medical grade poly(ε-caprolactone) (PCL) scaffold with β-tricalcium phosphate microparticles (mPCL-TCP). Specimens were characterised by biomechanical testing, microcomputed tomography and histology. Bridging was observed within 3 months for the autograft and both rhBMP-7 treatments. No significant difference was observed between the low and high rhBMP-7 dosages or between any of the rhBMP-7 groups and autograft implantation. Scaffolds alone did not induce comparable levels of bone formation compared to the autograft and rhBMP-7 groups. In summary, the mPCL-TCP scaffold with the lower rhBMP-7 dose led to equivalent results to autograft transplantation or the high BMP dosage. Our data suggest a promising clinical future for BMP application in scaffold-based bone tissue engineering, lowering and optimising the amount of required BMP.

Australia’s first Allied Health Prescribing Program – exploring participants' understanding and confidence in clinical therapy choices for patient management

BACKGROUND Globally there are emerging trends for non-medical health professionals to expand their scope of practice into prescribing. The NPS Prescribing Competencies Framework and the Health Professionals Prescribing Pathway Program are recent initiatives to assist with implementation of prescribing for allied health professionals (AHPs). For AHPs to become prescribers, training programmes must be designed to extend their knowledge of medicines information and medicine management principles with the aim of optimising medicines related outcomes for patients. AIM To explore the understanding and confidence in clinical therapeutic choices for patient management of those AHPs enrolled in the Allied Health Prescribing Training Program Module One: Introduction to clinical therapeutics for prescribers, delivered by Queensland University of Technology, Brisbane. METHOD A pre-post survey was developed to explore key themes around understanding and confidence in selecting therapeutic choices for patients with varying complexities of conditions. Data were collected from participants in week one and 13 of the module via an online survey using a five-point Likert scale (1 = Strongly Agree (SA) to 5 = Strongly Disagree (SD)). RESULTS In the pre-Module survey the AHPs had a limited degree (D/SD) of understanding and confidence regarding the safe and effective use of medicines and appropriate therapeutic choices for managing patients, particularly with complex patients. This improved significantly in the post Module survey (A/SA).

An open pit multistage mine production scheduling model for drilling, blasting and excavating operations

This paper proposes a new multi-resource multi-stage scheduling problem for optimising the open-pit drilling, blasting and excavating operations under equipment capacity constraints. The flow process is analysed based on the real-life data from an Australian iron ore mine site. The objective of the model is to maximise the throughput and minimise the total idle times of equipment at each stage. The following comprehensive mining attributes and constraints have been considered: types of equipment; operating capacities of equipment; ready times of equipment; speeds of equipment; block-sequence-dependent movement times of equipment; equipment-assignment-dependent operation times of blocks; distances between each pair of blocks; due windows of blocks; material properties of blocks; swell factors of blocks; and slope requirements of blocks. It is formulated by mixed integer programming and solved by ILOG-CPLEX optimiser. The proposed model is validated with extensive computational experiments to improve mine production efficiency at the operational level. The model also provides an intelligent decision support tool to account for the availability and usage of equipment units for drilling, blasting and excavating stages.

A communication model and partitioning algorithm for streaming applications for an embedded MPSoC

Energy efficient embedded computing enables new application scenarios in mobile devices like software-defined radio and video processing. The hierarchical multiprocessor considered in this work may contain dozens or hundreds of resource efficient VLIW CPUs. Programming this number of CPU cores is a complex task requiring compiler support. The stream programming paradigm provides beneficial properties that help to support automatic partitioning. This work describes a compiler for streaming applications targeting the self-build hierarchical CoreVA-MPSoC multiprocessor platform. The compiler is supported by a programming model that is tailored to fit the streaming programming paradigm. We present a novel simulated-annealing (SA) based partitioning algorithm, called Smart SA. The overall speedup of Smart SA is 12.84 for an MPSoC with 16 CPU cores compared to a single CPU implementation. Comparison with a state of the art partitioning algorithm shows an average performance improvement of 34.07%.

Classification of inventory data suit for managing Australian railway bridges

With a focus to optimising the life cycle performance of Australian Railway bridges, new bridge classification and environmental classification systems are proposed. The new bridge classification system is mainly to facilitate the implementation of novel Bridge Management System (BMS) which optimise the life cycle cost both at project level and network level while environment classification is mainly to improve accuracy of Remaining Service Potential (RSP) module of the proposed BMS. In fact, limited capacity of the existing BMS to trigger the maintenance intervention point is an indirect result of inadequacies of the existing bridge and environmental classification systems. The proposed bridge classification system permits to identify the intervention points based on percentage deterioration of individual elements and maintenance cost, while allowing performance based rating technique to implement for maintenance optimisation and prioritisation. Simultaneously, the proposed environment classification system will enhance the accuracy of prediction of deterioration of steel components.