<p>This research assessed landuse changes and trends in vegetation cover in the Sandveld, using remote sensing images. Landsat TM satellite images of 1990, 2004 and 2007 were classified using the maximum likelihood classifier into seven landuse classes, namely water, agriculture, fire patches, natural vegetation, wetlands, disturbed veld, and open sands. Change detection using remote sensing algorithms and landscape metrics was performed on these multi-temporal landuse maps using the Land Change Modeller and Patch Analyst respectively. Markov stochastic modelling techniques were used to predict future scenarios in landuse change based on the classified images and their transitional probabilities. MODIS NDVI multi-temporal datasets with a 16day temporal resolution were used to assess seasonal and annual trends in vegetation cover using time series analysis (PCA and time profiling).Results indicated that natural vegetation decreased from 46% to 31% of the total landscape between 1990 and 2007 and these biodiversity losses were attributed to an increasing agriculture footprint. Predicted future scenario based on transitional probabilities revealed a continual loss in natural habitat and increase in the agricultural footprint. Time series analysis results (principal components and temporal profiles) suggested that the landscape has a high degree of overall dynamic change with pronounced inter and intra-annual changes and there was an overall increase in greenness associated with increase in agricultural activity. The study concluded that without future conservation interventions natural habitats would continue to disappear, a condition that will impact heavily on biodiversity and significant waterdependent ecosystems such as wetlands. This has significant implications for the long-term provision of water from ground water reserves and for the overall sustainability of current agricultural practices.</p>

The objective of this work was to understand microbial population density and diversity, both spatially and temporally, in wetland mesocosms to gain a better fundamental understanding for use in the optimization and design of constructed wetlands (CWs). A standardized community level physiological profiling (CLPP) data analysis protocol was adapted and utilized for CW mesocosms. A new one-dimensional metric was developed to track community divergence using BIOLOGTM ECO plate data. The method proved easy to use, did not require a background in multivariate statistics, and accurately described community divergence in mesocosm systems. To study mesocosm biofilm-bound bacterial communities an appropriate detachment protocol was required. Various shaking protocols were evaluated for their effectiveness in the detachment of bacteria from mesocosm pea gravel, with a focus on detachment of viable and representative bacterial communities. A protocol based on mechanical shaking with buffer and enzymes was identified as an optimal approach and used further in this study. The bacterial communities associated with the interstitial water, pea gravel media, and rhizospheric regions from both planted and unplanted CW mesocosms were profiled using the CLPP method and compared. Vertical community stratification was observed for all mesocosm systems. Rhizospheric communities were found to be significantly more active than their gravel-associated counterparts, suggesting that although rhizospheric bacteria were less abundant in the mesocosms they may play a more significant role in the removal and fate of water born contaminants. The start-up dynamics of CW mesocosms was investigated using the CLPP and standard CW characterization methods over an eight month period. All mesocosms showed a steep increase in interstitial community divergence until day 75-100, at which point a steady-state was reached. The interstitial communities were also characterized in terms of similarity based on experimental design treatments (planted/unplanted and origin of seeding inoculum). Four stages were identified during the start-up consisting of an initial stage where mesocosm communities were differentiated based on origin of the inoculum, a period where adjustments and shifts occurred in all mesocosm, a time where all mesocosm communities were quite similar, and a final state where community differentiations were made based plant presence in the mesocosms.

The objective of this work was to understand microbial population density and diversity, both spatially and temporally, in wetland mesocosms to gain a better fundamental understanding for use in the optimization and design of constructed wetlands (CWs). A standardized community level physiological profiling (CLPP) data analysis protocol was adapted and utilized for CW mesocosms. A new one-dimensional metric was developed to track community divergence using BIOLOGTM ECO plate data. The method proved easy to use, did not require a background in multivariate statistics, and accurately described community divergence in mesocosm systems. To study mesocosm biofilm-bound bacterial communities an appropriate detachment protocol was required. Various shaking protocols were evaluated for their effectiveness in the detachment of bacteria from mesocosm pea gravel, with a focus on detachment of viable and representative bacterial communities. A protocol based on mechanical shaking with buffer and enzymes was identified as an optimal approach and used further in this study. The bacterial communities associated with the interstitial water, pea gravel media, and rhizospheric regions from both planted and unplanted CW mesocosms were profiled using the CLPP method and compared. Vertical community stratification was observed for all mesocosm systems. Rhizospheric communities were found to be significantly more active than their gravel-associated counterparts, suggesting that although rhizospheric bacteria were less abundant in the mesocosms they may play a more significant role in the removal and fate of water born contaminants. The start-up dynamics of CW mesocosms was investigated using the CLPP and standard CW characterization methods over an eight month period. All mesocosms showed a steep increase in interstitial community divergence until day 75-100, at which point a steady-state was reached. The interstitial communities were also characterized in terms of similarity based on experimental design treatments (planted/unplanted and origin of seeding inoculum). Four stages were identified during the start-up consisting of an initial stage where mesocosm communities were differentiated based on origin of the inoculum, a period where adjustments and shifts occurred in all mesocosm, a time where all mesocosm communities were quite similar, and a final state where community differentiations were made based plant presence in the mesocosms.

