No description available.

O presente trabalho implementa a otimização dos ganhos de um controle ativo com regulador linear quadrático (LQR), em um modelo veicular completo sujeito a um perfil de estrada proposto pela ISO 8608, para atenuação das acelerações RMS transmitidas para a carroceria e o assento do motorista. Dado que o ganho do controle LQR é formulado a partir das matrizes Q e R, o procedimento determina as matrizes ótimas do controle para a minimização das acelerações RMS transmitidas. O modelo é analisado no domínio do tempo por meio da formulação de espaço-estado, e o procedimento de otimização é avaliado pelo método dos algoritmos genéticos. Os parâmetros Q e R, que fornecem o melhor ganho para minimização do problema de otimização, reduzem em até 1000 vezes as acelerações RMS quando comparadas à situação sem atuação do controle. Após otimizar Q e R, são analisadas a influência nos demais graus de liberdade e as forças necessárias para os resultados obtidos. / This work aims to optimize the gains of an active control with linear quadratic regulator (LQR), applied in a full vehicle model subject to a random road surface profile proposed by ISO 8608, for reduction of RMS accelerations transmitted to the driver’s seat and the vehicle body. Since the gain of LQR control is formulated from the matrices Q and R, the procedure determines the optimal control matrices that minimize the RMS accelerations transmitted. The model is analyzed in the time domain through state-space formulation, and the optimization process evaluated by the method of genetic algorithms. The parameters Q and R, which provide the best gain for minimizing the optimization problem, reduce by up to 1000 times the RMS accelerations when compared to the situation without active control. Finally, after optimizing Q e R, are analyzed the influence to the other degrees of freedom and the forces necessary for the results obtained.

Transcription factor (TF) binding has been studied extensively in relation to binding site affinity and chromosome modifications; however, the relationship between genome spatial organisation and transcription factor binding is not well studied. Using the recently available high resolution Hi-C contact map of human GM12878 lymphoblastoid cells, we investigated computationally the genome-wide spatial co-localization of transcription factor binding sites, for both within the same type and between different types. First, we observed a strong positive correlation between site occupancy and homotypic TF co-localization based on Hi-C contacts, consistent with our predictions from biophysical simulations of TF target search. This trend is more prominent in binding sites with weak binding sequences and within enhancers, suggesting genome spatial organisation plays an essential role in determining binding site occupancy, especially for weak regulatory elements. Furthermore, when investigating spatial co-localization between different TFs, we discovered two distinct co-localization networks of TFs in lymphoblastoid cells, one of which is enriched in lymphocyte specific pathways and distal enhancer binding. These two TF networks have strong biases for either the A1 or A2 chromosome subcompartment, but nonetheless are still preserved within each, indicating a potential causal link between cell-type-specific transcription factor binding and chromosome subcompartment segregation. We called 40 pairs of significantly co-localized TFs according to the genome wide Hi-C contact map, which are enriched in previously reported, physical interactions, thus linking TF spatial network to co-functioning. In addition to the above main project, I also worked on a side project to find compute-efficient ways in scaling binding site strength across different TFs based on Position-Weight-Matrices (PWM). While common bioinformatics tools produce scores that can reflect the binding strength between a specific TF and the DNA, these scores are not directly comparable between different TFs. We provided two approaches in estimating a scaling parameter $\lambda$ to the PWM score for different TFs. The first approach uses a PWM and background genomic sequence as input to estimate $\lambda$ for a specific TF, which we applied to show that $\lambda$ distributions for different TF families correspond with their DNA binding properties. Our second method can reliably convert $\lambda$ between different PWMs of the same TF, which allows us to directly compare PWMs that were generated by different approaches.

O presente trabalho implementa a otimização dos ganhos de um controle ativo com regulador linear quadrático (LQR), em um modelo veicular completo sujeito a um perfil de estrada proposto pela ISO 8608, para atenuação das acelerações RMS transmitidas para a carroceria e o assento do motorista. Dado que o ganho do controle LQR é formulado a partir das matrizes Q e R, o procedimento determina as matrizes ótimas do controle para a minimização das acelerações RMS transmitidas. O modelo é analisado no domínio do tempo por meio da formulação de espaço-estado, e o procedimento de otimização é avaliado pelo método dos algoritmos genéticos. Os parâmetros Q e R, que fornecem o melhor ganho para minimização do problema de otimização, reduzem em até 1000 vezes as acelerações RMS quando comparadas à situação sem atuação do controle. Após otimizar Q e R, são analisadas a influência nos demais graus de liberdade e as forças necessárias para os resultados obtidos. / This work aims to optimize the gains of an active control with linear quadratic regulator (LQR), applied in a full vehicle model subject to a random road surface profile proposed by ISO 8608, for reduction of RMS accelerations transmitted to the driver’s seat and the vehicle body. Since the gain of LQR control is formulated from the matrices Q and R, the procedure determines the optimal control matrices that minimize the RMS accelerations transmitted. The model is analyzed in the time domain through state-space formulation, and the optimization process evaluated by the method of genetic algorithms. The parameters Q and R, which provide the best gain for minimizing the optimization problem, reduce by up to 1000 times the RMS accelerations when compared to the situation without active control. Finally, after optimizing Q e R, are analyzed the influence to the other degrees of freedom and the forces necessary for the results obtained.

