Please read the abstract in the section 00front of this document / Dissertation (M Eng (Industrial Engineering))--University of Pretoria, 2006. / Industrial and Systems Engineering / unrestricted

Given the energy intensive nature of the South African economy and the country’s dependence on fossil fuels, the reduction of greenhouse gas (GHG) emissions poses a serious problem to poverty alleviation, economic growth and employment. This study assesses the inter-industry and macro-economic impacts of carbon dioxide emissions reduction in South Africa. A monetary energy input-output table was developed using data from supply and use tables and a physical energy-emissions input-output table was developed from the national energy balance and the country’s GHG inventory. Both tables were used to develop the energy-emissions input-output model. Carbon dioxide taxes and energy subsidy reform were selected as potential economic policy instruments for analysis in South Africa. The energy-emissions input-output model was used to analyse the implications of the selected policy scenarios in terms of their effect on gross domestic product (GDP), employment, household consumption, energy consumption and energy emissions reduction. According to the energy-emissions input-output model developed in this study, financial and community services, construction and accommodation and machinery and equipment have the largest final demand and value added while nuclear energy, natural gas and biomass have the smallest final demand and value added. Renewable energy is labour intensive but not energy intensive as this energy sector has the highest labour to value added and the lowest energy to labour and energy to value added ratios. The petroleum products sector is the least labour intensive and the most energy intensive as it has a low labour to value added ratio and high energy to labour and energy to value added ratios. For every one unit increase in biomass, renewable energy and nuclear energy results in the largest increase in output, income and employment while machinery and equipment, natural gas and gold and other mining sectors have the lowest increase in simple and total output, income and employment multipliers. There is not much movement between natural gas, nuclear energy, renewable energy and biomass and the rest of the economy. Coal and crude oil have a relatively moderate impact and are moderately impacted on by other industries in the economy. Although almost all other industries in the economy depend heavily on electricity and petroleum products, these two industries are not as heavily dependent on other industries. Coal is responsible for the largest direct primary energy emissions followed by crude oil while natural gas; nuclear energy, renewable energy and biomass have a low direct impact. The electricity sector accounts for the highest indirect impact on coal emissions and petroleum products have the highest indirect impact on crude oil emissions. The petroleum products sector has the highest indirect impact on natural gas emissions. The electricity sector is largely responsible for the direct impact on coal emissions in terms of total economic output and the petroleum products sector accounts for all crude oil emissions from output. Natural gas, renewable energy, nuclear energy and biomass have no effect on direct emission output ratio. The iron and metals sector has the largest direct impact on electricity emissions per output and transport and communication has the highest direct impact on petroleum products emission per output. The largest indirect coal pollution per output impact is in the electricity sector, followed by petroleum products and iron and metals, while machinery and equipment has the smallest indirect impact on coal emissions per output. Petroleum products have the largest indirect crude oil pollution per output and the petroleum products sector is the only sector with an indirect impact on natural gas emissions per output. The iron and metals sector has the largest indirect electricity emission per output followed by household consumption and financial and community services while natural gas has the smallest indirect electricity emissions per output followed by machinery and equipment. Nuclear energy, renewable energy and biomass have no indirect petroleum products emissions per output. Machinery and equipment and crude oil have the lowest indirect petroleum products emissions per output. Inter-industry analysis indicates that the tax on coal results in the largest decrease in total output in the electricity and petroleum products sectors while output in the petroleum products and gold and other mining sectors decreases the most with the tax on oil. The tax on electricity has the largest negative impact on the iron and metals and financial and community services sectors and the tax on petroleum products results in the largest decrease in the transport and communication and financial and community services sectors. The electricity and coal mining sectors suffer the largest decrease in output as a result of energy subsidy reform. Macro-economic impacts were analysed according to real and marginal decreases. Real changes were used to assess the impact of each policy in terms of direct changes to each specific variable. Marginal decreases were calculated as a ratio of decreasing GDP for each variable hence marginal employment equals change in employment as a ratio of change in GDP and marginal household consumption equals change in household consumption as change a ratio of change in GDP. Marginal excess burden of taxes was calculated as changes in tax revenue, as a ratio of decrease in GDP. In terms of decreasing GDP, employment and household consumption, the lower the marginal burden the better the policy. Although the tax on coal offers the highest reduction in real energy emissions, this scenario also results in the highest reduction in GDP, employment and household consumption. Therefore the coal tax is not considered as the best option for carbon dioxide emissions reduction in South Africa. The electricity tax offers a moderate reduction in real energy emissions, GDP, employment and household consumption. It is concluded that the electricity tax could be an option for carbon dioxide emissions reduction in South Africa. However energy subsidy reform offers higher energy emissions reduction and a moderate reduction in GDP, employment and household consumption. This scenario is recognised as the most efficient option for carbon dioxide reduction in South Africa in terms of real changes. The tax on coal indicates high marginal decreases in employment and household consumption, moderate marginal tax revenue and moderate marginal decrease in energy consumption and energy emissions reduction. The tax on crude oil indicates low marginal decreases in employment and household consumption, low marginal excess burden on taxes, low marginal decrease in energy consumption and a moderate marginal decrease in energy emissions. The tax on petroleum products indicates low marginal decreases in employment and household consumption, low marginal excess burden on taxes and a high marginal decrease in energy consumption and energy emissions. Energy subsidy reform offers moderate marginal decreases in employment and household consumption, low marginal excess burden on taxes and a low marginal decrease in energy consumption and energy emissions. The comparison of marginal burdens of energy emissions reduction policies indicates that energy subsidy reform offers the best option as this scenario has moderate marginal decreases in employment and household consumption, low marginal excess burden on taxes and a low marginal decrease in energy consumption and energy emissions. The tax on crude oil is selected as the second best alternative as this scenario has low marginal decreases in employment and household consumption, low marginal excess burden on taxes, low marginal decrease in energy consumption and a moderate marginal decrease in energy emissions. Therefore in terms of real and marginal reduction in energy emissions, energy consumption, GDP, employment and household consumption, energy subsidy reform proves to be the best policy instrument in terms of energy emissions reduction, energy consumption, poverty alleviation, economic growth and employment. / Thesis (PhD)--University of Pretoria, 2008. / Agricultural Economics, Extension and Rural Development / unrestricted

