Strategizing public sector human resource management : the implications of working network

Networks have come to the fore as a means by which government can achieve its strategic objectives, particularly when addressing complex or “wicked” issues. Such joined-up arrangements differ in their operations from other forms of organizing as they require collaborative effort to deliver the collaborative advantage. Strategic Human Resource Management is concerned with the matching of human resource practices to the strategic direction of organizations. It is argued that the strategic direction of government has been towards network involvement and that, as a result, a reconfiguration of Human Resource Management practices is needed to support this new direction. Drawing on eight network case studies findings are presented in relation to the roles government is expected to play in networks and conclusions are drawn about what types of human resource management practices would best support those roles. Implications for Strategic Human Resource Management are posited.

Application-level simulation for network security

NeSSi (network security simulator) is a novel network simulation tool which incorporates a variety of features relevant to network security distinguishing it from general-purpose network simulators. Its capabilities such as profile-based automated attack generation, traffic analysis and support for detection algorithm plug-ins allow it to be used for security research and evaluation purposes. NeSSi has been successfully used for testing intrusion detection algorithms, conducting network security analysis and developing overlay security frameworks. NeSSi is built upon the agent framework JIAC, resulting in a distributed and extensible architecture. In this paper, we provide an overview of the NeSSi architecture as well as its distinguishing features and briefly demonstrate its application to current security research projects.

A study of postdiagnosis breast cancer concerns for women living in rural and remote Queensland. Part II : support issues

This paper presents the recent findings from a study on the postdiagnosis support needs of women with breast cancer living in rural and remote Queensland. The findings presented in this discussion focus on support needs from the perspective of the women experiencing breast cancer as well as health service providers. The tyranny of distance imposes unique hardships, such as separation from family and friends, during a time of great vulnerability for treatment, the need to travel long distances for support and follow-up services, and extra financial burdens, which can combine to cause strains on the marital relationship and family cohesion. Positive indications are, however, that the rural communities operate on strong, informal networks of support. This network of family, friends and community can, and does, play an active role in the provision of emotional and practical support.

Sessional Academic Success : A distributed framework for academic support and development

With approximately half of Australian university teaching now performed by sessional academics, there has been growing recognition of the contribution they make to student learning. At the same time, sector-wide research and institutional audits continue to raise concerns about academic development, quality assurance, recognition and belonging. In response, universities have increasingly begun to offer academic development programs for sessional academics. However, such programs may be centrally delivered, generic in nature, and contained within the moment of delivery, while the Faculty contexts and cultures that sessional academics work within are diverse, and the need for support unfolds in ad-hoc and often unpredictable ways. In this paper we present the Sessional Academic Success (SAS) program–a new framework that complements and extends the central academic development program for sessional academic staff at Queensland University of Technology. This program recognises that experienced sessional academics have much to contribute to the advancement of learning and teaching, and harnesses their expertise to provide school-based academic development opportunities, peer-to-peer support, and locally contextualized community building. We describe the program’s implementation and explain how Sessional Academic Success Advisors (SASAs) are employed, trained and supported to provide advice and mentorship and, through a co-design methodology, to develop local development opportunities and communities of teaching practice within their schools. Besides anticipated benefits to new sessional academics in terms of timely and contextual support and improved sense of belonging, we explain how SAS provides a pathway for building leadership capacity and academic advancement for experienced sessional academics. We take a collaborative, dialogic and reflective practice approach to this paper, interlacing insights from the Associate Director, Academic: Sessional Development who designed the program, and two Sessional Academic Success Advisors who have piloted it within their schools.

