Orientador: Marcos Cesar Ferreira / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-25T23:24:24Z (GMT). No. of bitstreams: 1 Messias_CassianoGustavo_M.pdf: 23638977 bytes, checksum: e704bfa68254415de27c9956b5cdab14 (MD5) Previous issue date: 2014 / Resumo: As paisagens rurais vêm sendo transformadas continuamente pela ocupação humana, principalmente em razão de adaptações técnicas requeridas para o desenvolvimento da agricultura. De maneira geral, estas alterações antrópicas estão diretamente ligadas à utilização dos recursos naturais como insumos da produção agrícola. Os recursos mais impactados por este modo de produção são a vegetação e o solo. Dentre as formas de avaliação do grau de comprometimento da paisagem em razão da exploração agrícola do território é o mapeamento da fragilidade ambiental. Esta pesquisa teve como principal objetivo avaliar os graus de fragilidade ambiental de diferentes áreas do Parque Nacional da Serra da Canastra, situado no sudoeste de Minas Gerais. Criado em 1972, o parque tem como meta principal preservar ecossistemas naturais ainda existentes no bioma do cerrado brasileiro. A metodologia de mapeamento da fragilidade ambiental utilizada neste trabalho baseia-se em cinco variáveis geográficas: índice de vegetação, probabilidade de ocorrência de chuvas intensas, declividades, densidade de estradas e densidade de lineamentos estruturais. Estas variáveis foram processadas em sistemas de informação geográfica, por meio de técnicas de análise espacial, utilizadas para a transformação destas, mapeadas segundo a lógica booleana, em variáveis probabilísticas fuzzy. Os mapas fuzzy foram combinados por meio de algoritmo baseado em soma ponderada, gerando-se um mapa de fragilidade ambiental do Parque Nacional. Este mapa final foi comparado a mapas de processos erosivos e de movimento de massa, checados em campo, com o objetivo de se atribuir pesos às variáveis ambientais por meio do teste de Kolmogorov-Smirnov. A metodologia se mostrou eficiente para a identificação e mapeamento de áreas com maior grau de fragilidade no parque, considerando-se a evidência dos processos erosivos e dos movimentos de massa / Abstract: Rural landscapes have been continually transformed by human occupation, mainly due to technical adjustments required for the development of agriculture. In general, these anthropogenic changes are directly linked to the use of natural resources as inputs into agricultural production. The resources most impacted by this mode of production are the vegetation and soil. Among the manners of assessing the degree of landscape vulnerability is the environmental fragility mapping. This research aimed to assess the degree of environmental vulnerability of different areas located within the Serra da Canastra National Park, located in southwestern Minas Gerais. Created in 1972, the park has as main goal to preserve remaining natural ecosystems of the Brazilian Cerrado biome. The methodology for environmental fragility mapping, used in this work, is based on five geographic variables: vegetation index, probability of intense rainfall, slope terrain, roads and structural lineaments densities. These variables, mapped according to the Boolean logic, were processed in a geographic information system through spatial analysis techniques and transformed in fuzzy probabilistic variables. The fuzzy maps were combined by means of algorithm based on a weighted sum, generating the environmental fragility map of the National Park. The estimative of weights values of the five environmental variables was carried out comparing erosion-mass movement maps and the fragility map, using the D-value of Kolmogorov-Smirnov test. Considering the evidence of erosion and mass movements, we concluded that the methodology is efficient for the identification and mapping of areas with the high degree of fragility in the park / Mestrado / Análise Ambiental e Dinâmica Territorial / Mestre em Geografia

