This master’s thesis studies the effect of weathering on thermally modified Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) with different surface treatments. Most importantly colour changes were analysed, especially, the greying. However, other aspects of appearance changes, like cracks, mould and chemical changes on the surface were included. Special emphasis was on analysing the influence of tree species, type of thermal modification and surface treatment on these properties. Furthermore, it was tested if near infra-red (NIR) spectroscopy allows to estimate the colour, in addition, to measuring chemical changes. The whole study was set up as a decking of a gangway in Northern Sweden and evaluated after the first year of exposure. With the help of colorimetry, changes in colour based on the CIE L*C*hab colour space were measured. Test results showed that within one year all surfaces turned greyer significantly due to changes in content of lignin and cellulose measured with NIR spectroscopy. Differences could neither be observed between the uses of the two tree species nor between the uses of the thermal modifications, pressurised saturated steam at a temperature of 180 °C and superheated steam at a temperature of 212 °C. However, the surface treatment affects the colour change. Timber treated with a silicon based treatment had from the beginning a greyer colour and turned greyest after one year, while oil and pigmented oil stain slowed down the greying compared to untreated and iron vitriol treated timber. After one year of exposure for none of the treatments the colour had stabilised. Qualitative analysis of cracks and mould growth on the surface indicated some dependence on thermal modification and surface treatment. The PLS model for the prediction was not good, so no universally valid conclusions could be drawn of them. Timber with silicon based treatment showed a tendency for mould growth and timber thermally modified with pressurised saturated steam at a temperature of 180 °C tends to have cracks more often. It was possible to estimate the colour from NIR spectroscopy. Best estimations were achieved for the Chroma, followed by lightness and hue. Even better prediction of the Chroma could be achieved by fitting different models based on the surface treatments.  Hence, NIR spectroscopy allows a good estimation of the greying without needing a further measurement instrument, like a colorimeter.

Title: Shell-like structures in the ISM: Observation versus simulations Author: Vojtěch Sidorin (vojtech.sidorin@gmail.com)1 Department: Astronomical Institute of Charles University2 Supervisor: Prof. RNDr. Jan Palouš, DrSc. (palous@asu.cas.cz), Astronomical Institute of the Czech Academy of Sciences3 Abstract: Shell-like structures are objects found in large numbers in the inter- stellar medium (ISM). They usually appear as bubbles or segments of bubbles and are believed to result from the deposition of mass and energy into the ISM by stars, gamma-ray bursts, or high-velocity clouds. Interstellar turbulence may play a role in their creation too. These structures influence the dynamics of the ISM and are also linked to star formation. In this thesis, I review our current knowledge of the ISM, interstellar turbulence, and shell-like structures in the ISM. Then I present the research into the GLIMPSE bubble N107 conducted in collaboration with my colleagues. N107 is a dusty shell-like structure found in our Galaxy. We explored its atomic, molecular, and radio-continuum components; derived its distance (3.6 kpc), size (radius of 12 pc), and expansion velocity (8 km s−1 ); and identified 49 associated molecular clumps. Using numerical simulations, we estimated the conditions under which N107 formed and concluded...

The rapidly increasing capabilities and complexity of numerical software present a growing challenge to software development productivity. While many open source projects enable the community to share experiences, learn and collaborate; estimating individual developer productivity becomes more difficult as projects expand. In this work, we analyze some HPC software Git repositories with issue trackers and compute productivity metrics that can be used to better understand and potentially improve development processes. Evaluating productivity in these communities presents additional challenges because bug reports and feature requests are often done by using mailing lists instead of issue tracking, resulting in difficult-to-analyze unstructured data. For such data, we investigate automatic tag generation by using natural language processing techniques. We aim to produce metrics that help quantify productivity improvement or degradation over the projects lifetimes. We also provide an objective measurement of productivity based on the effort estimation for the developer's work.

