419 resultados para Residential variation statistics
Resumo:
An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008. By 2011, both the peak demand and grid supplied electricity consumption had decreased to below pre-intervention levels. This case study research explored the relationship developed between the utility, community and individual consumer from the residential customer perspective through qualitative research of 22 residential households. It is proposed that an energy utility can be highly successful at peak demand reduction by becoming a community member and a peer to residential consumers and developing the necessary trust, access, influence and partnership required to create the responsive environment to change. A peer-community approach could provide policymakers with a pathway for implementing pro-environmental behaviour for low carbon communities, as well as peak demand reduction, thereby addressing government emission targets while limiting the cost of living increases from infrastructure expenditure.
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This paper presents the results of a full-scale research project undertaken to assess scour losses/gains for modular tray green roof specimens placed on a mock-up building, and identify important factors to consider for wind design criteria. Visual assessment of the experimental results showed that usage of vegetation, parapet height, wind direction, and test duration were the predominant factors affecting scour resistance of the growth media in tested specimens. Statistical analysis results indicated that the differences in soil losses measured among Phase 2’s test trials were more significant than those in Phase 1. This was attributed to the lack of parapet, cornering wind conditions, and longer test duration found in Phase 2. Findings presented in this paper constitute a benchmark for future research to improve the knowledge gap that exists in green roof wind design.
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The effect of nitrogen on the growth of vertically oriented graphene nanosheets on catalyst-free silicon and glass substrates in a plasma-assisted process is studied. Different concentrations of nitrogen were found to act as versatile control knobs that could be used to tailor the length, number density and structural properties of the nanosheets. Nanosheets with different structural characteristics exhibit markedly different optical properties. The nanosheet samples were treated with a bovine serum albumin protein solution to investigate the effects of this variation on the optical properties for biosensing through confocal micro-Raman spectroscopy and UV-Vis spectrophotometry. © 2012 Optical Society of America.
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This study was conducted to assess the vulnerability of coastal road infrastructures due to climate change induced sea level rise and extreme weather conditions through the estimation of road subgrade strength reduction as a result of changes in soil moisture content. The study area located in the Gold Coast, Australia highlighted that the risk is significant. In wet seasons or areas with wet condition, the groundwater table is already high, so even a small change in the groundwater table can raise the risk of inundation; particularly, in areas with existing shallow groundwater. The predicted risk of a high groundwater table on road infrastructure is a long-term hazard. Therefore, there is time to undertake some management plans to decrease the possible risks, for instance, some deep root plants could be planted along the roads with a high level of risk, to decrease the groundwater table elevation.
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Background Accurate diagnosis is essential for prompt and appropriate treatment of malaria. While rapid diagnostic tests (RDTs) offer great potential to improve malaria diagnosis, the sensitivity of RDTs has been reported to be highly variable. One possible factor contributing to variable test performance is the diversity of parasite antigens. This is of particular concern for Plasmodium falciparum histidine-rich protein 2 (PfHRP2)-detecting RDTs since PfHRP2 has been reported to be highly variable in isolates of the Asia-Pacific region. Methods The pfhrp2 exon 2 fragment from 458 isolates of P. falciparum collected from 38 countries was amplified and sequenced. For a subset of 80 isolates, the exon 2 fragment of histidine-rich protein 3 (pfhrp3) was also amplified and sequenced. DNA sequence and statistical analysis of the variation observed in these genes was conducted. The potential impact of the pfhrp2 variation on RDT detection rates was examined by analysing the relationship between sequence characteristics of this gene and the results of the WHO product testing of malaria RDTs: Round 1 (2008), for 34 PfHRP2-detecting RDTs. Results Sequence analysis revealed extensive variations in the number and arrangement of various repeats encoded by the genes in parasite populations world-wide. However, no statistically robust correlation between gene structure and RDT detection rate for P. falciparum parasites at 200 parasites per microlitre was identified. Conclusions The results suggest that despite extreme sequence variation, diversity of PfHRP2 does not appear to be a major cause of RDT sensitivity variation.
