As health care costs continue to increase at rates higher than the general inflation rate, there is increased focus on controlling health care expenditures in the public and private sectors. In particular, there is a compelling need for more creative and informed allocation decisions for limited government public health funds. This thesis suggests several methods for better forecasting the demand for health care and allocating health care resources more efficiently. First, productivity of dental sealant programs is studied and suggestions are made for increased efficiency. Using simulation and data from several states programs, guidelines are offered for optimal programs based on program size, distance to site, and practice act requirements. We find that under most conditions, it is better to carry an extra dental assistant to every program. The cost of satisfying practice act requirements is also quantified. Second, a model for allocating health resources to Community Health Centers (CHCs) is provided. Using the state of Georgia as a prototype, local estimation is used to forecast county insurance types, disease prevalence, and likelihood of using a clinic. Then, the optimal locations and service portfolios to be offered under financial constraints are determined using a developed mixed-integer programming model. Finally, shortcomings in current Markovian modeling of chronic disease are analyzed. Common forecasting techniques can overestimate or underestimate the population in need of care, as illustrated by analytic results and an example with lung cancer data. The chapter presents suggestions for improving such modeling. Each of these issues affect the planning models for scarce resources in health care, and improving those models can positively impact utilization of those services. Through this research, models are presented that can positively impact public health decisions in coming years, particularly those for growing high-risk and low-income groups.

Most of the Web is published in languages that are not accessible to many potential users who are only able to read and understand their local languages. Many of these local languages are Resources Scarce Languages (RSLs) and lack the necessary resources, such as machine translation tools, to make available content more accessible. State of the art preprocessing tools and retrieval methods are tailored for Web dominant languages and, accordingly, documents written in RSLs are lowly ranked and difficult to access in search results, resulting in a struggling and frustrating search experience for speakers of RSLs. In this thesis, we propose the use of language similarities to match, re-rank and return search results written in closely related languages to improve the quality of search results and user experience. We also explore the use of shared morphological features to build multilingual stemming tools. Focusing on six Bantu languages spoken in Southeastern Africa, we first explore how users would interact with search results written in related languages. We conduct a user study, examining the usefulness and user preferences for ranking search results with different levels of intelligibility, and the types of emotions users experience when interacting with such results. Our results show that users can complete tasks using related language search results but, as intelligibility decreases, more users struggle to complete search tasks and, consequently, experience negative emotions. Concerning ranking, we find that users prefer that relevant documents be ranked higher, and that intelligibility be used as a secondary criterion. Additionally, we use a User-Centered Design (UCD) approach to investigate enhanced interface features that could assist users to effectively interact with such search results. Usability evaluation of our designed interface scored 86% using the System Usability Scale (SUS). We then investigate whether ranking models that integrate relevance and intelligibility features would improve retrieval effectiveness. We develop these features by drawing from traditional Information Retrieval (IR) models and linguistics studies, and employ Learning To Rank (LTR) and unsupervised methods. Our evaluation shows that models that use both relevance and intelligibility feature(s) have better performance when compared to models that use relevance features only. Finally, we propose and evaluate morphological processing approaches that include multilingual stemming, using rules derived from common morphological features across Bantu family of languages. Our evaluation of the proposed stemming approach shows that its performance is competitive on queries that use general terms. Overall, the thesis provides evidence that considering and matching search results written in closely related languages, as well as ranking and presenting them appropriately, improves the quality of retrieval and user experience for speakers of RSLs.

Increasing competition and regulation are raising the pressure on manufacturing organisations to innovate their products. Innovation is fraught by significant uncertainty of whole product life cycle costs and this can lead to hesitance in investing which may result in a loss of competitive advantage. Innovative products exist when the minimum information for creating accurate cost models through contemporary forecasting methods does not exist. The scientific research challenge is that there are no forecasting methods available where cost data from only one time period suffices for their application. The aim of this research study was to develop a framework for forecasting cost uncertainty using cost data from only one time period. The developed framework consists of components that prepare minimum information for conversion into a future uncertainty range, forecast a future uncertainty range, and propagate the uncertainty range over time. The uncertainty range is represented as a vector space representing the state space of actual cost variance for 3 to n reasons, the dimensionality of that space is reduced through vector addition and a series of basic operators is applied to the aggregated vector in order to create a future state space of probable cost variance. The framework was validated through three case studies drawn from the United States Department of Defense. The novelty of the framework is found in the use of geometry to increase the amount of insights drawn from the cost data from only one time period and the propagation of cost uncertainty based on the geometric shape of uncertainty ranges. In order to demonstrate its benefits to industry, the framework was implemented at an aerospace manufacturing company for identifying potentially inaccurate cost estimates in early stages of the whole product life cycle.

