L'Imagerie par Résonance Magnétique pondérée en Diffusion (IRM-D) permet l'accès à l'information structurelle des tissus au travers de la lecture du mouvement brownien des molécules d'eau. Ses applications sont nombreuses en imagerie cérébrale, tant en milieu clinique qu'en recherche. Néanmoins le mouvement physiologique créé une perte de signal supplémentaire au cours de l'encodage de la diffusion. Cette perte de signal liée au mouvement limite les applications de l'IRM-D quant à l'imagerie cardiaque. L'utilisation de faibles valeurs de pondération (b) réduit cette sensibilité mais permet seulement l'imagerie du mouvement incohérent intra-voxel (IVIM) qui contient la circulation sanguine et la diffusion des molécules d'eau. L'imagerie IVIM possède pourtant de nombreuses applications en IRM de l'abdomen, depuis la caractérisation tissulaire à la quantification de la perfusion, mais reste inexplorée pour l'imagerie du coeur. Mon travail de thèse correspond à l'évaluation des conditions d'application de l'IRM-D à faibles valeurs de b pour le coeur humain, afin de proposer des contributions méthodologiques et d'appliquer les techniques développées expérimentalement. Nous avons identifié le mouvement cardiaque comme une des sources majeures de perte de signal. Bien que le mouvement global puisse être corrigé par un recalage non-rigide, la perte de signal induite par le mouvement perdure et empêche l'analyse précise par IRM-D du myocarde. L'étude de cette perte de signal chez un volontaire a fourni une fenêtre temporelle durable où le mouvement cardiaque est au minimum en diastole. Au sein de cette fenêtre optimale, la fluctuation de l'intensité atteste d'un mouvement variable résiduel. Une solution de répéter les acquisitions avec un déclenchement décalé dans le temps permet la capture des minimas du mouvement, c.-à-d. des maximas d'intensité en IRM-D. La projection du maximum d'intensité dans le temps (TMIP) permet ensuite de récupérer des images pondérées en diffusion avec un minimum de perte de signal lié au mouvement. Nous avons développé et évalué différentes séquences d'acquisition combinées avec TMIP : la séquence d'imagerie écho-planaire classique par écho de spin (SE-EPI) peut être adaptée mais souffre du repliement d'image ; une séquence Carr-Purcell-Meiboom-Gill combinée avec une préparation d'encodage de diffusion est plus robuste aux distorsions spatiales mais des artefacts de bandes noires empêchent son applicabilité ; finalement une séquence double-SE-EPI compensant les courants de Foucault et pleinement optimisée produit des images IRM-D moins artefactées. Avec cette séquence, l'IRM-D-TMIP permet la réduction significative de la perte de signal liée au mouvement pour l'imagerie cardiaque pondérée en diffusion. L'inconvénient avec TMIP vient de l'amplification du bruit positif d'intensité. Afin de compenser cette sensibilité du TMIP, nous séparons le bruit d'intensité des fluctuations lentes liées au mouvement grâce à une nouvelle approche basée sur l'analyse en composantes principales (PCA). La décomposition préserve les détails anatomiques tout en augmentant les rapports signal et contraste-à-bruit (SNR, CNR). Avec l'IRM-D-PCATMIP, nous augmentons à la fois l'intensité finale et la qualité d'image (SNR) en théorie et expérimentalement. Les bénéfices ont été quantifiés en simulation avant d'être validés sur des volontaires. De plus la technique a montré des résultats reproductibles sur des patients post-infarctus aigue du myocarde, avec un contraste cohérent avec la position et l'étendue de la zone pathologique. Contrairement à l'imagerie cérébrale, l'imagerie IRM-D par faibles valeurs de pondération in vivo doit être différentiée des analyses IRM-D ex-vivo. Ainsi l'IRM-D-PCATMIP offre une technique sans injection pour l'exploration du myocarde par imagerie IVIM. Les premiers résultats sont encourageants pour envisager l'application sur un modèle expérimental d'une maladie cardiovasculaire [etc...] / Diffusion weighted magnetic resonance imaging (DW-MRI, or DWI) enables the access to the structural information of body tissues through the reading of water molecules Brownian motion. Its applications are many in brain imaging, from clinical practice to research. However physiological motion induces an additional signal-loss when diffusion encoding is applied. This motion-induced signal-loss limits