his thesis implements a multivariate analysis in the current cut- based search for the charged Higgs bosons, which are new scalar particles predicted by several extensions to the Standard Model. Heavy charged Higgs bosons (mH± mtop) produced in association with a top quark de- caying via H± → τν are considered. The final state contains a hadronic τ decay, missing transverse energy and a hadronically decaying top quark. This study is based on Monte Carlo samples simulated at CM-energy √ s = 13 TeV for signal and backgrounds. The figure of merit to measure the improvement of the new method with respect to the old analysis is the separation between the signal and background distributions. Four mass points (mH± = 200, 400, 600, 1000 GeV) are considered, and an increase of the separation ranging from 2.6% (1000 GeV) to 29.2% (200 GeV) com- pared to the current cut-based analysis is found. / Denna studie implementerar en flervariabel-analys till den befintliga snitt-baserade analysen av laddade Higgs-bosoner, nya skal ̈arpartiklar fo ̈rutsagda av flertalet fo ̈rl ̈angningar av Standardmodellen. Studien antar tunga lad- dade Higgs-bosoner (mH± mtop) producerade tillsammans med en top- kvark som fo ̈rfaller via H± → τν. Sluttillst ̊andet best ̊ar av ett hadroniskt τ-so ̈nderfall, f ̈orlorad transversell energi och en hadroniskt so ̈nderfallande √ toppkvark. Studien a ̈r baserad p ̊a data f ̈or signal och bakgrund. Fo ̈r att ma ̈ta fo ̈rba ̈ttringen av analysens ka ̈nslighet anva ̈nds avst ̊and mellan bakgrundens och signalens distribu- tioner som godhetstal. Fyra masspunkter (mH± = 200, 400, 600, 1000 GeV) anva ̈nds, och en o ̈kning av avst ̊and fr ̊an 2.6% (1000 GeV) till 29.2% (200 GeV) hittades.

The maturing of Artificial intelligence provides great opportunities for healthcare, but also comes with new challenges. For Artificial intelligence to be adequate a comprehensive analysis of the data is necessary along with testing the data in multiple algorithms to determine which algorithm is appropriate to use. In this study collection of data has been gathered that consists of patients who have either been readmitted or not readmitted to hospital within 30-days after being admitted. The data has then been analyzed and compared in different algorithms to determine the most appropriate algorithm to use.

With exploding human populations and landscapes that are changing, an increasing number of wildlife species are brought to the brink of extinction. In Canada, the eastern hog-nosed snake, 'Heterodon platirhinos', is found in a limited portion of southern Ontario. Designated as threatened by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC), this reptile has been losing its habitat at an alarming rate. Due to the increase in development of southern Ontario, it is crucial to document what limits the snake’s habitat to direct conservation efforts better, for the long-term survival of this species. The goals of this study are: 1) to examine what environmental parameters are linked to the presence of the species at a landscape scale; 2) to predict where the snakes can be found in Ontario through GIS-based habitat suitability models (HSMs); and 3) to assess the role of biotic interactions in HSMs. Three models with high predictive power were employed: Maxent, Boosted Regression Trees (BRTs), and the Genetic Algorithm for Rule-set Production (GARP). Habitat suitability maps were constructed for the eastern hog-nosed snake for its entire Canadian distribution and models were validated with both threshold dependent and independent metrics. Maxent and BRT performed better than GARP and all models predict fewer areas of high suitability when landscape variables are used with current occurrences. Forest density and maximum temperature during the active season were the two variables that contributed the most to models predicting the current distribution of the species. Biotic variables increased the performance of models not by representing a limiting resource, but by representing the inequality of sampling and areas where forest remains. Although habitat suitability models rely on many assumptions, they remain useful in the fields of conservation and landscape management. In addition to help identify critical habitat, HSMs may be used as a tool to better manage land to allow for the survival of species at risk.