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A study of ten Polish entrepreneurs operating in Leicester, UK is reported in this article. The concepts of social, cultural and economic capital are used as the lens through which to explore the way the capital they access is employed and converted into entrepreneurial activity. Ethnic entrepreneurship takes place within wider social, political and economic institutional frameworks and opportunity structures and so this is taken into account by differentiating two groups – post-war and contemporary Polish entrepreneurs. The differing origins and amounts of forms of capital they can access are shown as is how these are converted into valued outcomes. Combining the mixed embeddedness approach with a forms-of-capital analysis enables looking beyond social capital to elaborate on intra-ethnic variation in the UK’s Polish entrepreneurial community.
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Basic mathematical skills are critical to a student’s ability to successfully undertake an introductory statistics course. Yet in business education this vitally important area of mathematics and statistics education is under-researched. The question therefore arises as to what level of mathematical skill a typical business studies student will possess as they enter the tertiary environment, and whether there are any common deficiencies that we can identify with a view to tackling the problem. This paper will focus on a study designed to measure the level of mathematical ability of first year business students. The results provide timely insight into a growing problem faced by many tertiary educators in this field.
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Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach -- the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the SLA-based and grid-based approaches perform equally well for spatially dense data.
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STAC is a mobile application (app) designed to promote the benefits of climate-aware urban development in Subtropical environments. Although, STAC is primarily tool for understanding climate efficient buildings in Brisbane, Australia, it also demonstrates how other exemplary buildings operate in other subtropical cities of the world. The STAC research and development team applied research undertaken by the Centre for Subtropical Design (Brisbane) to profile buildings past and present that have contributed to the creation of a vibrant society, a viable economy, a healthy environment, and an authentic sense of place. In collaboration with researchers from the field of Interaction Design, this knowledge and data was collated, processed and curated for presentation via a custom mobile application designed to distribute this important research for review and consideration on-location in local settings and for comparison across all other global subtropical regions and projects identified by this research. This collaboration adopted a Design-based Research (DBR) Methodology guided by the main tenets of research and design iteration and cross-discipline collaboration in real-world settings, resulting in the formulation of contextually-sensitive design principles, theories, and tools for design intervention. Combined with significant context review of available technology and data and subsequent case study analysis of exemplar design applications.
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Background: Extreme heat is a leading weather-related cause of illness and death in many locations across the globe, including subtropical Australia. The possibility of increasingly frequent and severe heat waves warrants continued efforts to reduce this health burden, which could be accomplished by targeting intervention measures toward the most vulnerable communities. Objectives: We sought to quantify spatial variability in heat-related morbidity in Brisbane, Australia, to highlight regions of the city with the greatest risk. We also aimed to find area-level social and environmental determinants of high risk within Brisbane. Methods: We used a series of hierarchical Bayesian models to examine city-wide and intracity associations between temperature and morbidity using a 2007–2011 time series of geographically referenced hospital admissions data. The models accounted for long-term time trends, seasonality, and day of week and holiday effects. Results: On average, a 10°C increase in daily maximum temperature during the summer was associated with a 7.2% increase in hospital admissions (95% CI: 4.7, 9.8%) on the following day. Positive statistically significant relationships between admissions and temperature were found for 16 of the city’s 158 areas; negative relationships were found for 5 areas. High-risk areas were associated with a lack of high income earners and higher population density. Conclusions: Geographically targeted public health strategies for extreme heat may be effective in Brisbane, because morbidity risk was found to be spatially variable. Emergency responders, health officials, and city planners could focus on short- and long-term intervention measures that reach communities in the city with lower incomes and higher population densities, including reduction of urban heat island effects.
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This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.
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This article examines the new Property Occupations Act 2014 (POA) and relevant provisions of the Agents Financial Administration Act 2014 (AFAA) and the impacts for property practitioners. The Acts are due to commence later in 2014 once regulations and relevant forms are drafted. Coinciding with the commencement of the Acts further versions of the REIQ Houses and Land Contract and REIQ Community Title Contract will also be released. The POA introduces changes for licencing of real estate agents, property developers and resident letting agents as well as significant changes for the contract formation process. The AFAA includes the trust account and claim fund provisions of PAMDA, which avoids duplication of these provisions across each of the industry-specific Bills. The most significant change is to the process for making a claim against the fund for the conduct of property agents.
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Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research.