The tumour suppressor p53 protein plays a central role in the DNA damage response/checkpoint pathways leading to DNA repair, cell cycle arrest, apoptosis and senescence. The activation of p53-mediated pathways is primarily facilitated by the binding of tetrameric p53 to two 'half-sites', each consisting of a decameric p53 response element (RE). Functional REs are directly adjacent or separated by a small number of 1-13 'spacer' base pairs (bp). The p53 RE is detected by exact or inexact matches to the palindromic sequence represented by the regular expression [AG][AG][AG]C[AT][TA]G[TC][TC][TC] or a position weight matrix (PWM). The use of matrix-based and regular expression pattern-matching techniques, however, leads to an overwhelming number of false positives. A more specific model, which combines multiple factors known to influence p53-dependent transcription, is required for accurate detection of the binding sites. In this thesis, we present a logistic regression based model which integrates sequence information and epigenetic information to predict human p53 binding sites. Sequence information includes the PWM score and the spacer length between the two half-sites of the observed binding site. To integrate epigenetic information, we analyzed the surrounding region of the binding site for the presence of mono- and trimethylation patterns of histone H3 lysine 4 (H3K4). Our model showed a high level of performance on both a high-resolution data set of functional p53 binding sites from the experimental literature (ChIP data) and the whole human genome. Comparing our model with a simpler sequence-only model, we demonstrated that the prediction accuracy of the sequence-only model could be improved by incorporating epigenetic information, such as the two histone modification marks H3K4me1 and H3K4me3.

Information and communication technology (ICT) greatly influence today’s business processes be it in public or private sectors. Everything that is done in business requires ICT in one way or the other. Research in ICTs is therefore critical. So much research was and is still carried out in projects that develop or enhance ICT but it is still apparent that the success rate of these projects is still very low. The extensive coverage of ICTs implies that if the success rate is still that low, many resources are being wasted in the failed projects; therefore, more research is needed to improve the success rate. Previous research has focussed on factors which are critical for the success of ICT projects, assuming that all ICT projects are the same. As a result, literature is full of different suggestions and guidelines of the factors critical to ICT projects’ success. This scenario brings challenges to project managers who end up using their own personal judgement to select which factors to consider for any project at hand. The end result is the high failure rate of ICT projects since there is a very high chance of applying the same critical success factors to different types of ICT projects. This research answered the question: which factors are critical to the success of computer networking projects in Zimbabwe and how these factors could be used for building a model that determines in advance the success of such projects? Literature reviewed indicated that most CSFs were not focused on specific types of ICT projects, hence were generalised. No literature was found on ICT projects’ CSFs in Zimbabwe. More so, no CSFs were found for computer networking projects as a specific instance of ICT projects. No model existed that predicts computer networking projects’ success. This study addressed the gaps by developing a CSF framework for ICT projects in Zimbabwe, determining CSFs for computer networking projects in Zimbabwe and the development of a logistic regression analysis model to predict computer networking projects’ success in Zimbabwe. Data was collected in Zimbabwe using a unique three-staged process which comprise metasynthesis analysis, questionnaire and interviews. The study was motivated by the fact that most available research focused on CSFs for general ICT projects and that no research was found on CSFs influencing projects in computer networking. Meta-synthesis analysis was therefore conducted on literature in order to identify CSFs as given in literature. The approach was appropriate since the researcher had noticed that there were extensive ICT projects’ CSFs and that no such research has been carried out in Zimbabwe. These CSFs formed the basis for the determination (using a questionnaire) of ICT projects CSFs for Zimbabwe in particular. Project practitioners’ viewpoints were sought through questionnaires. Once CSFs for ICT projects in Zimbabwe were determined, they formed the basis for the determination of unique critical success factors for computer networking projects in Zimbabwe. Interviews were used to get further information that would have been left out by questionnaires. The interview questions were set to clarify some unclear or conflicting responses from the questionnaire and providing in-depth insights into the factors critical to computer networking projects in Zimbabwe. The data i.e. critical success factors for computer networking projects guided the development of the logistic regression analysis model for the prediction of computer networking projects’ success in Zimbabwe. Data analysis from the questionnaire was analysed using SPSS Version 23.0. Factor analysis and principal component analysis were some of the techniques used in the analysis. Interview data was analysed through NVivo Version 10.0. From the results it was deduced that factors critical to ICT project management in Zimbabwe were closely related to those found in the literature. The only apparent difference was that CSFs for ICT projects in Zimbabwe were more specific thereby enhancing their applicability. Computer networking projects had fewer CSFs than general ICT projects. In addition, CSFs for general ICT projects were different from those critical to computer networking projects in Zimbabwe. The development of a comprehensive set of general ICT projects’ CSFs was the first contribution of this study. This was achieved through meta-synthesis analysis. The other contribution was the development of a CSF framework for ICT projects specific to Zimbabwe and those specific to computer networking projects in Zimbabwe. The major contribution was the development of the logistic regression analysis model that predicts computer networking projects’ success in Zimbabwe. These contributions will provide literature on ICT project management in Zimbabwe which will subsequently assist ICT project managers to concentrate on specific factors. The developed prediction model can be used by project managers to determine possible success or failure of ICT projects; thereby possible reducing wastage of resource. / School of Computing

