58 resultados para nucl-th

em Queensland University of Technology - ePrints Archive


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Total cross sections for neutron scattering from nuclei, with energies ranging from 10 to 600 MeV and from many nuclei spanning the mass range 6Li to 238U, have been analyzed using a simple, three-parameter, functional form. The calculated cross sections are compared with results obtained by using microscopic (g-folding) optical potentials as well as with experimental data. The functional form reproduces those total cross sections very well. When allowance is made for Ramsauer-like effects in the scattering, the parameters of the functional form required vary smoothly with energy and target mass. They too can be represented by functions of energy and mass.

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Raman spectra of the uranyl titanate mineral euxenite were analyzed and related to the mineral structure. A comparison is made with the Raman spectra of uranyl oxyhydroxide hydrates. The obsd. bands are attributed to the Ti[n.63743]O and (UO2)2+ stretching and bending vibrations, as well as lattice vibrations of rare-earth ions. The Raman bands of euxenite are in harmony with those of the uranyl oxyhydroxides. The mineral euxenite is metamict as is evidenced by the intensity of the U[n.63743]O stretching and bending modes, which are of lower intensity than expected, and with bands that are significantly broader.

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It is increasingly apparent that sea-level data (e.g. microfossil transfer functions, dated coral microatolls and direct observations from satellite and tidal gauges) vary temporally and spatially at regional to local scales, thus limiting our ability to model future sea-level rise for many regions. Understanding sealevel response at ‘far-field’ locations at regional scales is fundamental for formulating more relevant sea-level rise susceptibility models within these regions under future global change projections. Fossil corals and reefs in particular are valuable tools for reconstructing past sea levels and possible environmental phase shifts beyond the temporal constraints of instrumental records. This study used abundant surface geochronological data based on in situ subfossil corals and precise elevation surveys to determine previous sea level in Moreton Bay, eastern Australia, a far-field site. A total of 64 U-Th dates show that relative sea level was at least 1.1 m above modern lowest astronomical tide (LAT) from at least ˜6600 cal. yr BP. Furthermore, a rapid synchronous demise in coral reef growth occurred in Moreton Bay ˜5800 cal. yr BP, coinciding with reported reef hiatus periods in other areas around the Indo-Pacific region. Evaluating past reef growth patterns and phases allows for a better interpretation of anthropogenic forcing versus natural environmental/climatic cycles that effect reef formation and demise at all scales and may allow better prediction of reef response to future global change.

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In truck manufacturing, the exhaust and air inlet pipes are specialized equipment that requires highly skilled, heavy machinery and small batch production methods. This paper describes a project to develop the computer numerically controlled (CNC) pipe bending process for a truck component manufacturer. The company supplies a huge range of heavy duty truck parts to the domestic market and is a significant supplier in Australia. The company has been using traditional methods of machine assisted manual pipe bending techniques. In a drive of continuous improvement, the company has acquired a pre-owned CNC bending machine capable of bending pipes automatically up to 25 bends. However, due to process mismatch, this machine is only used for single bending operation. The researchers studied the bending system and changed the manufacturing process. Using an example exhaust pipe as the benchmark, a significant drop of manufacturing lead time from 70 minutes to 40 minutes for each pipe was demonstrated. There was also a decrease of material cost due to the multiple bends part in one piece without cutting excessive materials for each single bend like it used to be.

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Infertility is a social onus for women in Iran, who are expected to produce children early within marriage. With its estimated 1.5 million infertile couples, Iran is the only Muslim country in which assisted reproductive technologies (ARTs) using donor gametes and embryos have been legitimized by religious authorities and passed into law. Th is has placed Iran, a Shia-dominant country, in a unique position vis-à-vis the Sunni Islamic world, where all forms of gamete donation are strictly prohibited. In this article, we first examine the “Iranian ART revolution” that has allowed donor technologies to be admitted as a form of assisted reproduction. Then we examine the response of Iranian women to their infertility and the profound social pressures they face. We argue that the experience of infertility and its treatment are mediated by women’s socioeconomic position within Iranian society. Many women lack economic access to in vitro fertilization (IVF) technologies and fear the moral consequences of gamete donation. Thus, the benefits of the Iranian ART revolution are mixed: although many Iranian women have been able to overcome their infertility through ARTs, not all women’s lives are improved by these technologies.