abstract: The process of learning orchestra reductions on the piano is rather different from learning a piece originally written for the piano. Even though Dvořák’s Concerto for Cello and Orchestra in b minor is among the most performed works for cello and orchestra, and has been transcribed carefully by both the composer and other editors, the existing piano reductions are not always representative of many important aspects of the original orchestral score. Some reductions have large portions with unplayable or uncomfortable passages for pianists, or imprecise notations compared to the original orchestration, such as inaccurate indications for dynamics, rhythms, and notes. In rehearsal and performance, the pianist is challenged to adapt and transform one of the existing reductions into a playable and productive piano reduction, one which creates Dvořák’s full orchestral sonorities while retaining clarity of voicing. The resulting sound can be infinite in variety, as individual decisions and reductions may differ greatly. This paper will explore the following: how to reduce this orchestral score and solve the technical problem involved in orchestral writing for piano while effectively producing the sound of the orchestra in the piano reduction. There will be a literature review on important published reductions and a brief history of the work and composer. While it is not possible to discuss in detail each passage that has been revised or altered, this paper will focus on and analyze representative and substantial passages, including the perspective of two different reductions: Bärenreiter (2011) and Bärenreiter Praha (2004). It will provide a detailed demonstration of each example and will make suggestions for changes which will concentrate on capturing the essence of the orchestral score at the piano. Chapter one introduces and presents current editions. Chapters two, three and four will discuss each movement of the concerto with detailed explanations about changes in certain passages and sections. The appendix will feature a new revised reduction of Dvořák’s Cello concerto in B minor. / Dissertation/Thesis / Doctoral Dissertation Music 2020

