In recent years, communities that source raw water from the Dan River experienced two severe and unprecedented outbreaks of unpleasant tastes and odors in their drinking water. During both TandO events strong 'earthy', 'musty' odors were reported, but the source was not identified. The first TandO event began in early February, 2015 and coincided with an algal bloom in the Dan River. The algal bloom was thought to be the cause, but after the bloom dissipated, odors persisted until May 2015. The second TandO in October, 2015 did not coincide with observed algal blooms. On February 2, 2014 approximately 39,000 tons of coal ash from a Duke Energy coal ash pond was spilled into the Dan River near Eden, NC. As there were no documented TandO events before the spill, there is concern the coal ash adversely impacted water quality and biological communities in the Dan River leading to the TandO events. In addition to the coal ash spill, years of industrial and agricultural activity in the Dan River area may have contributed to the TandO events. The purpose of this research was to elucidate causes of the two TandO events and provide guidance to prevent future problems. Monthly water samples were collected from August, 2016 to September, 2017 from twelve sites along the Dan and Smith Rivers. Multivariate analyses were applied to look for underlying factors, spatial or temporal trends in the data. There were no reported TandO events during the project but sensory analysis, Flavor Profile Analysis, characterized earthy/musty odors present. No temporal or spatial trends of odors were observed. Seven earthy/musty odorants commonly associated with TandO events were detected. Odor intensity was mainly driven by geosmin, but no relationship between strong odors and odorants was observed. / Master of Science / In recent years, communities that source water from the Dan River experienced two severe and unprecedented outbreaks of unpleasant tastes and odors (T&O) in their drinking water. During both odor events strong ‘earthy’, ‘musty’ odors were reported, but the source was not identified. The first event began in early February, 2015 and coincided with an algal bloom in the Dan River. The algal bloom was thought to be the cause, but after the bloom dissipated, odors persisted until May 2015. The odors returned in October, 2015 but did not coincide with an algal bloom. On February 2, 2014 approximately 39,000 tons of coal ash from a Duke Energy coal ash pond was spilled into the Dan River near Eden, NC. As no documented odor events occurred before the spill, there is concern the coal ash adversely impacted the water quality in the Dan River leading to the odor events. The purpose of this research was to elucidate causes of the two odor events and provide guidance to prevent future problems. Monthly water samples were collected from August, 2016 to September, 2017 from twelve sites along the Dan and Smith Rivers. Multivariate analyses were applied to look for important factors. There were no reported odor events during the project but sensory analysis characterized earthy/musty odors present. No temporal or spatial trends of odors were observed. Seven earthy/musty odorants commonly associated with odor events were detected.

Pesquisas na área de Química Medicinal têm direcionado esforços na busca por métodos que acelerem o processo de descoberta de novos medicamentos. Dentre as diversas etapas relacionadas ao longo do processo de descoberta de substâncias bioativas está a análise das relações entre a estrutura química e a atividade biológica de compostos. Neste processo, os pesquisadores da área de Química Medicinal analisam conjuntos de dados que são caracterizados pela alta dimensionalidade e baixo número de observações. Dentro desse contexto, o presente trabalho apresenta uma abordagem computacional que visa contribuir para a análise de dados químicos e, consequentemente, a descoberta de novos medicamentos para o tratamento de doenças crônicas. As abordagens de análise exploratória de dados, utilizadas neste trabalho, combinam técnicas de redução de dimensionalidade e de agrupamento para detecção de estruturas naturais que reflitam a atividade biológica dos compostos analisados. Dentre as diversas técnicas existentes para a redução de dimensionalidade, são discutidas o escore de Fisher, a análise de componentes principais e a análise de componentes principais esparsas. Quanto aos algoritmos de aprendizado, são avaliados o k-médias, fuzzy c-médias e modelo de misturas ICA aperfeiçoado. No desenvolvimento deste trabalho foram utilizados quatro conjuntos de dados, contendo informações de substâncias bioativas, sendo que dois conjuntos foram relacionados ao tratamento da diabetes mellitus e da síndrome metabólica, o terceiro conjunto relacionado a doenças cardiovasculares e o último conjunto apresenta substâncias que podem ser utilizadas no tratamento do câncer. Nos experimentos realizados, os resultados alcançados sugerem a utilização das técnicas de redução de dimensionalidade juntamente com os algoritmos não supervisionados para a tarefa de agrupamento dos dados químicos, uma vez que nesses experimentos foi possível descrever níveis de atividade biológica dos compostos estudados. Portanto, é possível concluir que as técnicas de redução de dimensionalidade e de agrupamento podem possivelmente ser utilizadas como guias no processo de descoberta e desenvolvimento de novos compostos na área de Química Medicinal. / Researches in Medicinal Chemistry\'s area have focused on the search of methods that accelerate the process of drug discovery. Among several steps related to the process of discovery of bioactive substances there is the analysis of the relationships between chemical structure and biological activity of compounds. In this process, researchers of medicinal chemistry analyze data sets that are characterized by high dimensionality and small number of observations. Within this context, this work presents a computational approach that aims to contribute to the analysis of chemical data and, consequently, the discovery of new drugs for the treatment of chronic diseases. Approaches used in exploratory data analysis, employed in this work, combine techniques of dimensionality reduction and clustering for detecting natural structures that reflect the biological activity of the analyzed compounds. Among several existing techniques for dimensionality reduction, we have focused the Fisher\'s score, principal component analysis and sparse principal component analysis. For the clustering procedure, this study evaluated k-means, fuzzy c-means and enhanced ICA mixture model. In order to perform experiments, we used four data sets, containing information of bioactive substances. Two sets are related to the treatment of diabetes mellitus and metabolic syndrome, the third set is related to cardiovascular disease and the latter set has substances that can be used in cancer treatment. In the experiments, the obtained results suggest the use of dimensionality reduction techniques along with clustering algorithms for the task of clustering chemical data, since from these experiments, it was possible to describe different levels of biological activity of the studied compounds. Therefore, we conclude that the techniques of dimensionality reduction and clustering can be used as guides in the process of discovery and development of new compounds in the field of Medicinal Chemistry