O presente trabalho implementa a otimização dos ganhos de um controle ativo com regulador linear quadrático (LQR), em um modelo veicular completo sujeito a um perfil de estrada proposto pela ISO 8608, para atenuação das acelerações RMS transmitidas para a carroceria e o assento do motorista. Dado que o ganho do controle LQR é formulado a partir das matrizes Q e R, o procedimento determina as matrizes ótimas do controle para a minimização das acelerações RMS transmitidas. O modelo é analisado no domínio do tempo por meio da formulação de espaço-estado, e o procedimento de otimização é avaliado pelo método dos algoritmos genéticos. Os parâmetros Q e R, que fornecem o melhor ganho para minimização do problema de otimização, reduzem em até 1000 vezes as acelerações RMS quando comparadas à situação sem atuação do controle. Após otimizar Q e R, são analisadas a influência nos demais graus de liberdade e as forças necessárias para os resultados obtidos. / This work aims to optimize the gains of an active control with linear quadratic regulator (LQR), applied in a full vehicle model subject to a random road surface profile proposed by ISO 8608, for reduction of RMS accelerations transmitted to the driver’s seat and the vehicle body. Since the gain of LQR control is formulated from the matrices Q and R, the procedure determines the optimal control matrices that minimize the RMS accelerations transmitted. The model is analyzed in the time domain through state-space formulation, and the optimization process evaluated by the method of genetic algorithms. The parameters Q and R, which provide the best gain for minimizing the optimization problem, reduce by up to 1000 times the RMS accelerations when compared to the situation without active control. Finally, after optimizing Q e R, are analyzed the influence to the other degrees of freedom and the forces necessary for the results obtained.

Spatial econometrics can bring a useful approach to macroeconomic analysis of regional data. This thesis delineates suitable cross-section data models regarding their geographical location. Neighbourhood relation is used for the analysis. The relation of neighbourhood among the regions is expressed using spatial weight matrix. We focus on spatial autocorrelation tests and introduce processes of finding a suitable spatial model. Further, we describe regression coefficients estimates and estimates of spatial dependence coefficients, especially method of maximum likelihood estimates. Besides illustrative examples we apply chosen basic spatial models on real macroeconomic data. We examine how they describe relation between household incomes, GDP and unemployment rate in western Europe. Results are compared with a linear regression model.

In this study, we extend the spatial weight matrix defined by Getis and Aldstadt (2004) to a more general case. The modified spatial weight matrix performs better than the original spatial weight matrix since the modified spatial weight matrix adjusts weights of observations based on the distance from other observations. Both the simulation study and the application to the ecological process of invasion of non-native invasive plants (NNIPs) provide evidences for the better performance of the modified spatial weight matrix. We also develop procedures that can be used to quantify the invasion stages of NNIPs. The resultant map of invasion stage on county-level provides a useful and meaningful tool for policy makers; especially, it can be used to optimize allocation of management resources. The result of simultaneous autoregressive model shows that not only the biotic and abiotic factors but also human activities play an important role in the establishment and spread of multiflora rose in the Upper Midwest. It also shows the tendency of the establishment and spread of multiflora rose (Rosa Multiflora, Thunb. ex Murr.) in the Upper Midwest.

No description available.

Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from experimentally verified and aligned binding sequences. PWMs are then used to computationally discover novel putative binding sites for a given protein. DNA-binding proteins often show degeneracy in their binding requirement, the overall binding specificity of many proteins is unknown and remains an active area of research. Although PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. A previous study introduced a novel method to PWM training based on the known motifs to sample additional putative binding sites from a proximal promoter area. The core idea was further developed, implemented and tested in this thesis with a large scale application. Improved mono- and dinucleotide PWMs were computed for Drosophila melanogaster. The Matthews correlation coefficient was used as an optimization criterion in the PWM refinement algorithm. New PWMs keep an account of non-uniform background nucleotide distributions on the promoters and consider a larger number of new binding sites during the refinement steps. The optimization included the PWM motif length, the position on the promoter, the threshold value and the binding site location. The obtained predictions were compared for mono- and dinucleotide PWM versions with initial matrices and with conventional tools. The optimized PWMs predicted new binding sites with better accuracy than conventional PWMs.

Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from experimentally verified and aligned binding sequences. PWMs are then used to computationally discover novel putative binding sites for a given protein. DNA-binding proteins often show degeneracy in their binding requirement, the overall binding specificity of many proteins is unknown and remains an active area of research. Although PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. A previous study introduced a novel method to PWM training based on the known motifs to sample additional putative binding sites from a proximal promoter area. The core idea was further developed, implemented and tested in this thesis with a large scale application. Improved mono- and dinucleotide PWMs were computed for Drosophila melanogaster. The Matthews correlation coefficient was used as an optimization criterion in the PWM refinement algorithm. New PWMs keep an account of non-uniform background nucleotide distributions on the promoters and consider a larger number of new binding sites during the refinement steps. The optimization included the PWM motif length, the position on the promoter, the threshold value and the binding site location. The obtained predictions were compared for mono- and dinucleotide PWM versions with initial matrices and with conventional tools. The optimized PWMs predicted new binding sites with better accuracy than conventional PWMs.