Yes / The main objective of this research is to investigate solid-state destabilization mechanism and kinetics of the model cocrystal caffeine : malonic acid (CA:MO) in presence of oxalic acid (OX) as a structural competitor. Competitive destabilization of CA:MO and subsequent formation of CA:OX takes place at temperatures significantly below its melting point. Destabilization mechanism was found to be mediated by sublimation of both CA:MO and OX. During CA:MO destabilization, free CA could not be detected and direct transformation to CA:OX cocrystal was observed. The destabilization kinetics follow Prout-Tompkins nucleation and crystal growth model with activation energy of 133.91 kJ/mol and subsequent CA:OX growth kinetic follow Ginstling – Brounshtien diffusion model with activation energy of kJ/mol.

The dissertation forms part of an ongoing project for the modelling and eventual control of an electric arc furnace (EAF) process. The main motivation behind such a project is the potential benefits that can result from automation of a process that has largely been operator controlled, often with results that leave sufficient room for improvement. Previous work in the project has resulted in the development of a generic model of the process. A later study concentrated on the control of the EAF where economic factors were taken into account. Simulation results from both studies clearly demonstrate the benefits that can accrue from successful implementation of process control. A major drawback to the practical implementation of the results is the lack of a model that is proven to be an accurate depiction of the specific plant where control is to be applied. Furthermore, the accuracy of any process model can only be verified against actual process data. There lies the raison d'etre for this dissertation: to take the existing model from the simulation environment to the real process. The main objective is to obtain a model that is able to mimic a selected set of process outputs. This is commonly a problem of system identification (SID): to select an appropriate model then fit the model to plant input/output data until the model response is similar to the plant under the same inputs (and initial conditions). The model fitting is carried out on an existing EAF model primarily by estimation of the model parameters for the EAF refining stage. Therefore the contribution of this dissertation is a model that is able to depict the EAF refining stage with reasonable accuracy. An important aspect of model fitting is experiment design. This deals with the selection of inputs and outputs that must be measured in order to estimate the desired parameters. This constitutes the problem of identifiability: what possibilities exist for estimating parameters using available I/O data or, what additional data is necessary to estimate desired parameters. In the dissertation an analysis is carried out to determine which parameters are estimable from available data. For parameters that are not estimable recommendations are made about additional measurements required to remedy the situation. Additional modelling is carried out to adapt the model to the particular process. This includes modelling to incorporate the oxyfuel subsystem, the bath oxygen content, water cooling and the effect of foaming on the arc efficiency. / Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2006. / Electrical, Electronic and Computer Engineering / unrestricted

Numerous biological phenomena occur as a result of macromolecular interactions. Metal-ion-biomolecule binding account for a large portion of these reactions, and unsurprisingly, a vast amount of new research in this area is constantly emerging. Gaining insight into the characteristics that define these interactions; including equilibrium fluctuations, metal center formation, global stability perturbation, cooperativity, allostery, and site-specific binding are all significant. As with all chemical reactions, biological interactions are regulated by thermodynamics; and the development of novel tools and methods by which to study these interactions becomes highly relevant. In this dissertation, three systems involving macromolecular binding are studied using well established biophysical techniques in conjunction with a critical look at appropriate uses for mathematical modeling. The first system studied is that of the serpin plasminogen activator inhibitor-1 (PAI-1). PAI-1 is a protease inhibitor that specifically effects fibrinolysis, or the process that prevents the formation of blood clots, and misregulation of this enzyme leads to uncontrollable hemorrhaging. ITC was utilized to investigate the thermodynamics of copper binding to PAI-1. Human carbonic anhydrase II (CA) was the second system investigated. Studies were conducted on zinc(II) and copper(II) binding to CA, a metalloenzyme responsible for acid-base balances in the blood and the transport of carbon dioxide. Interestingly, CA binds two copper(II) ions, one at the active site, and one at a higher affinity N-terminal site. Temperature dependent ITC, CD and GdnHCl denaturation studies were performed to explore the impact of copper(II) binding, particularly at the higher affinity N-terminal site. Finally, protein binding to inorganic gold nanoparticles (AuNPs) was investigated. AuNPS are utilized in areas of diagnostics, biological sensing and drug delivery. We studied binding of nanoparticles to a set of six biologically relevant proteins; glutathione, wild-type GB3, K19C GB3 (a variant at position 19), bovine CA, bovine serum albumin, and fibrinogen. Nanoparticle-protein binding was monitored via UV-Visible extinction and polarized resonance synchronous spectroscopy (PRS2). The UV extinction maxima wavelength shifts were fit with two models, a Langmuir isotherm model and a mass action-derived model. The models fit the data equally well, however, they predict very different Kd values, specifically for smaller sized AuNPs.

In the past decades, a great variety of model fitting and model free (isoconversional) methods have been developed for extracting kinetic parameters for solid state reactions from thermally stimulated experimental data (TGA, DSC, DTA etc.). However, these methods have met with significant controversies about their methodologies. Firstly, model-fitting methods have been strongly criticized because almost any reaction mechanism can be used to fit the experimental data satisfactorily with drastic variations of the kinetic parameters, and no good criterion exists to tell which mechanism is the best choice. Secondly, previous model free methods originated from the isoconversional principle, which is often called the basic assumption; previous studies comparing the accuracy of model free methods have not paid attention to the influence of the principle on model free methods and, therefore, their conclusions are problematic. This work gives, firstly, a critical study of previous methods for evaluating kinetic parameters of solid state reactions and a critical analysis of the isoconversional principle of model free methods. Then an analysis is given of the invariant kinetic parameters method and recommends an incremental version of it. Based on the incremental method and model free method, a comprehensive method is proposed that predicts the degree of the dependences of activation energy on heating programs, and obtains reliable kinetic parameters. In addition, this work also compares the accuracy of previous methods and gives recommendations to apply them to kinetic studies.