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-04-04T11:46:54Z No. of bitstreams: 1 Allan de Barcelos Silva_.pdf: 2298557 bytes, checksum: dc876b1dd44e7a7095219195e809bb88 (MD5) / Made available in DSpace on 2018-04-04T11:46:55Z (GMT). No. of bitstreams: 1 Allan de Barcelos Silva_.pdf: 2298557 bytes, checksum: dc876b1dd44e7a7095219195e809bb88 (MD5) Previous issue date: 2017-12-14 / Nenhuma / Na área de Processamento de Linguagem Natural, a avaliação da similaridade semântica textual é considerada como um elemento importante para a construção de recursos em diversas frentes de trabalho, tais como a recuperação de informações, a classificação de textos, o agrupamento de documentos, as aplicações de tradução, a interação através de diálogos, entre outras. A literatura da área descreve aplicações e técnicas voltadas, em grande parte, para a língua inglesa. Além disso, observa-se o uso prioritário de recursos probabilísticos, enquanto os aspectos linguísticos são utilizados de forma incipiente. Trabalhos na área destacam que a linguística possui um papel fundamental na avaliação de similaridade semântica textual, justamente por ampliar o potencial dos métodos exclusivamente probabilísticos e evitar algumas de suas falhas, que em boa medida são resultado da falta de tratamento mais aprofundado de aspectos da língua. Este contexto é potencializado no tratamento de frases curtas, que consistem no maior campo de utilização das técnicas de similaridade semântica textual, pois este tipo de sentença é composto por um conjunto reduzido de informações, diminuindo assim a capacidade de tratamento probabilístico eficiente. Logo, considera-se vital a identificação e aplicação de recursos a partir do estudo mais aprofundado da língua para melhor compreensão dos aspectos que definem a similaridade entre sentenças. O presente trabalho apresenta uma abordagem para avaliação da similaridade semântica textual em frases curtas no idioma português brasileiro. O principal diferencial apresentado é o uso de uma abordagem híbrida, na qual tanto os recursos de representação distribuída como os aspectos léxicos e linguísticos são utilizados. Para a consolidação do estudo, foi definida uma metodologia que permite a análise de diversas combinações de recursos, possibilitando a avaliação dos ganhos que são introduzidos com a ampliação de aspectos linguísticos e também através de sua combinação com o conhecimento gerado por outras técnicas. A abordagem proposta foi avaliada com relação a conjuntos de dados conhecidos na literatura (evento PROPOR 2016) e obteve bons resultados. / One of the areas of Natural language processing (NLP), the task of assessing the Semantic Textual Similarity (STS) is one of the challenges in NLP and comes playing an increasingly important role in related applications. The STS is a fundamental part of techniques and approaches in several areas, such as information retrieval, text classification, document clustering, applications in the areas of translation, check for duplicates and others. The literature describes the experimentation with almost exclusive application in the English language, in addition to the priority use of probabilistic resources, exploring the linguistic ones in an incipient way. Since the linguistic plays a fundamental role in the analysis of semantic textual similarity between short sentences, because exclusively probabilistic works fails in some way (e.g. identification of far or close related sentences, anaphora) due to lack of understanding of the language. This fact stems from the few non-linguistic information in short sentences. Therefore, it is vital to identify and apply linguistic resources for better understand what make two or more sentences similar or not. The current work presents a hybrid approach, in which are used both of distributed, lexical and linguistic aspects for an evaluation of semantic textual similarity between short sentences in Brazilian Portuguese. We evaluated proposed approach with well-known and respected datasets in the literature (PROPOR 2016) and obtained good results.

