An algebraic approach for determination of DG parameters to support voltage profiles in radial distribution networks

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.

A new distributed control strategy to coordinate multiple dstatcoms in LV network

Voltage rise is the main issue which limits the capacity of Low Voltage (LV) network to accommodate more Renewable Energy (RE) sources. In addition, voltage drop at peak load period is a significant power quality concern. This paper proposes a new robust voltage support strategy based on distributed coordination of multiple distribution static synchronous compensators (DSTATCOMs). The study focuses on LV networks with PV as the RE source for customers. The proposed approach applied to a typical LV network and its advantages are shown comparing with other voltage control strategies.

The association between social support and levels of psychological distress in pregnant women in Australia

The purpose of this study was to explore associations between forms of social support and levels of psychological distress during pregnancy. Methods: A cross-sectional analysis of 2,743 pregnant women from south-east Queensland, Australia, was conducted utilising data collected between 2007-2011 as part of the Environments for Healthy Living (EFHL) project, Griffith University. Psychological distress was measured using the Kessler 6; social support was measured using the following four factors: living with a partner, living with parents or in-laws, self-perceived social network, and area satisfaction. Data were analysed using an ordered logistic regression model controlling for a range of socio-demographic factors. Results: There was an inverse association between self-perceived strength of social networks and levels of psychological distress (OR = 0.77; 95%CI: 0.70, 0.85) and between area satisfaction and levels of psychological distress (OR = 0.77; 95%CI: 0.69, 0.87). There was a direct association between living with parents or in-laws and levels of psychological distress (OR = 1.50; 95%CI: 1.16, 1.96). There was no statistically significant association between living with a partner and the level of psychological distress of the pregnant woman after accounting for household income. Conclusion: Living with parents or in-laws is a strong marker for psychological distress. Strategies aiming to build social support networks for women during pregnancy have the potential to provide a significant benefit. Policies promoting stable family relationships and networks through community development could also be effective in promoting the welfare of pregnant women.

SWCNT networks on nanoporous silica catalyst support : morphological and connectivity control for nanoelectronic, gas-sensing, and biosensing devices

Effective control of morphology and electrical connectivity of networks of single-walled carbon nanotubes (SWCNTs) by using rough, nanoporous silica supports of Fe catalyst nanoparticles in catalytic chemical vapor deposition is demonstrated experimentally. The very high quality of the nanotubes is evidenced by the G-to-D Raman peak ratios (>50) within the range of the highest known ratios. Transitions from separated nanotubes on smooth SiO2 surface to densely interconnected networks on the nanoporous SiO2 are accompanied by an almost two-order of magnitude increase of the nanotube density. These transitions herald the hardly detectable onset of the nanoscale connectivity and are confirmed by the microanalysis and electrical measurements. The achieved effective nanotube interconnection leads to the dramatic, almost three-orders of magnitude decrease of the SWCNT network resistivity compared to networks of similar density produced by wet chemistry-based assembly of preformed nanotubes. The growth model, supported by multiscale, multiphase modeling of SWCNT nucleation reveals multiple constructive roles of the porous catalyst support in facilitating the catalyst saturation and SWCNT nucleation, consistent with the observed higher density of longer nanotubes. The associated mechanisms are related to the unique surface conditions (roughness, wettability, and reduced catalyst coalescence) on the porous SiO2 and the increased carbon supply through the supporting porous structure. This approach is promising for the direct integration of SWCNT networks into Si-based nanodevice platforms and multiple applications ranging from nanoelectronics and energy conversion to bio- and environmental sensing.

