Orientador: Zacarias Xavier Barros / Resumo: O uso de ferramentas computacionais e da tecnologia desenvolvida por ferramentas do sensoriamento remoto para o mapeamento e estimação da fertilidade do solo são importantes para o desenvolvimento de áreas com práticas agrícolas. O emprego da técnica por meio da análise espacial pode proporcionar melhorias no manejo do solo. O objetivo deste trabalho foi estimar a fertilidade dos solos da Fazenda Experimental Lageado que pertence a Faculdade de Ciências Agronômicas- UNESP, localizada na cidade de Botucatu/São Paulo através dos atributos do solo e as bandas espectrais do vermelho, verde e azul do Vant (Veículo Aéreo Não Tripulado) e do satélite LandSat- 8. Neste contexto, o atributo do solo é essencial para entendermos a sua fertilidade. Foi utilizado um conjunto de amostras de solos com 52 amostras para correlacionar com as respostas espectrais dos solos nos comprimentos de onda do visível, para a geração do modelo. Os dados foram separados em três áreas e apresentaram coeficientes de correlação variam entre -0,58 a 0,56 para imagens de Vant entretanto para as imagens de satélites foram correlações fracas. Para o Vant os preditores mais correlacionados ph e matéria orgânica. Os resultados obtidos pela análise de regressão r² foram considerados baixos, sendo não indicado não espacialização e a geração do modelo de fertilidade. / Abstract: The use of computational tools and the technology developed by remote sensing tools for mapping and estimating soil fertility are important for the development of areas with agricultural practices. The use of the technique through spatial analysis can provide improvements in soil management. The objective of this work was to estimate the fertility of the soils of the Experimental Lageado Farm which belongs to the Faculty of Agronomic Sciences - UNESP, located in the city of Botucatu / São Paulo through the soil attributes and the spectral bands of the red, green and blue of the drone and the LandSat- 8 satellite. In this context, the soil attribute is essential to understand its fertility. A set of soil samples with 52 samples was used to correlate with the spectral responses of the soils in the visible wavelengths, for the generation of the model. The data were separated into three areas and showed correlation coefficients ranging from -0.58 to 0.56 for drone images however for satellite images they were weak correlations. For Vant, the most correlated predictors are ph and organic matter. The results obtained by the r² regression analysis were considered low, with no spatialization and the generation of the fertility model being indicated. / Doutor

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Big data is an enabling technology in digital transformation. It perfectly complements ordinary linear models and generalized linear models, as training well-performed ordinary linear models and generalized linear models require huge amounts of data. With the help of big data, ordinary and generalized linear models can be well-trained and thus offer better services to human beings. However, there are still many challenges to address for training ordinary linear models and generalized linear models in big data. One of the most prominent challenges is the computational challenges. Computational challenges refer to the memory inflation and training inefficiency issues occurred when processing data and training models. Hundreds of algorithms were proposed by the experts to alleviate/overcome the memory inflation issues. However, the solutions obtained are locally optimal solutions. Additionally, most of the proposed algorithms require loading the dataset to RAM many times when updating the model parameters. If multiple model hyper-parameters needed to be computed and compared, e.g. ridge regression, parallel computing techniques are applied in practice. Thus, multiple learning with sufficient statistics arrays are proposed to tackle the memory inflation and training inefficiency issues.

The goal of the thesis „Econometric model of flat prices in Brno“ is to create econometric model based on data from housing market. The theoretical part of the thesis defines variables, and use descriptive statistics. The practical part of the thesis deals with creation econometric model and interactive calculator.

Response reconstruction is used to obtain accurate replication of vehicle structural responses of field recorded measurements in a laboratory environment, a crucial step in the process of Accelerated Destructive Testing (ADT). Response Reconstruction is cast as an inverse problem whereby the desired input is inferred using the measured outputs of a system. ADT typically involves large shock loadings resulting in a nonlinear response of the structure. A promising linear regression technique known as Spanning Basis Transformation Regression (SBTR) in con- junction with non-overlapping windows casts the low dimensional nonlinear problem as a high dimensional linear problem. However, it is determined that the original implementation of SBTR struggles to invert a broader class of sensor configurations. A new windowing method called AntiDiagonal Averaging (ADA) is developed to overcome the shortcomings of the SBTR im- plementation. ADA introduces overlaps within the predicted time signal windows and averages them. The newly proposed method is tested on a numerical quarter car model and is shown to successfully invert a broader range of sensor configurations as well as being capable of describing nonlinearities in the system. / Dissertation (MEng)--University of Pretoria, 2021. / Mechanical and Aeronautical Engineering / MEng / Unrestricted

Advances in computing has undoubtfully been one of the main catalysts in the formation of the discipline always known as Statistics. A fundamental question addressed here is whether computing facilities, such as parallel or high performance computing, could assist in the development of methodologies that render stronger results, based on some predetermined optimality criterion. The candidate at the hand of which this enquiry is made, is the arc length of some statistical function. Estimation, goodness-of-fit, linear regression and non-linear regression, which may all be considered as central themes in Statistics, are revisited, and redefined in terms of this new measure. The results resulting from these arc length methodologies are obtained from simulation, as well as from real case studies, and contrasted to that obtained using their classical counterparts. Mathematical premises for the proposed methods are provided, together with the documentation accompanying the companion R package, along with the data utilised for the applications. / Thesis (PhD)--University of Pretoria, 2017. / National Research Foundation of South Africa, Unique Grant No. 94108. / Statistics / PhD / Unrestricted

