211 resultados para melody (parameter)


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The collective purpose of these two studies was to determine a link between the V02 slow component and the muscle activation patterns that occur during cycling. Six, male subjects performed an incremental cycle ergometer exercise test to determine asub-TvENT (i.e. 80% of TvENT) and supra-TvENT (TvENT + 0.75*(V02 max - TvENT) work load. These two constant work loads were subsequently performed on either three or four occasions for 8 mins each, with V02 captured on a breath-by-breath basis for every test, and EMO of eight major leg muscles collected on one occasion. EMG was collected for the first 10 s of every 30 s period, except for the very first 10 s period. The V02 data was interpolated, time aligned, averaged and smoothed for both intensities. Three models were then fitted to the V02 data to determine the kinetics responses. One of these models was mono-exponential, while the other two were biexponential. A second time delay parameter was the only difference between the two bi-exponential models. An F-test was used to determine significance between the biexponential models using the residual sum of squares term for each model. EMO was integrated to obtain one value for each 10 s period, per muscle. The EMG data was analysed by a two-way repeated measures ANOV A. A correlation was also used to determine significance between V02 and IEMG. The V02 data during the sub-TvENT intensity was best described by a mono-exponential response. In contrast, during supra-TvENT exercise the two bi-exponential models best described the V02 data. The resultant F-test revealed no significant difference between the two models and therefore demonstrated that the slow component was not delayed relative to the onset of the primary component. Furthermore, only two parameters were deemed to be significantly different based upon the two models. This is in contrast to other findings. The EMG data, for most muscles, appeared to follow the same pattern as V02 during both intensities of exercise. On most occasions, the correlation coefficient demonstrated significance. Although some muscles demonstrated the same relative increase in IEMO based upon increases in intensity and duration, it cannot be assumed that these muscles increase their contribution to V02 in a similar fashion. Larger muscles with a higher percentage of type II muscle fibres would have a larger increase in V02 over the same increase in intensity.

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Hydrogel polymers are used for the manufacture of soft (or disposable) contact lenses worldwide today, but have a tendency to dehydrate on the eye. In vitro methods that can probe the potential for a given hydrogel polymer to dehydrate in vivo are much sought after. Nuclear magnetic resonance (NMR) has been shown to be effective in characterising water mobility and binding in similar systems (Barbieri, Quaglia et al., 1998, Larsen, Huff et al., 1990, Peschier, Bouwstra et al., 1993), predominantly through measurement of the spin-lattice relaxation time (T1), the spinspin relaxation time (T2) and the water diffusion coefficient (D). The aim of this work was to use NMR to quantify the molecular behaviour of water in a series of commercially available contact lens hydrogels, and relate these measurements to the binding and mobility of the water, and ultimately the potential for the hydrogel to dehydrate. As a preliminary study, in vitro evaporation rates were measured for a set of commercial contact lens hydrogels. Following this, comprehensive measurement of the temperature and water content dependencies of T1, T2 and D was performed for a series of commercial hydrogels that spanned the spectrum of equilibrium water content (EWC) and common compositions of contact lenses that are manufactured today. To quantify material differences, the data were then modelled based on theory that had been used for similar systems in the literature (Walker, Balmer et al., 1989, Hills, Takacs et al., 1989). The differences were related to differences in water binding and mobility. The evaporative results suggested that the EWC of the material was important in determining a material's potential to dehydrate in this way. Similarly, the NMR water self-diffusion coefficient was also found to be largely (if not wholly) determined by the WC. A specific binding model confirmed that the we was the dominant factor in determining the diffusive behaviour, but also suggested that subtle differences existed between the materials used, based on their equilibrium we (EWC). However, an alternative modified free volume model suggested that only the current water content of the material was important in determining the diffusive behaviour, and not the equilibrium water content. It was shown that T2 relaxation was dominated by chemical exchange between water and exchangeable polymer protons for materials that contained exchangeable polymer protons. The data was analysed using a proton exchange model, and the results were again reasonably correlated with EWC. Specifically, it was found that the average water mobility increased with increasing EWe approaching that of free water. The T1 relaxation was also shown to be reasonably well described by the same model. The main conclusion that can be drawn from this work is that the hydrogel EWe is an important parameter, which largely determines the behaviour of water in the gel. Higher EWe results in a hydrogel with water that behaves more like bulk water on average, or is less strongly 'bound' on average, compared with a lower EWe material. Based on the set of materials used, significant differences due to composition (for materials of the same or similar water content) could not be found. Similar studies could be used in the future to highlight hydrogels that deviate significantly from this 'average' behaviour, and may therefore have the least/greatest potential to dehydrate on the eye.