Parent's socio-economic status and social support as risks for child mortality : consideration of health equity in The Gambia

A matched case-control study of mortality to children under age five was conducted to consider associations with parents' socio-economic status and social support in the Farafenni Demographic Surveillance Site (DSS). Cases and controls were selected from Farafenni DSS, matched on date of birth, and parents were interviewed about personal resources and social networks. Parents with the lowest personal socio-economic status and social support were identified. Multivariate multinomial regression was used to consider whether the children of these parents were at increased risk of either infant or 1-4 mortality, in separate models using either parents' characteristics. There was no benefit found for higher SES or better social support with respect to child mortality. Children of fathers who had the poorest social support had lower 1-4 mortality risk (OR=0.52, p=0.037). Given that socio-economic status was not associated with child mortality, it seems unlikely that the explanation for the link between father's social support and mortality is linked to resource availability. Explanations for the risk effect of father's social ties may lie in decision-making around health maintenance and health care for children.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Surviving (thriving) in academia : feminist support networks and women ECRs

In this paper, we reflect upon our experiences and those of our peers as doctoral students and early career researchers in an Australian political science department. We seek to explain and understand the diverse ways that participating in an unofficial Feminist Reading Group in our department affected our experiences. We contend that informal peer support networks like reading groups do more than is conventionally assumed, and may provide important avenues for sustaining feminist research in times of austerity, as well as supporting and enabling women and emerging feminist scholars in academia. Participating in the group created a community of belonging and resistance, providing women with personal validation, information and material support, as well as intellectual and political resources to understand and resist our position within the often hostile spaces of the University. While these experiences are specific to our context, time and location, they signal that peer networks may offer critical political resources for responding to the ways that women’s bodies and concerns are marginalised in increasingly competitive and corporatised university environments.

Identifying influential users of micro-blogging services : a dynamic action-based network approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our actionbaseduser influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered.

Support for an auto-associative model of spoken cued recall: Evidence from fMRI

Cued recall and item recognition are considered the standard episodic memory retrieval tasks. However, only the neural correlates of the latter have been studied in detail with fMRI. Using an event-related fMRI experimental design that permits spoken responses, we tested hypotheses from an auto-associative model of cued recall and item recognition [Chappell, M., & Humphreys, M. S. (1994). An auto-associative neural network for sparse representations: Analysis and application to models of recognition and cued recall. Psychological Review, 101, 103-128]. In brief, the model assumes that cues elicit a network of phonological short term memory (STM) and semantic long term memory (LTM) representations distributed throughout the neocortex as patterns of sparse activations. This information is transferred to the hippocampus which converges upon the item closest to a stored pattern and outputs a response. Word pairs were learned from a study list, with one member of the pair serving as the cue at test. Unstudied words were also intermingled at test in order to provide an analogue of yes/no recognition tasks. Compared to incorrectly rejected studied items (misses) and correctly rejected (CR) unstudied items, correctly recalled items (hits) elicited increased responses in the left hippocampus and neocortical regions including the left inferior prefrontal cortex (LIPC), left mid lateral temporal cortex and inferior parietal cortex, consistent with predictions from the model. This network was very similar to that observed in yes/no recognition studies, supporting proposals that cued recall and item recognition involve common rather than separate mechanisms.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Submission to the draft Migration Assurance Policy - Improving the transition to the fixed-line national broadband network review

The rights of individuals to self-determination and participation in social, political and economic life are recognised and supported by Articles 1, 3 and 25 of the International Covenant on Civil and Political Rights 1966.4 Article 1 of the United Nations’ Human Rights Council’s Resolution on the Promotion and Protection of Human Rights on the Internet of July 2012 confirms individuals have the same rights online as offline. Access to the internet is essential and as such the UN: Calls upon all States to promote and facilitate access to the Internet and international cooperation aimed at the development of media and information and communications facilities in all countries (Article 3) Accordingly, access to the internet per se is a fundamental human right, which requires direct State recognition and support.5 The obligations of the State to ensure its citizens are able, and are enabled, to access the internet, are not matters that should be delegated to commercial parties. Quite simply – access to the internet, and high-speed broadband, by whatever means are “essential services” and therefore “should be treated as any other utility service”...

