This research work has investigated the electrochemistry of glutathione (GSH)and homocysteine (HCSH) in order to develop sensors for these biological thiols.Ru(bpy)33+ and IrCl62− have been used as mediators for the electrooxidation of GSH andHCSH because direct oxidation of these thiols is slow at most conventional electrodes.The electrochemical detection of GSH and HCSH has been pursued because of their biological roles. Concerted proton electron transfer (CPET) and stepwise proton electron transfer(PT/ET) pathways have been observed in the electrooxidation of GSH and HCSH.Oxidation of GSH by Ru(bpy)33+ carried out in deuterated and undeuterated buffered (pH= pD = 5.0) and unbuffered solutions (pH = pD 5.0−9.0) indicates a CPET pathway. AtpH 7.0 buffered solution, the involvement of the buffer was obvious, with rate increasing as the buffer concentration increases − an indication of a general base catalysis. The oxidation of GSH by IrCl62− follows through CPET at pH 7.0 when the optimum concentration of the buffer is established. The plot of the rate vs. buffer concentration gave a curvature at lower buffer concentration and then a plateau at higher concentration,which implies a change in the rate determining step as the buffer concentration increases.At lower buffer concentration, proton transfer was seen to be the rate determining step asthe reduction current increases upon scan rate increase. In the oxidation of HCSH by IrCl62−, CPET was observed at pH = pD values of7.0 and 8.0, whereas PT/ET was seen at pH = pD values of 9.0 and 10. Increase in the buffer concentration at pH 7.0 revealed the contribution of the buffer, in that, the oxidation proceeds more efficiently, seeing that the catalytic peak current shifts more negatively and the peak broadness diminishes. Increase in the temperature for the electrooxidation of HCSH resulted in increase in the rate.

Return on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done. The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with. To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with. Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE. / Mathematical Sciences / M. Sc. (Statistics)

有鑑於國內投資人已漸漸注重基本分析，因此本研究將以電器電纜業為例，針對一般股票評價模式作研究。首先比較各種評價理論所計算出的結果與市場實際價格之間的落差，進行研究之後，分析各種評價法落差的情形，進而尋求對電器電纜業最適當的評價方法。並以此預測電器電纜公司88年底之實質價值，再與實際價格比較之後，提供投資大眾買進賣出之參考。 首先將過去文獻資料中影響公司成長的因素，利用相關分析以及逐步迴歸法，找出影響電器電纜公司銷售額成長的主要因素，以這些因素建構一條迴歸方程式，作為計算各公司未來成長率的依據。以過去最常用到的六種評價模式：現金流量折現法、會計盈餘折現法、本益比法、價格/帳面價值比法、價格/銷售比法及選擇權定價法來研究其效果。實際作法是分別計算各電器電纜公司79年至83年的價值，再與其各年度之實際股價比較，以Theil’s U值找出最佳之評價模式。最後則是利用所選出最適合我國電器電纜業的股票評價模式，配合第一部份所得之成長率，推算電器電纜業公司89-93年之財務報表，藉以算出其88年底的實質價值。 本研究之實證結果為：由總體經濟自變數之相關分析中可得，本研究在經濟面採用我國經濟成長率（E1）、台幣兌美元匯率（E2）、躉受物價指數（E4）、貨幣市場利率（E5）以及股票價格指數（E6）等五個變數；再加上影響公司營運成果的11個財務比率，引入逐步迴歸模式中。結果發現電器電纜業銷售額模式中，投入變數順序為固定資產週轉率(C4)、存貨週轉率(C3)、總資產週轉率(C5)、賺得利息倍數(C7)、躉售物價指數(E4)、負債比率(C6)以及貨幣市場利率(E5)。第二部分的實證結果結果發現市價帳面價值法為電線電纜業最佳的評價模式，其次為市價銷售額法。因此本研究最後以市價帳面價值法來計算電器電纜公司88年底之股價，提供大眾作為投資時的參考。