The present work has objective to define wich is the distribution of probability that analysis of nutritional contents are adjusted, selecting a function that better explain its behavior and to estimate the limits values of nutrients contents for it classification on several vegetables tissues. It were used 176 datas et colected from 15,534 nutritional analysis of three forest species, Acacia mearnsii, Eucalyptus sp. and Pinus sp., determinated for 1999 to 2004 period, in Forest Ecological Laboratory, in UFSM. The analysis were separated by subgroups in macro and micronutrients, vegetables components (leaves, bark, wood, root), species and year of determination. The database determination trough it statistical describe with mean, standart-deveation, number of observations, maximum and dataset adjusts a Normal distribution, it was aplicated the Lilliofors Test. Where the dataset didn´t adjust the Normal, it was tested the functions Log-normal, Beta, Gamma and Weibull through Kolmogorov-Smirnov Test, with 5%. It was possible determinated that 70% of all dataset valued adjusts one of functions, being the Normal presented of 33.5% of settlings, the Log-normal 27.3%, the Gamma 3.4%, the Weibull 5.7% and the Beta function didn´t presented no one settling. Of all amount dataset valued, 30.1% didn´t adjust any one functions. The studied functions, exception Beta, can explain the results from chemical analysis of vegetables tissues and establis hed the control limits of nutrient contents in each species. / O presente trabalho teve como objetivo definir qual é a distribuição de probabilidade que os resultados de análises nutricionais se ajustam, selecionando uma função que melhor explica o comportamento dos teores e estimar os valores limites dos macro e micronutrientes para sua classificação nos diversos tecidos vegetais. Foram utilizados 176 conjuntos de dados coletados em 15.534 análises nutricionais de três espécies florestais, Acacia mearnsii, Eucalyptus sp. e Pinus sp., realizadas durante o período de 1999 a 2004 e determinadas no Laboratório de Ecologia florestal, da UFSM. As análises foram separadas em subgrupos por macro e micronutrientes, componente vegetal (folhas, galhos, casca, madeira, raiz, etc.), espécie e ano de determinação. O banco de dados passou pela determinação das estatísticas descritivas média, desvio padrão, número de observações, valores máximo e mínimo. Para verificar se os dados seguiam a distribuição Normal, foi aplicado o teste de Lilliofors. Nos casos onde o teste não foi normal, foram testadas as funções Log-normal, Beta, Gamma e Weibull, via teste de Kolmogorov-Smirnov, com alfa 5%. Foi possível determinar que 70% do total avaliado seguiam alguma das funções, sendo que a Normal apresentou 33,5% de ajustes, a Log-normal 27,3%, a Gamma 3,4%, a Weibull 5,7% e a função Beta não apresentou nenhum ajuste. Do total de conjunto de dados avaliados, 30,1% não se aderiu a nenhuma das funções. As funções estudadas, com exceção da Beta, podem explicar a distribuição dos resultados das análises químicas de tecidos vegetais e estabelecer limites de controle dos teores de elementos químicos em cada espécie.

The doctoral thesis deals with spectrum sensing in cognitive radio networks (CRN). A number of international organizations are currently actively engaged in standardization of CRN and it points out to the fact that this technology will be widely used in the near future. One of the key features of this technology is a dynamic access to the spectrum, which can be affected by many different harmful factors occurring in the communication chain. The thesis investigates the influence of selected factors on the spectrum sensing process. Another contribution of the work is the optimization of the Kolmogorov - Smirnov statistical test that can be applied for the primary user signal detection. The work also incorporates the analysis of the influence of the harmful effects caused by the commonly used transmitters and receivers on various spectrum sensing methods. The investigations are verified by the results of the simulations and also by the measurements with experimental platforms based on the software-defined radio (SDR).

This project studies the signal detection and identification problem in high-dimensional noisy data and the possibility of using it on microbiome data. An extensive simulation study was performed on generated data using as well as a microbiome dataset collected on patients with Parkinson's disease, using Donoho and Jin's Higher criticism, Jager and Wellner's phi-divergence-based goodness-of-fit-test and Stepanova and Pavlenko's CsCsHM statistic . We present some novel approaches based on established theory that perform better than existing methods and show that it is possible to use the signal identification framework to detect differentially abundant features in microbiome data. Although the novel approaches produce good results, they lack substantial mathematical foundations and should be avoided if theoretical rigour is needed. We also conclude that while we have found that it is possible to use signal identification methods to find abundant features in microbiome data, further refinement is necessary before it can be properly used in research. / Detta projekt studerar signaldetekterings- och identifieringsproblemet i högdimensionell brusig data och möjligheten att använda det på mikrobiomdata från människor. En omfattande simuleringsstudie utfördes på genererad data samt ett mikrobiomdataset som samlats in på patienter med Parkinsons sjukdom, med hjälp av ett antal goodness-of-fit-metoder: Donoho och Jins Higher criticis , Jager och Wellners phi-divergenser och Stepanova och Pavelenkos CsCsHM. Vi presenterar några nya tillvägagångssätt baserade på vedertagen teori som visar sig fungera bättre än befintliga metoder och visar att det är möjligt att använda signalidentifiering för att upptäcka olika funktioner i mikrobiomdata. Även om de nya metoderna ger goda resultat saknar de betydande matematiska grunder och bör undvikas om teoretisk formalism är nödvändigt. Vi drar också slutsatsen att medan vi har funnit att det är möjligt att använda signalidentifieringsmetoder för att hitta information i mikrobiomdata, är ytterligare experiment nödvändiga innan de kan användas på ett korrekt sätt i forskning.