greatly its applications in cardiac imaging. Using low diffusion-weighting values (b) DWI reduces this sensitivity but permits only the imaging of intravoxel incoherent motion (IVIM), which combines both water diffusion and perfusion. IVIM imaging has many applications in body MRI, from tissue characterization to perfusion quantification but remains unexplored for the imaging of the heart. The purpose of this work was to evaluate the context of low b-values DWI imaging of the heart, propose methodological contributions and then apply the developed techniques experimentally. We identified cardiac motion as one of the major sources of motion-induced signal loss. Although bulk motion can be corrected with a non-rigid registration algorithm, additional signal-loss remains uncorrected for and prevents accurate DWI of the myocardium. The study of diffusion-weighted signal-loss induced by cardiac motion in a volunteer provided a time-window when motion is at minimum in diastole. Within this optimal time-window, fluctuation of intensity attests of variable remaining physiological motion. A solution to repeat acquisition with shifted trigger-times ease the capture of motion amplitude minima, i.e. DWI-intensity maxima. Temporal maximum intensity projection (TMIP) finally retrieves diffusion weighted images of minimal motion-induced signal-loss. We evaluated various attempts of sequence development with TMIP: usual spin-echo echo-planar imaging (se-EPI) sequence can be improved but suffers aliasing issues; a balanced steady-state free-precession (b-SSFP) combined with a diffusion preparation is more robust to spatial distortions but typical banding artifacts prevent its applicability; finally a state-of-the-art double-spin-echo EPI sequence produces less artifacted DWI results. With this sequence, TMIP-DWI proves to significantly reduce motion-induced signal-loss in the imaging of the myocardium. The drawback with TMIP comes from noise spikes that can easily be highlighted. To compensate for TMIP noise sensitivity, we separated noise spikes from smooth fluctuation of intensity using a novel approach based on localized principal component analysis (PCA). The decomposition was made so as to preserve anatomical features while increasing signal and contrast to noise ratios (SNR, CNR). With PCATMIP-DWI, both signal-intensity and SNR are increased theoretically and experimentally. Benefits were quantified in a simulation before being validated in volunteers. Additionally the technique showed reproducible results in a sample of acute myocardial infarction (AMI) patients, with a contrast matching the extent and location of the injured area. Contrarily to brain imaging, in vivo low b-values DWI should be differentiated from ex vivo DWI pure diffusion measurements. Thus PCATMIP-DWI might provide an injection-free technique for exploring cardiac IVIM imaging. Early results encourage the exploration of PCATMIP-DWI in an experimental model of cardiac diseases. Moreover the access to higher b values would permit the study of the full IVIM model for the human heart that retrieves and separates both perfusion and diffusion information

Made available in DSpace on 2014-12-17T15:42:31Z (GMT). No. of bitstreams: 1 MariaFGM.pdf: 2684798 bytes, checksum: 4d672e17bab00bdd5d88eb70b9c57edf (MD5) Previous issue date: 2007-03-30 / Petr?leo Brasileiro SA - PETROBRAS / Many studies on environmental ecosystems quality related to polycyclic aromatic hydrocarbons (PAH) have been carried out routinely due to their ubiquotus presence worldwide and to their potential toxicity after its biotransformation. PAH may be introduced into the environmet by natural and anthropogenic processes from direct runoff and discharges and indirect atmospheric deposition. Sources of naturally occurring PAHs include natural fires, natural oil seepage and recent biological or diagenetic processes. Anthropogenic sources of PAHs, acute or chronic, are combustion of organic matter (petroleum, coal, wood), waste and releases/spills of petroleum and derivatives (river runoff, sewage outfalls, maritime transport, pipelines). Besides the co-existence of multiples sources of PAH in the environmental samples, these compounds are subject to many processes that lead to geochemical fates (physical-chemical transformation, biodegradation and photo-oxidation), which leads to an