本研究的目的為研究台灣省在1990年之15-17歲的在學率，在找出可能影響因素並蒐集好相關的資料後，我們將蒐集到的資料分成兩個部份，一個部份用來建造模型，而另一個部份則用來測試所建立出來的模型。主要的過程是：先利用簡單迴歸模型了解各個可能的因素對於15-17歲的在學率的影響程度，經過許多分析及了解後再對這些變數採取可能的變數轉換(variable transformations)，而後再利用三種常用的統計迴歸方法﹝包含有逐步迴歸(stepwise regression)方法、前進選擇(forward selection)方法以及後退消除(backward elimination)方法﹞去發展出一個適當的複迴歸模型(multiple regression model)。對於這個模型，以實際的台灣在學情況來看，我們看不出它有任何的不合理；同時也利用圖形及檢定去驗證模型的假設，其次還做有關迴歸參數的推論(inferences about regression parameters)。再其次，我們運用變異數分析的結果(analysis of variance results)以及新觀察值的預測情形(predictions of new observations)來評估模型的預測能力。最後並利用所得到的最適當的模型，對如何提昇15-17歲青少年的在學率給予適當的建議。 / The objective of this research is to study what factors may affect the schooling rates of 15-17 years old in Taiwan province in 1990. After finding out some possible factors and collecting those data regarding those factors, we separate the data (by stratified random sampling) into two sets. One set is used to construct the model, and the other set shall be used to test the model. The main process to build a regression model is as follows. First, we shall use simple linear regression models to help us to see if each factor may have relation with the schooling rates. With the analysis of residuals and so on, we then make appropriate transformations on each of these factors. Finally, we use three common statistical regression techniques (including stepwise regression, forward selection, and backward elimination methods) to develop a suitable multiple regression model. It seems that, by our understanding of schooling rates in Taiwan, this model is not unreasonable. In addition, we verify the assumptions of the model by graphical methods and statistical tests. We also do the inferences about regression parameters. Furthermore, ye use the results of the analysis of variance and predictions of new observations to evaluate the prediction ability of the model. Finally, we use the most appropriate multiple regression model to give some suggestions to improve (or keep) the schooling rates of 15-17 years old.

<p>This work consists of two parts: the theoretical and the experimental. In the theoretical part, some general and some language specific theories of tense, aspect and aktionsart are presented, and the temporal systems of Swedish and Polish are compared. </p><p>The theoretical part is not a mere review of the literature on the subject. The comparison of the descriptions of aspect and aktionsart by Slavic researchers with the universal theory of Smith (1991) and (1977) and with description of aktionsart in Swedish in Teleman et al. (1999) has allowed me for some important observations as to the nature of the long-lasting dispute about the differences between aspect and aktionsart.</p><p>The experimental part is a cross-sectional study on the role of the linguistic context on the acquisition of the pluperfect by Polish learners Swedish as a foreign language. The informants are university students studying Swedish as a foreign language. The language samples were collected by means of two types of tests: gap-filling and translation from Polish. </p><p>Twelve linguistic factors, each divided into two subgroups, were hypothesised to have affected the correct use of the pluperfect. All those hypotheses as to which of the subgroups may inhibit and which may facilitate the correct use of the pluperfect are grounded in linguistic theories, i.e. presented in the form of linguistically-based discussions as to what kind of effect, facilitative or inhibiting, each of the linguistic factors may have had, and why. </p><p>The effect of those factors upon the correct use of the pluperfect has been tested by means of a step-wise multiple regression which measured the simultaneous effect of each factor upon the correct use of the pluperfect. This method has confirmed the facilitative effect of the following six linguistic factors: intrasentential indication of topic time (subordinate clause), unbounded verb indicating topic time, agentive meaning of the target verb, specifying subordinate clause, statal pluperfect and location of the time of action of pluperfect clause outside the temporal frame of narrative plot.</p>

<p>Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. Identifying covariates which explain differences between patients is important to discover patient subpopulations at risk of sub-therapeutic or toxic effects and for treatment individualization. Stepwise covariate modelling (SCM) is commonly used to this end. The aim of the current thesis work was to evaluate SCM and to develop alternative approaches. A further aim was to develop a mechanistic PK-PD model describing fasting plasma glucose, fasting insulin, insulin sensitivity and beta-cell mass.</p><p>The lasso is a penalized estimation method performing covariate selection simultaneously to shrinkage estimation. The lasso was implemented within NONMEM as an alternative to SCM and is discussed in comparison with that method. Further, various ways of incorporating information and propagating knowledge from previous studies into an analysis were investigated. In order to compare the different approaches, investigations were made under varying, replicated conditions. In the course of the investigations, more than one million NONMEM analyses were performed on simulated data. Due to selection bias the use of SCM performed poorly when analysing small datasets or rare subgroups. In these situations, the lasso method in NONMEM performed better, was faster, and additionally validated the covariate model. Alternatively, the performance of SCM can be improved by propagating knowledge or incorporating information from previously analysed studies and by population optimal design.</p><p>A model was also developed on a physiological/mechanistic basis to fit data from three phase II/III studies on the investigational drug, tesaglitazar. This model described fasting glucose and insulin levels well, despite heterogeneous patient groups ranging from non-diabetic insulin resistant subjects to patients with advanced diabetes. The model predictions of beta-cell mass and insulin sensitivity were well in agreement with values in the literature.</p>

