Developing a dashboard for real-time data stream composition and visualization

Technological advances have led to an influx of affordable hardware that supports sensing, computation and communication. This hardware is increasingly deployed in public and private spaces, tracking and aggregating a wealth of real-time environmental data. Although these technologies are the focus of several research areas, there is a lack of research dealing with the problem of making these capabilities accessible to everyday users. This thesis represents a first step towards developing systems that will allow users to leverage the available infrastructure and create custom tailored solutions. It explores how this notion can be utilized in the context of energy monitoring to improve conventional approaches. The project adopted a user-centered design process to inform the development of a flexible system for real-time data stream composition and visualization. This system features an extensible architecture and defines a unified API for heterogeneous data streams. Rather than displaying the data in a predetermined fashion, it makes this information available as building blocks that can be combined and shared. It is based on the insight that individual users have diverse information needs and presentation preferences. Therefore, it allows users to compose rich information displays, incorporating personally relevant data from an extensive information ecosystem. The prototype was evaluated in an exploratory study to observe its natural use in a real-world setting, gathering empirical usage statistics and conducting semi-structured interviews. The results show that a high degree of customization does not warrant sustained usage. Other factors were identified, yielding recommendations for increasing the impact on energy consumption.

Variability of metal composition and concentrations in road dust in the urban environment

Urban road dust comprises of a range of potentially toxic metal elements and plays a critical role in degrading urban receiving water quality. Hence, assessing the metal composition and concentration in urban road dust is a high priority. This study investigated the variability of metal composition and concentrations in road dust in 4 different urban land uses in Gold Coast, Australia. Samples from 16 road sites were collected and tested for selected 12 metal species. The data set was analyzed using both univariate and multivariate techniques. Outcomes of the data analysis revealed that the metal concentrations in road dust differ considerably within and between different land uses. Iron, aluminum, magnesium and zinc are the most abundant in urban land uses. It was also noted that metal species such as titanium, nickel, copper and zinc have the highest concentrations in industrial land use. The study outcomes revealed that soil and traffic related sources as key sources of metals deposited on road surfaces.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Antecedents of consumer brand loyalty in the Australian wine industry

The wine industry has become fiercely competitive worldwide and as a result, consumers are increasingly exposed to a wider range of wines in retail outlets. This expanding consumer choice means that there is a need for Australian wineries to develop and build consumer loyalty toward their brands. This paper aims to empirically examine the factors influencing consumer loyalty to wine brands. Using data from Australian wine consumers, the authors empirically test a model of antecedents of wine brand loyalty. The model considers wine brand trust, wine brand satisfaction, wine knowledge, and wine experience. Hypotheses were tested with structural equation modeling. The findings of this study show that wine knowledge and wine experience affect wine brand loyalty indirectly through wine brand trust and wine brand satisfaction. In addition, it is demonstrated that consumer satisfaction with a wine brand is the strongest driver of wine brand loyalty. The result of this study has value for Australian wineries, wine retailers, and wine marketers.

Elemental composition and potential toxicity of airborne particles at some urban schools

Although both the size and chemical composition of ambient particles are important parameters in determining their toxicities, their relative contributions are unclear (Heal et al., 2012). Children are particularly at risk to the detrimental health effects that have been linked to long term exposure to airborne particles (See e.g. Ruckerl et al., 2011). However, there is currently limited understanding of the health effects in children due to long term exposure to airborne particles. Schools are locations within an urban environment where children experience significant exposure to vehicle emissions, and to date there is limited information assessing children’s exposure at school. This study is a part of a large project aimed at gaining a holistic picture of the exposure of children to traffic related pollutants. In the current paper, results from the investigation of the elemental composition of airborne particle at urban schools are presented.

Entrepreneurial bricolage and young firm performance : the moderating effect of team composition

Many young firms face significant resource constraints during attempts to develop and grow. One promising theory that explicitly links to resource constraints is bricolage: a construct developed by Levi Strauss (1967). Bricolage aligns with notions of resourcefulness: using what’s on hand, through making do, and recombining resources for new or novel purposes. In this paper we further theorize and test the moderating effects of ownership team composition on bricolage and firm performance. Our findings suggest that team size, strong network ties, and functionality enhance the effects of bricolage in young firm performance.