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A review of the main rolling models is conducted to assess their suitability for modelling the foil rolling process. Two such models are Fleck and Johnson's Hertzian model and Fleck, Johnson, Mear and Zhang's Influence Function model. Both of these models are approximated through the use of perturbation methods. Decrease in the computation time resulted when compared with the numerical solution. The Hertzian model was approximated using the ratio of the yield stress of the strip to the plane-strain Young's Modulus of the rolls as the small perturbation parameter. The Influence Function model approximation takes advantage of the solution of the well-known Aerofoil Integral Equation to gain an insight into how the choice of interior boundary points affects the stability of numerical solution of the model's equations. These approximations require less computation than their full models and, in the case of the Hertzian approximation, only introduces a small error in the predictions of roll force roll torque. Hence the Hertzian approximate method is suitable for on-line control. The predictions from the Influence Function approximation underestimates the predictions from the numerical results. Better approximation of the pressure in the plastic reduction regions is the main source of this error.

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This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method. Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7. Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time. Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.

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Assessment of the condition of connectors in the overhead electricity network has traditionally relied on the heat dissipation or voltage drop from existing load current (50Hz) as a measurable parameter to differentiate between satisfactory and failing connectors. This research has developed a technique which does not rely on the 50Hz current and a prototype connector tester has been developed. In this system a high frequency signal is injected into the section of line under test and measures the resistive voltage drop and the current at the test frequency to yield the resistance in micro-ohms. From the value of resistance a decision as to whether a connector is satisfactory or approaching failure can be made. Determining the resistive voltage drop in the presence of a large induced voltage was achieved by the innovative approach of using a representative sample of the magnetic flux producing the induced voltage as the phase angle reference for the signal processing rather than the phase angle of the current, which can be affected by the presence of nearby metal objects. Laboratory evaluation of the connector tester has validated the measurement technique. The magnitude of the load current (50Hz) has minimal effect on the measurement accuracy. Addition of a suitable battery based power supply system and isolated communications, probably radio and refinement of the printed circuit board design and software are the remaining development steps to a production instrument.

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Several lines of evidence implicate the p38 mitogen-activated protein kinase (p38 MAPK) in the proinflammatory response to bacterial agents and cytokines. Equally, the transcription factor, nuclear factor (NF)-kappaB, is recognized to be a critical determinant of the inflammatory response in intestinal epithelial cells (IECs). However, the precise inter-relationship between the activation of p38 MAPK and activation of the transcription factor NF-kappaB in the intestinal epithelial cell (IEC) system, remains unknown. Here we show that interleukin (IL)-1beta activates all three MAPKs in Caco-2 cells. The production of IL-8 and monocyte chemotactic protein 1 (MCP-1) was attenuated by 50% when these cells were preincubated with the p38 MAPK inhibitor, SB 203580. Further investigation of the NF-kappaB signalling system revealed that the inhibitory effect was independent of the phosphorylation and degradation of IkappaBalpha, the binding partner of NF-kappaB. This effect was also independent of the DNA binding of the p65 Rel A subunit, as well as transactivation, determined by an NF-kappaB luciferase construct, using both SB 203580 and dominant-negative p38 MAPK. Evaluation of IL-8 and MCP-1 RNA messages by reverse transcription-polymerase chain reaction (RT-PCR) revealed that the inhibitory effect of SB 203580 was associated with a reduction in this parameter. Using an IL-8-luciferase promoter construct, an effect of p38 upon its activation by both pharmacological and dominant-negative p38 construct co-transfection was demonstrated. It is concluded that p38 MAPK influences the expression of chemokines in intestinal epithelial cells, through an effect upon the activation of the chemokine promoter, and does not directly involve the activation of the transcription factor NF-kappaB

