Doctor of Philosophy / Department of Civil Engineering / Mustaque Hossain / A Superpave asphalt mixture with 4.75-mm nominal maximum aggregate size (NMAS) is a promising, low-cost pavement preservation treatment for agencies such as the Kansas Department of Transportation (KDOT). The objective of this research study is to develop an optimized 4.75-mm NMAS Superpave mixture in Kansas. In addition, the study evaluated the residual tack coat application rate for the 4.75-mm NMAS mix overlay. Two, hot-in-place recycling (HIPR) projects in Kansas, on US-160 and K-25, were overlaid with a 15- to 19-mm thick layer of 4.75-mm NMAS Superpave mixture in 2007. The field tack coat application rate was measured during construction. Cores were collected from each test section for Hamburg wheel tracking device (HWTD) and laboratory bond tests performed after construction and after one year in service. Test results showed no significant effect of the tack coat application rate on the rutting performance of rehabilitated pavements. The number of wheel passes to rutting failure observed during the HWTD test was dependent on the aggregate source as well as on in-place density of the cores. Laboratory pull-off tests showed that most cores were fully bonded at the interface of the 4.75-mm NMAS overlay and the HIPR layer, regardless of the tack application rate. The failure mode during pull-off tests at the HMA interface was highly dependent on the aggregate source and mix design of the existing layer material. This study also confirmed that overlay construction with a high tack coat application rate may result in bond failure at the HMA interface. Twelve different 4.75-mm NMAS mix designs were developed using materials from the aforementioned but two binder grades and three different percentages of natural (river) sand. Laboratory performance tests were conducted to assess mixture performance. Results show that rutting and moisture damage potential in the laboratory depend on aggregate type irrespective of binder grade. Anti-stripping agent affects moisture sensitivity test results. Fatigue performance is significantly influenced by river sand content and binder grade. Finally, an optimized 4.75-mm NMAS mixture design was developed and verified based on statistical analysis of performance data.

Plusieurs applications modernes nécessitent un traitement de flux massifs de données XML, cela crée de défis techniques. Parmi ces derniers, il y a la conception et la mise en ouvre d'outils pour optimiser le traitement des requêtes XPath et fournir une estimation précise des coûts de ces requêtes traitées sur un flux massif de données XML. Dans cette thèse, nous proposons un nouveau modèle de prédiction de performance qui estime a priori le coût (en termes d'espace utilisé et de temps écoulé) pour les requêtes structurelles de Forward XPath. Ce faisant, nous réalisons une étude expérimentale pour confirmer la relation linéaire entre le traitement de flux, et les ressources d'accès aux données. Par conséquent, nous présentons un modèle mathématique (fonctions de régression linéaire) pour prévoir le coût d'une requête XPath donnée. En outre, nous présentons une technique nouvelle d'estimation de sélectivité. Elle se compose de deux éléments. Le premier est le résumé path tree: une présentation concise et précise de la structure d'un document XML. Le second est l'algorithme d'estimation de sélectivité: un algorithme efficace de flux pour traverser le synopsis path tree pour estimer les valeurs des paramètres de coût. Ces paramètres sont utilisés par le modèle mathématique pour déterminer le coût d'une requête XPath donnée. Nous comparons les performances de notre modèle avec les approches existantes. De plus, nous présentons un cas d'utilisation d'un système en ligne appelé "online stream-querying system". Le système utilise notre modèle de prédiction de performance pour estimer le coût (en termes de temps / mémoire) d'une requête XPath donnée. En outre, il fournit une réponse précise à l'auteur de la requête. Ce cas d'utilisation illustre les avantages pratiques de gestion de performance avec nos techniques / Many modern applications require processing of massive streams of XML data, creating difficult technical challenges. Among these, there is the design and implementation of applications to optimize the processing of XPath queries and to provide an accurate cost estimation for these queries processed on a massive steam of XML data. In this thesis, we propose a novel performance prediction model which a priori estimates the cost (in terms of space used and time spent) for any structural query belonging to Forward XPath. In doing so, we perform an experimental study to confirm the linear relationship between stream-processing and data-access resources. Therefore, we introduce a mathematical model (linear regression functions) to predict the cost for a given XPath query. Moreover, we introduce a new selectivity estimation technique. It consists of two elements. The first one is the path tree structure synopsis: a concise, accurate, and convenient summary of the structure of an XML document. The second one is the selectivity estimation algorithm: an efficient stream-querying algorithm to traverse the path tree synopsis for estimating the values of cost-parameters. Those parameters are used by the mathematical model to determine the cost of a given XPath query. We compare the performance of our model with existing approaches. Furthermore, we present a use case for an online stream-querying system. The system uses our performance predicate model to estimate the cost for a given XPath query in terms of time/memory. Moreover, it provides an accurate answer for the query's sender. This use case illustrates the practical advantages of performance management with our techniques