Building Healthy Communities : GP Network, Medicare Local and Primary Health Care in the Wheatbelt Region of Western Australia

• Government reports consistently recognise the importance of Primary Health Care to an efficient health system. Barriers identified in Australia’s Primary Health Care include workforce pressures, increase rate of chronic disease, and equitable access to Primary Health Care services. • General Practitioners (GPs) are the key to the successful delivery of Primary Health Care especially in rural and remote regions such as the Wheatbelt region in Western Australia (WA). • The Wheatbelt region of WA is vast: some 72,500 residents spread across 150,000km2 in 43 Local Government Authorities catchments. Majority of the Wheatbelt residents live in small towns. There is a higher reported rates of chronic disease, more at risk of chronic diseases and less utilisation of Primary Health Care services in this region. • General practice patients in the Wheatbelt are among those most in need of Primary Health Care services. • Wheatbelt GP Network (the “Network”) was established in 1998. It is a key health service delivery stakeholder in the Wheatbelt. • The Network has responded to the health needs of the community by creating a mobile Allied Health Team that works closely with GPs and is adaptive to ensure priority needs are met. • The Medicare Local model introduced by the Australian Government in 2011 aimed to improve the delivery of Primary Health Care services by improved health planning and coordinating service delivery. • Little if any recognition has been given to the outstanding work that many Divisions of General Practice have done in improving the delivery of Primary Health Care services such as the Network. • The Network has continued to support GPs and general practices and created a complementary system that integrated general practice with the work of an Allied Health Team. Its program mix is extensive. • The Network has consistently delivered on-required contract outputs and has a fifteen (15) years history of operating successfully in a large geographical area comprising in the main smaller communities that cannot support the traditional health services model. • The complexity of supporting International Medical Graduates in the region requires special attention. • The introduction of the Medicare Local in the South West of WA and their intention to take over the delivery of health services, thus effectively shutting the Network will have catastrophic consequences and cannot be supported economically. • The Network proposes to create a new model, built on its past work that increases the delivery of Primary Health Care services through its current Allied Health Team. • The proposal uses the Wheatbelt GP Super Clinic currently under construction in Northam, part of the Network and funded by the Australian Government is a key to the proposed new model. • Wheatbelt GP Super Clinic is different from existing models of GP Super Clinics around Australia which focus predominately on co-location of services. Wheatbelt GP Super Clinic utilises a hub and spoke model of service outreach to small rural towns to ensure equitable Primary Health Care coverage and continuum of care in a financially responsible and viable manner. In particular, the Wheatbelt GP Super Clinic recognises the importance of Allied Health Professionals and will involve them in a collaborative model with rural general practice. • The proposed model advocated by the Network aims to substitute the South West WA Medicare Local direct service delivery proposed for the Wheatbelt. The Network’s proposed model is to expand on the current hub and spoke model of Primary Health Care delivery to otherwise small unviable Wheatbelt towns. A flexible and adaptive skill mix of Allied Health Professionals, Nurse Practitioners and GPs ensure equitable access to service. Expanded scope of practices are utilised to reduce duplication of service and concentration of services in major towns. This involves a partnership approach. • If the proposed model not funded, the Network and the Wheatbelt region will stand to lose 16 Allied Health Professionals and defeats the purpose of Australian Government current funding for the construction of the Wheatbelt GP Super Clinic. • The Network has considered how its model can best be funded. It proposes a re-allocation of funds made available to the South West WA Medicare Local. • This submission argues that the proposal for the South West WA Medicare Local to take over the service delivery of Primary Health Care services in the Wheatbelt makes no economic sense when an existing agency (the Network) has the infrastructure in place, is experienced in working in this geographical area that has special needs and is capable to expand its programs to meet demand.