Orientador: Alice Kimie Miwa Libardi / Banca: Thiago de Melo / Banca: Washington Mio / Resumo: O objetivo desta dissertação é apresentar uma versão detalhada do artigo: "A Tutorial on Spectral Clustering" de U. von Luxburg sobre agrupamentos através de grafos Laplacianos, suas propriedades e mostrar alguns resultados da teoria de agrupamentos. Além disso, serão apresentados três algoritmos de agrupamentos e ilustraremos um deles com uma aplicação no cultivo da soja em diferentes condições de cultivo / Abstract: The main goal of this dissertation is to present a detailed version of the paper: " A Tutorial on Spectral Clustering" of U. von Luxburg on clusters, through Laplacian graphs, their properties and to show some results of the cluster theory. In addition, it will be presented three clustering algorithms and we will illustrate one of them with an application in the soybean cultivation, under different conditions / Mestre

Today paper is to find everywhere, and the production factories always need to increase the productivity if they want to stay competitive. Stora Enso Kvarnsveden has one of the biggest magazine paper machines in the world, which produces around 1900 meters of paper per minute. The production process is highly automatized, which reduces the number of operators that work on the machine. Still, process variations can cause brakes in the paper web and lead to loss of income, energy and paper production. It may also have a direct impact on the paper quality. This report is focusing the following question: How to keep the Paper Machine production process under controlled conditions? To make a data analysis fully relevant, we need to use the most important variables of the machine. By analyzing these data some unexpected behavior and variation of process values can be pointed out. The analyzing tool needs to be fast and portable, and therefore a software system has been developed. By comparing process data with reference data this software can make a powerful analysis. The created software is intended to be used either by operators or engineers. The most important results are collected in a file. In this text file, the comparison function gives the results which are stored in a CSV-format. Furthermore, an auto-update function allows the users to run it automatically. Graphical presentations are supporting the interpretation of the results.

A disseminação de sistemas de aquisição de dados sobre a qualidade e o desempenho de produtos e processos de fabricação deu origem a novos tipos de dados. Dado funcional é um conjunto de dados que formam um perfil ou uma curva. No perfil, a característica de qualidade é uma função dependente de uma ou mais variáveis exploratórias ou independentes. A análise de dados funcionais é um tema de pesquisa recente praticado em diversas áreas do conhecimento. Na indústria, os dados funcionais aparecem no controle de qualidade. A ausência de métodos apropriados a dados funcionais pode levar ao uso de métodos ineficientes e reduzir o desempenho e a qualidade de um produto ou processo. A análise de dados funcionais através de métodos multivariados pode ser inadequada devido à alta dimensionalidade e estruturas de variância e covariância dos dados. O desenvolvimento teórico de métodos para a análise de dados funcionais na área de Engenharia da Qualidade encontra-se defasado em relação ao potencial de aplicações práticas. Este trabalho identificou a existência dos dados funcionais tratados por métodos ineficientes. Os métodos atuais para controle de qualidade de dados são adaptados a situações específicas, conforme o tipo de dado funcional e a fase do monitoramento. Este trabalho apresenta propostas para métodos de análise de dados funcionais aplicáveis a questões relevantes da área de pesquisa em Engenharia da Qualidade, tais como: (i) o uso da análise de variância em experimentos com dados funcionais; (ii) gráficos de controle para monitoramento de perfis; e (iii) a análise e seleção de perfis de fornecedores em projetos inovadores. / The dissemination of data acquisition systems on the quality and performance of products and manufacturing process has given rise to new types of data. Functional data are a collection of data points organized as a profile or curve. In profile, the quality characteristic is a function dependent on one or more exploratory or independent variables. The functional data analysis is a recent research topic practiced in various areas of knowledge. In industry, the functional data appears in quality control. The lack of suitable methods can lead to use of inefficient methods and reducing the performance and quality of a product or process. The analysis of functional data by multivariate methods may be inadequate due to the high dimensionality and variance and covariance structures of the data. The development of theoretical methods for the analysis of functional data in Quality Engineering area is lagged behind the potential for practical applications. This work identified the existence of functional data processed by inefficient methods. Current methods for data quality control are adapted to specific situations, depending on the type of functional data and the phase of monitoring. This paper presents proposals for functional data analysis methods applicable to relevant research questions in the area of Quality Engineering such as: (i) the use of analysis of variance in experiments with functional data; (ii) control charts for monitoring profiles; and (iii) the analysis and selection of supplier profiles on innovative projects.