Pesquisas na área de Química Medicinal têm direcionado esforços na busca por métodos que acelerem o processo de descoberta de novos medicamentos. Dentre as diversas etapas relacionadas ao longo do processo de descoberta de substâncias bioativas está a análise das relações entre a estrutura química e a atividade biológica de compostos. Neste processo, os pesquisadores da área de Química Medicinal analisam conjuntos de dados que são caracterizados pela alta dimensionalidade e baixo número de observações. Dentro desse contexto, o presente trabalho apresenta uma abordagem computacional que visa contribuir para a análise de dados químicos e, consequentemente, a descoberta de novos medicamentos para o tratamento de doenças crônicas. As abordagens de análise exploratória de dados, utilizadas neste trabalho, combinam técnicas de redução de dimensionalidade e de agrupamento para detecção de estruturas naturais que reflitam a atividade biológica dos compostos analisados. Dentre as diversas técnicas existentes para a redução de dimensionalidade, são discutidas o escore de Fisher, a análise de componentes principais e a análise de componentes principais esparsas. Quanto aos algoritmos de aprendizado, são avaliados o k-médias, fuzzy c-médias e modelo de misturas ICA aperfeiçoado. No desenvolvimento deste trabalho foram utilizados quatro conjuntos de dados, contendo informações de substâncias bioativas, sendo que dois conjuntos foram relacionados ao tratamento da diabetes mellitus e da síndrome metabólica, o terceiro conjunto relacionado a doenças cardiovasculares e o último conjunto apresenta substâncias que podem ser utilizadas no tratamento do câncer. Nos experimentos realizados, os resultados alcançados sugerem a utilização das técnicas de redução de dimensionalidade juntamente com os algoritmos não supervisionados para a tarefa de agrupamento dos dados químicos, uma vez que nesses experimentos foi possível descrever níveis de atividade biológica dos compostos estudados. Portanto, é possível concluir que as técnicas de redução de dimensionalidade e de agrupamento podem possivelmente ser utilizadas como guias no processo de descoberta e desenvolvimento de novos compostos na área de Química Medicinal. / Researches in Medicinal Chemistry\'s area have focused on the search of methods that accelerate the process of drug discovery. Among several steps related to the process of discovery of bioactive substances there is the analysis of the relationships between chemical structure and biological activity of compounds. In this process, researchers of medicinal chemistry analyze data sets that are characterized by high dimensionality and small number of observations. Within this context, this work presents a computational approach that aims to contribute to the analysis of chemical data and, consequently, the discovery of new drugs for the treatment of chronic diseases. Approaches used in exploratory data analysis, employed in this work, combine techniques of dimensionality reduction and clustering for detecting natural structures that reflect the biological activity of the analyzed compounds. Among several existing techniques for dimensionality reduction, we have focused the Fisher\'s score, principal component analysis and sparse principal component analysis. For the clustering procedure, this study evaluated k-means, fuzzy c-means and enhanced ICA mixture model. In order to perform experiments, we used four data sets, containing information of bioactive substances. Two sets are related to the treatment of diabetes mellitus and metabolic syndrome, the third set is related to cardiovascular disease and the latter set has substances that can be used in cancer treatment. In the experiments, the obtained results suggest the use of dimensionality reduction techniques along with clustering algorithms for the task of clustering chemical data, since from these experiments, it was possible to describe different levels of biological activity of the studied compounds. Therefore, we conclude that the techniques of dimensionality reduction and clustering can be used as guides in the process of discovery and development of new compounds in the field of Medicinal Chemistry