The legacy of the apartheid education system in South Africa has created many challenges. Some of these challenges involve the management of public schools. There was a resistance to change, creating serious managerial problems at many schools which in turn led to deterioration in the quality of education. The first post-apartheid government led by former president, Nelson Mandela and, the subsequent administration led by presidents Thabo Mbeki and Jacob Zuma have emphasized the centrality of education as part of the human development strategy for South Africa. The current administration (Jacob Zuma) has taken this initiative a step further by giving education peak priority. In order to drive this initiative, the government has placed Mathematics, Science and Technology Education on the agenda for the development of scarce in the Further Education and Training (FET) band of the South African schooling system. This has led to the introduction of the National Strategy for Mathematics, Science and Technology Education (NSMSTE) as a policy option for the development of scarce skills in some selected schools throughout the nine provinces of South Africa. This study looks at how the implementation of this policy is managed in the Free State province. Since 1994 the government‘s attempts to transform education have been stifled by the failures to translate good policies into sound practices. In describing the failures of the African National Congress (ANC) led administration to implement policies, Biko (2013:191) uses an analogy of a failure to discern the dynamics of competing in a world competitive sport wherein all countries out-compete each other in the preparation of their children for the global workforce of the future. Biko (2013:191), further states that this global workforce does not have boundaries, it is not hindered by language barriers and it is also not tolerant of inflexibility. For the future generations, to be globally competitive, schools must provide quality education. This requires effective and efficient management. Like many other countries, South Africa has made strides in the area of access to education; quality education remains a major drawback. Mathematics and Science education is a flag point of every discussion on quality education. Many university‘s degrees and careers such as engineering require a good grounding in Mathematics and Science. Schools in South Africa incessantly produce few passes in Mathematics and Science. A study that looks into the management practices of the implementers of national policy on the development of scarce skills at school level is expedient. This is a Public Administration study that contributes towards the body of knowledge in finding the solutions for the school managers to implement NSMSTE. Although the challenges of managing schools are intricate and also permeate the broader spectrum of South African schools, provinces are unique in terms of infrastructure and human capital. It is for this reason that the scope of this study is only limited to the human capital of schools in the Free State province. Lessons derived from this study are likely to be applicable to other provinces of South Africa. / Thesis (PhD)--University of Pretoria, 2014. / lk2014 / School of Public Management and Administration (SPMA) / PhD / unrestricted

Accessible flood modeling for low-resource, data-scarce communities currently does not exist. This paper proposes using drone imagery to compensate for the lack of other flood modeling data (i.e. streamflow measurements). Three flood models were run for Dzaleka Refugee Camp, located in Dowa, Malawi. Two of the models (the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Center River Analysis System (HEC-RAS)) are commonly used hydrological-hydraulic based models. The third model, the Water Caused Erosion Patterns (WCEP) model, was proposed by the author to capitalize on the high-resolution drone imagery using geological-geomorphological information. The drone imagery used in this study has a resolution of 3.5cm and shows erosion patterns throughout the refugee camp. By comparing the erosion patterns to flow direction of the surface, the erosion patterns were determined to be water caused or not water caused, the erosion patterns considered water caused were defined as high-risk flood areas, creating the WCEP model. The three models were compared using locations of collapsed houses throughout the camp. It was found that the WCEP model represents the location of collapsed houses significantly better (misclassification rate below 17%) than the SWAT or HEC-RAS models (misclassification rate below 54%, and 67% respectively). The WCEP model was combined with the best hydrological-hydraulic model (SWAT) to create a hydrogeomorphological model which capitalizes on both the drone imagery and the hydrological process. / Master of Science / The negative impact flooding has on communities can be reduced through flood modeling. But commonly used flood models are not accessible to data-scarce communities because of the historical data the models require. This paper explores using aerial imagery taken by a drone to make-up for the lack of historical data at Dzaleka Refugee Camp located in Dowa, Malawi. Drone imagery has a very high spatial resolution (3.5cm), so it is able to provide a lot of details, including marks that show an increase of flooding in certain areas and elevation information. The flood model presented in this paper is created using the found flood marks in drone imagery. The presented model is then compared to two commonly used flood models, and all three flood models are compared to locations of houses that collapsed from flooding throughout the refugee camp. The model created using drone imagery did the best job predicting high-risk locations with misclassification rates below 17%. The drone imagery model was then combined with a commonly used model to create a more comprehensive flood model, capitalizing on all available data.