This work consists of two parts: the theoretical and the experimental. In the theoretical part, some general and some language specific theories of tense, aspect and aktionsart are presented, and the temporal systems of Swedish and Polish are compared. The theoretical part is not a mere review of the literature on the subject. The comparison of the descriptions of aspect and aktionsart by Slavic researchers with the universal theory of Smith (1991) and (1977) and with description of aktionsart in Swedish in Teleman et al. (1999) has allowed me for some important observations as to the nature of the long-lasting dispute about the differences between aspect and aktionsart. The experimental part is a cross-sectional study on the role of the linguistic context on the acquisition of the pluperfect by Polish learners Swedish as a foreign language. The informants are university students studying Swedish as a foreign language. The language samples were collected by means of two types of tests: gap-filling and translation from Polish. Twelve linguistic factors, each divided into two subgroups, were hypothesised to have affected the correct use of the pluperfect. All those hypotheses as to which of the subgroups may inhibit and which may facilitate the correct use of the pluperfect are grounded in linguistic theories, i.e. presented in the form of linguistically-based discussions as to what kind of effect, facilitative or inhibiting, each of the linguistic factors may have had, and why. The effect of those factors upon the correct use of the pluperfect has been tested by means of a step-wise multiple regression which measured the simultaneous effect of each factor upon the correct use of the pluperfect. This method has confirmed the facilitative effect of the following six linguistic factors: intrasentential indication of topic time (subordinate clause), unbounded verb indicating topic time, agentive meaning of the target verb, specifying subordinate clause, statal pluperfect and location of the time of action of pluperfect clause outside the temporal frame of narrative plot.

Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. Identifying covariates which explain differences between patients is important to discover patient subpopulations at risk of sub-therapeutic or toxic effects and for treatment individualization. Stepwise covariate modelling (SCM) is commonly used to this end. The aim of the current thesis work was to evaluate SCM and to develop alternative approaches. A further aim was to develop a mechanistic PK-PD model describing fasting plasma glucose, fasting insulin, insulin sensitivity and beta-cell mass. The lasso is a penalized estimation method performing covariate selection simultaneously to shrinkage estimation. The lasso was implemented within NONMEM as an alternative to SCM and is discussed in comparison with that method. Further, various ways of incorporating information and propagating knowledge from previous studies into an analysis were investigated. In order to compare the different approaches, investigations were made under varying, replicated conditions. In the course of the investigations, more than one million NONMEM analyses were performed on simulated data. Due to selection bias the use of SCM performed poorly when analysing small datasets or rare subgroups. In these situations, the lasso method in NONMEM performed better, was faster, and additionally validated the covariate model. Alternatively, the performance of SCM can be improved by propagating knowledge or incorporating information from previously analysed studies and by population optimal design. A model was also developed on a physiological/mechanistic basis to fit data from three phase II/III studies on the investigational drug, tesaglitazar. This model described fasting glucose and insulin levels well, despite heterogeneous patient groups ranging from non-diabetic insulin resistant subjects to patients with advanced diabetes. The model predictions of beta-cell mass and insulin sensitivity were well in agreement with values in the literature.

This doctoral study aims to develop a methodology for use in determining aerodynamic models and parameters from actual flight test data for different types of autonomous flight vehicles. The stepwise regression method and equation error method are utilized for the aerodynamic model identification and parameter estimation. A closed loop aerodynamic parameter estimation approach is also applied in this study which can be used to fine tune the model parameters. Genetic algorithm is used as the optimization kernel for this purpose. In the optimization scheme, an input error cost function is used together with a final position penalty as opposed to widely utilized output error cost function. Available methods in the literature are developed for and mostly applied to the aerodynamic system identification problem of piloted aircraft / a very limited number of studies on autonomous vehicles are available in the open literature. This doctoral study shows the applicability of the existing methods to aerodynamic model identification and parameter estimation problem of autonomous vehicles. Also practical considerations for the application of model structure determination methods to autonomous vehicles are not well defined in the literature and this study serves as a guide to these considerations.

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