Data analytics and optimization theory are well-developed techniques to describe, predict and optimize real-world systems, and they have been widely used in engineering and science. This dissertation focuses on applications in biomedical systems, ranging from the scale of microbial communities to problems relating to human disease and health care. Starting from the microbial level, the first problem considered is to design metabolic division of labor in microbial communities. Given a number of microbial species living in a community, the starting point of the analysis is a list of all metabolic reactions present in the community, expressed in terms of the metabolite proportions involved in each reaction. Leveraging tools from Flux Balance Analysis (FBA), the problem is formulated as a Mixed Integer Program (MIP) and new methods are developed to solve large scale instances. The strategies found reveal a large space of nuanced and non-intuitive metabolic division of labor opportunities, including, for example, splitting the Tricarboxylic Acid Cycle (TCA) cycle into two separate halves. More broadly, the landscape of possible 1-, 2-, and 3-strain solutions is systematically mapped at increasingly tight constraints on the number of allowed reactions. The second problem addressed involves the prediction and prevention of short-term (30-day) hospital re-admissions. To develop predictive models, a variety of classification algorithms are adapted and coupled with robust (regularized) learning and heuristic feature selection approaches. Using real, large datasets, these methods are shown to reliably predict re-admissions of patients undergoing general surgery, within 30-days of discharge. Beyond predictions, a novel prescriptive method is developed that computes specific control actions with the effect of altering the outcome. This method, termed Prescriptive Support Vector Machines (PSVM), is based on an underlying SVM classifier. Applied to the hospital re-admission data, it is shown to reduce 30-day re-admissions after surgery through better control of the patient’s pre-operative condition. Specifically, using the new method the patient’s pre-operative hematocrit is regulated through limited blood transfusion. In the last problem in this dissertation, a framework for parameter estimation in Regularized Mixed Linear Regression (MLR) problems is developed. In the specific MLR setting considered, training data are generated from a mixture of distinct linear models (or clusters) and the task is to identify the corresponding coefficient vectors. The problem is formulated as a Mixed Integer Program (MIP) subject to regularization constraints on the coefficient vectors. A number of results on the convergence of parameter estimates for MLR are established. In addition, experimental prediction results are presented comparing the prediction algorithm with mean absolute error regression and random forest regression, in terms of both accuracy and interpretability.

Diploma thesis "Mediation Analysis in Sociology" deals with mediation analysis and possibilities of its application in sociology, depending on the type of the dependent variable that enters the analysis. In the first case the dependent variable is continuous - in this case the SPSS software and its PROCESS add-on are used to directly analyse the mediation. In the second case the dependent variable that enters the analysis is binary - the PROCESS add-on doesn't allow this option; therefore, the analysis is performed in SPSS software by the set of linear and logistic regressions according to the Baron & Kenny method. Two case studies from the field of sociology, GSS (General Social Survey) and ISSP (International Social Survey Programme), are used in the thesis and the consequences of the transition from continuous dependent variable to binary are examined using the secondary analysis of these data.

This study is based on first-league football teams from England, Spain and Russia. It demonstrates that football tourism has a big potential because the attendance on football stadiums has a positive effect over the number of visitors in the city, and this influence is stronger among teams that are historically more popular. Football trips can also contribute to decreasing the seasonality and the centralization of tourism. The questionnaire designed for supporters demonstrates that a higher percentage of fans of currently less competitive teams tend to visit away games, but it is explained through the fact that more successful clubs have supporters located far away, for whom performing such a thing is more troublesome. English supporters spend less money in their football visiting trips, but their journeys are also shorter and without many activities, which makes their habits different from the ones of a typical tourist. At the same time, Spanish football tourists spend more time and money while travelling. The respondents from both countries face similar problems while visiting a match, and the majority of them are infrastructural. Russian football tourists spend the most time and money in their journeys, visiting many places except for the football match itself, which makes their behaviour the closest to a typical tourist. However, during matches they also suffer from more serious issues related to the unfriendliness of police and of hosts.

Arbuscular mycorrhizal (AM) fungi are key soil symbiotic microorganisms, intensively studied for their roles in improving plant fitness and their ubiquity in terrestrial ecosystems. Research on AM fungi is difficult because their obligate biotrophic nature makes it impossible to culture them in the absence of a host. Over the last three decades, Ri T-DNA transformed roots have been the gold standard to study AM fungi under in vitro conditions. However, only two host plant species (Daucus carota and Cichorium intybus) have been routinely used to in vitro propagate less than 5% of the known AM fungal species. There is much evidence that host identity can significantly affect AM symbioses, therefore, we investigated any potential host-specific effects of two novel Ri T-DNA transformed root species, Medicago truncatula and Nicotiana benthamiana, by associating them with seven AM fungal species selected based on their contrasting behaviors when grown with Ri T-DNA transformed D. carota roots. To evaluate the performance of new Ri T-DNA transformed roots to host and propagate AM fungal species, a factorial set-up was used to generate nine unique pairs of hosts (M. truncatula, N. benthamiana, D. carota) and AM fungi (Rhizophagus irregularis, R. clarus, Glomus sp.). Using statistical modeling, all pairs of hosts and AM fungi were compared by their symbiosis development (SD) and sporulation patterns in the hyphal compartments (HCs) of two-compartment Petri dishes. Our results show that 1) most of the variation between host and AM fungus pairs relating to SD or HC sporulation was explained by an interaction between host and AM fungal identity, i.e., host identity alone was not sufficient to explain AM fungal behaviour, 2) AM symbioses involving different combinations of symbiont identities trigger heterogenous fungal behaviours. This work provides a robust framework to develop and evaluate new Ri T-DNA roots for the in vitro propagation of AM fungi, an important asset for germplasm collections and biodiversity preservation.