17 resultados para Hoyt, Jesse.

em Queensland University of Technology - ePrints Archive


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The term self-selected (i.e., individual or comfortable walking pace or speed) is commonly used in the literature (Frost, Dowling, Bar-Or, & Dyson, 1997; Jeng, Liao, Lai, & Hou, 1997; Wergel-Kolmert & Wohlfart, 1999; Maltais, Bar-Or, Pienynowski, & Galea, 2003; Browning & Kram, 2005; Browning, Baker, Herron, & Kram, 2006; Hills, Byrne, Wearing, & Armstrong, 2006) and is identified as the most efficient walking speed, with increased efficiency defined by lower oxygen uptake (VO^sub 2^) per unit mechanical work (Hoyt & Taylor, 1981; Taylor, Heglund, & Maloiy, 1982; Hreljac, 1993). [...] assessing individual and group differences in metabolic energy expenditure using oxygen uptake requires individuals to be comfortable with, and able to accommodate to, the equipment.

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In this study, we provide an insight into how private equity players choose their targets and the bid arrangements they prefer. We test our expectations of the unique features of private equity targets using a sample of 23 listed private equity target firms during 2001–2007. We find, relative to a benchmark sample of 81 corporate targets matched by year and industry, the private equity target firms to be larger, more profitable, use their assets more efficiently, more highly levered and have greater cash flow. Multivariate testing indicates that private equity targets have relatively greater financial slack, greater financial stability, greater free cash flow and lower measurable growth prospects. All conclusions are found to be robust to a control sample of 502 takeover bids during 2001–2007.

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The deal value of private equity merger and takeover activity has achieved unprecedented growth in the last couple of years, in Australia and globally. Private equity deals are not a new feature of the market; however, such deals have been subject to increased academic, professional and policy interest. This study examines the particular features of 15 major deals involving listed company "targets" and provides evidence – based on a comparison with a benchmark sample – to demonstrate the role that private equity plays in the market for corporate control. The objective of this study was to assess the friendliness of private equity bids. Based on the indicia compiled, lower bid premiums, the presence of break fees and the intention to retain senior management are compellingly different for private equity bids than for the comparative sample of bids. Using these several characteristics of "friendliness", the authors show that private equity deals are generally friendly in nature, consistent with industry rhetoric, but perhaps inconsistent with the popular belief that private equity bidders are the "barbarians at the gate".

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Mathematical descriptions of birth–death–movement processes are often calibrated to measurements from cell biology experiments to quantify tissue growth rates. Here we describe and analyze a discrete model of a birth–death-movement process applied to a typical two–dimensional cell biology experiment. We present three different descriptions of the system: (i) a standard mean–field description which neglects correlation effects and clustering; (ii) a moment dynamics description which approximately incorporates correlation and clustering effects, and; (iii) averaged data from repeated discrete simulations which directly incorporates correlation and clustering effects. Comparing these three descriptions indicates that the mean–field and moment dynamics approaches are valid only for certain parameter regimes, and that both these descriptions fail to make accurate predictions of the system for sufficiently fast birth and death rates where the effects of spatial correlations and clustering are sufficiently strong. Without any method to distinguish between the parameter regimes where these three descriptions are valid, it is possible that either the mean–field or moment dynamics model could be calibrated to experimental data under inappropriate conditions, leading to errors in parameter estimation. In this work we demonstrate that a simple measurement of agent clustering and correlation, based on coordination number data, provides an indirect measure of agent correlation and clustering effects, and can therefore be used to make a distinction between the validity of the different descriptions of the birth–death–movement process.

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Introduction: Dengue poses a problem for safe transfusion of blood components with confirmed reports of transfusion-transmission in Hong Kong and Singapore. The largest outbreak in 50 years occurred in North Queensland during 2008/2009 with more than 1,000 confirmed cases in Cairns and Townsville. During this outbreak, supplementary questioning for all donors was implemented, and fresh components were not manufactured from at risk donors. We aim to determine the seroprevalence of dengue exposure in this population during this epidemic. Methods: Samples were collected from blood donors during the 2008/2009 epidemic and 3 months after the last confirmed case. These samples were tested for anti-Dengue IgM, IgG and NS1 antigen with commercially available ELISA based assay kits from PanBio. Results: Initial analyses revealed 2.7% of samples from deferred donors were IgM repeat reactive. Of these, 16% were also positive for anti-dengue IgG, while none of these were positive for the NS1 viral antigen. However, two NS1 positives were found in samples collected from deferred donors. Conclusions: This initial analysis represents recent and cumulative past exposure in a presumed asymptomatic population, and will provide documentation of the rate of asymptomatic dengue infection during the epidemic. This data can also be used to assess the risk of dengue becoming endemic in North Queensland given that the mosquito vector is established in this region.

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An important aspect of robotic path planning for is ensuring that the vehicle is in the best location to collect the data necessary for the problem at hand. Given that features of interest are dynamic and move with oceanic currents, vehicle speed is an important factor in any planning exercises to ensure vehicles are at the right place at the right time. Here, we examine different Gaussian process models to find a suitable predictive kinematic model that enable the speed of an underactuated, autonomous surface vehicle to be accurately predicted given a set of input environmental parameters.

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A new method for the direct aryl iodination of isoindolines and isoindoline nitroxides which utilizes periodic acid and potassium iodide in sulfuric acid is presented. Di-iodo functionalized tetramethyl and tetraethyl isoindolines and a di-iodo tetramethyl isoindoline nitroxide were prepared in high yield (70-82%). The analogous mono-iodo species were afforded in modest yield (34-48%). Iodinated nitrones were also obtained from a tetraethyl isoindoline nitroxide.