We consider the problem of testing whether r (>=2) samples are drawn from the same continuous distribution F(x). The test statistic we will study in some detail is defined as the maximum of the circular differences of the empirical distribution functions, a generalization of the classical 2-sample Kolmogorov-Smirnov test to r (>=2) independent samples. For the case of equal sample sizes we derive the exact null distribution by counting lattice paths confined to stay in the scaled alcove $\mathcal{A}_r$ of the affine Weyl group $A_{r-1}$. This is done using a generalization of the classical reflection principle. By a standard diffusion scaling we derive also the asymptotic distribution of the test statistic in terms of a multivariate Dirichlet series. When the sample sizes are not equal the reflection principle no longer works, but we are able to establish a weak convergence result even in this case showing that by a proper rescaling a test statistic based on a linear transformation of the circular differences of the empirical distribution functions has the same asymptotic distribution as the test statistic in the case of equal sample sizes. / Series: Research Report Series / Department of Statistics and Mathematics

The two and k-sample tests of equality of the survival distributions against the alternatives including cross-effects of survival functions, proportional and monotone hazard ratios, are given for the right censored data. The asymptotic power against approaching alternatives is investigated. The tests are applied to the well known chemio and radio therapy data of the Gastrointestinal Tumor Study Group. The P-values for both proposed tests are much smaller then in the case of other known tests. Differently from the test of Stablein and Koutrouvelis the new tests can be applied not only for singly but also to randomly censored data.

The purpose of this analysis is to evaluate the usefulness of Fourier Transform Infrared (FTIR) spectroscopy in the detection of Herpes Simplex Virus 1 (hsv1) infection at an early stage. The raw absorption values were standardized to eliminate inter-sampling error. Wilcoxon-Mann-Whitney (WMW) statistic's Z score was calculated to select significant spectral regions. Partial least squares modeling was performed because of multicollinearity. Kolmogorov-Smirnov statistic showed models for healthy tissues from different time groups were not from same distribution. The additional 24 hour dataset was evaluated using the following methods. Variables were selected by WMW Z score. Difference of Composites statistic, DC, was created as a disease indicator and evaluated using area under the ROC curve, specificities, and confidence intervals using bootstrap algorithm. The specificity of DC was high, however the confidence intervals were large. Future studies are required with larger sample sizes to test this statistic's usefulness.

This thesis presents several statistical analysis on a cooperative project with Dr. Pallas and Yuting Mao from Biology Department of Georgia State University. This research concludes the impact of cauterize size of animals’ midbrain on auditory and visual response in brains. Besides some already commonly used statistical analysis method, such as MANOVA and Frequency Test, a unique combination of Permutation Test, Kolmogorov-Smirnov Test and Wilcoxon Rank Sum Test is applied to our non-parametric data. Some simulation results show the Permutation Test we used has very good powers, and fits the need for this study. The result confirms part of the Biology Department’s hypothesis statistically and enhances more complete understanding of the experiments and the potential impact of helping patients with Acquired Brain Injury.

Identifying previously unknown adverse drug reactions becomes more important as the number of drugs and the extent of their use increases. The aim of this Master’s thesis project was to evaluate the performance of a novel approach for highlighting potential adverse drug reactions, also known as signal detection. The approach was based on deviating time-to-onset patterns and was implemented as a two-sample Kolmogorov-Smirnov test for non-vaccine data in the safety report database, VigiBase. The method was outperformed by both disproportionality analysis and the multivariate predictive model vigiRank. Performance estimates indicate that deviating time-to-onset patterns is not a suitable approach for signal detection for non-vaccine data in VigiBase.

Measuring the research performance of a university is important to the universities themselves, governments, and students alike. Among other metrics, the number of publications is easy to obtain, and due to the large number of publications each university produces during one year, it suggests to be one accurate metric. However, the number of publications depends largely on the size of the institution, suggesting, if not addressed, that larger universities are better. Thus, one might intuitively try to normalize by size and use publications per researcher instead. A better institution would allow individual researchers to have more publications each year. However, publications, like many other things, might follow a power-law distribution, where most researchers have few, and only a few researchers have very many publications. These power-law distributions violate the assumptions the central limit the orem makes, for example, having a well-defined mean or variance. Specifically, one can not normalize or use averages from power-law distributed data, making the comparison of university publications impossible if they indeed follow a power-law distribution. While it has been shown that some scientific domains or universities show this power-law distribution, it is not known if Swedish universities also show this phenomenon. Thus, here we collect publication data for Swedish universities and determine whether or not, they are power-law distributed. Interestingly, if they are, one might use the slope of the power-law distribution as a proxy to determine research output. If the slope is steep, it suggests that the ratio between highly published authors and those with few publications is small. Where as a flatter slope suggests that a university has more highly published authors than a university with a steeper slope. Thus, the second objective here is to assess if the slope of the distribution can be determined or to which extent this is possible. This study will show that eight of the fifteen Swedish universities considered follow a power-law distribution (Kolmogorov-Smirnov statistic<0.05), while the remaining seven do not. The key determinant is the total number of publications. The difficulty here is that often the total number of publications is so small that one can not reject a power-law distribution, and it is also impossible to determine the slope of the distribution with any accuracy in those cases. While this study suggests that in principle, the slopes of the power-law distributions can be used as a comparative metric, it also showed that for half of Sweden’s universities, the data is insufficient for this type of analysis.