This research explores factor analysis applied to data from skewed distributions for the general skew model, the selection-elliptical model, the selection-normal model, the skew-elliptical model and the skew-normal model for finite sample sizes. In terms of asymptotics, or large sample sizes, quasi-maximum likelihood methods are broached numerically. The skewed models are formed using selection distribution theory, which is based on Rao’s weighted distribution theory. The models assume the observed variable of the factor model is from a skewed distribution by defining the distribution of the unobserved common factors skewed and the unobserved unique factors symmetric. Numerical examples are provided using maximum likelihood selection skew-normal factor analysis. The numerical examples, such as maximum likelihood parameter estimation with the resolution of the “sign switching” problem and model fitting using likelihood methods, illustrate that the selection skew-normal factor analysis model better fits skew-normal data than does the normal factor analysis model.

No description available.

This thesis deals with automatic localization of thin surgical tools such as needles or electrodes in 3D ultrasound images. The precise and reliable localization is important for medical interventions such as needle biopsy or electrode insertion into tissue. The reader is introduced to basics of medical ultrasound (US) imaging. The state of the art localization methods are reviewed in the work. Many methods such as Hough transform (HT) or Parallel Integral Projection (PIP) are based on projections. As the existing PIP implementations are relatively slow, we suggest an acceleration by using a multiresolution approach. We propose to use model fitting approach which uses randomized sample consensus (RANSAC) and local optimization. It is a fast method suitable for real-time use and it is robust with respect to the presence of other high-intensity structures in the background. We propose two new shape and appearance models of tool in 3D US images. Tool localization can be improved by exploiting its tubularity. We propose a tool model which uses line filtering and we incorporated it into the model fitting scheme. The robustness of such localization algorithm is improved at the expense of additional time for pre-processing. The real-time localization using the shape model is demonstrated by implementation on the 3D US scanner Ultrasonix RP. All proposed methods were tested on simulated data, phantom US data (a replacement for a tissue) and real tissue US data of breast with biopsy needle. The proposed methods had comparable accuracy and the lower number of failures than the state of the art projection based methods.

Prescribed (i.e., controlled) burning is a common practice used in many vegetation types in the world to accomplish a wide range of land management objectives including wildfire risk reduction, wildlife habitat improvement, forest regeneration, and land clearing. To properly apply controlled fire and reduce unwanted fire behavior, an improved understanding of fundamental processes related to combustion of live and dead vegetation is needed. Since the combustion process starts with pyrolysis, there is a need for more data and better models of pyrolysis of live and dead fuels. In this study, slow pyrolysis experiments were carried out in a pyrolyzer apparatus and a Thermogravimetric analyzer (TGA) under oxygen free environment in three groups of experiments. In the first group, the effects of temperature (400–800 °C), a slow heating rate (H.R.) (5–30 °C min−1), and carrier gas flow rate (50–350 ml min−1) on yields of tar and light gas obtained from pyrolysis of dead longleaf pine litter in the pyrolyzer apparatus were investigated to find the optimum condition which results in the maximum tar yield. In the second group of experiments, 14 plant species (live and dead) native to forests in the southern United States, were heated in the pyrolyzer apparatus at the optimum condition. A gas chromatograph equipped with a mass spectrometer (GC–MS) and a gas chromatograph equipped with a thermal conductivity detector (GC-TCD) were used to study the speciation of tar and light gases, respectively. In the third group of experiments, the slow pyrolysis experiments for all plant species (live and dead) were carried out in the TGA at 5 different heating rates ranged from 10 to 30 ℃ min-1 to study the kinetics of pyrolysis. The results showed that the highest tar yield was obtained at a temperature of 500 °C, heating rate of 30 °C min−1, and sweep gas flow rate of 100 ml min−1. In addition, the tar composition is dominated by oxygenated aromatic compounds consisting mainly of phenols. The light gas analysis showed that CO and CO2 were the dominant light gas species for all plant samples on a dry wt% basis, followed by CH4 and H2. The kinetics of pyrolysis was studied using one model-free method and three model-fitting methods. First, the model-free method of Kissinger-Akahira-Sunose (KAS) was used to calculate the rates of pyrolysis as a function of the extent of conversion. The results showed that different plant species had different rates at different conversions. Then, three model fitting methods were used to find the kinetic parameters to potentially provide a single rate for each plant species. The results showed that the simple one-step model did not fit the one-peak pyrolysis data as well as the distributed activation energy model (DAEM) model. The multiple-reaction DAEM model provided very good fits to the experimental data where multiple peaks were observed, even at different heating rates.

No description available.

Mortality information is very important for national planning and health of a country. Mortality rate forecasting is a basic contribution for the projection of financial improvement of pension plans, well-being and social strategy planning. In the first part of the thesis, we fit the selected mortality rate models, namely the Power-exponential function based model, the ModifiedPerks model and the Heligman and Pollard (HP4) model to the data obtained from the HumanMortality Database [22] for the male population ages 1–70 of the USA, Japan and Australia. We observe that the Heligman and Pollard (HP4) model performs well and better fit the data as compared to the Power-exponential function based model and the Modified Perks model. The second part is to systematically compare the quality of the mortality rate forecasting using the second order Lee–Carter method with the selected mortality rate models. The results indicate that Power-exponential function based model and the Heligman and Pollard (HP4) model gives a more reliable forecast depending on individual countries.