Stakeholder engagement by governance networks

As the problems involving infrastructure delivery have become more complex and contentious, there has been an acknowledgement that these problems cannot be resolved by any one body working alone. This understanding has driven multi-sectoral collaboration and has led to an expansion of the set of actors, including stakeholders, who are now involved in delivery of infrastructure projects and services. However, more needs to be understood about how to include stakeholders in these processes and ways of developing the requisite combination of stakeholders to achieve effective outcomes. This thesis draws on stakeholder theory and governance network theory to obtain insights into how three multi-level networks within the Roads Alliance in Queensland engage with stakeholders in the delivery of complex and sensitive infrastructure services and projects. New knowledge about stakeholders will be obtained by testing a model of Stakeholder Salience and Engagement which combines and extends the stakeholder identification and salience theory, ladder of stakeholder management and engagement and the model of stakeholder engagement and moral treatment of stakeholders. By applying this model, the broad research question: “Who or what decides how stakeholders are engaged by governance networks delivering public outcomes?” will be addressed. The case studies will test a theoretical model of stakeholder salience and engagement which links strategic decisions about stakeholder salience with the quality and quantity of engagement strategies for engaging different types of stakeholders. A multiple embedded case study design has been selected as the overall approach to explore, describe, explain and evaluate how stakeholder engagement occurs in three governance networks delivering road infrastructure in Queensland. The research design also incorporates a four stage approach to data collection: observations, stakeholder analysis, telephone survey questionnaire and semi-structured interviews. The outcomes of this research will contribute to and extend stakeholder theory by showing how stakeholder salience impacts on decisions about the types of engagement processes implemented. Governance network theory will be extended by showing how governance networks interact with stakeholders through the concepts of stakeholder salience and engagement. From a practical perspective this research will provide governance networks with an indication of how to optimise engagement with different types of stakeholders. 2

Photo-cross-linked poly(D,L-lactide)-based networks. Structural characterization by HR-MAS NMR spectroscopy and hydrolytic degradation behavior

To date, biodegradable networks and particularly their kinetic chain lengths have been characterized by analysis of their degradation products in solution. We characterize the network itself by NMR analysis in the solvent-swollen state under magic angle spinning conditions. The networks were prepared by photoinitiated cross-linking of poly(dl-lactide)−dimethacrylate macromers (5 kg/mol) in the presence of an unreactive diluent. Using diffusion filtering and 2D correlation spectroscopy techniques, all network components are identified. By quantification of network-bound photoinitiator fragments, an average kinetic chain length of 9 ± 2 methacrylate units is determined. The PDLLA macromer solution was also used with a dye to prepare computer-designed structures by stereolithography. For these networks structures, the average kinetic chain length is 24 ± 4 methacrylate units. In all cases the calculated molecular weights of the polymethacrylate chains after degradation are maximally 8.8 kg/mol, which is far below the threshold for renal clearance. Upon incubation in phosphate buffered saline at 37 °C, the networks show a similar mass loss profile in time as linear high-molecular-weight PDLLA (HMW PDLLA). The mechanical properties are preserved longer for the PDLLA networks than for HMW PDLLA. The initial tensile strength of 47 ± 2 MPa does not decrease significantly for the first 15 weeks, while HMW PDLLA lost 85 ± 5% of its strength within 5 weeks. The physical properties, kinetic chain length, and degradation profile of these photo-cross-linked PDLLA networks make them most suited materials for orthopedic applications and use in (bone) tissue engineering.