Neste trabalho, estudam-se métodos que consideram a natureza funcional da Estrutura a Termo da Taxa de Juros (ETTJ) para fazer previsões fora da amostra. São estimados modelos não-paramétricos para dados funcionais (NP-FDA) e séries temporais funcionais (FTS). O primeiro se baseia em um estimador de regressão proposto por Ferraty e Vieu (2006), que utiliza funções Kernel para atribuir pesos localmente às variáveis funcionais. Já o segundo se baseia no trabalho de Hays, Shen e Huang (2012), que estimam a ETTJ através de um modelo de fatores dinâmicos, que por sua vez são estimados através de análise de componentes principais funcional. Testa-se a capacidade de previsão dos modelos com a ETTJ americana, para os horizontes de 1, 3, 6 e 12 meses, e comparam-se os resultados com modelos benchmark, como Diebold e Li (2006) e o passeio aleatório. Principal foco deste trabalho, as estimações com métodos NP-FDA não tiveram resultado muito bons, obtendo sucesso apenas com maturidades e horizontes muito curtos. Já as estimações com FTS tiveram, no geral, desempenho melhor que os métodos escolhidos como benchmark. / This work studies methods that takes the Yield Curve's functional nature into account to produce out-of-sample forecasts. These methods are based in nonparametric functional data analysis (NP-FDA) and functional time series (FTS). The former are based in a functional regressor estimator proposed by Ferraty e Vieu (2006) that includes Kernel functions to do local weighting between the functional variables. The latter are based on the paper by Hays, Shen and Huang (2012), that forecasts the Yield Curve based in a dynamic factors model, in which the factors are determined by functional principal component analysis. Their forecasting capability is tested for the american's Yield Curve database for 1, 3, 6 and 12 months. The results from the functional methods models are then compared to benchmarks widely used in the literature, such as the random walk and the Diebold and Li (2006). Main focus on this work, the NP-FDA methods didn't produce very good forecasts, being successful only for very low maturities and short forecast horizons. The forecasts generated by the FTS methods were, in general, better than our chosen benchmarks.

The Top Scoring Pair (TSP) classifier, based on the notion of relative ranking reversals in the expressions of two marker genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. We introduce the AUC-based TSP classifier, which is based on the Area Under the ROC (Receiver Operating Characteristic) Curve. The AUCTSP classifier works according to the same principle as TSP but differs from the latter in that the probabilities that determine the top scoring pair are computed based on the relative rankings of the two marker genes across all subjects as opposed to for each individual subject. Although the classification is still done on an individual subject basis, the generalization that the AUC-based probabilities provide during training yield an overall better and more stable classifier. Through extensive simulation results and case studies involving classification in ovarian, leukemia, colon, and breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative pivot genes. The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across {\em all} subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selection of non-informative (pivot) genes as members of the top-scoring pair.\\ We have also proposed the use of the AUC test statistic in order to reduce the computational cost of the TSP in selecting the most informative pair of genes for diagnosing a specific disease. We have proven the efficacy of our proposed method through case studies in ovarian, colon, leukemia, breast and prostate cancers and diffuse large b-cell lymphoma in selecting informative genes. We have compared the selected pairs, computational cost and running time and classification performance of a subset of differentially expressed genes selected based on the AUC probability with the original TSP in the aforementioned datasets. The reduce sized TSP has proven to dramatically reduce the computational cost and time complexity of selecting the top scoring pair of genes in comparison to the original TSP in all of the case studies without degrading the performance of the classifier. Using the AUC probability, we were able to reduce the computational cost and CPU running time of the TSP by 79\% and 84\% respectively on average in the tested case studies. In addition, the use of the AUC probability prior to applying the TSP tends to avoid the selection of genes that are not expressed (``pivot'' genes) due to the imposed condition. We have demonstrated through LOOCV and 5-fold cross validation that the reduce sized TSP and TSP have shown to perform approximately the same in terms of classification accuracy for smaller threshold values. In conclusion, we suggest the use of the AUC test statistic in reducing the size of the dataset for the extensions of the TSP method, e.g. the k-TSP and TST, in order to make these methods feasible and cost effective.

