587 resultados para Split application


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Proper application of sunscreen is essential as an effective public health strategy for skin cancer prevention. Insufficient application is common among sunbathers, results in decreased sun protection and may therefore lead to increased UV damage of the skin. However, no objective measure of sunscreen application thickness (SAT) is currently available for field-based use. We present a method to detect SAT on human skin for determining the amount of sunscreen applied and thus enabling comparisons to manufacturer recommendations. Using a skin swabbing method and subsequent spectrophotometric analysis, we were able to determine SAT on human skin. A swabbing method was used to derive SAT on skin (in mg sunscreen per cm2 of skin area) through the concentration–absorption relationship of sunscreen determined in laboratory experiments. Analysis differentiated SATs between 0.25 and 4 mg cm−2 and showed a small but significant decrease in concentration over time postapplication. A field study was performed, in which the heterogeneity of sunscreen application could be investigated. The proposed method is a low cost, noninvasive method for the determination of SAT on skin and it can be used as a valid tool in field- and population-based studies.

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This paper presents a Genetic Algorithms (GA) approach to resolve traffic conflicts at a railway junction. The formulation of the problem for the suitable application of GA will be discussed and three neighborhoods have been proposed for generation evolution. The performance of the GA is evaluated by computer simulation. This study paves the way for more applications of artificial intelligence techniques on a rather conservative industry.

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Reactive oxygen species (ROS) and related free radicals are considered to be key factors underpinning the various adverse health effects associated with exposure to ambient particulate matter. Therefore, measurement of ROS is a crucial factor for assessing the potential toxicity of particles. In this work, a novel profluorescent nitroxide, BPEAnit, was investigated as a probe for detecting particle-derived ROS. BPEAnit has a very low fluorescence emission due to inherent quenching by the nitroxide group, but upon radical trapping or redox activity, a strong fluorescence is observed. BPEAnit was tested for detection of ROS present in mainstream and sidestream cigarette smoke. In the case of mainstream cigarette smoke, there was a linear increase in fluorescence intensity with an increasing number of cigarette puffs, equivalent to an average of 101 nmol ROS per cigarette based on the number of moles of the probe reacted. Sidestream cigarette smoke sampled from an environmental chamber exposed BPEAnit to much lower concentrations of particles, but still resulted in a clearly detectible increase in fluorescence intensity with sampling time. It was calculated that the amount of ROS was equivalent to 50 ± 2 nmol per mg of particulate matter; however, this value decreased with ageing of the particles in the chamber. Overall, BPEAnit was shown to provide a sensitive response related to the oxidative capacity of the particulate matter. These findings present a good basis for employing the new BPEAnit probe for the investigation of particle-related ROS generated from cigarette smoke as well as from other combustion sources.

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A composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. Hence, this model was able to quickly quantify the time spent in each segment within the considered zone, as well as the composition and position of the requisite segments based on the vehicle fleet information, which not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bi-directional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. Although the CLSE model is intended to be applied in traffic management and transport analysis systems for the evaluation of exposure, as well as the simulation of vehicle emissions in traffic interrupted microenvironments, the bus station model can also be used for the input of initial source definitions in future dispersion models.

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This overview focuses on the application of chemometrics techniques for the investigation of soils contaminated by polycyclic aromatic hydrocarbons (PAHs) and metals because these two important and very diverse groups of pollutants are ubiquitous in soils. The salient features of various studies carried out in the micro- and recreational environments of humans, are highlighted in the context of the various multivariate statistical techniques available across discipline boundaries that have been effectively used in soil studies. Particular attention is paid to techniques employed in the geosciences that may be effectively utilized for environmental soil studies; classical multivariate approaches that may be used in isolation or as complementary methods to these are also discussed. Chemometrics techniques widely applied in atmospheric studies for identifying sources of pollutants or for determining the importance of contaminant source contributions to a particular site, have seen little use in soil studies, but may be effectively employed in such investigations. Suitable programs are also available for suggesting mitigating measures in cases of soil contamination, and these are also considered. Specific techniques reviewed include pattern recognition techniques such as Principal Components Analysis (PCA), Fuzzy Clustering (FC) and Cluster Analysis (CA); geostatistical tools include variograms, Geographical Information Systems (GIS), contour mapping and kriging; source identification and contribution estimation methods reviewed include Positive Matrix Factorisation (PMF), and Principal Component Analysis on Absolute Principal Component Scores (PCA/APCS). Mitigating measures to limit or eliminate pollutant sources may be suggested through the use of ranking analysis and multi criteria decision making methods (MCDM). These methods are mainly represented in this review by studies employing the Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and its associated graphic output, Geometrical Analysis for Interactive Aid (GAIA).