alteration of their composition. All these facts make the identification of the hydrocarbons sources, if petrogenic, pyrolytic or natural, a challenge. One of the objectives of this study is to establish tools to identify the origin of hydrocarbons in environmental samples. PAH diagnostic ratios and PAH principal component analysis were tested on a critical area: Guanabara Bay sediments. Guanabara Bay is located in a complex urban area of Rio de Janeiro with a high anthropogenic influence, being an endpoint of chronic pollution from the Greater Rio and it was the scenario of an acute event of oil release in January 2000. It were quantified 38 compounds, parental and alkylated PAH, in 21 sediment samples collected in two surveys: 2000 and 2003. The PAH levels varied from 400 to 58439 ng g-1. Both tested techniques for origin identification of hydrocarbons have shown their applicability, being able to discriminate the PAH sources for the majority of the samples analysed. The bay sediments were separated into two big clusters: sediments with a clear pattern of petrogenic introduction of hydrocarbons (from intertidal area) and sediments with combustion characteristics (from subtidal region). Only a minority of the samples could not display a clear contribution of petrogenic or pyrolytic input. The diagnostic ratios that have exhibited high ability to distinguish combustion- and petroleum-derived PAH inputs for Guanabara Bay sediments were Phenanthrene+Anthracene/(Phenanthrene+Anthracene+C1Phenanthrene); Fluorantene/(Fluorantene+Pyrene); Σ (other 3-6 ring PAHs)/ Σ (5 alkylated PAH series). The PCA results prooved to be a useful tool for PAH source identification in the environment, corroborating the diagnostic indexes. In relation to the temporal evaluation carried out in this study, it was not verified significant changes on the class of predominant source of the samples. This result indicates that the hydrocarbons present in the Guanabara Bay sediments are mainly related to the long-term anthropogenic input and not directly related to acute events such as the oil spill of January 2000. This findings were similar to various international estuarine sites. Finally, this work had a complementary objective of evaluating the level of hydrocarbons exposure of the aquatic organisms of Guanabara Bay. It was a preliminary study in which a quantification of 12 individual biliar metabolites of PAH was performed in four demersal fish representing three different families. The analysed metabolites were 1-hydroxynaphtalene, 2-hidroxinaphtalene, 1hydroxyphenanthrene, 9-hydroxyphenanthrene, 2-hydroxyphenanthrene, 1hydroxypyrene, 3-hidroxibiphenil, 3- hydroxyphenanthrene, 1-hydroxychrysene, 9hydroxyfluorene, 4-hydroxyphenanthrene, 3-hydroxybenz(a)pyrene. The metabolites concentrations were found to be high, ranging from 13 to 177 ?g g-1, however they were similar to worldwide regions under high anthropogenic input. Besides the metabolites established by the used protocol, it was possible to verified high concentrations of three other compounds not yet reported in the literature. They were related to pyrolytic PAH contribution to Guanabara Bay aquatic biota: 1-hydroxypyrine and 3-hydroxybenz(a)pyrine isomers / In?meros estudos da qualidade de ecossistemas naturais em rela??o a contamina??o de hidrocarbonetos polic?ciclos arom?ticos (HPA) t?m sido desenvolvidos continuadamente face a sua presen?a ub?quoa em todo o planeta e ao seu potencial t?xico ap?s a biotransforma??o. A introdu??o dos HPA no meio ambiente pode ocorrer atrav?s de processos naturais e antropog?nicos, atrav?s de despejos e/ou drenagens e deposi??o atmosf?rica indireta. Fontes naturais de HPA incluem queimadas naturais, exsuda??es de ?leo e processos biog?nicos recentes. Fontes antropog?nicas de HPA, advindas de eventos cr?nicos ou agudos, s?o a combust?o incompleta de ?leo combust?vel automotivo e industrial, a queima intencional de madeira e planta??es, os despejos dom?sticos e industriais, as drenagens pluviais urbanas, os efluentes da ind?stria petrol?fera, os derrames acidentais de ?leo e derivados. Al?m da coexist?ncia de m?ltiplas fontes destes hidrocarbonetos nas amostras ambientais, os HPA est?o sujeitos a v?rios processos geoqu?micos que conduzem ? altera??o de sua composi??o qu?mica ao longo do tempo, tornando a identifica??o das fontes contaminantes, se