The experience of forging new emergency nursing curriculum development, through a course advisory network

Queensland University of Technology (QUT), School of Nursing (SoN), has offered a postgraduate Graduate Certificate in Emergency Nursing since 2003, for registered nurses practising in an emergency clinical area, who fulfil key entry criteria. Feedback from industry partners and students evidenced support for flexible and extended study pathways in emergency nursing. Therefore, in the context of a growing demand for emergency health services and the need for specialist qualified staff, it was timely to review and redevelop our emergency specialist nursing courses. The QUT postgraduate emergency nursing study area is supported by a course advisory group, whose aim is to provide input and focus development of current and future course planning. All members of the course advisory were invited to form an expert panel to review current emergency course documents. A half day “brainstorm session”, planning and development workshop was held to review the emergency courses to implement changes from 2009. Results from the expert panel planning day include: proposal for a new emergency specialty unit; incorporation of the College of Emergency Nurses (CENA) Standards for Emergency Nursing Specialist in clinical assessment; modification of the present core emergency unit; enhancing the focus of the two other units that emergency students undertake; and opening the emergency study area to the Graduate Diploma in Nursing (Emergency Nursing) and Master of Nursing (Emergency Nursing). The conclusion of the brainstorm session resulted in a clearer conceptualisation, of the study pathway for students. Overall, the expert panel group of enthusiastic emergency educators and clinicians provided viable options for extending the career progression opportunities for emergency nurses. In concluding, the opportunity for collaboration across university and clinical settings has resulted in the design of a course with exciting potential and strong clinical relevance.

Support Frameworks for the Use of Social Media by Emergency Management Organisations

Social media platforms such as Facebook and Twitter are now widely recognised as playing an increasingly important role in the dissemination of information during crisis events. They are used by emergency management organisations as well as by the public to share information and advice. However, the official use of social media for crisis communication within emergency management organisations is still relatively new and ad hoc, rather than being systematically embedded within or effectively coordinated across agencies. This policy report suggests a more effectively coordinated approach to leverage social media use, involving stronger networking between social media staff within emergency management organisations. This could be realised by establishing a national network of social media practitioners managed by the Australia-New Zealand Emergency Management Committee (ANZEMC), reinforced by a Federal government task force that promotes further policy initiatives in this space.

Life events and social support among patients with major depressive disorder

The study is part of a research project of 269 psychiatric patients with major depression, Vantaa Depression Study, in the Department of Mental Health and Alcohol Research of the National Public Health Institute and the Department of Psychiatry of the Peijas Medical Care District. The aim was to study at the onset of MDE psychosocial differences in subgroups of patients and clustering of events into time before depression and its prodromal phase, to study whether more severe life events and less social support predict poorer outcome in all patients, but most among those currently in partial remission, whether social support declines as a consequence of time spent in MDE, is sensitive to improvement, and whether social support is influenced by neuroticism and extraversion. After screening, a semistructured interview (SCAN, version 2.0) was used for the presence of DSM-IV MDE, and other psychiatric diagnoses. Life events and social support were studied with semistructured methods (IRLE, Paykel 1983; IMSR, Brugha et al. 1987), perceived social support and neuroticism/extraversion with questionnaires (PSSS-R, Blumenthal et al. 1987; EPI, Eysenck and Eysenck 1964) at baseline, 6 and 18 months. At the onset of depression life events were common. No major differences between subgroups of patients were found; the younger had more events, whereas those with comorbid alcoholism and personality disorders perceived less support. Although events were distributed evenly between the time before depression, the prodromal phase and the index MDE, two thirds of the patients attributed their depression to some life event. Adversities and poor perceived support influenced the outcome of all psychiatric patients, most in the subgroup of full remission. In the partial remission group, the impact of severe events and in the MDE, perceived support was important. Low objective and subjective support were predicted by longer time spent in MDE. Along with improvement subjective support improved. Neuroticism and extraversion were associated with the size of social network and perceived support and predicted change of perceived support. In conclusion, adversities were common in all phases of depression. They may thus have many roles; before depression they may precipitate it, in the prodromal phase worsen symptoms, and during the MDE, the outcome of depression. Patients often attributed their depression to a life event. Psychosocial subgroup differences were quite small. Perceived support predicted the outcome of depression, and time spent in MDE objective and subjective support. Neuroticism and extraversion may modify the level and change particularly in perceived support, thereby indirectly effecting vulnerability to depression.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.