579 resultados para BIOLOGICAL NETWORKS
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Introduction Informal caring networks contribute significantly to end-of-life (EOL) care in the community. However, to ensure that these networks are sustainable, and unpaid carers are not exploited, primary carers need permission and practical assistance to gather networks together and negotiate the help they need. Our aim in this study was to develop an understanding of how formal and informal carers work together when care is being provided in a dying person's home. We were particularly interested in formal providers’ perceptions and knowledge of informal networks of care and in identifying barriers to the networks working together. Methods Qualitative methods, informed by an interpretive approach, were used. In February-July 2012, 10 focus groups were conducted in urban, regional, and rural Australia comprising 88 participants. Findings Our findings show that formal providers are aware, and supportive, of the vital role informal networks play in the care of the dying at home. A number of barriers to formal and informal networks working together more effectively were identified. In particular, we found that the Australian policy of health-promoting palliative is not substantially translating to practice. Conclusion Combinations of formal and informal caring networks are essential to support people and their primary carers. Formal service providers do little to establish, support, or maintain the informal networks although there is much goodwill and scope for them to do so. Further re-orientation towards a health-promoting palliative care and community capacity building approach is suggested.
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In this paper we describe the benefits of a performance-based approach to modeling biological systems for use in robotics. Specifically, we describe the RatSLAM system, a computational model of the navigation processes thought to drive navigation in a part of the rodent brain called the hippocampus. Unlike typical computational modeling approaches, which focus on biological fidelity, RatSLAM’s development cycle has been driven primarily by performance evaluation on robots navigating in a wide variety of challenging, real world environments. We briefly describe three seminal results, two in robotics and one in biology. In addition, we present current research on brain-inspired learning algorithms with the aim of enabling a robot to autonomously learn how best to use its sensor suite to navigate, without requiring any specific knowledge of the robot, sensor types or environment characteristics. Our aim is to drive discussion on the merits of practical, performance-focused implementations of biological models in robotics.
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The Warburton-Cooper basins, central Australia, include a multitude of reactivated fracture-fault networks related to a complex, and poorly understood, tectonic evolution. We investigated authigenic illites from a granitic intrusion and sedimentary rocks associated with prominent structural features (Gidgealpa-Merrimelia-Innamincka Ridge and the Nappamerri Trough). These were analysed by 40Ar-39Ar, 87Rb-87Sr and 147Sm-143Nd geochronology to explore the thermal and tectonic histories of central Australian basins. The combined age data provide evidence for three major periods of fault reactivation throughout the Phanerozoic. While Carboniferous (323.3 ± 9.4 Ma) and Late Triassic ages (201.7 ± 9.3 Ma) derive from basin-wide hydrothermal circulation, Cretaceous ages (~128 to ~86 Ma) reflect episodic fluid flow events restricted to the synclinal Nappamerri Trough. Such events result from regional extensional tectonism derived from the transferral of far-field stresses to mechanically and thermally weakened regions of the Australian continent. Specifically, Cretaceous ages reflect continent-wide transmission of tensional stress from a > 2500 km long rifting event on the Eastern (and southern) Australian margin associated with break-up of Gondwana and opening of the Tasman Sea. By integrating 40Ar-39Ar, 87Rb-87Sr and 147Sm-143Nd dating, this study highlights the use of authigenic illite in temporally constraining the tectonic evolution of intracontinental basins that would otherwise remain unknown. Furthermore, combining Sr- and Ar-isotopic systems enables more accurate dating of authigenesis whilst significantly reducing geochemical pitfalls commonly associated with these radioisotopic dating methods.
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A Remote Sensing Core Curriculum (RSCC) development project is currently underway. This project is being conducted under the auspices of the National Center for Geographic Information and Analysis (NCGIA). RSCC is an outgrowth of the NCGIA GIS Core Curriculum project. It grew out of discussions begun at NCGIA, Initiative 12 (I-12): 'Integration of Remote Sensing and Geographic Information Systems'. This curriculum development project focuses on providing professors, teachers and instructors in undergraduate and graduate institutions with course materials from experts in specific subject matter for areas use in the class room.