Clinical dyslipidaemia is associated with changes in the lipid composition and inflammatory properties of apolipoprotein-B-containing lipoproteins from women with type 2 diabetes

The aim of this study was to use lipidomics to determine if the lipid composition of apolipoprotein-B-containing lipoproteins is modified by dyslipidaemia in type 2 diabetes and if any of the identified changes potentially have biological relevance in the pathophysiology of type 2 diabetes. VLDL and LDL from normolipidaemic and dyslipidaemic type 2 diabetic women and controls were isolated and quantified with HPLC and mass spectrometry. A detailed molecular characterisation of VLDL triacylglycerols (TAG) was also performed using the novel ozone-induced dissociation method, which allowed us to distinguish vaccenic acid (C18:1 n-7) from oleic acid (C18:1 n-9) in specific TAG species. Lipid class composition was very similar in VLDL and LDL from normolipidaemic type 2 diabetic and control participants. By contrast, dyslipidaemia was associated with significant changes in both lipid classes (e.g. increased diacylglycerols) and lipid species (e.g. increased C16:1 and C20:3 in phosphatidylcholine and cholesteryl ester and increased C16:0 [palmitic acid] and vaccenic acid in TAG). Levels of palmitic acid in VLDL and LDL TAG correlated with insulin resistance, and VLDL TAG enriched in palmitic acid promoted increased secretion of proinflammatory mediators from human smooth muscle cells. We showed that dyslipidaemia is associated with major changes in both lipid class and lipid species composition in VLDL and LDL from women with type 2 diabetes. In addition, we identified specific molecular lipid species that both correlate with clinical variables and are proinflammatory. Our study thus shows the potential of advanced lipidomic methods to further understand the pathophysiology of type 2 diabetes.

Phospholipid composition of the rat lens is independent of diet

Dietary fatty acids are known to influence the phospholipid composition of many tissues in the body, with lipid turnover occurring rapidly. The aim of this study was to investigate whether changes in the fatty acid composition of the diet can affect the phospholipid composition of the lens. Male Sprague-Dawley rats were fed three diets with distinct profiles in both essential and non-essential fatty acids. After 8 weeks, lenses and skeletal muscle were removed, and the lenses sectioned into nuclear and cortical regions. In these experiments, the lens cortex was synthesised during the course of the variable lipid diet. Phospholipids were then identified by electrospray ionisation tandem mass spectrometry, and quantified via the use of internal standards. The phospholipid compositions of the nuclear and cortical regions of the lens differed slightly between the two regions, but comparison of the equivalent regions across the diet groups showed remarkable similarity. In contrast, the phospholipid composition of skeletal muscle (medial gastrocnemius) in these rats varied significantly. This study provides the first direct evidence to show that the phospholipid composition of the lens is tightly regulated and thus appears to be independent of diet. As phospholipids determine membrane fluidity and influence the activity and function of integral membrane proteins, regulation of their composition may be important for the function of the lens. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.

Systematic differences in membrane acyl composition associated with varying body mass in mammals occur in all phospholipid classes : an analysis of kidney and brain

The acyl composition of membrane phospholipids in kidney and brain of mammals of different body mass was examined. It was hypothesized that reduction in unsaturation index (number of double bonds per 100 acyl chains) of membrane phospholipids with increasing body mass in mammals would be made-up of similar changes in acyl composition across all phospholipid classes and that phospholipid class distribution would be regulated and similar in the same tissues of the different-sized mammals. The results of this study supported both hypotheses. Differences in membrane phospholipid acyl composition (i. e. decreased omega-3 fats, increased monounsaturated fats and decreased unsaturation index with increasing body size) were not restricted to any specific phospholipid molecule or to any specific phospholipid class but were observed in all phospholipid classes. With increase in body mass of mammals both monounsaturates and use of less unsaturated polyunsaturates increases at the expense of the long-chain highly unsaturated omega-3 and omega-6 polyunsaturates, producing decreases in membrane unsaturation. The distribution of membrane phospholipid classes was essentially the same in the different-sized mammals with phosphatidylcholine (PC) and phosphatidylethanolamine (PE) together constituting similar to 91% and similar to 88% of all phospholipids in kidney and brain, respectively. The lack of sphingomyelin in the mouse tissues and higher levels in larger mammals suggests an increased presence of membrane lipid rafts in larger mammals. The results of this study support the proposal that the physical properties of membranes are likely to be involved in changing metabolic rate.