petrog?nica, pirol?tica ou natural, um verdadeiro desafio. Desta forma, um dos objetivos deste estudo foi estabelecer ferramentas que possibilitem a determina??o das fontes de hidrocarbonetos no meio ambiente. Foram utilizadas raz?es diagn?sticas e an?lise de componentes principais de HPA, tendo sido quantificados 38 compostos, incluindo os HPA parentais e alquilados, em 21 amostras de sedimento da Ba?a de Guanabara, coletadas nos anos de 2000 e 2003. A Ba?a de Guanabara ? um ecossistema estuarino com elevada influ?ncia antropog?nica, que recebe polui??o cr?nica da regi?o metropolitana do Rio de Janeiro e que foi cen?rio de um derrame de ?leo em janeiro de 2000. As concentra??es de HPA nos sedimentos estudados apresentaram-se na faixa de 400 a 58439 ng g-1. Ambas as t?cnicas de diferencia??o de fontes de HPA testadas, raz?es diagn?sticas e an?lise de componentes principais, demonstraram sua aplicabilidade, permitindo a diferencia??o das fontes de HPA para a maioria dos sedimentos da ba?a, que foram divididos em dois grandes grupos: sedimentos com padr?es de introdu??o de hidrocarbonetos predominantemente petrog?nicos e sedimentos com caracter?sticas de combust?o. Apenas uma minoria de amostras n?o apresentou com nitidez a natureza de sua contamina??o. As raz?es que apresentaram maior capacidade em diferenciar as fontes de HPA foram Fluoranteno / (Fluoranteno + Pireno), (Fenantreno + Antraceno) / (Fenantreno + Antraceno + C1Fenantreno) e o ?ndice pirol?tico, Σ (HPA parentais de 3-6 an?is) /Σ (5 s?ries de HPA alquilados). Na avalia??o temporal realizada neste estudo n?o foram verificadas varia??es significativas na natureza das fontes contaminantes predominantes na ba?a, revelando que os hidrocarbonetos presentes est?o correlacionados principalmente com os aportes cr?nicos e n?o diretamente com eventos agudos como o derrame de ?leo ocorrido em janeiro de 2000. Este estudo teve como segundo objetivo a avalia??o preliminar do n?vel de exposi??o a que os organismos aqu?ticos da Ba?a de Guanabara est?o submetidos, atrav?s da quantifica??o de 12 metab?litos individuais de HPA presentes em bile de peixe de quatro esp?cies demersais representativas de tr?s fam?lias diferentes. Os metab?litos analisados foram 1-hidroxinaftaleno, 1-hidroxifenantreno, 9hidroxifenantreno, 2-hidroxinaftaleno, 2-hidroxifenantreno, 1-hidroxipireno, 3hidroxibifenila, 3-hidroxifenantreno, 1-hidroxicriseno, 9-hidroxifluoreno, 4hidroxifenantreno, 3-hidroxibenzo(a)pireno. As concentra??es encontradas nas esp?cies de peixes analisadas mostraram-se elevadas, na faixa de 13 a 177 ?g g1, por?m similares ?s encontradas em algumas regi?es de grande influ?ncia antropog?nica, tanto no Brasil quanto no exterior. Al?m dos metab?litos estabelecidos pela metodologia utilizada, foi poss?vel quantificar tr?s compostos, ainda n?o reportados na literatura, em concentra??es relevantes. Estes metab?litos, relacionados a contribui??o pirol?tica de HPA aos organismos aqu?ticos da Ba?a de Guanabara, s?o is?meros de 1-hidroxipireno e de 3-hidroxibenzo(a)pireno

The Ceratitis capitata is a very important agricultural pest, whose reproduction behaviour is controled by chemical signals. Males initiate mating by creating leks, where they release sexual pheromones to attract females. The main goal of this diploma thesis was to determine the influence of host plants on the composition of male sex-pheromones C. capitata and to compare emanations of wild males with those originating from laboratory population. We studied the chemical composition of volatiles, released by calling males C. capitata from laboratory and two wild populations, using two-dimensional gas chromatography with time-of-flight mass spectrometric detection (GC×GC-TOFMS), gas chromatography with electroantennographic and flame ionization detection (GC-EAD-FID). All data were statistically analyzed by multivariate data analyses. Significant differences were observed in the quantitative and qualitative composition of the chemical emanations among males from the three populations. The GC-EAD-FID analyses revealed fourteen antenally active compounds with a possible behavioral function. Isomenthone, geraniol, bornyl acetate, geranyl acetone and ethyl octanoate were newly identified antenally active compounds of C. capitata male sex pheromone. Statistical analyses indicated that males and females of...

