A tbesis sutmitted to the Faculty of Engineering in fulfilment of the requirements for the degree of Doctor of Philosophy. / Available data on particularly viscosities of titaniferous melts under oxidizing conditions are reviewed, and an attempt is made to explain the high fluidity of such melts. (Abbreviation abstract) / Andrew Chakane 2018

Greater knowledge of the short- and long-term effects of biomass production practices on soil biological and chemical properties is needed to determine influences on sustainable land management. Soil samples under switchgrass (Panicum virgatum L.), other forage grasses, cultivated crops, and forest were collected seasonally at six locations. Soil organic C (SOC), total N, soil microbial biomass C (SMBC) and N (SMBN), soil mineralizable C and N, and basal soil respiration (BSR) were in general greatest under long-term coastal bermudagrass [Cynodon dactylon (L.) Pers.] pasture (>40 years), second highest under Alamo switchgrass and kleingrass (Panicum coloratum L.) planted in 1992 and forest, followed by Alamo switchgrass planted in 1997, and was lowest under the cultivated soils. Soil organic C at 0-5 cm was 42-220% greater in soils under Alamo switchgrass planted in 1992 than cultivated soils, except at College Station where SOC values under Alamo planted in 1992 and the cultivated rotation were not significantly different. Although the rotation treatment is cultivated at this location, two high residue crops are used, wheat (Triticum aestivum L.) and sorghum [Sorghum bicolor (L.) Moench.]. Similar trends were noted for total N, SMBC, SMBN, mineralizable C and N, BSR, and the ratio of SMBC/SOC. Insufficient information was collected in this study to determine whether the parameters evaluated for forest and switchgrass were different. In addition to its high yield potential, adaptation to marginal sites and tolerance to water and nutrient limitations, switchgrass appeared to be a competitive crop in terms of land sustainability, resulting in enhanced soil quality characteristics compared to long-term cultivated soils.

Greater knowledge of the short- and long-term effects of biomass production practices on soil biological and chemical properties is needed to determine influences on sustainable land management. Soil samples under switchgrass (Panicum virgatum L.), other forage grasses, cultivated crops, and forest were collected seasonally at six locations. Soil organic C (SOC), total N, soil microbial biomass C (SMBC) and N (SMBN), soil mineralizable C and N, and basal soil respiration (BSR) were in general greatest under long-term coastal bermudagrass [Cynodon dactylon (L.) Pers.] pasture (>40 years), second highest under Alamo switchgrass and kleingrass (Panicum coloratum L.) planted in 1992 and forest, followed by Alamo switchgrass planted in 1997, and was lowest under the cultivated soils. Soil organic C at 0-5 cm was 42-220% greater in soils under Alamo switchgrass planted in 1992 than cultivated soils, except at College Station where SOC values under Alamo planted in 1992 and the cultivated rotation were not significantly different. Although the rotation treatment is cultivated at this location, two high residue crops are used, wheat (Triticum aestivum L.) and sorghum [Sorghum bicolor (L.) Moench.]. Similar trends were noted for total N, SMBC, SMBN, mineralizable C and N, BSR, and the ratio of SMBC/SOC. Insufficient information was collected in this study to determine whether the parameters evaluated for forest and switchgrass were different. In addition to its high yield potential, adaptation to marginal sites and tolerance to water and nutrient limitations, switchgrass appeared to be a competitive crop in terms of land sustainability, resulting in enhanced soil quality characteristics compared to long-term cultivated soils.

No description available.

Peptide and protein sequences are most commonly represented as a strings: a series of letters selected from the twenty character alphabet of abbreviations for the naturally occurring amino acids. Here, we experiment with representations of small peptide sequences that incorporate more physiochemical information. Specifically, we develop three different physiochemical representations for a set of roughly 700 HIV–I protease substrates. These different representations are used as input to an array of six different machine learning models which are used to predict whether or not a given peptide is likely to be an acceptable substrate for the protease. Our results show that, in general, higher–dimensional physiochemical representations tend to have better performance than representations incorporating fewer dimensions selected on the basis of high information content. We contend that such representations are more biologically relevant than simple string–based representations and are likely to more accurately capture peptide characteristics that are functionally important. / Singapore-MIT Alliance (SMA)