Studies of sexual dimorphism of the sacrum have generally been conducted as part of broader population research or on living persons and cadavers, making the anthropological literature sparse. The greater sciatic notch and the preauricular sulcus of the ilium have both been found to show sexual dimorphism, although studies of these traits often have ambiguous definitions of characteristics and lack the standardization of measurements. This research was designed to reexamine and test the accuracy of standard scoring systems and measurements of the posterior pelvis used to determine sex and to establish new formulae combining traits and measurements to accurately determine sex using logistic regression analysis. A series of metric measurements and morphological scores were recorded for 104 males and 106 females of both European- and African-American ancestry from the William M. Bass and Terry Collections. In order to reexamine previous research conducted on the posterior pelvis, standard ratios of metric measurements were analyzed to determine ranges and cut-off values for males and females in this sample. The ratio of ala width to the maximum transverse diameter of the sacral base and the ratio of the length and width of the sciatic notch have proven to be the most useful ratios in sex determination, though not as accurate as the formulae created using logistic regression. These data were also analyzed in SPSS using logistic regression to assess the usefulness of metric measurements and morphological scores of the posterior pelvis in sex determination. Using stepwise logistic regression, a combination of traits for both the sacrum and posterior ilium that are the most reliable and accurate for sex determination have been determined. The values for these selected traits can be incorporated into the log odds formulas which will classify an individual as male or female. The ultimate goal of this research was to provide physical anthropologists with iii logistic regression equations that can be used to estimate the sex of the posterior ilium and sacrum. Two equations ranging in accuracy from 79-84% were developed to determine sex of the posterior pelvis.

The focus of this research was to identify variables reported in the 2008-2009 Florida School Indicator Report (FSIR) that had a statistical impact, positive or negative, on the likelihood that a school would achieve Adequate Yearly Progress (AYP) in reading or mathematics using the logistic regression technique. This study analyzed four broad categories reported by the FSIR to include academic, school, student, and teacher characteristics. FSIR and AYP data was collected for 468 Florida high schools that were categorized by the Florida Department of Education as presenting a comprehensive curriculum to grades 9-12 or grades 10-12. It was determined in this study that academic data associated with ACT results and the grade 11 FCAT Science were effective predictors of a school’s academic health in reading and mathematics. Student absenteeism showed the greatest impact on a school obtaining AYP in reading while the percentage of students qualifying for free and disabled populations within a school showed the greatest impact on a school obtaining AYP in mathematics. Teachers teaching out of field were identified as having a negative influence on AYP in reading and mathematics while a teacher’s experience was considered a positive influence on AYP in mathematics only. Further research is necessary to fully explore the use of logistic regression as a predictive tool at the state, school district, and school level.

In this thesis, we present the use of logistic regression method to develop a credit scoring modelusing the raw data of 4447 customers of a bank. The data of customers is collected under 14independent explanatory variables and 1 default indicator. The objective of this thesis is toidentify optimal coefficients. In order to clean data, the raw data set was put through variousdata calibration techniques such as Kurtosis, Skewness, Winsorization to eliminate outliers.On this winsorized dataset, LOGIT analysis is applied in two rounds with multiple statisticaltests. These tests aim to estimate the significance of each independent variable and modelfitness. The optimal coefficients can be used to obtain the credit scores for new customers witha new data set and rank them according to their credit risk.