With exploding human populations and landscapes that are changing, an increasing number of wildlife species are brought to the brink of extinction. In Canada, the eastern hog-nosed snake, 'Heterodon platirhinos', is found in a limited portion of southern Ontario. Designated as threatened by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC), this reptile has been losing its habitat at an alarming rate. Due to the increase in development of southern Ontario, it is crucial to document what limits the snake’s habitat to direct conservation efforts better, for the long-term survival of this species. The goals of this study are: 1) to examine what environmental parameters are linked to the presence of the species at a landscape scale; 2) to predict where the snakes can be found in Ontario through GIS-based habitat suitability models (HSMs); and 3) to assess the role of biotic interactions in HSMs. Three models with high predictive power were employed: Maxent, Boosted Regression Trees (BRTs), and the Genetic Algorithm for Rule-set Production (GARP). Habitat suitability maps were constructed for the eastern hog-nosed snake for its entire Canadian distribution and models were validated with both threshold dependent and independent metrics. Maxent and BRT performed better than GARP and all models predict fewer areas of high suitability when landscape variables are used with current occurrences. Forest density and maximum temperature during the active season were the two variables that contributed the most to models predicting the current distribution of the species. Biotic variables increased the performance of models not by representing a limiting resource, but by representing the inequality of sampling and areas where forest remains. Although habitat suitability models rely on many assumptions, they remain useful in the fields of conservation and landscape management. In addition to help identify critical habitat, HSMs may be used as a tool to better manage land to allow for the survival of species at risk.

The purpose of this doctoral thesis is to create a new bankruptcy prediction model and also to design how to use this model for the purposes of credit risk measuring. The starting-point of this work is the analysis of traditional bankruptcy models. It was found out that the traditional bankruptcy model are not enough effective in the current economic conditions and it is necessary to create a new ones. Based on the identified deficiencies of the traditional models a set of two new model series was created. The first series of the created models is based on the use of parametric methods, and the second one is based on the use of newer nonparametric approach. Moreover, a set of factors which are able to identify an imminent bankruptcy was analyzed. It was found, that significant signs of imminent bankruptcy can be identified even five years before the bankruptcy occurs. Based on these findings a new model was created. This model incorporates variables of static and even dynamic character for bankruptcy prediction purposes. The overall classification accuracy of this model is 92.27% of correctly classified active companies and 95.65% of correctly classified bankrupt companies.

This thesis investigates the pair production of Higgs bosons (di-Higgs events) at the ATLAS experiment in the Large Hadron Collider (LHC), focusing on the channel where one Higgs boson decays into two bottom quarks and the other decays into two tau leptons. The main objective was to determine whether introducing a split in the invariant mass of the decay products from the two Higgs bosons (the di-Higgs mass) and using this as an analysis variable improves the sensitivity of the Boosted Decision Tree (BDT) machine learning algorithm to the di-Higgs signal. A mass split was performed at 350 GeV, and the BDT algorithm was trained on both the split and un-split data sets, where the split data set included a high-mass region (di-Higgs mass above 350 GeV) using the Standard Model Higgs boson coupling constant of 1 and a low-mass region (di-Higgs mass below 350 GeV) using the enhanced coupling constant of 10 to create a low-mass region more sensitive to the signal.  The results showed that the BDT algorithm training performed on the split data set provided a 3.6% improvement in the exclusion limits, indicating an improvement in the algorithm's sensitivity to the di-Higgs signal compared to the training performed on the un-split data set. This finding suggests that the introduction of a split at 350 GeV can enhance the accuracy and efficiency of machine learning algorithms in detecting di-Higgs boson production at the LHC.  The improvement in sensitivity was attributed to the enhanced discrimination between signal and background events provided by the split in the di-Higgs mass analysis variable. The improved separation between the signal and background events lead to a higher signal-to-background ratio and a corresponding increase in the BDT algorithm's sensitivity to the di-Higgs signal.  In conclusion, this thesis provided evidence that introducing a split in the di-Higgs mass analysis variable can improve the sensitivity of machine learning algorithms to the di-Higgs signal in the channel where one Higgs boson decays into two bottom quarks and the other into two tau particles. This finding has important implications for future research on di-Higgs boson production at the LHC and could lead to more accurate and efficient detection of this rare and important process.