Made available in DSpace on 2014-06-11T19:23:10Z (GMT). No. of bitstreams: 0 Previous issue date: 2012-02-27Bitstream added on 2014-06-13T20:30:04Z : No. of bitstreams: 1 silvajunior_jf_me_jabo.pdf: 787738 bytes, checksum: 7b3e41cb53972e8468ad03ecb64cfbe1 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Os óxidos de ferro são importantes indicadores ambientais e possuem grande influência nos atributos físicos e químicos do solo. Entretanto, para o melhor entendimento de sua formação e ocorrência são necessários estudos de variabilidade espacial. A geoestatística é a melhor ferramenta para este estudo, sendo que a Krigagem ordinária (KO) é um interpolador de mínimos quadrados, mais utilizado em ciência do solo. Porém, ela suaviza os detalhes locais da variação espacial, superestimando pequenos valores e subestimando altos valores. Por isso, ela revela-se inadequada na avaliação da verdadeira variabilidade espacial dos óxidos de ferro. Embora a krigagem mostre a melhor estimativa de um atributo Z, ele não representa bem a variação. Deste modo, a alternativa é simulação geoestatística que avalia e quantifica a incerteza de um atributo, de maneira mais realista. A técnica de simulação estocástica mais utilizada é a simulação sequencial gaussiana (SSG), por ser, rápida e direta na construção da função de densidade de probabilidade acumulada condicional (f.d.p.a.c), sendo, portanto preferível à estimação pela KO. Desta forma, é importante testar outros métodos para caracterizar espacial dos óxidos de ferro. Objetivando avaliar o desempenho da (KO), krigagem ordinária dos dados padronizados (KOP) e da simulação seqüencial gaussiana (SSG) da variabilidade espacial dos óxidos de ferro em uma área com pedoformas: côncava e convexa foram coletados 121 amostras de duas malhas nos dois relevos classificados como: côncavo e convexo com espaçamento de 10 metros. O erro médio, o erro quadrático médio e o erro quadrático médio padronizado foram os critérios de avaliação da acurácia da predição. Os valores altos dos coeficientes de variação dos óxidos de ferro afetaram o desempenho dos métodos... / Iron oxides are important environmental indicators and have great influence on the physical and chemical properties of soil. However, for a better understanding of their formation and occurrence studies are necessary spatial variability. Geostatistics is a tool for this study, ordinary kriging (OK) is a least squares interpolator, is the most widely used in soil science. However smooths out local details of spatial, small values overestimating and underestimating high values. Therefore, is inadequate in the evaluation of iron oxides. Although the kriging show the best estimate of an attribute Z, it does not represent well the variation. Thus, the alternative is geroestatisctical simulation that evaluates and quantify the uncertainty of an attribute, more realistically. Since the most widely used is the sequential Gaussian simulation (SGS), to be quickly and directly in the construction of the density function of cumulative probability (fdpac), and are preferred to the estimation. Thus, is important to test other methods to characterize spatially of iron oxides. In order to evaluate the performance of (OK), ordinary kriging of standardized data (OKS) and sequential Gaussian simulation (SGS) of the spatial variability of iron oxides in an area with landform: concave and convex 121 samples were collected data in two loops with two reliefs classified as concave and convex with spacing of 10 meters. The average error, the mean square error and root mean square error were standardized criteria for evaluating the accuracy of prediction. High values of coefficient of variation of iron oxides harmed the performance of the methods. The landforms showed variable differentiated by SGS. The maps of OKS and E-type had higher accuracy and greater detail in relation to landforms in the OK. It is recommended that E-type maps instead of maps OK... (Complete abstract click electronic access below)