Résumé : Objectif : Identifier les facteurs associés avec le succès d’une intervention multidisciplinaire de prise en charge et de prévention des maladies chroniques dans un contexte de soins de santé de première ligne. Devis : Étude corrélationnelle prédictive d’analyse secondaire des données du projet PR1MaC, un essai randomisé contrôlé analysant les effets d’une intervention intégrant un programme de prise en charge et de prévention Contexte : Huit cliniques de soins de première ligne de la région Saguenay-Lac-Saint-Jean. Participants : un échantillon de 160 patients (52,5% d’hommes) référés par des professionnels de première ligne. L’analyse a porté sur le groupe intervention seulement. Mesure de résultats primaire : Mesure d’amélioration significative dans les huit domaines du «Health Education Impact Questionnaire». Résultat : L’analyse de régression multivariée a démontré qu’être plus jeune, être célibataire et avoir un salaire plus bas a mené à plus d’amélioration au niveau du domaine « Bien-être émotionnel »; avoir de bonnes habitudes alimentaires et cibler moins de facteurs de risque durant l’intervention a mené à plus d’amélioration au niveau du domaine « Approches et attitudes constructives »; être plus jeune, avoir plus de temps de contact avec les professionnels et avoir une concertation des professionnels a mené à plus d’amélioration dans le domaine « Approches et attitudes constructives »; avoir plus de temps de contact avec les professionnels a aussi eu une influence sur l’amélioration du domaine « Engagement positif et actif dans la vie » et avoir un plus grand nombre de professionnels intervenant chez une même personne a démontré plus d’amélioration dans le domaine « Acquisition des techniques et habiletés ». Aucun facteur prédictif n’a pu être identifié pour les domaines « Comportements de santé », « Intégration sociale et soutien » et « Auto-surveillance et discernement ». Seulement les résultats statistiquement significatifs sont présentés (valeur p ≥ 0,05). La petite taille de l'échantillon ainsi que la possibilité d'une perte de signification des résultats après certains ajustements statistiques suggèrent que ces observations devraient faire l'objet d'une validation plus approfondie dans d'autres études. Conclusion : La tentative d’identification des facteurs prédictifs de résultats de cette recherche contribue à la compréhension des mécanismes complexes de l’efficacité et offre des pistes quant à l’optimisation des programmes de prévention et de gestion des maladies chroniques. // Abstract : Context : Research on the factors associated with the successes of chronic disease prevention and management (CDPM) interventions is scarce. Objectives : To identify the factors associated with the successes of an interprofessional CDPM intervention among adult patients in primary healthcare (PHC) settings. Design : Secondary analysis of data from the PR1MaC project; a pragmatic randomized controlled trial looking at the effects of an intervention involving the integration of CDPM services in PHC. Settings : Eight PHC practices in the Saguenay - Lac - Saint - Jean region of Quebec, Canada. Participants : A sample of 160 patients (84 males) referred by PHC providers constituted the sample (mean age 52.66 ± 11.5 years); 98.5% presented two or more chronic conditions analysis focused on the intervention arm sample only. Main and secondary outcome measures : Dichotomic substantive improvement in the eight domains of the Health Education Impact questionnaire (hei Q) measured at baseline and three months later. Results : Multivariate logistic regression analysis showed that being younger, being single and having a lower family income led to a better improvement in the emotional wellbeing domain; having healthy eating habits and less objectives during the intervention led to improvement in the constructive attitudes and approaches domain; being younger, a longer intervention and a consensus of professionals led to improvement in the health services navigation domain; a longer intervention led to improvement in the positive and active engagement in life domain and having more professionals involved led to improvement in the Skills and techniques acquisition domain. No predictive factors were identified for the Health - directed behaviour, Social interaction and support and S elf - monitoring and insight domains. Only significant results are presented here (p - value ≥ 0.05). The small sample and the lost of significance after statistical adjustments suggest that observations should be validated by other studies. Conclusion: In an attempt to make causal inferences in regards to improvement, this research contributes to the understanding of the complex mechanisms of efficiency and provides information about the optimisation of CDPM program delivery.

This study was conducted in the Nkangala district, in the province of Mpumalanga in South Africa. This province remains the largest forestry production region in South Africa. The majority of people living in Mpumalanga are farmers and they have contributed immensely to promote food security. The objective of the study was to determine the level of climate change awareness among small scale maize producers in Mpumalanga province. Random sampling techniques was used to select two hundred and fifty one (251) farmers to be interviewed. A pre-tested questionnaire was administered to maize farmers, focusing on matters relating to climate change awareness in maize production. Data was captured and analysed using software package for social science (SPSS version 20 of 2012). Descriptive statistics were applied to analyse and describe the data. Logistic regression analysis followed to demonstrate the significance of the independent variables on climate change awareness. The results of the analysis indicated that the information received and the size of the farm had an impact on climate change awareness in the area of study. It was therefore recommended that the majority of farmers in Mpumalanga needed to be made aware of climate change in order to assist them to build the adaptive capacity, increase resilience and reduce vulnerability. Information on climate change awareness should be disseminated well to ensure that it will attract the attention of the farmers / Agriculture and  Animal Health / M.Sc. (Agriculture)