In this thesis, we design, develop and implement parallel algorithms for irregular problems on heterogeneous multi-core architectures. Irregular problems exhibit random and unpredictable memory access patterns, poor spatial locality and input dependent control flow. Heterogeneous multi-core processors vary in: clock frequency, power dissipation, programming model (MIMD vs. SIMD), memory design and computing units, scalar versus vector units. The heterogeneity of the processors makes designing efficient parallel algorithms for irregular problems on heterogeneous multicore processors challenging. Techniques of mapping tasks or data on traditional parallel computers can not be used as is on heterogeneous multi-core processors due to the varying hardware. In an attempt to understand the efficiency of futuristic heterogeneous multi-core architectures on applications we study several computation and bandwidth oriented irregular problems on one heterogeneous multi-core architecture, the IBM Cell Broadband Engine (Cell BE). The Cell BE consists of a general processor and eight specialized processors and addresses vector/data-level parallelism and instruction-level parallelism simultaneously. Through these studies on the Cell BE, we provide some discussions and insight on the performance of the applications on heterogeneous multi-core architectures. Verifying these experimental results require some performance modeling. Due to the diversity of heterogeneous multi-core architectures, theoretical performance models used for homogeneous multi-core architectures do not provide accurate results. Therefore, in this thesis we propose an analytical performance prediction model that considers the multitude architectural features of heterogeneous multi-cores (such as DMA transfers, number of instructions and operations, the processor frequency and DMA bandwidth). We show that the execution time from our prediction model is comparable to the execution time of the experimental results for a complex medical imaging application.

In this thesis, we design, develop and implement parallel algorithms for irregular problems on heterogeneous multi-core architectures. Irregular problems exhibit random and unpredictable memory access patterns, poor spatial locality and input dependent control flow. Heterogeneous multi-core processors vary in: clock frequency, power dissipation, programming model (MIMD vs. SIMD), memory design and computing units, scalar versus vector units. The heterogeneity of the processors makes designing efficient parallel algorithms for irregular problems on heterogeneous multicore processors challenging. Techniques of mapping tasks or data on traditional parallel computers can not be used as is on heterogeneous multi-core processors due to the varying hardware. In an attempt to understand the efficiency of futuristic heterogeneous multi-core architectures on applications we study several computation and bandwidth oriented irregular problems on one heterogeneous multi-core architecture, the IBM Cell Broadband Engine (Cell BE). The Cell BE consists of a general processor and eight specialized processors and addresses vector/data-level parallelism and instruction-level parallelism simultaneously. Through these studies on the Cell BE, we provide some discussions and insight on the performance of the applications on heterogeneous multi-core architectures. Verifying these experimental results require some performance modeling. Due to the diversity of heterogeneous multi-core architectures, theoretical performance models used for homogeneous multi-core architectures do not provide accurate results. Therefore, in this thesis we propose an analytical performance prediction model that considers the multitude architectural features of heterogeneous multi-cores (such as DMA transfers, number of instructions and operations, the processor frequency and DMA bandwidth). We show that the execution time from our prediction model is comparable to the execution time of the experimental results for a complex medical imaging application.