The development of linguistic data, especially annotated corpora, is imperative for the human language technology enablement of any language. The annotation process is, however, often time-consuming and expensive. As such, various projects make use of several strategies to expedite the development of human language technology resources. For resource-scarce languages – those with limited resources, finances and expertise – the efficiency of these strategies has not been conclusively established. This study investigates the efficiency of some of these strategies in the development of resources for resource-scarce languages, in order to provide recommendations for future projects facing decisions regarding which strategies they should implement. For all experiments, Afrikaans is used as an example of a resource-scarce language. Two tasks, viz. lemmatisation of text data and orthographic transcription of audio data, are evaluated in terms of quality and in terms of the time required to perform the task. The main focus of the study is on the skill level of the annotators, software environments which aim to improve the quality and time needed to perform annotations, and whether it is beneficial to annotate more data, or to increase the quality of the data. We outline and conduct systematic experiments on each of the three focus areas in order to determine the efficiency of each. First, we investigated the influence of a respondent’s skill level on data annotation by using untrained, sourced respondents for annotation of linguistic data for Afrikaans. We compared data annotated by experts, novices and laymen. From the results it was evident that the experts outperformed the non-experts on both tasks, and that the differences in performance were statistically significant. Next, we investigated the effect of software environments on data annotation to determine the benefits of using tailor-made software as opposed to general-purpose or domain-specific software. The comparison showed that, for these two specific projects, it was beneficial in terms of time and quality to use tailor-made software rather than domain-specific or general-purpose software. However, in the context of linguistic annotation of data for resource-scarce languages, the additional time needed to develop tailor-made software is not justified by the savings in annotation time. Finally, we compared systems trained with data of varying levels of quality and quantity, to determine the impact of quality versus quantity on the performance of systems. When comparing systems trained with gold standard data to systems trained with more data containing a low level of errors, the systems trained with the erroneous data were statistically significantly better. Thus, we conclude that it is more beneficial to focus on the quantity rather than on the quality of training data. Based on the results and analyses of the experiments, we offer some recommendations regarding which of the methods should be implemented in practice. For a project aiming to develop gold standard data, the highest quality annotations can be obtained by using experts to double-blind annotate data in tailor-made software (if provided for in the budget or if the development time can be justified by the savings in annotation time). For a project that aims to develop a core technology, experts or trained novices should be used to single-annotate data in tailor-made software (if provided for in the budget or if the development time can be justified by the savings in annotation time). / PhD (Linguistics and Literary Theory), North-West University, Potchefstroom Campus, 2014

The mining engineering world is continuously experiencing new challenges whether it is economically, technologically or socially, there will always be something new in sight. Training and Development fulfils the role of the “breeder” of competence workforce and therefore it is essential to assure constant growth and progress within the skills and development environment. Personal attribute and dedication are the drivers of our success and achievements in our careers. The capacity of our trade and occupation does not set the limits and pace of our intensification to strive for the bigger and better in future. The career background and training and development opportunities are essential aspects to be renowned with before engaging with the learning intervention. Assure you start your career in the artisan trade that you desire to be a successful example in and discover the engineering world where the spectrum is never-ending. Development opportunities exist for all, irrespective of the trade and occupation. This study illustrates that even the smaller trade‟s artisans as focused on in this study, namely the Rigger Ropesman, can achieve higher laurels if the energy and efforts are exploited in the applicable avenues. The starting blocks, must be the blocks that fit yourself as individual learner, if not, even the first steps in the career will be started in stumbling mode. This qualitative research design study discovered numerous persons who started a career without knowing what it entails. The Rigger Ropesman artisans, Engineering Foremen and Learners who were interviewed as the target group contributed to achieving the aim of the study. The aim of the study was the possible training and development opportunities for the Rigger Ropesman in the Mining and Minerals Sector. Future studies can be conducted on career guidance and training and development opportunities in other engineering trades as this study only focused on the Rigger Ropesman trade in the Mining and Minerals Sector. / MEd (Training and Development), North-West University, Potchefstroom Campus, 2014