Much ado about nothing? Tracing the progress of innovations borne on enterprise social network sites

Enterprise social networks are organizationally bounded online platforms for users to interact with another and maintain interpersonal relationships. The allure of these technologies is often seen in intra-organizational communication, collaboration and innovation. How these technologies actually support organizational innovation efforts remains unclear. A specific challenge is whether digital content on these platforms converts to actual innovation development efforts. In this study we set out to examine innovation-centric content flows on enterprise social networking platforms, and advance a conceptual model that seeks to explain which innovation conveyed in the digital content will traverse from the digital platform into regular processes. We describe important constructs of our model and offer strategies for the operationalization of the constructs. We conclude with an outlook to our ongoing empirical study that will explore and validate the key propositions of our model, and we sketch some potential implications for industry and academia.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

A rural health education, training and research network

The purpose of the Rural Health Education, Training and Research Network is to support the education and training of rural health practitioners and research in rural health through the optimum use of appropriate information and communication technologies to link and inform all individuals and organisation involved in the teaching, planning and delivery of health care in rural and remote Queensland. The health care of people in rural areas has the potential to be enhanced, through providing the rural and remote health professionals in Queensland with the same access to educational and training opportunities as their metropolitan colleagues. This consultative, coordinated approach should be cost-effective through both increasing awareness and utilisation of existing and developing networks, and through more efficient and rational use of both the basic and sophisticated technologies which support them. Technological hardware, expertise and infrastructure are already in place in Queensland to support a Rural Health Education, Training and Research Network, but are not being used to their potential, more often due to a lack of awareness of their existence and utility than to their perceived costs. Development of the network has commenced through seeding funds provided by Queensland Health. Future expansion will ensure access by health professionals to existing networks within Queensland. This paper explores the issues and implications of a network for rural health professionals in Queensland and potentially throughout Australia, with a specific focus on the implications for rural and isolated health professional.

A reference station-based GNSS computing mode to support unified precise point positioning and real-time kinematic services

Currently, the GNSS computing modes are of two classes: network-based data processing and user receiver-based processing. A GNSS reference receiver station essentially contributes raw measurement data in either the RINEX file format or as real-time data streams in the RTCM format. Very little computation is carried out by the reference station. The existing network-based processing modes, regardless of whether they are executed in real-time or post-processed modes, are centralised or sequential. This paper describes a distributed GNSS computing framework that incorporates three GNSS modes: reference station-based, user receiver-based and network-based data processing. Raw data streams from each GNSS reference receiver station are processed in a distributed manner, i.e., either at the station itself or at a hosting data server/processor, to generate station-based solutions, or reference receiver-specific parameters. These may include precise receiver clock, zenith tropospheric delay, differential code biases, ambiguity parameters, ionospheric delays, as well as line-of-sight information such as azimuth and elevation angles. Covariance information for estimated parameters may also be optionally provided. In such a mode the nearby precise point positioning (PPP) or real-time kinematic (RTK) users can directly use the corrections from all or some of the stations for real-time precise positioning via a data server. At the user receiver, PPP and RTK techniques are unified under the same observation models, and the distinction is how the user receiver software deals with corrections from the reference station solutions and the ambiguity estimation in the observation equations. Numerical tests demonstrate good convergence behaviour for differential code bias and ambiguity estimates derived individually with single reference stations. With station-based solutions from three reference stations within distances of 22–103 km the user receiver positioning results, with various schemes, show an accuracy improvement of the proposed station-augmented PPP and ambiguity-fixed PPP solutions with respect to the standard float PPP solutions without station augmentation and ambiguity resolutions. Overall, the proposed reference station-based GNSS computing mode can support PPP and RTK positioning services as a simpler alternative to the existing network-based RTK or regionally augmented PPP systems.

Building adaptable agent-based models – application to the electricity distribution network