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No estudo das comunidades florestais, estabelecer a importância relativa dos fatores que definem a composição e a distribuição das espécies é um desafio. Em termos de gradientes ambientais o estudo das respostas das espécies arbóreas são essenciais para a compreensão dos processos ecológicos e decisões de conservação. Neste sentido, para contribuir com a elucidação dos processos ecológicos nas principais formações florestais do Estado de São Paulo (Floresta Ombrófila Densa de Terras Baixas, Floresta Ombrófila Densa Submontana, Floresta Estacional Semidecidual e Savana Florestada) este trabalho objetivou responder as seguintes questões: (I) a composição florística e a abundância das espécies arbóreas, em cada unidade fitogeográfica, variam conforme o gradiente edáfico e topográfico?; (II) características do solo e topografia podem influenciar na previsibilidade de ocorrência de espécies arbóreas de ampla distribuição em diferentes tipos vegetacionais? (III) existe relação entre o padrão de distribuição espacial de espécies arbóreas e os parâmetros do solo e topografia? O trabalho foi realizado em parcelas alocadas em unidades de conservação (UC) que apresentaram trechos representativos, em termos de conservação e tamanho, das quatro principais formações florestais presentes no Estado de São Paulo. Em cada UC foram contabilizados os indivíduos arbóreos (CAP ≥ 15 cm), topografia, dados de textura e atributos químicos dos solos em uma parcela de 10,24 ha, subdividida em 256 subparcelas. Análises de correspodência canônica foram aplicadas para estabelecer a correspondência entre a abundância das espécies e o gradiente ambiental (solo e topografia). O método TWINSPAN modificado foi aplicado ao diagrama de ordenação da CCA para avaliar a influência das variáveis ambientais (solo e topografia) na composição de espécies. Árvores de regressão \"ampliadas\" (BRT) foram ajustadas para a predição da ocorrência das espécies segundo as variáveis de solo e topografia. O índice de Getis-Ord (G) foi utilizado para determinar a autocorrelação espacial das variáveis ambientais utilizadas nos modelos de predição da ocorrência das espécies. Nas unidades fitogeográficas analisadas, a correspondência entre o gradiente ambiental (solo e topografia) e a abundância das espécies foi significativa, especialmente na Savana Florestada onde observou-se a maior relação. O solo e a topografia também se relacionaram com a semelhança na composição florística das subparcelas, com exceção da Floresta Estacional Semicidual (EEC). As principais variáveis de solo e topografia relacionadas a flora em cada UC foram: (1) Na Floresta Ombrófila Densa de Terras Baixas (PEIC) - teor de alumínio na camada profunda (Al (80-100 cm)) que pode refletir os teor de Al na superfície, acidez do solo (pH(H2O) (5-25 cm)) e altitude, que delimitou as áreas alagadas; (2) Na Floresta Ombrófila Densa Submontana (PECB) - altitude, fator que, devido ao relevo acidentado, influencia a temperatura e incidência de sol no sub-bosque; (3) Na Savana Florestada (EEA) - fertilidade, tolerância ao alumínio e acidez do solo. Nos modelos de predição BRT, as variáveis químicas dos solos foram mais importantes do que a textura, devido à pequena variação deste atributo no solo nas áreas amostradas. Dentre as variáveis químicas dos solos, a capacidade de troca catiônica foi utilizada para prever a ocorrência das espécies nas quatro formações florestais, sendo particularmente importante na camada mais profunda do solo da Floresta Ombrófila Densa de Terras Baixas (PEIC). Quanto à topografia, a altitude foi inserida na maioria dos modelos e apresentou diferentes influências sobre as áreas de estudo. De modo geral, para presença das espécies de ampla distribuição observou-se uma mesma tendência quando à associação com os atributos dos solos, porém com amplitudes dos descritores edáficos que variaram de acordo com a área de estudo. A ocorrência de Guapira opposita e Syagrus romanzoffiana, cujo padrão variou conforme a escala, foi explicada por variáveis com padrões espaciais agregados que somaram entre 30% e 50% de importância relativa no modelo BRT. A presença de A. anthelmia, cujo padrão também apresentou certo nível de agregação, foi associada apenas a uma variável com padrão agregado, a altitude (21%), que pode ter exercido grande influência na distribuição da espécie ao delimitar áreas alagadas. T. guianensis se associou a variáveis ambientais preditoras com padrão espacial agregado que somaram cerca de 70% de importância relativa, o que deve ter sido suficiente para estabelecer o padrão agregado em todas as escalas. No entanto, a influência dos fatores ambientais no padrão de distribuição da espécie não depende apenas do ótimo ambiental da espécie, mas um resultado da interação espécie-ambiente. Concluiu-se que: (I) características edáficas e topográficas explicaram uma pequena parcela da composição florística, em cada unidade fitogeográfica, embora a ocorrência de algumas espécies tenha se associado ao gradiente edáfico e topográfico; (II) a partir de características dos solos e da topografia foi possível prever a presença de espécies arbóreas, que apresentaram particularidades em relação a sua associação