En imagerie médicale, des collaborations internationales ont lançé l'acquisition de centaines de Terabytes de données - et en particulierde données d'Imagerie par Résonance Magnétique fonctionelle (IRMf) -pour les mettre à disposition de la communauté scientifique.Extraire de l'information utile de ces données nécessite d'importants prétraitements et des étapes de réduction de bruit. La complexité de ces analyses rend les résultats très sensibles aux paramètres choisis. Le temps de calcul requis augmente plus vite que linéairement: les jeux de données sont si importants qu'il ne tiennent plus dans le cache, et les architectures de calcul classiques deviennent inefficaces.Pour réduire les temps de calcul, nous avons étudié le feature-grouping commetechnique de réduction de dimension. Pour ce faire, nous utilisons des méthodes de clustering. Nous proposons un algorithme de clustering agglomératif en temps linéaire: Recursive Nearest Agglomeration (ReNA). ReNA prévient la création de clusters énormes, qui constitue un défaut des méthodes agglomératives rapidesexistantes. Nous démontrons empiriquement que cet algorithme de clustering engendre des modèles très précis et rapides, et permet d'analyser de grands jeux de données avec des ressources limitées.En neuroimagerie, l'apprentissage statistique peut servir à étudierl'organisation cognitive du cerveau. Des modèles prédictifs permettent d'identifier les régions du cerveau impliquées dans le traitement cognitif d'un stimulus externe. L'entraînement de ces modèles est un problème de très grande dimension, et il est nécéssaire d'introduire un a priori pour obtenir un modèle satisfaisant.Afin de pouvoir traiter de grands jeux de données et d'améliorer lastabilité des résultats, nous proposons de combiner le clustering etl'utilisation d'ensembles de modèles. Nous évaluons la performance empirique de ce procédé à travers de nombreux jeux de données de neuroimagerie. Cette méthode est hautement parallélisable et moins coûteuse que l'état del'art en temps de calcul. Elle permet, avec moins de données d'entraînement,d'obtenir de meilleures prédictions. Enfin, nous montrons que l'utilisation d'ensembles de modèles améliore la stabilité des cartes de poids résultantes et réduit la variance du score de prédiction. / In medical imaging, collaborative worldwide initiatives have begun theacquisition of hundreds of Terabytes of data that are made available to thescientific community. In particular, functional Magnetic Resonance Imaging --fMRI-- data. However, this signal requires extensive fitting and noise reduction steps to extract useful information. The complexity of these analysis pipelines yields results that are highly dependent on the chosen parameters.The computation cost of this data deluge is worse than linear: as datasetsno longer fit in cache, standard computational architectures cannot beefficiently used.To speed-up the computation time, we considered dimensionality reduction byfeature grouping. We use clustering methods to perform this task. We introduce a linear-time agglomerative clustering scheme, Recursive Nearest Agglomeration (ReNA). Unlike existing fast agglomerative schemes, it avoids the creation of giant clusters. We then show empirically how this clustering algorithm yields very fast and accurate models, enabling to process large datasets on budget.In neuroimaging, machine learning can be used to understand the cognitiveorganization of the brain. The idea is to build predictive models that are used to identify the brain regions involved in the cognitive processing of an external stimulus. However, training such estimators is a high-dimensional problem, and one needs to impose some prior to find a suitable model.To handle large datasets and increase stability of results, we propose to useensembles of models in combination with clustering. We study the empirical performance of this pipeline on a large number of brain imaging datasets. This method is highly parallelizable, it has lower computation time than the state-of-the-art methods and we show that, it requires less data samples to achieve better prediction accuracy. Finally, we show that ensembles of models improve the stability of the weight maps and reduce the variance of prediction accuracy.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The reduction of waste is a constant concern for companies that form part of a supply chain. In industrial processors, these are related to logistics solutions, because the production process of the different products is highly automated. In the case of the Peruvian potato, this model is not applicable due to its irregular characteristics. In this context, this paper proposes an improvement in the process of elaboration of processed potatoes in order to reduce or eliminate waste in food sector companies. Identification tools are used for activities that do not generate value, such as the VSM, and other continuous improvement tools such as Kaizen and 5S, as well as a simulation model. In the validation, an 89% increase in the product yield, as well as a 72% efficiency increase, is obtained.

Manganese is amongst the most abundant transition metals on earth. Playing several roles in enzymatic function, manganese is largely considered biocompatible and, in comparison to most transition metals, it is relatively inexpensive. It is surprising then, that manganese remains poorly explored in the field of pincer-based homogenous catalysis. PN3(P) pincer ligands have proved to impart different kinetic and thermodynamic properties to the complexes they are a part of when compared to analogous complexes of ligands with CH2 spacers. In part I of this work, we present unexpected results from a thorough investigation of the coordination chemistry between a PN3 phenanthroline-based ligand and several manganese salts that suggest that the coordination environment may promote a disproportionation reaction. We also present an efficient route towards dichloride substituted PN3 manganese complexes. In Part II, we investigate the reactivity of manganese(II) pincer compounds in ester reduction reactions and probe the promising results afforded by reduction through borohydride.