Project-based organisations : overcoming lack of trust and social networks within small and medium project teams

In sustainable development projects, as well as other types of projects, knowledge transfer is important for the organisations managing the project. Nevertheless, knowledge transfer among employees does not happen automatically and it has been found that the lack of social networks and the lack of trust among employees are the major barriers to effective knowledge transfer. Social network analysis has been recognised as a very important tool for improving knowledge transfer in the project environment. Transfer of knowledge is more effective where it depends heavily on social networks and informal dialogue. Based on the theory of social capital, social capital consists of two parts: conduits network and resource exchange network. This research studies the relationships among performance, the resource exchange network (such as the knowledge network) and the relationship network (such as strong ties network, energy network, and trust network) at the individual and project levels. The aim of this chapter is to present an approach to overcoming the lack of social networks and lack of trust to improve knowledge transfer within project-based organisations. This is to be done by identifying the optimum structure of relationship networks and knowledge networks within small and medium projects. The optimal structure of the relationship networks and knowledge networks is measured using two dimensions: intra-project and inter-project. This chapter also outlines an extensive literature review in the areas of social capital, knowledge management and project management, and presents the conceptual model of the research approach.

The role of informal networks in providing effective work opportunities for people with an intellectual disability

Being in paid employment is socially valued, and is linked to health, financial security and time use. Issues arising from a lack of occupational choice and control, and from diminished role partnerships are particularly problematic in the lives of people with an intellectual disability. Informal support networks are shown to influence work opportunities for people without disabilities, but their impact on the work experiences of people with disability has not been thoroughly explored. The experience of 'work' and preparation for work was explored with a group of four people with an intellectual disability (the participants) and the key members of their informal support networks (network members) in New South Wales, Australia. Network members and participants were interviewed and participant observations of work and other activities were undertaken. Data analysis included open, conceptual and thematic coding. Data analysis software assisted in managing the large datasets across multiple team members. The insight and actions of network members created and sustained the employment and support opportunities that effectively matched the needs and interests of the participants. Recommendations for future research are outlined.

Defending the patch : a labour geography analysis of unions’ construction of and responses to the problem of declining union membership amongst youth

If the trade union movement is to remain an influential force in the industrial, economic and socio/political arenas of industrialised nations it is vital that its recruitment of young members improve dramatically. Australian union membership levels have declined markedly over the last three decades and youth union membership levels have decreased more than any age group. Currently around 10% of young workers aged between 16-24 years are members of unions in Australia compared to 26% of workers aged 45-58 (Oliver, 2008). This decline has occurred throughout the union movement, in all states and in almost all industries and occupations. This research, which consists of interviews with union organisers and union officials, draws on perspectives from the labour geography literature to explore how union personnel located in various places, spaces and scales construct the issue of declining youth union membership. It explores the scale of connections within the labour movement and the extent to which these connections are leveraged to address the problem of youth union membership decline. To offer the reader a sense of context and perspective, the thesis firstly outlines the historical development of the union movement. It also reviews the literature on youth membership decline. Labour geography offers a rich and apposite analytical tool for investigation of this area. The notion of ‘scale’ as a dynamic, interactive, constructed and reconstructed entity (Ellem, 2006) is an appropriate lens for viewing youth-union membership issues. In this non-linear view, scale is a relational element which interplays with space, place and the environment (Howett, in Marston, 2000) rather than being ‘sequential’ and hierarchical. Importantly, the thesis investigates the notion of unions as ‘spaces of dependence’ (Cox, 1998a, p.2), organisations whose space is centred upon realising essential interests. It also considers the quality of unions’ interactions with others – their ‘spaces of engagement‘(Cox, 1998a, p.2), and the impact that this has upon their ability to recruit youth. The findings reveal that most respondents across the spectrum of the union movement attribute the decline in youth membership levels to factors external to the movement itself, such as changes to industrial relations legislation and the impact of globalisation on employment markets. However, participants also attribute responsibility for declining membership levels to the union movement itself, citing factors such as a lack of resourcing and a need to change unions’ perceived identity and methods of operation. The research further determined that networks of connections across the union movement are tenuous and, to date, are not being fully utilised to assist unions to overcome the youth recruitment dilemma. The study concludes that potential connections between unions are hampered by poor resourcing, workload issues and some deeply entrenched attitudes related to unions ‘defending (and maintaining) their patch’.