Differences in membrane acyl phospholipid composition between an endothermic mammal and an ectothermic reptile are not limited to any phospholipid class

This study examined questions concerning differences in the acyl composition of membrane phospholipids that have been linked to the faster rates of metabolic processes in endotherms versus ectotherms. In liver, kidney, heart and brain of the ectothermic reptile, Trachydosaurus rugosus, and the endothermic mammal, Rattus norvegicus, previous findings of fewer unsaturates but a greater unsaturation index (UI) in membranes of the mammal versus those of the reptile were confirmed. Moreover, the study showed that the distribution of phospholipid head-group classes was similar in the same tissues of the reptile and mammal and that the differences in acyl composition were present in all phospholipid classes analysed, suggesting a role for the physical over the chemical properties of membranes in determining the faster rates of metabolic processes in endotherms. The most common phosphatidylcholine (PC) molecules present in all tissues (except brain) of the reptile were 16:0/18:1, 16:0/18:2, 18:0/18:2, 18:1/18:1 and 18:1/18:2, whereas arachidonic acid (20:4), containing PCs 16:0/ 20: 4, 18: 0/ 20: 4, were the common molecules in the mammal. The most abundant phosphatidylethanolamines ( PE) used in the tissue of the reptile were 18:0/18:2, 18:0/20:4, 18:1/18:1, 18:1/18:2 and 18:1/20:4, compared to 16: 0/ 18: 2, 16: 0/ 20: 4, 16: 0/ 22: 6, 18: 0/ 20: 4, 18: 0/ 22: 6 and 18:1/20: 4 in the mammal. UI differences were primarily due to arachidonic acid found in both PC and PEs, whereas docosahexaenoic acid (22:6) was a lesser contributor mainly within PEs and essentially absent in the kidney. The phospholipid composition of brain was more similar in the reptile and mammal compared to those of other tissues.

Development and application of a predictive model of freshwater fish assemblage composition to evaluate river health in eastern Australia

Multivariate predictive models are widely used tools for assessment of aquatic ecosystem health and models have been successfully developed for the prediction and assessment of aquatic macroinvertebrates, diatoms, local stream habitat features and fish. We evaluated the ability of a modelling method based on the River InVertebrate Prediction and Classification System (RIVPACS) to accurately predict freshwater fish assemblage composition and assess aquatic ecosystem health in rivers and streams of south-eastern Queensland, Australia. The predictive model was developed, validated and tested in a region of comparatively high environmental variability due to the unpredictable nature of rainfall and river discharge. The model was concluded to provide sufficiently accurate and precise predictions of species composition and was sensitive enough to distinguish test sites impacted by several common types of human disturbance (particularly impacts associated with catchment land use and associated local riparian, in-stream habitat and water quality degradation). The total number of fish species available for prediction was low in comparison to similar applications of multivariate predictive models based on other indicator groups, yet the accuracy and precision of our model was comparable to outcomes from such studies. In addition, our model developed for sites sampled on one occasion and in one season only (winter), was able to accurately predict fish assemblage composition at sites sampled during other seasons and years, provided that they were not subject to unusually extreme environmental conditions (e.g. extended periods of low flow that restricted fish movement or resulted in habitat desiccation and local fish extinctions).

