Defining mega project success in Australian defence : a relation-based framework for value co-creation with project stakeholders

Defining success in mega projects has been a challenging exercise for Australian Defence. The inherent conflict between nation capability building and cost efficiency raises questions about how to appropriately define mega project success. Contrary to the traditional output-focused project methodology, the value creation perspective argues for the importance of creating new knowledge, processes, and systems for suppliers and customers. Stakeholder involvement is important in this new perspective, as the balancing of competing needs of stakeholders in mega projects becomes a major challenge in managing the value co-creation process. In our earlier study reported interview data from three Australian defence mega projects and reported that those senior executives have a more complex understanding of project success than traditional iron triangle measures. In these mega defence projects, customers and other stakeholders actively engage in the value creation process, and over time both content and process value are created to increase defence and national capability. Value created and captured during and post projects are the key to true success. We aim to develop a comprehensive theoretical model the capture the value co-creation process as a way of re-conceptualising success in mega projects. We propose a new framework redefine project value as multi-dimensional, contextual and temporal construct that emerges from the interactions among multiple stake holders over the complete project life cycle. The framework distinguishes between exploitation and exploration types of projects, and takes into consideration the requisite governance structures.

Trialling low-cost level crossing warning devices in Australia

This paper discusses the methodology and design of the Cooperative Research Centre for Rail Innovation’s national low-cost level crossing trial programme currently being conducted in Australia. Three suppliers of innovative low-cost level crossing warning devices were chosen through a tendering and evaluation process. The paper outlines the acceptance criteria that were used to select the suppliers and describes the different types of train detection technologies and innovative cost- reduction solutions that are being tested as part of the trial. The trial is being hosted by three major railways in three different regions in Australia, where systems from the three suppliers have been installed parallel to a baseline conventional track-circuit based level crossing at each site. The paper discusses our experience to date, the trialling process and the challenges that the project has confronted in order to develop a nationally consistent trialling programme.

The increasing cost of healthy food

OBJECTIVE: To assess changes in the cost and availability of a standard basket of healthy food items (the Healthy Food Access Basket [HFAB]) in Queensland. METHODS: Analysis of five cross-sectional surveys (1998, 2000, 2001, 2004 and 2006) describes changes over time. Eighty-nine stores in five remoteness categories were surveyed during May 2006. For the first time a sampling framework based on randomisation of towns throughout the state was applied and the survey was conducted by Queensland Treasury. RESULTS: Compared with the costs in major cities, in 2006 the mean cost of the HFAB was $107.81 (24.2%) higher in very remote stores in Queensland, but $145.57 (32.6%) higher in stores more than 2,000 kilometres from Brisbane. Over six years the cost of the HFAB has increased by around 50% ($148.87) across Queensland and, where data was available, by more than the cost of less healthy alternatives. The Consumer Price Index for food in Brisbane increased by 32.5% over the same period. CONCLUSIONS AND IMPLICATIONS: Australians, no matter where they live, need access to affordable, healthy food. Issues of food security in the face of rising food costs are of concern particularly in the current global economic downturn. There is an urgent need to nationally monitor, but also sustainably address the factors affecting the price of healthy foods, particularly for vulnerable groups who suffer a disproportionate burden of poor health.

The human cost of change : tales from the campus about personal change fatigue, resistance, and resilience