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

Syfte: Att ta fram en metod för att hjälpa mindre sågverk att bättre tillvarata mesta möjliga virke från en timmerstock. Metod: En kvantitativ studie där tre iterationer genomförts enligt Design Science. Resultat: För att skapa en effektiv algoritm som ska utföra volymberäkningar i ett punktmoln som består av cirka två miljoner punkter i ett industriellt syfte ligger fokus i att algoritmen är snabb och visar rätt data. Det primära målet för att göra algoritmen snabb är att bearbeta punktmolnet ett minimalt antal gånger. Den algoritm som uppfyller delmålen i denna studie är Algoritm C. Algoritmen är både snabb och har en låg standardavvikelse på mätfelen. Algoritm C har komplexiteten O(n) vid analys av delpunktmoln. Implikationer: Med utgångspunkt från denna studies algoritm skulle det vara möjligt att använda stereokamerateknik för att hjälpa mindre sågverk att bättre tillvarata mesta möjliga virke från en timmerstock. Begränsningar: Studiens algoritm har utgått från att inga punkter har skapats inuti stocken vilket skulle kunna leda till felplacerade punkter. Om en stock skulle vara krokig överensstämmer inte stockens centrum med z-axelns placering. Detta är något som skulle kunna innebära att z-värdet hamnar utanför stocken, i extremfall, vilket algoritmen inte kan hantera. / Purpose: To develop a method that can help smaller sawmills to better utilize the greatest possible amount of wood from a log. Method: A quantitative study where three iterations has been made using Design Science. Findings: To create an effective algorithm that will perform volume calculations in a point cloud consisting of about two million points for an industrial purpose, the focus is on the algorithm being fast and that it shows the correct data. The primary goal of making the algorithm quick is to process the point cloud a minimum number of times. The algorithm that meets the goals in this study is Algorithm C. The algorithm is both fast and has a low standard deviation of the measurement errors. Algorithm C has the complexity O(n) in the analysis of sub-point clouds. Implications: Based on this study’s algorithm, it would be possible to use stereo camera technology to help smaller sawmills to better utilize the most possible amount of wood from a log. Limitations: The study’s algorithm assumes that no points have been created inside the log, which could lead to misplaced points. If a log would be crooked, the center of the log would not match the z-axis position. This is something that could mean that the z-value is outside of the log, in extreme cases, which the algorithm cannot handle.

This diploma thesis deals with separation of backgroud and moving objects in video. Video can be represented as series of frames and each frame represented as low - rank structure - matrix. This thesis describe sparse representation of signals and robust principal component analysis. It also presents and implements algorithms - models for reconstruction of real video.

This diploma thesis deals with separation of backgroud and moving objects in video. Video can be represented as series of frames and each frame represented as low - rank structure - matrix. This thesis describe sparse representation of signals and robust principal component analysis. It also presents and implements algorithms - models for reconstruction of real video.

This diploma thesis deals with separation of backgroud and moving objects in video. Video can be represented as series of frames and each frame represented as low - rank structure - matrix. This thesis describe sparse representation of signals and robust principal component analysis. It also presents and implements algorithms - models for reconstruction of real video.

Design space exploration (DSE) is a tool used to evaluate and compare designs as part of the design selection process. While evaluating every possible design in a design space is infeasible, understanding design behavior and response throughout the design space may be accomplished by evaluating a subset of designs and interpolating between them using surrogate models. Surrogate modeling is a technique that uses low-cost calculations to approximate the outcome of more computationally expensive calculations or analyses, such as finite element analysis (FEA). While surrogates make quick predictions, accuracy is not guaranteed and must be considered. This research addressed the need to improve the accuracy of surrogate predictions in order to improve DSE of structural responses. This was accomplished by performing comparative analyses of linear and nonlinear dimensionality-reduction-based radial basis function (RBF) surrogate models for emulating various FEA nodal results. A total of four dimensionality reduction methods were investigated, namely principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (ISOMAP), and locally linear embedding (LLE). These methods were used in conjunction with surrogate modeling to predict nodal stresses and coordinates of a compressor blade. The research showed that using an ISOMAP-based dual-RBF surrogate model for predicting nodal stresses decreased the estimated mean error of the surrogate by 35.7% compared to PCA. Using nonlinear dimensionality-reduction-based surrogates did not reduce surrogate error for predicting nodal coordinates. A new metric, the manifold distance ratio (MDR), was introduced to measure the nonlinearity of the data manifolds. When applied to the stress and coordinate data, the stress space was found to be more nonlinear than the coordinate space for this application. The upfront training cost of the nonlinear dimensionality-reduction-based surrogates was larger than that of their linear counterparts but small enough to remain feasible. After training, all the dual-RBF surrogates were capable of making real-time predictions. This same process was repeated for a separate application involving the nodal displacements of mode shapes obtained from a FEA modal analysis. The modal assurance criterion (MAC) calculation was used to compare the predicted mode shapes, as well as their corresponding true mode shapes obtained from FEA, to a set of reference modes. The research showed that two nonlinear techniques, namely LLE and KPCA, resulted in lower surrogate error in the more complex design spaces. Using a RBF kernel, KPCA achieved the largest average reduction in error of 13.57%. The results also showed that surrogate error was greatly affected by mode shape reversal. Four different approaches of identifying reversed mode shapes were explored, all of which resulted in varying amounts of surrogate error. Together, the methods explored in this research were shown to decrease surrogate error when performing DSE of a turbomachine compressor blade. As surrogate accuracy increases, so does the ability to correctly make engineering decisions and judgements throughout the design process. Ultimately, this will help engineers design better turbomachines.