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There is a long and fundamental connection between rural place and the land. Whereas land is simply the foundation for the construction of towns and cities, whose urban culture and economy thrives on human ingenuity and industry that may have little direct attachment to the physical ground over which it occurs, historical discourses of rurality place the land at the heart of the rural economy and society. Rural people, such discourses hold, live on the land, work the land, tend the land and know the land. The land formed not only the base of the rural economy (as ‘a physical, tangible resource which can be ploughed, sown, grazed, built upon’ (Macnaghten and Urry 1998, 200)), but also shaped rural culture and the rural calendar, and contributed to the constitution of the rural character (see Bell 1994). As such, the land is central to rural sense of place.

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China’s biggest search engine has a constitutional right to filter its search results, a US court found last month. But that’s just the start of the story. Eight New York-based pro-democracy activists sued Baidu Inc in 2011, seeking damages because Baidu prevents their work from showing up in search results. Baidu follows Chinese law that requires it to censor politically sensitive results. But in what the plaintiffs’ lawyer has dubbed a “perfect paradox”, US District Judge Jesse Furman has dismissed the challenge, explaining that to hold Baidu liable for its decisions to censor pro-democracy content would itself infringe the right to free speech.

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One cannot help but be impressed by the inroads that digital oilfield technologies have made into the exploration and production (E&P) industry in the past decade. Today’s production systems can be monitored by “smart” sensors that allow engineers to observe almost any aspect of performance in real time. Our understanding of how reservoirs are behaving has improved considerably since the dawn of this revolution, and the industry has been able to move away from point answers to more holistic “big picture” integrated solutions. Indeed, the industry has already reaped the rewards of many of these kinds of investments. Many billions of dollars of value have been delivered by this heightened awareness of what is going on within our assets and the world around them (Van Den Berg et al. 2010).

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This article develops methods for spatially predicting daily change of dissolved oxygen (Dochange) at both sampled locations (134 freshwater sites in 2002 and 2003) and other locations of interest throughout a river network in South East Queensland, Australia. In order to deal with the relative sparseness of the monitoring locations in comparison to the number of locations where one might want to make predictions, we make a classification of the river and stream locations. We then implement optimal spatial prediction (ordinary and constrained kriging) from geostatistics. Because of their directed-tree structure, rivers and streams offer special challenges. A complete approach to spatial prediction on a river network is given, with special attention paid to environmental exceedances. The methodology is used to produce a map of Dochange predictions for 2003. Dochange is one of the variables measured as part of the Ecosystem Health Monitoring Program conducted within the Moreton Bay Waterways and Catchments Partnership.

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The upstream oil & gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data”—that is, the ability to apply more sophisticated types of analytical tools to information in a way that extracts new insights or creates new forms of value—is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil & gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This paper examines existing data management practices in the upstream oil & gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the Big Data revolution. The comparison shows that, in companies that are leading the Big Data revolution, data is regarded as a valuable asset. The presented evidence also shows, however, that this is usually not true within the oil & gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how upstream oil & gas companies could potentially extract more value from data, and concludes with a series of specific technical and management-related recommendations to this end.

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The importance of clean drinking water in any community is absolutely vital if we as the consumers are to sustain a life of health and wellbeing. Suspended particles in surface waters not only provide the means to transport micro-organisms which can cause serious infections and diseases, they can also affect the performance capacity of a water treatment plant. In such situations pre-treatment ahead of the main plant is recommended. Previous research carried out using non-woven synthetic as a pre-filter materials for protecting slow sand filters from high turbidity showed that filter run times can be extended by several times and filters can be regenerated by simply removing and washing of the fabric ( Mbwette and Graham, 1987 and Mbwette, 1991). Geosynthetic materials have been extensively used for soil retention and dewatering in geotechnical applications and little research exists for the application of turbidity reduction in water treatment. With the development of new materials in geosynthetics today, it was hypothesized that the turbidity removal efficiency can be improved further by selecting appropriate materials. Two different geosynthetic materials (75 micron) tested at a filtration rate of 0.7 m/h yielded 30-45% reduction in turbidity with relatively minor head loss. It was found that the non-woven geotextile Propex 1701 retained the highest performance in both filtration efficiency and head loss across the varying turbidity ranges in comparison to other geotextiles tested. With 5 layers of the Propex 1701 an average percent reduction of approximately 67% was achieved with a head loss average of 4mm over the two and half hour testing period. Using the data collected for the Propex 1701 a mathematical model was developed for predicting the expected percent reduction given the ability to control the cost and as a result the number of layers to be used in a given filtration scenario.

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The upstream oil and gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data” is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil and gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This viewpoint examines existing data management practices in the upstream oil and gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the way in Big Data. The comparison shows that, in companies that are widely considered to be leaders in Big Data analytics, data is regarded as a valuable asset—but this is usually not true within the oil and gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how the industry could potentially extract more value from data, and concludes with a series of policy-related questions to this end.

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A key component of robotic path planning is ensuring that one can reliably navigate a vehicle to a desired location. In addition, when the features of interest are dynamic and move with oceanic currents, vehicle speed plays an important role in the planning exercise to ensure that vehicles are in the right place at the right time. Aquatic robot design is moving towards utilizing the environment for propulsion rather than traditional motors and propellers. These new vehicles are able to realize significantly increased endurance, however the mission planning problem, in turn, becomes more difficult as the vehicle velocity is not directly controllable. In this paper, we examine Gaussian process models applied to existing wave model data to predict the behavior, i.e., velocity, of a Wave Glider Autonomous Surface Vehicle. Using training data from an on-board sensor and forecasting with the WAVEWATCH III model, our probabilistic regression models created an effective method for forecasting WG velocity.