There is a growing demand for studies applying quantitative methods to large-scale data sets for the purpose of evaluating the effects of educational reforms (UVK, 2010). In this thesis the statistical method, Propensity Score Analysis (PSA), is presented and explored in the evaluating context of an extensive educational initiative within science and technology education; the Science and Technology for All-program (NTA). The research question put forward reads; under what conditions are PSA-analyses a useful method when measuring the effects from a school improvement initiative in S &amp; T? The study considers the use of PSA when looking for long-term effects that could be measured, what to take into consideration to be able to measure this, and how this could be done. The baseline references (outcome variables) used in order to measure/evaluate the long-term effects from the studied program is students’ achievements in the national test (score and grades) and their grades in year 9. Some findings revealed regarding the object of study (long-term effects from using NTA) are also presented. The PSA method is found to be a useful tool that makes it possible to create artificial control groups when experimental studies are impossible or inappropriate; which is often the case in school education research. The method opens up for making use of the rich source of registry data gathered by authorities. PSA proves reliable and relatively insensitive to the effects of covariates and heterogeneous effecter if the number of samples is large enough. The use of PSA (or other statistical methods) also makes it possible to measure outcomes several years after treatment. There are issues of concern when using PSA. One is the obvious demand for organized collection of measurement data. Another issue of concern is the choice of outcome variables. In this study the chosen outcome variables (pupils’ score and grading in national tests and grades in year 9) open up for discussions regarding aspects that might not be reflected/measured in national tests and/or teachers’ grading. Findings regarding the long-term effects from using NTA) show significantly positive effects in physics on test scores (average increase 16.5%) and test grades, but not in biology and chemistry. In this study no significant effects are found for course grades. PSA approach has proved to be a reliable method. There is however a limitation in terms of the method's ability to capture more subtle aspects of learning. A combination of quantitative and qualitative approach when studying long-term effects from educational intervention is therefore suggested. / <p>QC 20131120</p>

Coals, from mines in the Highveld coalfield, as well as gasification ash samples were characterised, in order to understand the mineralogical and chemical properties of the individual components in the gasification feedstocks. X-ray diffraction of low temperature oxygen-plasma ash indicates that the coals contain significant proportions of kaolinite, quartz and a fluxing elements-bearing mineral (dolomite), plus minor concentrations of illite and other fluxing elements-bearing minerals namely calcite, pyrite and siderite. Of the feed coal, the -75+53 mm size fraction has a high pyrite, and to a lesser extent a high calcite and dolomite content. However, the small proportion of iron-bearing phases (from the reaction between kaolinite and pyrite) in samples taken from the gasifier implies that pyrite contributes minimally to sintering or slagging in this case. Calcite is mainly present in the >1.8 g/cm3 density fraction of the feed coal, whereas dolomite is mainly present in the 1.5-1.8 g/cm3 density fraction, as inclusions or fine cleats in the coal matrix. Electron microprobe analyses of coals from the six different South African mines confirmed that some Ca, Mg, Al, Si, Na, K, Ti and Fe are present in the organic matrix in the coal samples tested in this study, but the amounts of these are small compared with the fluxing elements in minerals. XRD and microprobe analyses indicate that the ash clinker samples taken from the gasifiers contain a number of crystalline high temperature phases, including anorthite, mullite, cristobalite, quartz and diopside. FactSage confirmed that anorthite and mullite are equilibrium phases at elevated temperatures in the ash clinkers and heated rock fragments. Limited reaction takes place between the included coal minerals and the extraneous rock fragments. / Thesis (PhD)--University of Pretoria, 2008. / Materials Science and Metallurgical Engineering / unrestricted

Image Super-Resolution (ISR) is a technology that aims to increase image resolution while preserving as much content and detail as possible. In this study, we evaluate four different Deep Learning models (EDSR, LapSRN, ESPCN, and FSRCNN) to determine their effectiveness in increasing the resolution of lowresolution images. The study builds on previous research in the field as well as the results of the comparison between the different deep learning models. The problem statement for this study is: “Which of the four Deep Learning-based models, EDSR, LapSRN, ESPCN, and FSRCNN, generates an upscaled image with the best quality from a low-resolution image on a dataset of Abyssinian cats, with a factor of four, based on quantitative results?” The study utilizes a dataset consisting of pictures of Abyssinian cats to evaluate the performance and results of these different models. Based on the quantitative results obtained from RMSE, PSNR, and Structural Similarity (SSIM) measurements, our study concludes that EDSR is the most effective Deep Learning-based model. / Bildsuperupplösning (ISR) är en teknik som syftar till att öka bildupplösningen samtidigt som så mycket innehåll och detaljer som möjligt bevaras. I denna studie utvärderar vi fyra olika Deep Learning modeller (EDSR, LapSRN, ESPCN och FSRCNN) för att bestämma deras effektivitet när det gäller att öka upplösningen på lågupplösta bilder. Studien bygger på tidigare forskning inom området samt resultatjämförelser mellan olika djupinlärningsmodeller. Problemet som studien tar upp är: “Vilken av de fyra Deep Learning-baserade modellerna, EDSR, LapSRN, ESPCN och FSRCNN generarar en uppskalad bild med bäst kvalité, från en lågupplöst bild på ett dataset med abessinierkatter, med skalningsfaktor fyra, baserat på kvantitativa resultat?” Studien använder en dataset av bilder på abyssinierkatter för att utvärdera prestandan och resultaten för dessa olika modeller. Baserat på de kvantitativa resultaten som erhölls från RMSE, PSNR och Structural Similarity (SSIM) mätningar, drar vår studie slutsatsen att EDSR är den mest effektiva djupinlärningsmodellen.