[pt] As descargas atmosféricas são de grande importância para o setor elétrico, sendo frequentemente responsáveis por desligamentos de linhas de transmissão, que podem desencadear uma sequência de eventos que levem o sistema elétrico interligado ao colapso. As longas extensões de linhas de transmissão, expostas a intemperes climáticas, determinam uma probabilidade significativa de incidência direta de descargas atmosféricas nestes equipamentos. Devido ao caráter estratégico das linhas para o fornecimento de energia e a constatação de que descargas atmosféricas estão entre as principais causas de desligamentos, torna-se importante o estudo do comportamento das descargas atmosféricas, antes do instante da ocorrência do desligamento das linhas de transmissão, para compreender os padrões característicos potenciais causadores destes desligamentos. Os estudos encontrados atualmente estão orientados na eficiência das redes de detecção de descargas atmosféricas e na identificação de condições climáticas que indiquem a ocorrência de raios de forma preditiva, sem correlação a ocorrências em linhas de transmissão. Assim, essa dissertação consiste na determinação do risco de desligamentos de linhas de transmissão por descargas atmosféricas, visando fornecer informações antecipadas e possibilitar ações operativas para manter a segurança do sistema elétrico. O modelo desenvolvido nesse estudo, denominado Risco de Desligamentos de Linhas de Transmissão por Raios (RDLR), é composto de dois módulos principais, sendo o primeiro o agrupamento do conjunto amostral de descargas atmosféricas, realizado através de um método baseado em densidade. Nesse módulo, os ruídos são eliminados de forma eficiente e são formados grupos representativos de descargas atmosféricas. O segundo módulo consiste em uma etapa classificatória, baseado em redes neurais artificiais para identificar padrões de grupos de descargas que representem riscos de desligamentos de linhas de transmissão. Visando a otimização do modelo, foi aplicado um método de seleção das variáveis, através de componentes principais, para determinar aquelas que mais contribuem na caracterização desses eventos. O modelo RDLR foi testado com dados reais dos registros de desligamentos de linhas de transmissão, associado a outro banco com dados reais contendo milhões de registros de descargas atmosféricas oriundos das redes de detecção de raios, sendo obtidos excelentes resultados na determinação dos riscos de desligamentos de linhas de transmissão por descargas atmosféricas. / [en] Atmospheric discharges are of great importance to power systems, and are often responsible for outages of transmission lines, which can trigger a sequence of events that leads to a system collapse. The long extensions of transmission lines, exposed to climatic conditions, create significant probability of direct incidence of atmospheric discharges in these equipments. Due to the strategic nature of power supply lines and the fact that atmospheric discharges are among the main causes of outages, it is important to study atmospheric discharges characteristics before failure of transmission lines and understand patterns that are responsible for interruptions. Current studies focus on efficiency of lightning detection networks and on identification of climatic conditions that indicate lightning occurrence in a predictive approach, without any correlation with transmission lines outages. Therefore, this thesis consists on real-time risk determination of transmission lines outage by lightning, providing early information to enabling operational procedures for power system safety. The proposed model, named Transmission Lines Outage Risk by Lightning (TLORL) is composed of two main modules: Atmospheric Discharge Data Clustering and Classification. In the atmospheric discharges data-clustering module, performed by a density-based method, the outages are efficiently eliminated and representative groups of atmospheric discharges are formed. The second module consists of a classification step, based on artificial neural networks, to identify patterns of discharges groups that represent risks to cause transmission lines outages. Aiming at improving the proposed model, principal components analysis (PCA) was applied to determine the input variables that most contribute to the events characterization. The TLORL model was tested with real data transmission line outages, associated to another database with millions lightning records from the detection networks, producing excellent results of transmission lines outages caused by atmospheric discharges.

Le but principal de cette thèse était d'étudier les caractéristiques de la matière organique dissoute (MOD) dans le bassin versant de la Seine. Ce travail a été réalisé dans le cadre du programme de recherche PIREN-Seine. Les travaux présentés ici visaient plus particulièrement à identifier les sources de MOD et à suivre son évolution dans les zones d’étude. L’analyse des propriétés optiques (UV-Visible, fluorescence) de la MOD, couplée aux traitements PARAFAC et ACP, a permis de discriminer différentes sources de MOD et de mettre en évidence des variations spatio-temporelles de ses propriétés. L’axe Seine, en aval de Paris, a notamment été caractérisé par l'activité biologique la plus forte. La MOD du bassin de l’Oise a montré des caractéristiques plus "humiques", tandis que le bassin de la Marne a été caractérisé par un troisième type spécifique de MOD. Il a d’autre part été mis en évidence la présence de MODs spécifiques dans chaque zone pour les échantillons prélevés en périodes d’étiage, alors qu’une distribution homogène des composants a été obtenue pour l’ensemble des échantillons prélevés en période de crue.Le rôle environnemental des colloïdes naturels étant étroitement lié à leur taille, il a d’autre part été développé une technique analytique/séparative originale pour l’étude de ce matériel complexe, un fractionnement par couplage flux/force avec flux asymétrique (AF4). Le fractionnement par AF4 des échantillons a confirmé la variabilité spatio-temporelle en composition et en taille de la MOD d'un site de prélèvement à un autre et a permis de distinguer différentes sources de MOD colloïdale confirmant les résultats de l’étude de ses propriétés optiques. / The main goal of this thesis was to investigate the characteristics of dissolvedorganic matter (DOM) within the Seine River catchment in the Northern part of France. ThisPhD thesis was performed within the framework of the PIREN-Seine research program. Theapplication of UV/visible absorbance and EEM fluorescence spectroscopy combined toPARAFAC and PCA analyses allowed us to identify different sources of DOM andhighlighted spatial and temporal variations of DOM properties. The Seine River wascharacterized by the strongest biological activity. DOM from the Oise basin seemed to havemore "humic" characteristics, while the Marne basin was characterized by a third specifictype of DOM. For samples collected during low-water periods, the distributions of the 7components determined by PARAFAC treatment varied between the studied sub-basins,highlighting different organic materials in each zone. A homogeneous distribution of thecomponents was obtained for the samples collected in period of flood.Then, a semi-quantitative asymmetrical flow field-flow fractionation (AF4) methodology wasdeveloped to fractionate DOM. The following optimized parameters were determined: across-flow rate of 2 ml min-1 during the focus step with a focusing time of 2 min and anexponential gradient of cross-flow from 3.5 to 0.2 ml min-1 during the elution step. Thefluorescence properties of various size-based fractions of DOM were evaluated by applyingthe optimized AF4 methodology to fractionate 13 samples, selected from the three sub-basins.The fluorescence properties of these fractions were analysed, allowing us to discriminatebetween the terrestrial or autochthonous origin of DOM.