Education for the creative economy : innovation, transdisciplinarity, and networks

The question posed in this chapter is: To what extent does current education theory and practice prepare graduates for the creative economy? We first define what we mean by the term creative economy, explain why we think it is a significant point of focus, derive its key features, describe the human capital requirements of these features, and then discuss whether current education theory and practice are producing these human capital requirements. The term creative economy can be critiqued as a shibboleth, but as a high level metaphor, it nevertheless has value in directing us away from certain sorts of economic activity and toward other kinds. Much economic activity is in no way creative. If I have a monopoly on some valued resource, I do not need to be creative. Other forms of economic activity are intensely creative. If I have no valued resources, I must create something that is valued. At its simplest and yet most profound, the idea of a creative economy suggests a capacity to compete based on engaging in a gainful activity that is different from everyone else’s, rather than pursuing the same endeavor more competitively than everyone else. The ability to differentiate on novelty is key to the concept of creative economy and key to our analysis of education for this economy. Therefore, we follow Potts and Cunningham (2008, p. 18) and Potts, Cunningham, Hartley, and Ormerod (2008) in their discussion of the economic significance of the creative industries and see the creative economy not as a sector but as a set of economic processes that act on the economy as a whole to invigorate innovation based growth. We see the creative economy as suffused with all industry rather than as a sector in its own right. These economic processes are essentially concerned with the production of new ideas that ultimately become new products, service, industry sectors, or, in some cases, process or product innovations in older sectors. Therefore, our starting point is that modern economies depend on innovation, and we see the core of innovation as new knowledge of some kind. We commence with some observations about innovation.

Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.

Reliability analysis and economic equipment replacement appraisal for substation and sub-transmission systems with explicit inclusion of non-repairable failures

The modern society has come to expect the electrical energy on demand, while many of the facilities in power systems are aging beyond repair and maintenance. The risk of failure is increasing with the aging equipments and can pose serious consequences for continuity of electricity supply. As the equipments used in high voltage power networks are very expensive, economically it may not be feasible to purchase and store spares in a warehouse for extended periods of time. On the other hand, there is normally a significant time before receiving equipment once it is ordered. This situation has created a considerable interest in the evaluation and application of probability methods for aging plant and provisions of spares in bulk supply networks, and can be of particular importance for substations. Quantitative adequacy assessment of substation and sub-transmission power systems is generally done using a contingency enumeration approach which includes the evaluation of contingencies, classification of the contingencies based on selected failure criteria. The problem is very complex because of the need to include detailed modelling and operation of substation and sub-transmission equipment using network flow evaluation and to consider multiple levels of component failures. In this thesis a new model associated with aging equipment is developed to combine the standard tools of random failures, as well as specific model for aging failures. This technique is applied in this thesis to include and examine the impact of aging equipments on system reliability of bulk supply loads and consumers in distribution network for defined range of planning years. The power system risk indices depend on many factors such as the actual physical network configuration and operation, aging conditions of the equipment, and the relevant constraints. The impact and importance of equipment reliability on power system risk indices in a network with aging facilities contains valuable information for utilities to better understand network performance and the weak links in the system. In this thesis, algorithms are developed to measure the contribution of individual equipment to the power system risk indices, as part of the novel risk analysis tool. A new cost worth approach was developed in this thesis that can make an early decision in planning for replacement activities concerning non-repairable aging components, in order to maintain a system reliability performance which economically is acceptable. The concepts, techniques and procedures developed in this thesis are illustrated numerically using published test systems. It is believed that the methods and approaches presented, substantially improve the accuracy of risk predictions by explicit consideration of the effect of equipment entering a period of increased risk of a non-repairable failure.

Corrupt police networks : uncovering hidden relationship patterns, functions and roles

This article applies social network analysis techniques to a case study of police corruption in order to produce findings which will assist in corruption prevention and investigation. Police corruption is commonly studied but rarely are sophisticated tools of analyse engaged to add rigour to the field of study. This article analyses the ‘First Joke’ a systemic and long lasting corruption network in the Queensland Police Force, a state police agency in Australia. It uses the data obtained from a commission of inquiry which exposed the network and develops hypotheses as to the nature of the networks structure based on existing literature into dark networks and criminal networks. These hypotheses are tested by entering the data into UCINET and analysing the outcomes through social network analysis measures of average path distance, centrality and density. The conclusions reached show that the network has characteristics not predicted by the literature.