In this thesis, improving the performance of the automatic recognition of the Doppler radar targets is studied. The radar used in this study is a ground-surveillance doppler radar. Target types are car, truck, bus, tank, helicopter, moving man and running man. The input of this thesis is the output of the real doppler radar signals which are normalized and preprocessed (TRP vectors: Target Recognition Pattern vectors) in the doctorate thesis by Erdogan (2002). TRP vectors are normalized and homogenized doppler radar target signals with respect to target speed, target aspect angle and target range. Some target classes have repetitions in time in their TRPs. By the use of these repetitions, improvement of the target type classification performance is studied. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for doppler radar target classification and the results are evaluated. Before classification PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), NMF (Nonnegative Matrix Factorization) and ICA (Independent Component Analysis) are implemented and applied to normalized doppler radar signals for feature extraction and dimension reduction in an efficient way. These techniques transform the input vectors, which are the normalized doppler radar signals, to another space. The effects of the implementation of these feature extraction algoritms and the use of the repetitions in doppler radar target signals on the doppler radar target classification performance are studied.

<p>This research assessed landuse changes and trends in vegetation cover in the Sandveld, using remote sensing images. Landsat TM satellite images of 1990, 2004 and 2007 were classified using the maximum likelihood classifier into seven landuse classes, namely water, agriculture, fire patches, natural vegetation, wetlands, disturbed veld, and open sands. Change detection using remote sensing algorithms and landscape metrics was performed on these multi-temporal landuse maps using the Land Change Modeller and Patch Analyst respectively. Markov stochastic modelling techniques were used to predict future scenarios in landuse change based on the classified images and their transitional probabilities. MODIS NDVI multi-temporal datasets with a 16day temporal resolution were used to assess seasonal and annual trends in vegetation cover using time series analysis (PCA and time profiling).Results indicated that natural vegetation decreased from 46% to 31% of the total landscape between 1990 and 2007 and these biodiversity losses were attributed to an increasing agriculture footprint. Predicted future scenario based on transitional probabilities revealed a continual loss in natural habitat and increase in the agricultural footprint. Time series analysis results (principal components and temporal profiles) suggested that the landscape has a high degree of overall dynamic change with pronounced inter and intra-annual changes and there was an overall increase in greenness associated with increase in agricultural activity. The study concluded that without future conservation interventions natural habitats would continue to disappear, a condition that will impact heavily on biodiversity and significant waterdependent ecosystems such as wetlands. This has significant implications for the long-term provision of water from ground water reserves and for the overall sustainability of current agricultural practices.</p>

<p>Diss. (sammanfattning) Umeå : Umeå universitet, 1990, härtill 5 uppsatser.</p> / digitalisering@umu.se