Small businesses growth and profitability is a national problem in South Africa (SA) and the Emerging Contractors (ECs) in the construction industry in Gauteng Province are no exception. The SA government expect ECs to become the main players in economic transformation and growth, job creation and income generation. However, ECs fail to live up to the expectations as some are characterised by low performance levels and are at risk of business closure. The purpose of this research is to examine the perceptions of Small Business Executives (SBEs) on determinants of business performance so that a performance model that could guide ECs within the construction business is developed. The researcher used a mixed approach to examine the perceptions of SBEs on determinants of ECs’ performance by applying both qualitative and quantitative research approaches. The population from which the sample was drawn comprised of 1890 urban based ECs registered with CIDB in the construction industry in Gauteng. Systematic random sampling was utilized and a sample size of 501 ECs was used. A structured questionnaire and interview guide were administered as the data collection instruments. A pilot survey was conducted where 2 key informants (SBEs) and 15 respondents (SBEs) were interviewed as representatives of ECs. A survey was used to assess the four hypotheses in the study. Content analysis was used to identify themes that emerged from qualitative data. The analysis of qualitative data demonstrated the perceptions of SBEs that financial factors, manpower, materials, machinery and equipment, project implementation, quality of work, legal and environmental and strategic planning were the main determinants that should be addressed when setting up and running construction businesses to reduce the negative impact on business performance. Based on the perceptions of SBEs, in quantitative, the univariate and multivariate statistical methods were performed to measure the level of significance between and among the performance determinants were tested at 5% confidence interval. Multiple linear regressions carried out based on the perceptions of SBEs identified strategic planning, project implementation and project performance as the most statistically significant factors in predicting the performance of ECs in Gauteng Province. The results from both qualitative and quantitative methods were synthesised and analysed through data transformation, typological development and case study analysis and the perceptions of respondents indicated a strong agreement between the qualitative and quantitative results. Based on multivariate results which were a result of the perceptions of SBEs, a performance prediction model was developed which is the core contribution of the study. The structure of the model, how it is used, its advantages and disadvantages were presented. The model provides a platform upon which ECs could predict performance and this would benefit ECs, clients, community and policymakers. In conclusion, the study established three significant determinants that include strategic planning, project implementation and project performance as highly relevant variables to improve construction business performance. Based on the perceptions of the respondents, the study recommends ECs to acquire and develop financial management skills that would enable them to understand and manage financial requirements that stakeholders such as financial institutions need in the areas of budgeting and cash flow management, bookkeeping knowledge, financial policies and controls and project pricing strategy. On the shortage of skills, the study recommends SBEs to take an initiative role in training employees, motivating and retaining competent workforce. The government need to establish technical colleges for training workers (apprenticeship) in order to equip them with appropriate industry knowledge, skills and experience. Lack of collateral security was widely reported by most SBEs and the study recommends the formation of a sector bank that would support emerging contractor businesses. / Business Management / D.B.L.

Small businesses growth and profitability is a national problem in South Africa (SA) and the Emerging Contractors (ECs) in the construction industry in Gauteng Province are no exception. The SA government expect ECs to become the main players in economic transformation and growth, job creation and income generation. However, ECs fail to live up to the expectations as some are characterised by low performance levels and are at risk of business closure. The purpose of this research is to examine the perceptions of Small Business Executives (SBEs) on determinants of business performance so that a performance model that could guide ECs within the construction business is developed. The researcher used a mixed approach to examine the perceptions of SBEs on determinants of ECs’ performance by applying both qualitative and quantitative research approaches. The population from which the sample was drawn comprised of 1890 urban based ECs registered with CIDB in the construction industry in Gauteng. Systematic random sampling was utilized and a sample size of 501 ECs was used. A structured questionnaire and interview guide were administered as the data collection instruments. A pilot survey was conducted where 2 key informants (SBEs) and 15 respondents (SBEs) were interviewed as representatives of ECs. A survey was used to assess the four hypotheses in the study. Content analysis was used to identify themes that emerged from qualitative data. The analysis of qualitative data demonstrated the perceptions of SBEs that financial factors, manpower, materials, machinery and equipment, project implementation, quality of work, legal and environmental and strategic planning were the main determinants that should be addressed when setting up and running construction businesses to reduce the negative impact on business performance. Based on the perceptions of SBEs, in quantitative, the univariate and multivariate statistical methods were performed to measure the level of significance between and among the performance determinants were tested at 5% confidence interval. Multiple linear regressions carried out based on the perceptions of SBEs identified strategic planning, project implementation and project performance as the most statistically significant factors in predicting the performance of ECs in Gauteng Province. The results from both qualitative and quantitative methods were synthesised and analysed through data transformation, typological development and case study analysis and the perceptions of respondents indicated a strong agreement between the qualitative and quantitative results. Based on multivariate results which were a result of the perceptions of SBEs, a performance prediction model was developed which is the core contribution of the study. The structure of the model, how it is used, its advantages and disadvantages were presented. The model provides a platform upon which ECs could predict performance and this would benefit ECs, clients, community and policymakers. In conclusion, the study established three significant determinants that include strategic planning, project implementation and project performance as highly relevant variables to improve construction business performance. Based on the perceptions of the respondents, the study recommends ECs to acquire and develop financial management skills that would enable them to understand and manage financial requirements that stakeholders such as financial institutions need in the areas of budgeting and cash flow management, bookkeeping knowledge, financial policies and controls and project pricing strategy. On the shortage of skills, the study recommends SBEs to take an initiative role in training employees, motivating and retaining competent workforce. The government need to establish technical colleges for training workers (apprenticeship) in order to equip them with appropriate industry knowledge, skills and experience. Lack of collateral security was widely reported by most SBEs and the study recommends the formation of a sector bank that would support emerging contractor businesses. / Business Management / D.B.L.