For the last decade, one question has haunted me: what helps people to cope with large-scale organisational change in their workplace? This study explores the construct of personal change resilience, and its potential for identifying solutions to the problems of change fatigue and change resistance. The thesis has emerged from the fields of change management, leadership, training, mentoring, evaluation, management and trust within the context of higher education in Australia at the beginning of the twenty-first century. In this thesis I present a theoretical model of the factors to consider in increasing peoples’ personal change resilience as they navigate large-scale organisational change at work, thereby closing a gap in the literature on the construct of change resilience. The model presented is based on both the literature in the realms of business and education, and on the findings of the research. In this thesis, an autoethnographic case study of two Australian university projects is presented as one narrative, resulting in a methodological step forward in the use of multiple research participants’ stories in the development of a single narrative. The findings describe the experiences of workers in higher education and emphasise the importance of considerate management in the achievement of positive experiences of organisational change. This research makes a significant contribution to new knowledge in three ways. First, it closes a gap in the literature in the realm of change management around personal change resilience as a solution to the problem of change fatigue by presenting models of both change failure and personal change resilience. Second, it is methodologically innovative in the use of personae to tell the stories of multiple participants in one coherent tale presented as a work of ethnographic fiction seen through an autoethnographic lens. By doing so, it develops a methodology for giving a voice to those to whom change is done in the workplace. Third, it provides a perspective on organisational change management from the view of the actual workers affected by change, thereby adding to the literature that currently exists, which is based on the views of those with responsibility for leading or managing change rather than those it affects. This thesis is intended as a practical starting point for conversations by actual change managers in higher education, and it is written in such a way as to help them see how theory can be applied in real life, and how empowering and enabling the actual working staff members, and engaging with them in a considerate way before, during and even after the change process, can help to make them resilient enough to cope with the change, rather than leaving them burned out or disengaged and no longer a well-functioning member of the institution. This thesis shows how considerately managed large-scale organisational change can result in positive outcomes for both the organisation and the individuals who work in it.

A reciprocal collaborative method using relevance feedback and feature importance

In a people-to-people matching systems, filtering is widely applied to find the most suitable matches. The results returned are either too many or only a few when the search is generic or specific respectively. The use of a sophisticated recommendation approach becomes necessary. Traditionally, the object of recommendation is the item which is inanimate. In online dating systems, reciprocal recommendation is required to suggest a partner only when the user and the recommended candidate both are satisfied. In this paper, an innovative reciprocal collaborative method is developed based on the idea of similarity and common neighbors, utilizing the information of relevance feedback and feature importance. Extensive experiments are carried out using data gathered from a real online dating service. Compared to benchmarking methods, our results show the proposed method can achieve noticeable better performance.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

Low cost localisation for agricultural robotics

This paper presents a pose estimation approach that is resilient to typical sensor failure and suitable for low cost agricultural robots. Guiding large agricultural machinery with highly accurate GPS/INS systems has become standard practice, however these systems are inappropriate for smaller, lower-cost robots. Our positioning system estimates pose by fusing data from a low-cost global positioning sensor, low-cost inertial sensors and a new technique for vision-based row tracking. The results first demonstrate that our positioning system will accurately guide a robot to perform a coverage task across a 6 hectare field. The results then demonstrate that our vision-based row tracking algorithm improves the performance of the positioning system despite long periods of precision correction signal dropout and intermittent dropouts of the entire GPS sensor.

CPD : Using evidence based clinical practice guidelines in the management of anaemia in patients with chronic renal failure

Anaemia is a chronic problem in patients with renal insufficiency, especially chronic renal failure (CRF). In patients with CRF, anaemia is primarily due to a deficiency in erythropoietin (EPO), a glycoprotein growth factor that stimulates RBC production. The long-term effects and burden of anaemia for patients with CRF can be physical, emotional and financial. With efficient, systematic management of anaemia, clinicians have the potential to realise not only better clinical outcomes for CRF patients but also significant cost savings for them and the health system. During the last decade, significant advances have been made in clinicians’ understanding of how best to manage anaemia in this vulnerable population. One of the most important efforts to improve clinical practice has been the development of best practice guidelines.