Industrial chemicals in European Union produced or imported in volumes above 1 tonne annually, necessitate a registration within REACH. A common problem, concerning these chemicals, is deficient information and lack of data for assessing the hazards posed to human health and the environment. Animal studies for the type of toxicological information needed are both expensive and time consuming, and to that an ethical aspect is added. Alternative methods to animal testing are thereby requested. REACH have called for an increased use of in silico tools for non-testing data as structure-activity relationships (SARs), quantitative structure-activity relationships (QSARs), and read-across. The main objective of the studies underlying this thesis is related to explore and refine the use of in silico tools in a risk assessment context of industrial chemicals. In particular, try to relate properties of the molecular structure to the toxic effect of the chemical substance, by using principles and methods of computational chemistry. The initial study was a survey of all industrial chemicals; the Industrial chemical map was created. A part of this map was identified including chemicals of potential concern. Secondly, the environmental pollutants, polychlorinated biphenyls (PCBs) were examined and in particular the non-dioxin-like PCBs (NDL-PCBs). A set of 20 NDL-PCBs was selected to represent the 178 PCB congeners with three to seven chlorine substituents. The selection procedure was a combined process including statistical molecular design for a representative selection and expert judgements to be able to include congeners of specific interest. The 20 selected congeners were tested in vitro in as much as 17 different assays. The data from the screening process was turned into interpretable toxicity profiles with multivariate methods, used for investigation of potential classes of NDL-PCBs. It was shown that NDL-PCBs cannot be treated as one group of substances with similar mechanisms of action. Two groups of congeners were identified. A group including in general lower chlorinated congeners with a higher degree of ortho substitution showed a higher potency in more assays (including all neurotoxic assays). A second group included abundant congeners with a similar toxic profile that might contribute to a common toxic burden. To investigate the structure-activity pattern of PCBs effect on DAT in rat striatal synaptosomes, ten additional congeners were selected and tested in vitro. NDL-PCBs were shown to be potent inhibitors of DAT binding. The congeners with highest DAT inhibiting potency were tetra- and penta-chlorinated with 2-3 chlorine atoms in ortho-position. The model was not able to distinguish the congeners with activities in the lower μM range, which could be explained by a relatively unspecific response for the lower ortho chlorinated PCBs. / Den europeiska kemikalielagstiftningen REACH har fastställt att kemikalier som produceras eller importeras i en mängd över 1 ton per år, måste registreras och riskbedömmas. En uppskattad siffra är att detta gäller för 30 000 kemikalier. Problemet är dock att data och information ofta är otillräcklig för en riskbedömning. Till stor del har djurförsök använts för effektdata, men djurförsök är både kostsamt och tidskrävande, dessutom kommer den etiska aspekten in. REACH har därför efterfrågat en undersökning av möjligheten att använda in silico verktyg för att bidra med efterfrågad data och information. In silico har en ungefärlig betydelse av i datorn, och innebär beräkningsmodeller och metoder som används för att få information om kemikaliers egenskaper och toxicitet. Avhandlingens syfte är att utforska möjligheten och förfina användningen av in silico verktyg för att skapa information för riskbedömning av industrikemikalier. Avhandlingen beskriver kvantitativa modeller framtagna med kemometriska metoder för att prediktera, dvs förutsäga specifika kemikaliers toxiska effekt. I den första studien (I) undersöktes 56 072 organiska industrikemikalier. Med multivariata metoder skapades en karta över industrikemikalierna som beskrev dess kemiska och fysikaliska egenskaper. Kartan användes för jämförelser med kända och potentiella miljöfarliga kemikalier. De mest kända miljöföroreningarna visade sig ha liknande principal egenskaper och grupperade i kartan. Genom att specialstudera den delen av kartan skulle man kunna identifiera fler potentiellt farliga kemiska substanser. I studie två till fyra (II-IV) specialstuderades miljögiftet PCB. Tjugo PCBs valdes ut så att de strukturellt och fysiokemiskt representerade de 178 PCB kongenerna med tre till sju klorsubstituenter. Den toxikologiska effekten hos dessa 20 PCBs undersöktes i 17 olika in vitro assays. De toxikologiska profilerna för de 20 testade kongenerna fastställdes, dvs vilka som har liknande skadliga effekter och vilka som skiljer sig åt. De toxicologiska profilerna användes för klassificering av PCBs. Kvantitativa modeller utvecklades för prediktioner, dvs att förutbestämma effekter hos ännu icke testade PCBs, och för att få ytterligare kunskap om strukturella egenskaper som ger icke önskvärda effekter i människa och natur. Information som kan användas vid en framtida riskbedömning av icke-dioxinlika PCBs. Den sista studien (IV) är en struktur-aktivitets studie som undersöker de icke-dioxinlika PCBernas hämmande effekt av signalsubstansen dopamin i hjärnan.