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. Understanding the nature of functional relationships (such as prey-predator) between species is important for building predictive models. However, modelling the interactions with external stressors over time and space is also essential for ecosystem-based approaches to fisheries management. With the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, fewer assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data and combined with existing knowledge. In this thesis, we explore Bayesian network modelling approaches, accounting for latent effects to reveal species dynamics within geographically different marine ecosystems. First, we introduce the concept of functional equivalence between different fish species and generalise trophic structure from different marine ecosystems in order to predict influence from natural and anthropogenic sources. The importance of a hidden variable in fish community change studies of this nature was acknowledged because it allows causes of change which are not purely found within the constrained model structure. Then, a functional network modelling approach was developed for the region of North Sea that takes into consideration unmeasured latent effects and spatial autocorrelation to model species interactions and associations with external factors such as climate and fisheries exploitation. The proposed model was able to produce novel insights on the ecosystem's dynamics and ecological interactions mainly because it accounts for the heterogeneous nature of the driving factors within spatially differentiated areas and their changes over time. Finally, a modified version of this dynamic Bayesian network model was used to predict the response of different ecosystem components to change in anthropogenic and environmental factors. Through the development of fisheries catch, temperature and productivity scenarios, we explore the future of different fish and zooplankton species and examine what trends of fisheries exploitation and environmental change are potentially beneficial in terms of ecological stability and resilience. Thus, we were able to provide a new data-driven modelling approach which might be beneficial to give strategic advice on potential response of the system to pressure.

This dissertation explores how students’ beliefs and attitudes interact with their identities as physics people, motivated by calls to increase participation in science, technology, engineering, and mathematics (STEM) careers. This work combines several theoretical frameworks, including Identity theory, Future Time Perspective theory, and other personality traits to investigate associations between these factors. An enriched understanding of how these attitudinal factors are associated with each other extends prior models of identity and link theoretical frameworks used in psychological and educational research. The research uses a series of quantitative and qualitative methodologies, including linear and logistic regression analysis, thematic interview analysis, and an innovative analytic technique adapted for use with student educational data for the first time: topological data analysis via the Mapper algorithm. Engineering students were surveyed in their introductory engineering courses. Several factors are found to be associated with physics identity, including student interest in particular engineering majors. The distributions of student scores on these affective constructs are simultaneously represented in a map of beliefs, from which the existence of a large “normative group” of students (according to their beliefs) is identified, defined by the data as a large concentration of similarly minded students. Significant differences exist in the demographic representation of this normative group compared to other students, which has implications for recruitment efforts that seek to increase diversity in STEM fields. Select students from both the normative group and outside the normative group were selected for subsequent interviews investigating their associations between physics and engineering, and how their physics identities evolve during their engineering careers. Further analyses suggest a more complex model of physics and engineering identity which is not necessarily uniform for all engineering students, including discipline-specific differences that should be further investigated. Further, the use of physics identity as a model to describe engineering student choices may be limited in applicability to early college. Interview analysis shows that physics recognition beliefs become contextualized in engineering as students begin to view physics as an increasingly distinct domain from engineering.