Ontology-based product's reputation model

Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Second-order inelastic analysis of composite framed structures based on the refined plastic hinge method

Composite steel-concrete structures experience non-linear effects which arise from both instability-related geometric non-linearity and from material non-linearity in all of their component members. Because of this, conventional design procedures cannot capture the true behaviour of a composite frame throughout its full loading range, and so a procedure to account for those non-linearities is much needed. This paper therefore presents a numerical procedure capable of addressing geometric and material non-linearities at the strength limit state based on the refined plastic hinge method. Different material non-linearity for different composite structural components such as T-beams, concrete-filled tubular (CFT) and steel-encased reinforced concrete (SRC) sections can be treated using a routine numerical procedure for their section properties in this plastic hinge approach. Simple and conservative initial and full yield surfaces for general composite sections are proposed in this paper. The refined plastic hinge approach models springs at the ends of the element which are activated when the surface defining the interaction of bending and axial force at first yield is reached; a transition from the first yield interaction surface to the fully plastic interaction surface is postulated based on a proposed refined spring stiffness, which formulates the load-displacement relation for material non-linearity under the interaction of bending and axial actions. This produces a benign method for a beam-column composite element under general loading cases. Another main feature of this paper is that, for members containing a point of contraflexure, its location is determined with a simple application of the method herein and a node is then located at this position to reproduce the real flexural behaviour and associated material non-linearity of the member. Recourse is made to an updated Lagrangian formulation to consider geometric non-linear behaviour and to develop a non-linear solution strategy. The formulation with the refined plastic hinge approach is efficacious and robust, and so a full frame analysis incorporating geometric and material non-linearity is tractable. By way of contrast, the plastic zone approach possesses the drawback of strain-based procedures which rely on determining plastic zones within a cross-section and which require lengthwise integration. Following development of the theory, its application is illustrated with a number of varied examples.

Effects of wireless sensor network uncertainties on output-only modal-based damage identification

The use of Wireless Sensor Networks (WSNs) for Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data synchronization error and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research examining effects of uncertainties of generic WSN platform and verifying the capability of SHM-oriented WSNs, particularly on demanding SHM applications like modal analysis and damage identification of real civil structures. This article first reviews the major technical uncertainties of both generic and SHM-oriented WSN platforms and efforts of SHM research community to cope with them. Then, effects of the most inherent WSN uncertainty on the first level of a common Output-only Modal-based Damage Identification (OMDI) approach are intensively investigated. Experimental accelerations collected by a wired sensory system on a benchmark civil structure are initially used as clean data before being contaminated with different levels of data pollutants to simulate practical uncertainties in both WSN platforms. Statistical analyses are comprehensively employed in order to uncover the distribution pattern of the uncertainty influence on the OMDI approach. The result of this research shows that uncertainties of generic WSNs can cause serious impact for level 1 OMDI methods utilizing mode shapes. It also proves that SHM-WSN can substantially lessen the impact and obtain truly structural information without having used costly computation solutions.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

Structured feature extraction using association rules

As of today, opinion mining has been widely used to iden- tify the strength and weakness of products (e.g., cameras) or services (e.g., services in medical clinics or hospitals) based upon people's feed- back such as user reviews. Feature extraction is a crucial step for opinion mining which has been used to collect useful information from user reviews. Most existing approaches only find individual features of a product without the structural relationships between the features which usually exists. In this paper, we propose an approach to extract features and feature relationship, represented as tree structure called a feature hi- erarchy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature hierarchy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that the proposed feature extraction approach can identify more correct features than the baseline model. Even though the datasets used in the experiment are about cameras, our work can be ap- plied to generate features about a service such as the services in hospitals or clinics.

Enhanced n-gram extraction using relevance feature discovery

Guaranteeing the quality of extracted features that describe relevant knowledge to users or topics is a challenge because of the large number of extracted features. Most popular existing term-based feature selection methods suffer from noisy feature extraction, which is irrelevant to the user needs (noisy). One popular method is to extract phrases or n-grams to describe the relevant knowledge. However, extracted n-grams and phrases usually contain a lot of noise. This paper proposes a method for reducing the noise in n-grams. The method first extracts more specific features (terms) to remove noisy features. The method then uses an extended random set to accurately weight n-grams based on their distribution in the documents and their terms distribution in n-grams. The proposed approach not only reduces the number of extracted n-grams but also improves the performance. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms the state-of-art methods underpinned by Okapi BM25, tf*idf and Rocchio.