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Installation of domestic rooftop photovoltaic cells (PVs) is increasing due to feed–in tariff and motivation driven by environmental concerns. Even though the increase in the PV installation is gradual, their locations and ratings are often random. Therefore, such single–phase bi–directional power flow caused by the residential customers can have adverse effect on the voltage imbalance of a three–phase distribution network. In this chapter, a voltage imbalance sensitivity analysis and stochastic evaluation are carried out based on the ratings and locations of single–phase grid–connected rooftop PVs in a residential low voltage distribution network. The stochastic evaluation, based on Monte Carlo method, predicts a failure index of non–standard voltage imbalance in the network in presence of PVs. Later, the application of series and parallel custom power devices are investigated to improve voltage imbalance problem in these feeders. In this regard, first, the effectiveness of these two custom power devices is demonstrated vis–à–vis the voltage imbalance reduction in feeders containing rooftop PVs. Their effectiveness is investigated from the installation location and rating points of view. Later, a Monte Carlo based stochastic analysis is utilized to investigate their efficacy for different uncertainties of load and PV rating and location in the network. This is followed by demonstrating the dynamic feasibility and stability issues of applying these devices in the network.
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The application of artificial neural networks (ANN) in finance is relatively new area of research. We employed ANNs that used both fundamental and technical inputs to predict future prices of widely held Australian stocks and used these predicted prices for stock portfolio selection over a 10-year period (2001-2011). We found that the ANNs generally do well in predicting the direction of stock price movements. The stock portfolios selected by the ANNs with median accuracy are able to generate positive alpha over the 10-year period. More importantly, we found that a portfolio based on randomly selected network configuration had zero chance of resulting in a significantly negative alpha but a 27% chance of yielding a significantly positive alpha. This is in stark contrast to the findings of the research on mutual fund performance where active fund managers with negative alphas outnumber those with positive alphas.
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Rapidly increasing electricity demands and capacity shortage of transmission and distribution facilities are the main driving forces for the growth of Distributed Generation (DG) integration in power grids. One of the reasons for choosing a DG is its ability to support voltage in a distribution system. Selection of effective DG characteristics and DG parameters is a significant concern of distribution system planners to obtain maximum potential benefits from the DG unit. This paper addresses the issue of improving the network voltage profile in distribution systems by installing a DG of the most suitable size, at a suitable location. An analytical approach is developed based on algebraic equations for uniformly distributed loads to determine the optimal operation, size and location of the DG in order to achieve required levels of network voltage. The developed method is simple to use for conceptual design and analysis of distribution system expansion with a DG and suitable for a quick estimation of DG parameters (such as optimal operating angle, size and location of a DG system) in a radial network. A practical network is used to verify the proposed technique and test results are presented.
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Bandwidths and offsets are important components in vehicle traffic control strategies. This article proposes new methods for quantifying and selecting them. Bandwidth is the amount of green time available for vehicles to travel through adjacent intersections without the requirement to stop at the second traffic light. The offset is the difference between the starting-time of ``green'' periods at two adjacent intersections, along a given route. The core ideas in this article were developed during the 2013 Maths and Industry Study Group in Brisbane, Australia. Analytical expressions for computing bandwidth, as a function of offset, are developed. An optimisation model, for selecting offsets across an arterial, is proposed. Arterial roads were focussed upon, as bandwidth and offset have a greater impact on these types of road as opposed to a full traffic network. A generic optimisation-simulation approach is also proposed to refine an initial starting solution, according to a specified metric. A metric that reflects the number of stops, and the distance between stops, is proposed to explicitly reduce the dissatisfaction of road users, and to implicitly reduce fuel consumption and emissions. Conceptually the optimisation-simulation approach is superior as it handles real-life complexities and is a global optimisation approach. The models and equations in this article can be used in road planning and traffic control.