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This paper investigates how to interface the wireless application protocol (WAP) architecture to the SCADA system running distributed network protocol (DNP) in a power process plant. DNP is a well-developed protocol to be applied in the supervisory control and data acquisition (SCADA) system but the system control centre and remote terminal units (RTUs) are presently connected through a local area network. The conditions in a process plant are harsh and the site is remote. Resources for data communication are difficult to obtain under these conditions, thus, a wireless channel communication through a mobile phone is practical and efficient in a process plant environment. The mobile communication industries and the public have a strong interest in the WAP technology application in mobile phone networks and the WAP application programming interface (API) in power industry applications is one area that requires extensive investigation.

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Photo-curable biodegradable macromers were prepared by ring opening polymerization of D,L-lactide (DLLA), (similar to)-caprolactone (CL) and 1,3-trimethylene carbonate (TMC) in the presence of glycerol or sorbitol as initiator and stannous octoate as catalyst, and subsequent methacrylation of the terminal hydroxyl groups. These methacrylated macromers, ranging in molecular weight from approximately 700 to 6000 g/mol, were cross-linked using ultraviolet (UV) light to form biodegradable networks. Homogeneous networks with high gel contents were prepared. One of the resins based on PTMC was used to prepare three-dimensional structures by stereo-lithography using a commercially available apparatus.

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Photo-curable biodegradable macromers were prepared by ring opening polymerization of D,L-lactide (DLLA), ε-caprolactone (CL) and 1,3-trimethylene carbonate (TMC) in the presence of glycerol or sorbitol as initiator and stannous octoate as catalyst, and subsequent methacrylation of the terminal hydroxyl groups. These methacrylated macromers, ranging in molecular weight from approximately 700 to 6000 g/mol, were cross-linked using ultraviolet (UV) light to form biodegradable networks. Homogeneous networks with high gel contents were prepared. One of the resins based on PTMC was used to prepare three-dimensional structures by stereo-lithography using a commercially available apparatus.

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This paper reports on the development of specifications for an on-board mass monitoring (OBM) application for regulatory requirements in Australia. An earlier paper reported on feasibility study and pilot testing program prior to the specification development [1]. Learnings from the pilot were used to refine this testing process and a full scale testing program was conducted from July to October 2008. The results from the full scale test and evidentiary implications are presented in this report. The draft specification for an evidentiary on-board mass monitoring application is currently under development.

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Given that what students learn is so strongly related to how they learn, the modes of delivery and assessment that we as teachers provide them with have a major impact on their ability to learn. As this paper shows, good learning environments are constructed from a range of modes that respond to student learning styles and seek to align activities and learning outcomes with assessment tasks, to better accommodate a diversity of student learning styles and backgrounds. This paper uses a number of models of learning to critique and analyse the traditional practices of assessment in an architectural design class, and then proposes and reports on an alternative pattern of assessment. It discusses the issues of accommodating a group of first-year architecture students at Queensland University of Technology in 2009. These students arrived with diverse prior learning backgrounds, the group being evenly split between those with drawing capabilities and those without. They also had a variety of learning style preferences. The experiment in alternative assessment patterns presented here shows that what has traditionally been considered a diverse and difficult cohort of students can benefit from the assessment of a range of task types at different stages in the learning cycle.

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Purpose To identify the challenges faced by local government in Indonesia when adopting a Public Asset Management Framework. Design A Case Study in South Sulawesi Provincial Government was used as the approach to achieving the research objective. The case study involved two data collection techniques - interviews and document analysis. Findings The result of the study indicates there are significant challenges that the Indonesian local government need to manage when adopting a public asset management framework. Those challenges are: absence of an institutional and legal framework to support the asset management application; non-profit principle of public assets; multiple jurisdictions involved in the public asset management processes; the complexity of local government objectives; unavailability of data for managing public property; and limited human resources. Research Limitation This research is limited to one case study. It is a preliminary study from larger research that uses multiple case studies. The main research also investigates opportunities for local government by adopting and implementing public asset management. Originality/Value Findings from this study provide useful input for the policy makers, academics and asset management practitioners in Indonesia to establish a public asset management framework resulting in efficient and effective organizations, as well as an increase of public services quality. This study has a potential application for other developing countries.