com o solo de cada fitofisionomia; (III) a partir de associações descritivas o solo e a topografia influenciam o padrão de distribuição espacial das espécies, na proporção em que contribuem para a presença das mesmas. / In the study of forest communities, establish the relative importance of the factors that define the composition and distribution of species is a challenge. In terms of environmental gradients study the responses of tree species are essential to the understanding of ecological processes and conservation decisions. In this regard, to contribute to the elucidation of ecological processes in the main forest formations of São Paulo (Dense Ombrophylous Forest of Lowlands, Submontane Dense Ombrophylous Forest, Semideciduous Forest and Savanna Woodland) this study aimed to answer the following questions: (I) floristic composition and tree species abundance in each phytogeographic unit change according to edaphic and topographic gradient?; (II) soil characteristics and topography can influence the occurrence of predictability of tree species widely distributed in different types of vegetation? (III) there is a relationship between spatial distribution pattern of tree species and the soil parameters and topography? The work was carried out in allocated plots in protected areas (PA) with the four main forest formations in terms of conservation and size of Sao Paulo. In each PA was sampled individual trees, topography, texture data and chemical properties of the soil on a plot of 10.24 ha, subdivided into 256 subplots. Canonical corresponding analyzes (CCA) were applied to establish the correspondence between the abundance of species and environmental gradient (soil and topography). The modified TWINSPAN method was applied to CCA ordination diagram to evaluate the influence of environmental variables (soil and topography) on species composition. Boosteed Regression Trees (BRT) were adjusted for predicting the occurrence of the species according to soil variables and topography. The Getis Ord-index (G) was used to determine the spatial autocorrelation of environmental variables used in the BRT models. In analyzed phytogeographic units, correspondence between the environmental gradient (soil and topography) and abundance of species was significant, especially in Savanna Woodland. The soil and topography also correlated with the floristic composition similarity of the subplots, with the exception of Semicidual Seasonal Forest (EEC). The main soil and topography variables related to floristic in each PA were: (1) Dense Ombrophylous Forest of Lowlands (PEIC) - aluminium content in the deep layer (Al (80-100 cm)) which may reflect the Al content at the surface, soil acidity (pH (H2O) (5-25 cm)) and altitude, which outlined the flooded areas; (2) Submontane Dense Ombrophylous Forest (PECB) - elevation, due to the rugged terrain influences the temperature and light incidence in the understory; (3) Savanna Woodland (EEA) - fertility, tolerance to aluminum and soil acidity. In BRT prediction models, the chemical soil variables were more important than the texture due to small variation of this soil attribute in the sampled area. Among the soil chemical variables, cation exchange capacity was used to predict the species occurrence in four forest formations and particularly important in the soil deepest layer on the Dense Ombrophylous Forest of Lowlands (PEIC). In relation to topography, elevation was included in most models and had different influences on the study areas. Overall, the species widely distributed showed the same trend as the association with the attributes of the soil, but with amplitudes of edaphic descriptors that change according to the study area. The occurrence of the Guapira opposita and Syagrus romanzoffiana, whose pattern change according to the scale, was explained by variables with aggregated spatial patterns that amounted to between 30% and 50% relative importance in the BRT model. The presence of A. anthelmia, which defaults also presented certain level of aggregation, was associated only with one aggregate variable, elevation (21%), which may have exerted great influence on the species distribution to delimit wetlands. T. guianensis was related with the predictive environmental variables of aggregate spatial pattern which totaled to about 70% relative importance, what must have been enough to establish the aggregate pattern at all scales. However, the influence of environmental factors (soil and topography) on the species distribution pattern depends not only on the environmental optimum of the species, but a result of species-environment interaction. We concluded that: (I) soil and topographical characteristics explain a small portion of the floristic composition in each phytogeographic unit, although the occurrence of some species have been associated to the soil and topographic gradient; (II) from soil characteristics and topography it was possible to predict the presence of tree species, which showed particular in relation to its association with the soil of each vegetation type; (III) from descriptive associations soil and topography influence the spatial distribution pattern of the species, to the extent that contribute to the presence of the same.