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BACKGROUND There is a growing volume of open source ‘education material’ on energy efficiency now available however the Australian government has identified a need to increase the use of such materials in undergraduate engineering education. Furthermore, there is a reported need to rapidly equip engineering graduates with the capabilities in conducting energy efficiency assessments, to improve energy performance across major sectors of the economy. In January 2013, building on several years of preparatory action-research initiatives, the former Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education (DIICCSRTE) offered $600,000 to develop resources for energy efficiency related graduate attributes, targeting Engineers Australia college disciplines, accreditation requirements and opportunities to address such requirements. PURPOSE This paper discusses a $430,000 successful bid by a university consortium led by QUT and including RMIT, UA, UOW, and VU, to design and pilot several innovative, targeted open-source resources for curriculum renewal related to energy efficiency assessments, in Australian engineering programs (2013-2014), including ‘flat-pack’, ‘media-bites’, ‘virtual reality’ and ‘deep dive’ case study initiatives. DESIGN/ METHOD The paper draws on literature review and lessons learned by the consortium partners in resource development over the last several years to discuss methods for selecting key graduate attributes and providing targeted resources, supporting materials, and innovative delivery options to assist universities deliver knowledge and skills to develop such attributes. This includes strategic industry and key stakeholders engagement. The paper also discusses processes for piloting, validating, peer reviewing, and refining these resources using a rigorous and repeatable approach to engaging with academic and industry colleagues. RESULTS The paper provides an example of innovation in resource development through an engagement strategy that takes advantage of existing networks, initiatives, and funding arrangements, while informing program accreditation requirements, to produce a cost-effective plan for rapid integration of energy efficiency within education. By the conference, stakeholder workshops will be complete. Resources will be in the process of being drafted, building on findings from the stakeholder engagement workshops. Reporting on this project “in progress” provides a significant opportunity to share lessons learned and take on board feedback and input. CONCLUSIONS This paper provides a useful reference document for others considering significant resource development in a consortium approach, summarising benefits and challenges. The paper also provides a basis for documenting the second half of the project, which comprises piloting resources and producing a ‘good practice guide’ for energy efficiency related curriculum renewal.
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This paper explores the impact that extreme weather events can have on communities. Using the Brisbane floods of 2011 to examine the recovery operations, the paper highlights the effectiveness of recovery and rebuilding in already strong and resilient communities. Our research has shown that communities which have a strong sense of identity, as well as organized places to meet, develop resilient networks that come into play in times of crisis. The increasing trend of the fly-in/fly-out (FIFO) or drive-in/drive-out (DIDO) workforce to service regional areas has undermined the resilience of existing communities. The first hint of this occurs with community groups not knowing who their neighbours are. The paper is based on research examining the needs of groups in regional communities with the goal to better equip regional communities with the capacity to respond positively to change (and crisis) through in-novative, evidence-based policies, resilience strategies and tools. Part of this process was to build an evidence-base to address a range of challenges associated with the place-based environments and the sharing of information systems within communities and decision makers. The first part of the paper explores the context in which communities have been required to mobilize in response to crises; the issues that have galvanized a common purpose; and the methods by which these communities shared their knowledge. The second part of the paper examines how communities could plan for and mitigate natural disasters in the future by developing better decision making tools. The paper defines the requirements for information systems that will link data models of built infrastruc-ture with data from the disaster and response plans. These will then form the basis for the use of social media to coordinate activities between official crews and the public to improve response coordination and provide the technology that could reduce the time required to allow communities to resume some semblance of normality.
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This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.
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This research has established a new privacy framework, privacy model, and privacy architecture to create more transparent privacy for social networking users. The architecture is designed into three levels: Business, Data, and Technology, which is based on The Open Group Architecture Framework (TOGAF®). This framework and architecture provides a novel platform for investigating privacy in Social Networks (SNs). This approach mitigates many current SN privacy issues, and leads to a more controlled form of privacy assessment. Ultimately, more privacy will encourage more connections between people across SN services.
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This study explored early career academics' experiences in using information to learn while building their networks for professional development. A 'knowledge ecosystem' model was developed consisting of informal learning interactions such as relating to information to create knowledge and engaging in mutually supportive relationships. Findings from this study present an alternative interpretation of information use for learning that is focused on processes manifesting as human interactions with informing entities revolving around the contexts of reciprocal human relationships.