Challenges of tracking topical discussion networks online

Attempts to map online networks, representing relationships between people and sites, have covered sites including Facebook, Twitter, and blogs. However, the predominant approach of static network visualization, treating months of data as a single case rather than depicting changes over time or between topics, remains a flawed process. As different events and themes provoke varying interactions and conversations, it is proposed that case-by-case analysis would aid studies of online social networks by further examining the dynamics of links and information flows. This study uses hyperlink analysis of a population of French political blogs to compare connections between sites from January to August 2009. Themes discussed in this period were identified for subsequent analysis of topic-oriented networks. By comparing static blogrolls with topical citations within posts, this research addresses challenges and methods in mapping online networks, providing new information on temporal aspects of linking behaviors and information flows within these systems.

Intrusion detection framework for encrypted networks

Network-based Intrusion Detection Systems (NIDSs) monitor network traffic for signs of malicious activities that have the potential to disrupt entire network infrastructures and services. NIDS can only operate when the network traffic is available and can be extracted for analysis. However, with the growing use of encrypted networks such as Virtual Private Networks (VPNs) that encrypt and conceal network traffic, a traditional NIDS can no longer access network traffic for analysis. The goal of this research is to address this problem by proposing a detection framework that allows a commercial off-the-shelf NIDS to function normally in a VPN without any modification. One of the features of the proposed framework is that it does not compromise on the confidentiality afforded by the VPN. Our work uses a combination of Shamir’s secret-sharing scheme and randomised network proxies to securely route network traffic to the NIDS for analysis. The detection framework is effective against two general classes of attacks – attacks targeted at the network hosts or attacks targeted at framework itself. We implement the detection framework as a prototype program and evaluate it. Our evaluation shows that the framework does indeed detect these classes of attacks and does not introduce any additional false positives. Despite the increase in network overhead in doing so, the proposed detection framework is able to consistently detect intrusions through encrypted networks.

Secure data aggregation in wireless sensor networks

A Wireless Sensor Network (WSN) is a set of sensors that are integrated with a physical environment. These sensors are small in size, and capable of sensing physical phenomena and processing them. They communicate in a multihop manner, due to a short radio range, to form an Ad Hoc network capable of reporting network activities to a data collection sink. Recent advances in WSNs have led to several new promising applications, including habitat monitoring, military target tracking, natural disaster relief, and health monitoring. The current version of sensor node, such as MICA2, uses a 16 bit, 8 MHz Texas Instruments MSP430 micro-controller with only 10 KB RAM, 128 KB program space, 512 KB external ash memory to store measurement data, and is powered by two AA batteries. Due to these unique specifications and a lack of tamper-resistant hardware, devising security protocols for WSNs is complex. Previous studies show that data transmission consumes much more energy than computation. Data aggregation can greatly help to reduce this consumption by eliminating redundant data. However, aggregators are under the threat of various types of attacks. Among them, node compromise is usually considered as one of the most challenging for the security of WSNs. In a node compromise attack, an adversary physically tampers with a node in order to extract the cryptographic secrets. This attack can be very harmful depending on the security architecture of the network. For example, when an aggregator node is compromised, it is easy for the adversary to change the aggregation result and inject false data into the WSN. The contributions of this thesis to the area of secure data aggregation are manifold. We firstly define the security for data aggregation in WSNs. In contrast with existing secure data aggregation definitions, the proposed definition covers the unique characteristics that WSNs have. Secondly, we analyze the relationship between security services and adversarial models considered in existing secure data aggregation in order to provide a general framework of required security services. Thirdly, we analyze existing cryptographic-based and reputationbased secure data aggregation schemes. This analysis covers security services provided by these schemes and their robustness against attacks. Fourthly, we propose a robust reputationbased secure data aggregation scheme for WSNs. This scheme minimizes the use of heavy cryptographic mechanisms. The security advantages provided by this scheme are realized by integrating aggregation functionalities with: (i) a reputation system, (ii) an estimation theory, and (iii) a change detection mechanism. We have shown that this addition helps defend against most of the security attacks discussed in this thesis, including the On-Off attack. Finally, we propose a secure key management scheme in order to distribute essential pairwise and group keys among the sensor nodes. The design idea of the proposed scheme is the combination between Lamport's reverse hash chain as well as the usual hash chain to provide both past and future key secrecy. The proposal avoids the delivery of the whole value of a new group key for group key update; instead only the half of the value is transmitted from the network manager to the sensor nodes. This way, the compromise of a pairwise key alone does not lead to the compromise of the group key. The new pairwise key in our scheme is determined by Diffie-Hellman based key agreement.