Patterns of diversity in fatty acid composition in the Australian groundnut germplasm collection

Knowledge of the amounts and types of fatty acids in groundnut oil is beneficial, particularly from a nutritional standpoint. Germplasm evaluation data for fatty acid composition on 819 accessions of groundnut (Arachis hypogaea L.) from the Australian Tropical Field Crops Genetic Resource Centre, Biloela, Queensland were examined. Data for eight quantitative fatty acid descriptors have been documented. Statistical assessment, via methods of pattern analysis, summarised and described the patterns of variation in fatty acid composition of the groundnut accessions in the Australian germplasm collection. Presentation of the results from principal components analysis and hierarchical cluster analysis using a biplot was shown to be a very useful interpretative tool. Such a biplot enables a simultaneous examination of the relationships among all the accessions and the fatty acids. Unlike that information available via database searches, the results from contribution analysis together with the biplot provide a global picture of the diversity available for use in plant breeding programs. The use of standardised data for eight fatty acids, compared to using three specific fatty acids, provided a better description of the total diversity available because it remains relevant with possible changes in the nutritional preferences for fatty acids. Fatty acid composition was found to vary in relation to the branching pattern of the accessions. This pattern is generally indicative of the botanical types of groundnuts; Virginia (alternate) compared to Spanish and Valencia (sequential) botanical types.

Structure- and composition-dependent electron field emission from nitrogenated carbon nanotips

The electron field emission (EFE) properties of nitrogenated carbon nanotips (NCNTPs) were studied under high-vacuum conditions. The NCNTPs were prepared in a plasma-assisted hot filament chemical vapor deposition system using CH4 and N2 as the carbon and nitrogen sources, respectively. The work functions of NCNTPs were measured using x-ray photoelectron spectroscopy. The morphological and structural properties of NCNTPs were studied by field emission scanning electron microscopy, micro-Raman spectroscopy, and x-ray photoelectron spectroscopy. The field enhancement factors of NCNTPs were calculated using relevant EFE models based on the Fowler-Nordheim approximation. Analytical characterization and modeling results were used to establish the relations between the EFE properties of NCNTPs and their morphology, structure, and composition. It is shown that the EFE properties of NCNTPs can be enhanced by the reduction of oxygen termination on the surface as well as by increasing the ratio of the NCNTP height to the radius of curvature at its top. These results also suggest that a significant amount of electrons is emitted from other surface areas besides the NCNTP tops, contrary to the common belief. The outcomes of this study advance our knowledge on the electron emission properties of carbonnanomaterials and contribute to the development of the next-generation of advanced applications in the fields of micro- and opto-electronics.

Composition-dependent structural and electronic properties of α-(Si1−xCx)3N4

The highly unusual structural and electronic properties of the α-phase of (Si1-xCx)3N4 are determined by density functional theory (DFT) calculations using the Generalized Gradient Approximation (GGA). The electronic properties of α-(Si 1-xCx)3N4 are found to be very close to those of α-C3N4. The bandgap of α-(Si 1-xCx)3N4 significantly decreases as C atoms are substituted by Si atoms (in most cases, smaller than that of either α-Si3N4 or α-C3N4) and attains a minimum when the ratio of C to Si is close to 2. On the other hand, the bulk modulus of α-(Si1-xCx)3N 4 is found to be closer to that of α-Si3N 4 than of α-C3N4. Plasma-assisted synthesis experiments of CNx and SiCN films are performed to verify the accuracy of the DFT calculations. TEM measurements confirm the calculated lattice constants, and FT-IR/XPS analysis confirms the formation and lengths of C-N and Si-N bonds. The results of DFT calculations are also in a remarkable agreement with the experiments of other authors.

Control of core-shell structure and elemental composition of binary quantum dots

The possibility of initial stage control of the elemental composition and core/shell structure of binary SiC quantum dots by optimizing temporal variation of Si and C incoming fluxes and surface temperatures is shown via hybrid numerical simulations. Higher temperatures and influxes encourage the formation of a stoichiometric outer shell over a small carbon-enriched core, whereas lower temperatures result in a larger carbon-enriched core, Si-enriched undershell, and then a stoichiometric SiC outer shell. This approach is generic and is applicable to a broad range of semiconductor materials and nanofabrication techniques. © 2007 American Institute of Physics.