This thesis investigates the factors that are associated with annual changes in the share price of Johannesburg Stock Exchange (JSE) listed companies. In this study, an increase in value of a share is when the share price of a company goes up by the end of the financial year as compared to the previous year. Secondary data that was sourced from McGregor BFA website was used. The data was from 2004 up to 2011. Deciding which share to buy is the biggest challenge faced by both investment companies and individuals when investing on the stock exchange. This thesis uses binary logistic regression to identify the variables that are associated with share price increase. The dependent variable was annual change in share price (ACSP) and the independent variables were assets per capital employed ratio, debt per assets ratio, debt per equity ratio, dividend yield, earnings per share, earnings yield, operating profit margin, price earnings ratio, return on assets, return on equity and return on capital employed. Different variable selection methods were used and it was established that the backward elimination method produced the best model. It was established that the probability of success of a share is higher if the shareholders are anticipating a higher return on capital employed, and high earnings/ share. It was however, noted that the share price is negatively impacted by dividend yield and earnings yield. Since the odds of an increase in share price is higher if there is a higher return on capital employed and high earning per share, investors and investment companies are encouraged to choose companies with high earnings per share and the best returns on capital employed. The final model had a classification rate of 68.3% and the validation sample produced a classification rate of 65.2% / Mathematical Sciences / M.Sc. (Statistics)

This study was conducted in the Nkangala district, in the province of Mpumalanga in South Africa. This province remains the largest forestry production region in South Africa. The majority of people living in Mpumalanga are farmers and they have contributed immensely to promote food security. The objective of the study was to determine the level of climate change awareness among small scale maize producers in Mpumalanga province. Random sampling techniques was used to select two hundred and fifty one (251) farmers to be interviewed. A pre-tested questionnaire was administered to maize farmers, focusing on matters relating to climate change awareness in maize production. Data was captured and analysed using software package for social science (SPSS version 20 of 2012). Descriptive statistics were applied to analyse and describe the data. Logistic regression analysis followed to demonstrate the significance of the independent variables on climate change awareness. The results of the analysis indicated that the information received and the size of the farm had an impact on climate change awareness in the area of study. It was therefore recommended that the majority of farmers in Mpumalanga needed to be made aware of climate change in order to assist them to build the adaptive capacity, increase resilience and reduce vulnerability. Information on climate change awareness should be disseminated well to ensure that it will attract the attention of the farmers / Agriculture and  Animal Health / M. Sc. (Agriculture)

企業發生財務危機，不僅使其經營陷入生死關頭之掙扎，更影響眾多投資人、債權人的利益，對於整個經濟環境亦造成一定的衝擊。因此，如何提早察覺企業之危機，以減少社會成本，實值得我們深入研究。 本研究主要目的為評估現金流量表揭露之資訊，於預測企業財務危機的有用性。本研究欲探討現金流量資訊是否為預測企業財務危機的良好指標，於建構企業財務危機預警模式之際，加入現金流量的財務指標是否會比僅以傳統財務比率建立之預警模式，更具預測能力。 本研究採用配對樣本設計，在我國上市公司中共選取了35家危機公司與68家正常公司。並利用Logit迴歸分析分別建立現金流量模式、應計財務模式與綜合模式，得到以下結論： 一、在財務危機發生之前一至三年，本研究所使用的應計基礎財務比率並非皆適合用來區分危機公司與正常公司。 二、除了營業活動現金流量相關比率具有顯著的區別能力外，部分投資與融資活動現金流量相關比率亦提供額外的財務危機警訊。 三、現金流量比率預警模式之預測力表現不遜於應計基礎比率模式；但在應計基礎比率中加入現金流量比率，並未顯著提高模式的預測能力。 / The objective of this study is to assess the usefulness of cash flow disclosures in the prediction of financial distress. This study also determines whether cash flow ratios are good indicator of financial distress and whether adding cash flow ratios in prediction model can improve the predictive ability of the model employing conventional accrual-based ratios. Using a matched pair design, this study examines a sample of 35 distress firms along with 68 non-distress firms. Also, a logistic regression analysis is used to establish the financial distress model with and without cash flow variables respectively, in order to test the hypotheses developed by this study and to derive the conclusion. The findings of this study are as follows. 1. During the period between 3 years to 1 year before financial distress, the accrual-based ratios used in this study aren't all good predictor in financial distress model. 2. The discriminate ability of operating cash flow data is significant. Also, the investing and financing cash flow data provide additional information in the prediction of business distress. 3. Cash flow ratios provide a superior measure for the prediction of financial distress over accrual-based ratios. However, no significant evidence shows that using cash flow ratios in conjunction with accrual-based ratios can improve the overall predictive power of accrual-based ratios alone.