Orientador: Gener Tadeu Pereira / Coorientador: José Marques Júnior / Banca: Ednaldo Carvalho Guimarães / Banca: Marcílio Vieira Martins Filho / Resumo: Os óxidos de ferro são importantes indicadores ambientais e possuem grande influência nos atributos físicos e químicos do solo. Entretanto, para o melhor entendimento de sua formação e ocorrência são necessários estudos de variabilidade espacial. A geoestatística é a melhor ferramenta para este estudo, sendo que a Krigagem ordinária (KO) é um interpolador de mínimos quadrados, mais utilizado em ciência do solo. Porém, ela suaviza os detalhes locais da variação espacial, superestimando pequenos valores e subestimando altos valores. Por isso, ela revela-se inadequada na avaliação da verdadeira variabilidade espacial dos óxidos de ferro. Embora a krigagem mostre a melhor estimativa de um atributo Z, ele não representa bem a variação. Deste modo, a alternativa é simulação geoestatística que avalia e quantifica a incerteza de um atributo, de maneira mais realista. A técnica de simulação estocástica mais utilizada é a simulação sequencial gaussiana (SSG), por ser, rápida e direta na construção da função de densidade de probabilidade acumulada condicional (f.d.p.a.c), sendo, portanto preferível à estimação pela KO. Desta forma, é importante testar outros métodos para caracterizar espacial dos óxidos de ferro. Objetivando avaliar o desempenho da (KO), krigagem ordinária dos dados padronizados (KOP) e da simulação seqüencial gaussiana (SSG) da variabilidade espacial dos óxidos de ferro em uma área com pedoformas: côncava e convexa foram coletados 121 amostras de duas malhas nos dois relevos classificados como: côncavo e convexo com espaçamento de 10 metros. O erro médio, o erro quadrático médio e o erro quadrático médio padronizado foram os critérios de avaliação da acurácia da predição. Os valores altos dos coeficientes de variação dos óxidos de ferro afetaram o desempenho dos métodos... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Iron oxides are important environmental indicators and have great influence on the physical and chemical properties of soil. However, for a better understanding of their formation and occurrence studies are necessary spatial variability. Geostatistics is a tool for this study, ordinary kriging (OK) is a least squares interpolator, is the most widely used in soil science. However smooths out local details of spatial, small values overestimating and underestimating high values. Therefore, is inadequate in the evaluation of iron oxides. Although the kriging show the best estimate of an attribute Z, it does not represent well the variation. Thus, the alternative is geroestatisctical simulation that evaluates and quantify the uncertainty of an attribute, more realistically. Since the most widely used is the sequential Gaussian simulation (SGS), to be quickly and directly in the construction of the density function of cumulative probability (fdpac), and are preferred to the estimation. Thus, is important to test other methods to characterize spatially of iron oxides. In order to evaluate the performance of (OK), ordinary kriging of standardized data (OKS) and sequential Gaussian simulation (SGS) of the spatial variability of iron oxides in an area with landform: concave and convex 121 samples were collected data in two loops with two reliefs classified as concave and convex with spacing of 10 meters. The average error, the mean square error and root mean square error were standardized criteria for evaluating the accuracy of prediction. High values of coefficient of variation of iron oxides harmed the performance of the methods. The landforms showed variable differentiated by SGS. The maps of OKS and E-type had higher accuracy and greater detail in relation to landforms in the OK. It is recommended that E-type maps instead of maps OK... (Complete abstract click electronic access below) / Mestre

Environmentally friendly reactions yielding cis-cyclopropanes are desired in the insecticide industry as these are most effective at reducing the population of insects while remaining benign to the environment. We are exploring the best parameters for the most effective cis-cyclopropanation reactions. An alkene and a diazo compound react together in the presence of a dirhodium catalyst to produce cyclopropanes. First however, the starting materials must be pure. Alfa Aesar supplies cyclohexene which is reported to be 99% pure, however, Gas column chromatography/Mass spectroscopy reveals that the sample is only 3% cyclohexene. Using an adopted published procedure, we discovered that distillation under nitrogen produces cyclohexene in greater than 95% pure. This is suitable for use in cyclopropanation reactions.

Environmentally friendly insecticides have long been desired in the production of food. Pyrethiod compounds not only decompose under light and heat, but they are highly toxic to insects while having effectively no toxicity to mammals. The cis-cyclopropane ring in pyrethiod insecticides is the most challenging component to synthesize. We are exploring the best parameters for the most effective cis-Cyclopropanation reactions. An alkene and a diazo compound react together in the presence of a dirhodium catalyst to produce cyclopropanes. First however, the starting materials must be pure. My part of the project is to purify ethyl diazoacetate(EDA). Column chromatography has been used, but yielded no fraction containing EDA as determined by Gas chromatography/Mass spectrometry. Extraction of EDA with sodium carbonate solution yielded similar results. Thus, I turned to distillation at reduced pressure. This method produced EDA in high enough purity to be used in cyclopropanation reactions. The purity of EDA was determined by H-1 nuclear magnetic resonance spectroscopy.

No description available.