Agent-based modelling (ABM), like other modelling techniques, is used to answer specific questions from real world systems that could otherwise be expensive or impractical. Its recent gain in popularity can be attributed to some degree to its capacity to use information at a fine level of detail of the system, both geographically and temporally, and generate information at a higher level, where emerging patterns can be observed. This technique is data-intensive, as explicit data at a fine level of detail is used and it is computer-intensive as many interactions between agents, which can learn and have a goal, are required. With the growing availability of data and the increase in computer power, these concerns are however fading. Nonetheless, being able to update or extend the model as more information becomes available can become problematic, because of the tight coupling of the agents and their dependence on the data, especially when modelling very large systems. One large system to which ABM is currently applied is the electricity distribution where thousands of agents representing the network and the consumers’ behaviours are interacting with one another. A framework that aims at answering a range of questions regarding the potential evolution of the grid has been developed and is presented here. It uses agent-based modelling to represent the engineering infrastructure of the distribution network and has been built with flexibility and extensibility in mind. What distinguishes the method presented here from the usual ABMs is that this ABM has been developed in a compositional manner. This encompasses not only the software tool, which core is named MODAM (MODular Agent-based Model) but the model itself. Using such approach enables the model to be extended as more information becomes available or modified as the electricity system evolves, leading to an adaptable model. Two well-known modularity principles in the software engineering domain are information hiding and separation of concerns. These principles were used to develop the agent-based model on top of OSGi and Eclipse plugins which have good support for modularity. Information regarding the model entities was separated into a) assets which describe the entities’ physical characteristics, and b) agents which describe their behaviour according to their goal and previous learning experiences. This approach diverges from the traditional approach where both aspects are often conflated. It has many advantages in terms of reusability of one or the other aspect for different purposes as well as composability when building simulations. For example, the way an asset is used on a network can greatly vary while its physical characteristics are the same – this is the case for two identical battery systems which usage will vary depending on the purpose of their installation. While any battery can be described by its physical properties (e.g. capacity, lifetime, and depth of discharge), its behaviour will vary depending on who is using it and what their aim is. The model is populated using data describing both aspects (physical characteristics and behaviour) and can be updated as required depending on what simulation is to be run. For example, data can be used to describe the environment to which the agents respond to – e.g. weather for solar panels, or to describe the assets and their relation to one another – e.g. the network assets. Finally, when running a simulation, MODAM calls on its module manager that coordinates the different plugins, automates the creation of the assets and agents using factories, and schedules their execution which can be done sequentially or in parallel for faster execution. Building agent-based models in this way has proven fast when adding new complex behaviours, as well as new types of assets. Simulations have been run to understand the potential impact of changes on the network in terms of assets (e.g. installation of decentralised generators) or behaviours (e.g. response to different management aims). While this platform has been developed within the context of a project focussing on the electricity domain, the core of the software, MODAM, can be extended to other domains such as transport which is part of future work with the addition of electric vehicles.

Modelling for the electricity distribution network

Global awareness for cleaner and renewable energy is transforming the electricity sector at many levels. New technologies are being increasingly integrated into the electricity grid at high, medium and low voltage levels, new taxes on carbon emissions are being introduced and individuals can now produce electricity, mainly through rooftop photovoltaic (PV) systems. While leading to improvements, these changes also introduce challenges, and a question that often rises is ‘how can we manage this constantly evolving grid?’ The Queensland Government and Ergon Energy, one of the two Queensland distribution companies, have partnered with some Australian and German universities on a project to answer this question in a holistic manner. The project investigates the impact the integration of renewables and other new technologies has on the physical structure of the grid, and how this evolving system can be managed in a sustainable and economical manner. To aid understanding of what the future might bring, a software platform has been developed that integrates two modelling techniques: agent-based modelling (ABM) to capture the characteristics of the different system units accurately and dynamically, and particle swarm optimization (PSO) to find the most economical mix of network extension and integration of distributed generation over long periods of time. Using data from Ergon Energy, two types of networks (3 phase, and Single Wired Earth Return or SWER) have been modelled; three-phase networks are usually used in dense networks such as urban areas, while SWER networks are widely used in rural Queensland. Simulations can be performed on these networks to identify the required upgrades, following a three-step process: a) what is already in place and how it performs under current and future loads, b) what can be done to manage it and plan the future grid and c) how these upgrades/new installations will perform over time. The number of small-scale distributed generators, e.g. PV and battery, is now sufficient (and expected to increase) to impact the operation of the grid, which in turn needs to be considered by the distribution network manager when planning for upgrades and/or installations to stay within regulatory limits. Different scenarios can be simulated, with different levels of distributed generation, in-place as well as expected, so that a large number of options can be assessed (Step a). Once the location, sizing and timing of assets upgrade and/or installation are found using optimisation techniques (Step b), it is possible to assess the adequacy of their daily performance using agent-based modelling (Step c). One distinguishing feature of this software is that it is possible to analyse a whole area at once, while still having a tailored solution for each of the sub-areas. To illustrate this, using the impact of battery and PV can have on the two types of networks mentioned above, three design conditions can be identified (amongst others): · Urban conditions o Feeders that have a low take-up of solar generators, may benefit from adding solar panels o Feeders that need voltage support at specific times, may be assisted by installing batteries · Rural conditions - SWER network o Feeders that need voltage support as well as peak lopping may benefit from both battery and solar panel installations. This small example demonstrates that no single solution can be applied across all three areas, and there is a need to be selective in which one is applied to each branch of the network. This is currently the function of the engineer who can define various scenarios against a configuration, test them and iterate towards an appropriate solution. Future work will focus on increasing the level of automation in identifying areas where particular solutions are applicable.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