Bovine mastitis adversely affects the dairy industry around the world. This disease is caused by a diverse range of bacteria, broadly categorised as minor and major pathogens. In-line tools that help identify these bacterial groupings in the early stages of the disease are advantageous as timely decisions could be made before the cow develops any clinical symptoms. The first objective of this research was to identify the most informative milk parameters for the detection of minor and major bacterial pathogens. The second objective of this research was to evaluate the potential of supervised and unsupervised neural network learning paradigms for the detection of minor infected and major infected quarters in the early stages of the disease. The third objective was to evaluate the effects of different proportions of infected to non-infected cases in the training data set on the correct classification rate of the supervised neural network models as there are proportionately more non-infected cases in a herd than infected cases. A database developed at Lincoln University was used to achieve the research objectives. Starting at calving, quarter milk samples were collected weekly from 112 cows for a period of fourteen weeks, resulting in 4852 samples with complete records for somatic cell count (SCC), electrical resistance, protein percentage, fat percentage, and bacteriological status. To account for the effects of the stage of lactation on milk parameters with respect to days in milking, data was divided into three days in milk ranges. In addition, cow variation was accounted for by the sire family from which the cow originated and the lactation number of each cow. Data was pre-processed before the application of advanced analytical techniques. Somatic cell score (SCS) and electrical resistance index were derived from somatic cell count and electrical resistance, respectively. After pre-processing, the data was divided into training and validation sets for the unsupervised neural network modelling experiment and, for the supervised neural network modelling experiments, the data was divided into training, calibration and validation sets. Prior to any modelling experiments, the data was analysed using statistical and multivariate visualisation techniques. Correlations (p<0.05) were found between the infection status of a quarter and its somatic cell score (SCS, 0.86), electrical resistance index (ERI, -0.59) and protein percentage (PP, 0.33). The multivariate parallel visualisation analysis validated the correlation analysis. Due to significant multicolinearity [Correlations: SCS and ERI (-0.65: p<0.05); SCS and PP (0.32: p<0.05); ERI and PP (-0.35: p<0.05)], the original variables were decorrelated using principle component analysis. SCS and ERI were found to be the most informative variables for discriminating between non-infected, minor infected and major infected cases. Unsupervised neural network (USNN) model was trained using the training data set which was extracted from the database, containing approximately equal number of randomly selected records for each bacteriological status [not infected (NI), infected with a major pathogen (MJI) and infected with a minor pathogen (MNI)]. The USNN model was validated with the remaining data using the four principle components, days in milk (DIM), lactation number (LN), sire number, and bacteriological status (BS). The specificity of the USNN model in correctly identifying non infected cases was 97%. Sensitivities for correctly detecting minor and major infections were 89% and 80%, respectively. The supervised neural network (SNN) models were trained, calibrated and validated with several sets of training, calibration and validation data, which were randomly extracted from the database in such a way that each set has a different proportion of infected to non-infected cases ranging from 1:1 to 1:10. The overall accuracy of these models based on validation data sets gradually increased with increase in the number of non-infected cases in the data sets (80% for the 1:1, 84% for 1:2, 86% for 1:4 and 93% for 1:10). Specificities of the best models for correctly recognising non-infected cases for the four data sets were 82% for 1:1, 91% for 1:2, 94% for 1:4 and 98% for 1:10. Sensitivities for correctly recognising minor infected cases for the four data sets were 86% for 1:1, 76% for 1:2, 71% for 1:4 and 44% for 1:10. Sensitivities for correctly recognising major infected cases for the four data sets were 20% for 1:1, 20% for 1:2, 30% for 1:4 and 40% for 1:10. Overall, sensitivity for the minor infected cases decreased while that of major infected cases increased with increase in the number non-infected cases in the training data set. Due to the very low prevalence of MJI category in this particular herd, results for this category may be inconclusive. This research suggests that somatic cell score and electrical resistance index of milk were the most effective variables for detecting the infection status of a quarter followed by milk protein and fat percentage. The neural network models were able to differentiate milk containing minor and major bacterial pathogens based on milk parameters associated with mastitis. It is concluded that the neural network models can be developed and incorporated into milking machines to provide an efficient and effective method for the diagnosis of mastitis.