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One major gap in transportation system safety management is the ability to assess the safety ramifications of design changes for both new road projects and modifications to existing roads. To fulfill this need, FHWA and its many partners are developing a safety forecasting tool, the Interactive Highway Safety Design Model (IHSDM). The tool will be used by roadway design engineers, safety analysts, and planners throughout the United States. As such, the statistical models embedded in IHSDM will need to be able to forecast safety impacts under a wide range of roadway configurations and environmental conditions for a wide range of driver populations and will need to be able to capture elements of driving risk across states. One of the IHSDM algorithms developed by FHWA and its contractors is for forecasting accidents on rural road segments and rural intersections. The methodological approach is to use predictive models for specific base conditions, with traffic volume information as the sole explanatory variable for crashes, and then to apply regional or state calibration factors and accident modification factors (AMFs) to estimate the impact on accidents of geometric characteristics that differ from the base model conditions. In the majority of past approaches, AMFs are derived from parameter estimates associated with the explanatory variables. A recent study for FHWA used a multistate database to examine in detail the use of the algorithm with the base model-AMF approach and explored alternative base model forms as well as the use of full models that included nontraffic-related variables and other approaches to estimate AMFs. That research effort is reported. The results support the IHSDM methodology.

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In Australia and many other countries worldwide, water used in the manufacture of concrete must be potable. At present, it is currently thought that concrete properties are highly influenced by the water type used and its proportion in the concrete mix, but actually there is little knowledge of the effects of different, alternative water sources used in concrete mix design. Therefore, the identification of the level and nature of contamination in available water sources and their subsequent influence on concrete properties is becoming increasingly important. Of most interest, is the recycled washout water currently used by batch plants as mixing water for concrete. Recycled washout water is the water used onsite for a variety of purposes, including washing of truck agitator bowls, wetting down of aggregate and run off. This report presents current information on the quality of concrete mixing water in terms of mandatory limits and guidelines on impurities as well as investigating the impact of recycled washout water on concrete performance. It also explores new sources of recycled water in terms of their quality and suitability for use in concrete production. The complete recycling of washout water has been considered for use in concrete mixing plants because of the great benefit in terms of reducing the cost of waste disposal cost and environmental conservation. The objective of this study was to investigate the effects of using washout water on the properties of fresh and hardened concrete. This was carried out by utilizing a 10 week sampling program from three representative sites across South East Queensland. The sample sites chosen represented a cross-section of plant recycling methods, from most effective to least effective. The washout water samples collected from each site were then analysed in accordance with Standards Association of Australia AS/NZS 5667.1 :1998. These tests revealed that, compared with tap water, the washout water was higher in alkalinity, pH, and total dissolved solids content. However, washout water with a total dissolved solids content of less than 6% could be used in the production of concrete with acceptable strength and durability. These results were then interpreted using chemometric techniques of Principal Component Analysis, SIMCA and the Multi-Criteria Decision Making methods PROMETHEE and GAIA were used to rank the samples from cleanest to unclean. It was found that even the simplest purifying processes provided water suitable for the manufacture of concrete form wash out water. These results were compared to a series of alternative water sources. The water sources included treated effluent, sea water and dam water and were subject to the same testing parameters as the reference set. Analysis of these results also found that despite having higher levels of both organic and inorganic properties, the waters complied with the parameter thresholds given in the American Standard Test Method (ASTM) C913-08. All of the alternative sources were found to be suitable sources of water for the manufacture of plain concrete.

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This paper suggests an approach for finding an appropriate combination of various parameters for extracting texture features (e.g. choice of spectral band for extracting texture feature, size of the moving window, quantization level of the image, and choice of texture feature etc.) to be used in the classification process. Gray level co-occurrence matrix (GLCM) method has been used for extracting texture from remotely sensed satellite image. Results of the classification of an Indian urban environment using spatial property (texture), derived from spectral and multi-resolution wavelet decomposed images have also been reported. A multivariate data analysis technique called ‘conjoint analysis’ has been used in the study to analyze the relative importance of these parameters. Results indicate that the choice of texture feature and window size have higher relative importance in the classification process than quantization level or the choice of image band for extracting texture feature. In case of texture features derived using wavelet decomposed image, the parameter ‘decomposition level’ has almost equal relative importance as the size of moving window and the decomposition of images up to level one is sufficient and there is no need to go for further decomposition. It was also observed that the classification incorporating texture features improves the overall classification accuracy in a statistically significant manner in comparison to pure spectral classification.

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This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

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This paper presents bonding technology of aluminum alloy by hot-dipping tin. The dissolution curve of copper in molten tin liquid was obtained in the experiment of hot-dipping Sn. Optimal hot-dipping parameter which was suitable for soldering was designed. To elucidate characteristics of interfacial evolution, the microstructure of the coatings, soldered joint were analyzed using optical microscopy, SEM and EDX. The shear strength of soldered joints was tested as high as 39.9Mpa, which is high enough to achieve the requirement of electronic industry.