Decentralized voltage support in a Low Voltage feeder by droop based voltage controlled PVs

Voltage drop at network peak hours is a significant power quality problem in Low Voltage (LV) distribution feeders. Recently, voltage rise due to high penetration of Photovoltaic cells (PVs) has been creating a new power quality problem during noon periods. In this paper, a voltage control strategy is proposed for the household installed PVs to regulate the voltage along the LV feeder. For this purpose, each PV is controlled to exchange reactive power with the grid. A droop control method is utilized to coordinate the reactive power exchange of each PV. The proposed method is a decentralized local voltage support since it is based on only local measurements and does not require any communication with other PVs. The required converter and filter structure and control algorithms are proposed to ensure the dynamic performance of the system. The study focuses on 3-phase PVs. The network is studied at network peak and off-peak periods, separately. The efficacy of the proposed voltage support concept is verified through numerical and dynamic analyses with MATLAB and PSCAD/EMTDC.

GNSS ambiguity resolution with controllable failure rate for long baseline network RTK

Many large-scale GNSS CORS networks have been deployed around the world to support various commercial and scientific applications. To make use of these networks for real-time kinematic positioning services, one of the major challenges is the ambiguity resolution (AR) over long inter-station baselines in the presence of considerable atmosphere biases. Usually, the widelane ambiguities are fixed first, followed by the procedure of determination of the narrowlane ambiguity integers based on the ionosphere-free model in which the widelane integers are introduced as known quantities. This paper seeks to improve the AR performance over long baseline through efficient procedures for improved float solutions and ambiguity fixing. The contribution is threefold: (1) instead of using the ionosphere-free measurements, the absolute and/or relative ionospheric constraints are introduced in the ionosphere-constrained model to enhance the model strength, thus resulting in the better float solutions; (2) the realistic widelane ambiguity precision is estimated by capturing the multipath effects due to the observation complexity, leading to improvement of reliability of widelane AR; (3) for the narrowlane AR, the partial AR for a subset of ambiguities selected according to the successively increased elevation is applied. For fixing the scalar ambiguity, an error probability controllable rounding method is proposed. The established ionosphere-constrained model can be efficiently solved based on the sequential Kalman filter. It can be either reduced to some special models simply by adjusting the variances of ionospheric constraints, or extended with more parameters and constraints. The presented methodology is tested over seven baselines of around 100 km from USA CORS network. The results show that the new widelane AR scheme can obtain the 99.4 % successful fixing rate with 0.6 % failure rate; while the new rounding method of narrowlane AR can obtain the fix rate of 89 % with failure rate of 0.8 %. In summary, the AR reliability can be efficiently improved with rigorous controllable probability of incorrectly fixed ambiguities.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.