<p>In this thesis the oxidation of monoterpenes by O3 and NO2 and factors that influenced the oxidation were studied. In the environment both ozone (O3) and nitrogen dioxide (NO2) are present as oxidising gases, which causes sampling artefacts when using Tenax TA as an adsorbent to sample organic compounds in the air. A scrubber was developed to remove O3 and NO2 prior to the sampling tube, and artefacts during sampling were minimised when using the scrubber. The main organic compounds sampled in this thesis were two monoterpenes, alfa-pinene and delta-3-carene, due to their presence in both indoor and outdoor air. The recovery of the monoterpenes through the scrubber varied between 75-97% at relative humidities of 15-75%.</p><p>The reactions of alfa-pinene and delta-3-carene with O 3, NO2 and nitric oxide (NO) at different relative humidities (RHs) and reaction times were studied in a dark reaction chamber. The experiments were planned and performed according to an experimental design were the factors influencing the reaction (O3, NO2, NO, RH and reaction times) were varied between high and low levels. In the experiments up to 13% of the monoterpenes reacted when O3, NO2, and reaction time were at high levels, and NO, and RH were at low levels. In the evaluation eight and seven factors (including both single and interaction factors) were found to influence the amount of alfa-pinene and delta-3-carene reacted, respectively. The three most influencing factors for both of the monoterpenes were the O 3 level, the reaction time, and the RH. Increased O3 level and reaction time increased the amount of monoterpene reacted, and increased RH decreased the amount reacted.</p><p>A theoretical model of the reactions occurring in the reaction chamber was created. The amount of monoterpene reacted at different initial settings of O3, NO2, and NO were calculated, as well as the influence of different reaction pathways, and the concentrations of O3 and NO2, and NO at specific reaction times. The results of the theoretical model were that the reactivity of the gas mixture towards alfa-pinene and delta-3-carene was underestimated. But, the calculated concentrations of O3, NO2, and NO in the theoretical model were found to correspond to a high degree with experimental results performed under similar conditions. The possible associations between organic compounds in indoor air, building variables and the presence of sick building syndrome were studied using principal component analysis. The most complex model was able to separate 71% of the “sick” buildings from the “healthy” buildings. The most important variables that separated the “sick” buildings from the “healthy” buildings were a more frequent occurrence or a higher concentration of compounds with shorter retention times in the “sick” buildings.</p><p>The outcome of this thesis could be summarised as follows;</p><p>-</p><p>-</p><p>-</p><p>-</p>

The quantitative estimation of reservoir properties directly from seismic data is a major goal of reservoir characterization. Integrated reservoir characterization makes use of different varieties of well and seismic data to construct detailed spatial estimates of petrophysical and fluid reservoir properties. The advantage of data integration is the generation of consistent and accurate reservoir models that can be used for reservoir optimization, management and development. This is particularly valuable in mature field settings where hydrocarbons are known to exist but their exact location, pay, lateral variations and other properties are poorly defined. Recent approaches of reservoir characterization make use of individual seismic attributes to estimate inter-well reservoir properties. However, these attributes share a considerable amount of information among them and can lead to spurious correlations. An alternative approach is to evaluate reservoir properties using multiple seismic attributes. This study reports the results of an investigation of the use of multivariate seismic attributes to predict lateral reservoir properties of gross thickness, net thickness, gross effective porosity, net-to-gross ratio and net reservoir porosity thickness product. This approach uses principal component analysis and principal factor analysis to transform eighteen relatively correlated original seismic attributes into a set of mutually orthogonal or independent PC??s and PF??s which are designated as multivariate seismic attributes. Data from the N-sand interval of Vermilion Block 50 field, Gulf of Mexico, was used in this study. Multivariate analyses produced eighteen PC??s and three PF??s grid maps. A collocated cokriging geostaistical technique was used to estimate the spatial distribution of reservoir properties of eighteen wells penetrating the N-sand interval. Reservoir property maps generated by using multivariate seismic attributes yield highly accurate predictions of reservoir properties when compared to predictions produced with original individual seismic attributes. To the contrary of the original seismic attribute results, predicted reservoir properties of the multivariate seismic attributes honor the lateral geological heterogeneities imbedded within seismic data and strongly maintain the proposed geological model of the N-sand interval. Results suggest that multivariate seismic attribute technique can be used to predict various reservoir properties and can be applied to a wide variety of geological and geophysical settings.