Equilibrium exchange constants were calculated for exchange of sulphite with other sulphur compounds. The equilibrium constant for sulphur Isotope exchange between sulphate in solution and solid calcium sulphate was calcu­lated and measured experimentally. In the chemical reduc­tion of sulphate to sulphide S^32O-4 reacted 2.5% faster than S340-4 , in agreement with the calculated kinetic isotope effect for the step sulphate to sulphite. The isotope effect in the reduction of sulphate by Desulphovibrio desulphuricans was found to vary from 0.0 to 2.5% The results were inter­preted on the basis of a mechanism involving two consecutive steps, pick-up of sulphate and reduction of sulphate to sul-phite, competing for control of the rate. The isotope effect in bacterial reduction of sulphite was studied briefly. / Thesis / Doctor of Philosophy (PhD)

This thesis presents both a shared memory parallel version of the hybrid classification algorithm IGSCR (iterative guided spectral class rejection) and a novel data reduction technique that can be used in conjuction with pIGSCR (parallel IGSCR). The parallel algorithm is motivated by a demonstrated need for more computing power driven by the increasing size of remote sensing datasets due to higher resolution sensors, larger study regions, and the like. Even with a fast algorithm such as pIGSCR, the reduction of dimension in a dataset is desirable in order to decrease the processing time further and possibly improve overall classification accuracy. pIGSCR was developed to produce fast and portable code using Fortran 95, OpenMP, and the Hierarchical Data Format version 5 (HDF5) and accompanying data access library. The applicability of the faster pIGSCR algorithm is demonstrated by classifying Landsat data covering most of Virginia, USA into forest and non-forest classes with approximately 90 percent accuracy. Parallel results are given using the SGI Altix 3300 shared memory computer and the SGI Altix 3700 with as many as 64 processors reaching speedups of almost 77. This fast algorithm allows an analyst to perform and assess multiple classifications to refine parameters. As an example, pIGSCR was used for a factorial analysis consisting of 42 classifications of a 1.2 gigabyte image to select the number of initial classes (70) and class purity (70%) used for the remaining two images. A feature selection or reduction method may be appropriate for a specific lassification method depending on the properties and training required for the classification method, or an alternative band selection method may be derived based on the classification method itself. This thesis introduces a feature reduction method based on the singular value decomposition (SVD). This feature reduction technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery acquired over a forested area in Virginia, USA and Rondonia, Brazil. Subsequent parallel iterative guided spectral class rejection (pIGSCR) forest/non-forest classifications were performed to determine the quality of the feature reduction. The classifications of the Virginia data were five times faster using SVD based feature reduction without affecting the classification accuracy. Feature reduction using the SVD was also compared to feature reduction using principal components analysis (PCA). The highest average accuracies for the Virginia dataset (88.34%) and for the Amazon dataset (93.31%) were achieved using the SVD. The results presented here indicate that SVD based feature reduction can produce statistically significantly better classifications than PCA. / Master of Science

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / This paper introduces a set of macros that automate the importing of antenna data into Excel and charting that data. These macros (as discussed here) import data from a ViaSat ACUs (Antenna Control Unit) and a TCS ACU (Telemetry & Communications Systems Inc). After the import is complete, the macros can build a set of charts, all formatted and labeled in a predetermined and standard manner. A task that may take half a day or more can be completed in minutes. The concept and layout of the macros lend them to quick adaptation to your data. In scenarios of “test and collect” followed by “import and chart”, the data can be imported and charted within the minute.

This research has been directed at the study of neighboring group participation in electrooxidation of thioethers. Controlled potential oxidation of substituted 1,3-dithiane in wet acetonitrile provides substituted 1,2-dithiolane 1-oxide in good yield. Thioethers appended with neighboring alcohols and carboxylate are catalytically oxidized in a redox cycle by bromide ion. The formation of the alkoxysulfonium salt intermediates in such reactions is confirmed by product study. On the other hand, the acyloxysulfonium salt intermediates in the electrooxidation of endo -6-methylthio-bicyclo [2.2.1] heptane-2- endo -carboxylic acid (1) are unstable at room temperature. Control experiments using ¹⁸O labeled compounds prove unequivocally the existent of the acyloxysulfonium salt intermediates. Diastereospecific oxidation of 1 and its methyl ester with DABCO.2Br₂ complex and m-CPBA is sterically controlled. In both cases, similar product ratios are observed which is explained by the participation of the carboxylic acid group in the case of DABCO.2Br₂ oxidation but not in the case of m-CPBA oxidation. The structure of one of the diastereomer of the endo acid sulfoxide is unequivocally proved by x-ray crystallographic analysis.