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Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

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Despite the important physiological role of periosteum in the pathogenesis and treatment of osteoporosis, little is known about the structural and cellular characteristics of periosteum in osteoporosis. To study the structural and cellular differences in both diaphyseal and metaphyseal periosteum of osteoporotic rats, samples from the right femur of osteoporotic and normal female Lewis rats were collected and tissue sections were stained with hematoxylin and eosin, antibodies or staining kit against tartrate resistant acid phosphatase (TRAP), alkaline phosphatase (ALP), vascular endothelial growth factor (VEGF), von Willebrand (vWF), tyrosine hydroxylase (TH) and calcitonin gene-related peptide (CGRP). The results showed that the osteoporotic rats had much thicker and more cellular cambial layer of metaphyseal periosteum compared with other periosteal areas and normal rats (P\0.001). The number of TRAP? osteoclasts in bone resorption pits, VEGF? cells and the degree of vascularization were found to be greater in the cambial layer of metaphyseal periosteum of osteoporotic rats (P\0.05), while no significant difference was detected in the number of ALP? cells between the two groups. Sympathetic nerve fibers identified by TH staining were predominantly located in the cambial layer of metaphyseal periosteum of osteoporotic rats. No obvious difference in the expression of CGRP between the two groups was found. In conclusion, periosteum may play an important role in the cortical bone resorption in osteoporotic rats and this pathological process may be regulated by the sympathetic nervous system.

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Experts in injection molding often refer to previous solutions to find a mold design similar to the current mold and use previous successful molding process parameters with intuitive adjustment and modification as a start for the new molding application. This approach saves a substantial amount of time and cost in experimental based corrective actions which are required in order to reach optimum molding conditions. A Case-Based Reasoning (CBR) System can perform the same task by retrieving a similar case which is applied to the new case from the case library and uses the modification rules to adapt a solution to the new case. Therefore, a CBR System can simulate human e~pertise in injection molding process design. This research is aimed at developing an interactive Hybrid Expert System to reduce expert dependency needed on the production floor. The Hybrid Expert System (HES) is comprised of CBR, flow analysis, post-processor and trouble shooting systems. The HES can provide the first set of operating parameters in order to achieve moldability condition and producing moldings free of stress cracks and warpage. In this work C++ programming language is used to implement the expert system. The Case-Based Reasoning sub-system is constructed to derive the optimum magnitude of process parameters in the cavity. Toward this end the Flow Analysis sub-system is employed to calculate the pressure drop and temperature difference in the feed system to determine the required magnitude of parameters at the nozzle. The Post-Processor is implemented to convert the molding parameters to machine setting parameters. The parameters designed by HES are implemented using the injection molding machine. In the presence of any molding defect, a trouble shooting subsystem can determine which combination of process parameters must be changed iii during the process to deal with possible variations. Constraints in relation to the application of this HES are as follows. - flow length (L) constraint: 40 mm < L < I 00 mm, - flow thickness (Th) constraint: -flow type: - material types: I mm < Th < 4 mm, unidirectional flow, High Impact Polystyrene (HIPS) and Acrylic. In order to test the HES, experiments were conducted and satisfactory results were obtained.

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the creative city is one that builds ‘clusters of clusters’ to enable the self-management of complexity, the emergence of new ideas, and the growth of knowledge. Clash and difference is what allows for change and innovation.

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This paper argues a model of open systems evolution based on evolutionary thermodynamics and complex system science, as a design paradigm for sustainable architecture. The mechanism of open system evolution is specified in mathematical simulations and theoretical discourses. According to the mechanism, the authors propose an intelligent building model of sustainable design by a holistic information system of the end-users, the building and nature. This information system is used to control the consumption of energy and material resources in building system at microscopic scale, to adapt the environmental performance of the building system to the natural environment at macroscopic scale, for an evolutionary emergence of sustainable performance of buildings.

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Cell-based therapy is one of the major potential therapeutic strategies for cardiovascular, neuronal and degenerative diseases in recent years. Synthetic biodegradable polymers have been utilized increasingly in pharmaceutical, medical and biomedical engineering. Control of the interaction of living cells and biomaterials surfaces is one of the major goals in the design and development of new polymeric biomaterials in tissue engineering. The aims of this study is to develop a novel bio-mimic polymeric materials which will facilitate the delivery cells, control cell bioactivities and enhance the focal integration of graft cells with host tissues.

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Without the virtually free services of nature like clean air and water, humans would not last long. Natural systems can be incorporated in existing urban structures or spaces to add public amenity, mitigate the heat island effect, reduce pollution, add oxygen, and ensure water, electricity and food security in urban areas. Th ere are many eco-solutions that could radically reduce resource consumption and pollution and even provide surplus ecosystem services in the built environment at little or no operational cost, if adequately supported by design. Th is paper is the fi rst of a two part paper that explains what eco-services are, then provides examples of how design can generate natural as well as social capital. Using examples of actual and notional solutions, both papers set out to challenge designers to ‘think again’, and invent ways of creating net positive environmental gains through built environment design.

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Without the virtually free services of nature like clean air and water, humans would not last long. Natural systems can be incorporated in existing urban structures or spaces to add public amenity, mitigate the heat island eff ect, reduce pollution, add oxygen, and ensure water, electricity and food security in urban areas. Th ere are many eco-solutions that could radically reduce resource consumption and pollution and even provide surplus ecosystem services in the built environment at little or no operational cost, if adequately supported by design. Th is is the second part of a two part paper that explains what eco-services are, then provides examples of how design can generate natural as well as social capital. Using examples of actual and notional solutions, both papers set out to challenge designers to ‘think again’, and invent ways of creating net positive environmental gains through built environment design.