In the studies underlying this thesis, various source tracing techniques were applied to environmental samples from the Baltic region. Comprehensive sampling and analysis of polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) in surface sediments in Swedish coastal and offshore areas resulted in a unique data set for this region. Nearly 150 samples of surface sediments were analyzed for all tetra- to octa-chlorinated PCDD/Fs. The levels showed large spatial variability with hotspots in several coastal regions. Neither Sweden nor the EU has introduced guideline values for PCDD/Fs in sediment, but comparisons to available guidelines and quality standards from other countries indicate that large areas of primarily coastal sediments may constitute a risk to marine organisms. Multivariate pattern analysis techniques and receptor models, such as Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF), were used to trace sources. These analyses suggested that three to six source types can explain most of the observed pattern variations found in the sediment samples. Atmospheric deposition was suggested as the most important source to offshore areas, thus confirming earlier estimates. However, spatial differences indicated a larger fraction of local/regional atmospheric sources, characterized by PCDFs, in the south. This was indicated by the identification of several patterns of atmospheric origin. In coastal areas, the influence of direct emission sources was larger, and among these, chlorophenol used for wood preservation and emissions from pulp/paper production and other wood related industry appeared to be most important. The historic emissions connected to processes involving chemical reactions with chlorine (e.g. pulp bleaching) were found to be of less importance except at some coastal sites. The analysis of PCDD/Fs in Baltic herring also revealed spatial variations in the levels and pollution patterns along the coast. The geographical match against areas with elevated sediment levels indicated that transfer from sediments via water to organisms was one possible explanation. Fugacity, a concept used to predict the net transport direction between environmental matrices, was used to explore the gas exchange of hexachlorocyclohexanes (HCHs) and polychlorinated biphenyls (PCBs) between air and water. These estimates suggested that, in the Kattegat Sea, the gaseous exchange of HCHs primarily resulted in net deposition while PCBs were net volatilized under certain environmental conditions. The study also indicated that, while the air concentrations of both PCBs and γ-HCH are mostly dependent upon the origin of the air mass, the fluctuations in α-HCH were primarily influenced by seasonal changes.

In this thesis, the expression variation problem in two-dimensional (2D) and three-dimensional (3D) face recognition is tackled. While discriminant analysis (DA) methods are effective solutions for recognizing expression-variant 2D face images, they are not directly applicable when only a single sample image per subject is available. This problem is addressed in this thesis by introducing expression subspaces which can be used for synthesizing new expression images from subjects with only one sample image. It is proposed that by augmenting a generic training set with the gallery and their synthesized new expression images, and then training DA methods using this new set, the face recognition performance can be significantly improved. An important advantage of the proposed method is its simplicity; the expression of an image is transformed simply by projecting it into another subspace. The above proposed solution can also be used in general pattern recognition applications. The above method can also be used in 3D face recognition where expression variation is a more serious issue. However, DA methods cannot be readily applied to 3D faces because of the lack of a proper alignment method for 3D faces. To solve this issue, a method is proposed for sampling the points of the face that correspond to the same facial features across all faces, denoted as the closest-normal points (CNPs). It is shown that the performance of the linear discriminant analysis (LDA) method, applied to such an aligned representation of 3D faces, is significantly better than the performance of the state-of-the-art methods which, rely on one-by-one registration of the probe faces to every gallery face. Furthermore, as an important finding, it is shown that the surface normal vectors of the face provide a higher level of discriminatory information rather than the coordinates of the points. In addition, the expression subspace approach is used for the recognition of 3D faces from single sample. By constructing expression subspaces from the surface normal vectors at the CNPs, the surface normal vectors of a 3D face with single sample can be synthesized under other expressions. As a result, by improving the estimation of the within-class scatter matrix using the synthesized samples, a significant improvement in the recognition performance is achieved.