The dynamics captured in the ocean surface current data provided by satellite altimetry has been a subject of debate since the past decade. In particular, the contribution of surface and interior dynamics to altimetry remains unclear. One avenue to settling this issue is to compare the turbulence (for example, the nature of spectra and interscale fluxes) captured by altimetry to theories of two-dimensional, surface and interior quasigeostrophic turbulence. In this thesis, we focus on mesoscales (i.e., scales of the order of few hundred kms) that are well resolved by altimetry data. Aspects of two dimensional, three dimensional, geotropic and surface quasigeostrophic turbulence are revisited and compared with the observations. Specifically, we compute kinetic energy (KE) spectra and fluxes in five geographical regions (all over the globe) using 21 years of 0.25◦resolution daily data as provided by the AVISO project. We report a strong forward cascade of KE at small scales (accompanied by a spectral scaling of the form k−3) and a robust inverse cascade at larger scales. Further, we show that the small diver-gent part in horizontal velocity data drives the strong forward flux of KE. Indeed, on considering only the non-divergent part of the flow, in accord with incompressible two-dimensional turbulence, the inverse cascade is unaffected, but the forward transfer becomes very weak and the spectral slopes over this range of scales tend to a relatively steeper k−3.5scaling. We note that our results do not agree with interior first bar clinic mode quasigeostrophic (incorrect strength of forward flux) or surface-quasigeostrophic (incorrect spectral slopes) turbulence. Rather, the results are compatible with rotating shallow water and rotating stratified Boussinesq models in which condition of geostrophic balance is dominant but the divergence of horizontal velocity field is not exactly zero. Having seen the “mean” picture of fluxes and spectra from altimetry, in the second part of the thesis we investigate the variability of these entities. In particular, we employ Empirical Or-thogonal Function (EOF) analysis and focus on the variability in the spectral flux. Remarkably, over the entire globe, irrespective of the region under consideration, we see that the first two EOFs explain a large part of the variability in flux anomalies. The geometry of these modes is distinct, the first represents a single signed transfer across scales (i.e. large to small or small to large depending on the sign of the associated principal component), while the second is a mixed mode in that it exhibits a forward/inverse transfer at large/small scales.

A análise de sinais provenientes de um eletrocardiograma (ECG) pode ser de grande importância para avaliação do comportamento cardíaco de um paciente. Os sinais de ECG possuem características específicas de acordo com os tipos de arritmias e sua classificação depende da morfologia do sinal. Neste trabalho é considerada uma abordagem híbrida utilizando análise de componentes principais (PCA) e mapas auto-organizáveis (SOM) para classificação de agrupamentos provenientes de arritmias como a taquicardia sinusal e, principalmente, fibrilação atrial. Nesse sentido, O PCA é utilizado como um pré-processador buscando suprimir sinais de atividades ventriculares, de maneira que a atividade atrial presente no ECG seja evidenciada sob a forma das ondas f. A Rede Neural SOM, é usada na classificação dos padrões de fibrilação atrial e seus agrupamentos / A análise de sinais provenientes de um eletrocardiograma (ECG) pode ser de grande importância para avaliação do comportamento cardíaco de um paciente. Os sinais de ECG possuem características específicas de acordo com os tipos de arritmias e sua classificação depende da morfologia do sinal. Neste trabalho é considerada uma abordagem híbrida utilizando análise de componentes principais (PCA) e mapas auto-organizáveis (SOM) para classificação de agrupamentos provenientes de arritmias como a taquicardia sinusal e, principalmente, fibrilação atrial. Nesse sentido, O PCA é utilizado como um pré-processador buscando suprimir sinais de atividades ventriculares, de maneira que a atividade atrial presente no ECG seja evidenciada sob a forma das ondas f. A Rede Neural SOM, é usada na classificação dos padrões de fibrilação atrial e seus agrupamentos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Monitoring of process control systems is extremely important for industries to ensure the quality of the product and the safety of the process. Predictive controllers, also known by MPC (Model Predictive Control), usually has a well performance initially. However, after a period, many factors contribute to the deterioration of its performance. This highlights the importance of monitoring the MPC control systems. In this work, tools based on multivariate statistical methods are discussed and applied to the problem of monitoring and Performance Assessment of predictive controllers. The methods presented here are: PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Both are techniques that use data collected directly from the process. The first is widely used in Performance Assessment of predictive controllers. The second is a more recent technique that has arisen, mainly in order to be used in fault detection systems. The analyzes are made when applied in simulated processes characteristic of the petrochemical industry operating under MPC control. / O monitoramento de sistemas de controle de processos é extremamente importante no que diz respeito às indústrias, para garantir a qualidade do que é produzido e a segurança do processo. Os controladores preditivos, também conhecidos pela sigla em inglês MPC (Model Predictive Control), costumam ter um bom desempenho inicialmente. Entretanto, após um certo período, muitos fatores contribuem para a deterioração de seu desempenho. Isto evidencia a importância do monitoramento dos sistemas de controle MPC. Neste trabalho aborda-se ferramentas, baseada em métodos estatísticos multivariados, aplicados ao problema de monitoramento e avaliação de desempenho de controladores preditivos. Os métodos aqui apresentados são: o PCA (Análise por componentes principais) e o ICA (Análise por componentes independentes). Ambas são técnicas que utilizam dados coletados diretamente do processo. O primeiro é largamente utilizado na avaliação de desempenho de controladores preditivos. Já o segundo, é uma técnica mais recente que surgiu, principalmente, com o intuito de ser utilizado em sistemas de detecção de falhas. As análises são feitas quando aplicadas em processos simulados característicos da indústria petroquímica operando sob controle MPC.