How can soil monitoring networks be used to improve predictions of organic carbon pool dynamics and CO2 fluxes in agricultural soils?

Abstract As regional and continental carbon balances of terrestrial ecosystems become available, it becomes clear that the soils are the largest source of uncertainty. Repeated inventories of soil organic carbon (SOC) organized in soil monitoring networks (SMN) are being implemented in a number of countries. This paper reviews the concepts and design of SMNs in ten countries, and discusses the contribution of such networks to reducing the uncertainty of soil carbon balances. Some SMNs are designed to estimate country-specific land use or management effects on SOC stocks, while others collect soil carbon and ancillary data to provide a nationally consistent assessment of soil carbon condition across the major land-use/soil type combinations. The former use a single sampling campaign of paired sites, while for the latter both systematic (usually grid based) and stratified repeated sampling campaigns (5–10 years interval) are used with densities of one site per 10–1,040 km². For paired sites, multiple samples at each site are taken in order to allow statistical analysis, while for the single sites, composite samples are taken. In both cases, fixed depth increments together with samples for bulk density and stone content are recommended. Samples should be archived to allow for re-measurement purposes using updated techniques. Information on land management, and where possible, land use history should be systematically recorded for each site. A case study of the agricultural frontier in Brazil is presented in which land use effect factors are calculated in order to quantify the CO2 fluxes from national land use/management conversion matrices. Process-based SOC models can be run for the individual points of the SMN, provided detailed land management records are available. These studies are still rare, as most SMNs have been implemented recently or are in progress. Examples from the USA and Belgium show that uncertainties in SOC change range from 1.6–6.5 Mg C ha−1 for the prediction of SOC stock changes on individual sites to 11.72 Mg C ha−1 or 34% of the median SOC change for soil/land use/climate units. For national SOC monitoring, stratified sampling sites appears to be the most straightforward attribution of SOC values to units with similar soil/land use/climate conditions (i.e. a spatially implicit upscaling approach). Keywords Soil monitoring networks - Soil organic carbon - Modeling - Sampling design

Urban maze : A typological investigation in porous morphology and dynamic pedestrian networks

Mixed use typologies and pedestrian networks are two strategies commonly applied in design of the contemporary city. These approaches, aimed towards the creation of a more sustainalble urban environment, have their roots in the traditional, pre-industrial towns; they characterize urban form, articulating the tension between privaate and public realms through a series of typological variations as well as stimulating commercial activity in the city centre. Arcades, loggias and verandas are just some of the elements which can mediate this tension. Historically they have defined physical and social spaces with particular character; in the contemporary city these features are applied to deform the urban form and create a porous, dynamic morphology. This paper, comparing case studies from Italy, Japan and Australia, investigates how the design of the transition zone can define hybrid pedestrian networks, where a clear distinction between the public and private realms is no longer applicable. Pedestrians use the city in a dynamic way, combining trajectories on the public street with ones on the fringe or inside of the private built environment. In some cases, cities offer different pedestrian network possibilities at different times, as the commercial precints are subject to variations in accessibility across various timeframes. These walkable systems have an impact on the urban form and identity of places, redefining typologies and requiring an in depth analysis through plan, section and elevation diagrams.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.