The development of linguistic data, especially annotated corpora, is imperative for the human language technology enablement of any language. The annotation process is, however, often time-consuming and expensive. As such, various projects make use of several strategies to expedite the development of human language technology resources. For resource-scarce languages – those with limited resources, finances and expertise – the efficiency of these strategies has not been conclusively established. This study investigates the efficiency of some of these strategies in the development of resources for resource-scarce languages, in order to provide recommendations for future projects facing decisions regarding which strategies they should implement. For all experiments, Afrikaans is used as an example of a resource-scarce language. Two tasks, viz. lemmatisation of text data and orthographic transcription of audio data, are evaluated in terms of quality and in terms of the time required to perform the task. The main focus of the study is on the skill level of the annotators, software environments which aim to improve the quality and time needed to perform annotations, and whether it is beneficial to annotate more data, or to increase the quality of the data. We outline and conduct systematic experiments on each of the three focus areas in order to determine the efficiency of each. First, we investigated the influence of a respondent’s skill level on data annotation by using untrained, sourced respondents for annotation of linguistic data for Afrikaans. We compared data annotated by experts, novices and laymen. From the results it was evident that the experts outperformed the non-experts on both tasks, and that the differences in performance were statistically significant. Next, we investigated the effect of software environments on data annotation to determine the benefits of using tailor-made software as opposed to general-purpose or domain-specific software. The comparison showed that, for these two specific projects, it was beneficial in terms of time and quality to use tailor-made software rather than domain-specific or general-purpose software. However, in the context of linguistic annotation of data for resource-scarce languages, the additional time needed to develop tailor-made software is not justified by the savings in annotation time. Finally, we compared systems trained with data of varying levels of quality and quantity, to determine the impact of quality versus quantity on the performance of systems. When comparing systems trained with gold standard data to systems trained with more data containing a low level of errors, the systems trained with the erroneous data were statistically significantly better. Thus, we conclude that it is more beneficial to focus on the quantity rather than on the quality of training data. Based on the results and analyses of the experiments, we offer some recommendations regarding which of the methods should be implemented in practice. For a project aiming to develop gold standard data, the highest quality annotations can be obtained by using experts to double-blind annotate data in tailor-made software (if provided for in the budget or if the development time can be justified by the savings in annotation time). For a project that aims to develop a core technology, experts or trained novices should be used to single-annotate data in tailor-made software (if provided for in the budget or if the development time can be justified by the savings in annotation time). / PhD (Linguistics and Literary Theory), North-West University, Potchefstroom Campus, 2014

The mining engineering world is continuously experiencing new challenges whether it is economically, technologically or socially, there will always be something new in sight. Training and Development fulfils the role of the “breeder” of competence workforce and therefore it is essential to assure constant growth and progress within the skills and development environment. Personal attribute and dedication are the drivers of our success and achievements in our careers. The capacity of our trade and occupation does not set the limits and pace of our intensification to strive for the bigger and better in future. The career background and training and development opportunities are essential aspects to be renowned with before engaging with the learning intervention. Assure you start your career in the artisan trade that you desire to be a successful example in and discover the engineering world where the spectrum is never-ending. Development opportunities exist for all, irrespective of the trade and occupation. This study illustrates that even the smaller trade‟s artisans as focused on in this study, namely the Rigger Ropesman, can achieve higher laurels if the energy and efforts are exploited in the applicable avenues. The starting blocks, must be the blocks that fit yourself as individual learner, if not, even the first steps in the career will be started in stumbling mode. This qualitative research design study discovered numerous persons who started a career without knowing what it entails. The Rigger Ropesman artisans, Engineering Foremen and Learners who were interviewed as the target group contributed to achieving the aim of the study. The aim of the study was the possible training and development opportunities for the Rigger Ropesman in the Mining and Minerals Sector. Future studies can be conducted on career guidance and training and development opportunities in other engineering trades as this study only focused on the Rigger Ropesman trade in the Mining and Minerals Sector. / MEd (Training and Development), North-West University, Potchefstroom Campus, 2014

M. Tech. (Business Administration, Faculty of Management Sciences): Vaal University of Technology / The purpose of this study is to investigate the influence of employee rewards on talent attraction and talent retention. Furthermore, the study intends to unearth the influence of rewards on employee attraction and retention and to suggest strategies that could be employed by FET colleges in Gauteng in the Sedibeng district of Gauteng. The attraction and retention of employees continues to be a key priority not only of human resource professionals but also of FET colleges generally in South Africa and in the Gauteng province in particular. The most valuable asset available to an organisation is its people, and consequently, retaining employees in their jobs is crucial for any organization. In South Africa, the retention of highly skilled employees is critical, particularly because of the need to contribute to economic growth, innovation and poverty eradication. Owing to the competition for scarce skills, the attraction and retention of quality employees has emerged as the biggest challenge in human capital management and this phenomenon has also arisen in FET colleges. To attract and retain employee, organisations need novel reward systems that satisfy employees. This study employs a quantitative research paradigm and a survey method was used to investigate a sample of 154 academic employees at Sedibeng FET College, Gauteng. A personal method was used to collect data using semi-structured questionnaire and the results of the correlations shows that employee rewards are significantly positively related to talent attraction and talent retention. Findings and recommendations of this study are important to employers as they are supposed to design a remuneration package that attract and retain the best candidates and satisfies their employee’s expectations, in that it is fair, equitable and free of bias. A remuneration package is one of the most important factors that influence people to take up employment and stay with the organization.