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This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

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A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

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Bridges are an important part of a nation’s infrastructure and reliable monitoring methods are necessary to ensure their safety and efficiency. Most bridges in use today were built decades ago and are now subjected to changes in load patterns that can cause localized distress, which can result in bridge failure if not corrected. Early detection of damage helps in prolonging lives of bridges and preventing catastrophic failures. This paper briefly reviews the various technologies currently used in health monitoring of bridge structures and in particular discusses the application and challenges of acoustic emission (AE) technology. Some of the results from laboratory experiments on a bridge model are also presented. The main objectives of these experiments are source localisation and assessment. The findings of the study can be expected to enhance the knowledge of acoustic emission process and thereby aid in the development of an effective bridge structure diagnostics system.

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Particulate pollution has been widely recognised as an important risk factor to human health. In addition to increases in respiratory and cardiovascular morbidity associated with exposure to particulate matter (PM), WHO estimates that urban PM causes 0.8 million premature deaths globally and that 1.5 million people die prematurely from exposure to indoor smoke generated from the combustion of solid fuels. Despite the availability of a huge body of research, the underlying toxicological mechanisms by which particles induce adverse health effects are not yet entirely understood. Oxidative stress caused by generation of free radicals and related reactive oxygen species (ROS) at the sites of deposition has been proposed as a mechanism for many of the adverse health outcomes associated with exposure to PM. In addition to particle-induced generation of ROS in lung tissue cells, several recent studies have shown that particles may also contain ROS. As such, they present a direct cause of oxidative stress and related adverse health effects. Cellular responses to oxidative stress have been widely investigated using various cell exposure assays. However, for a rapid screening of the oxidative potential of PM, less time-consuming and less expensive, cell-free assays are needed. The main aim of this research project was to investigate the application of a novel profluorescent nitroxide probe, synthesised at QUT, as a rapid screening assay in assessing the oxidative potential of PM. Considering that this was the first time that a profluorescent nitroxide probe was applied in investigating the oxidative stress potential of PM, the proof of concept regarding the detection of PM–derived ROS by using such probes needed to be demonstrated and a sampling methodology needed to be developed. Sampling through an impinger containing profluorescent nitroxide solution was chosen as a means of particle collection as it allowed particles to react with the profluorescent nitroxide probe during sampling, avoiding in that way any possible chemical changes resulting from delays between the sampling and the analysis of the PM. Among several profluorescent nitroxide probes available at QUT, bis(phenylethynyl)anthracene-nitroxide (BPEAnit) was found to be the most suitable probe, mainly due to relatively long excitation and emission wavelengths (λex= 430 nm; λem= 485 and 513 nm). These wavelengths are long enough to avoid overlap with the background fluorescence coming from light absorbing compounds which may be present in PM (e.g. polycyclic aromatic hydrocarbons and their derivatives). Given that combustion, in general, is one of the major sources of ambient PM, this project aimed at getting an insight into the oxidative stress potential of combustion-generated PM, namely cigarette smoke, diesel exhaust and wood smoke PM. During the course of this research project, it was demonstrated that the BPEAnit probe based assay is sufficiently sensitive and robust enough to be applied as a rapid screening test for PM-derived ROS detection. Considering that for all three aerosol sources (i.e. cigarette smoke, diesel exhaust and wood smoke) the same assay was applied, the results presented in this thesis allow direct comparison of the oxidative potential measured for all three sources of PM. In summary, it was found that there was a substantial difference between the amounts of ROS per unit of PM mass (ROS concentration) for particles emitted by different combustion sources. For example, particles from cigarette smoke were found to have up to 80 times less ROS per unit of mass than particles produced during logwood combustion. For both diesel and wood combustion it has been demonstrated that the type of fuel significantly affects the oxidative potential of the particles emitted. Similarly, the operating conditions of the combustion source were also found to affect the oxidative potential of particulate emissions. Moreover, this project has demonstrated a strong link between semivolatile (i.e. organic) species and ROS and therefore, clearly highlights the importance of semivolatile species in particle-induced toxicity.