Current excessive use and abuse of antibiotics has resulted in increasing bacterial resistance to common treatment options which is threatening to deprive us of a pillar of modern medicine. In this work methods to optimize the use of existing antibiotics and to help development of new antibiotics were developed and applied. Semi-mechanistic pharmacokinetic-pharmacodynamic (PKPD) models were developed to describe the time course of the dynamic effect and interaction of combinations of antibiotics. The models were applied to illustrate that colistin combined with a high dose of meropenem may overcome meropenem-resistant P. aeruginosa infections. The results from an in vivo dose finding study of meropenem was successfully predicted by the meropenem PKPD model in combination with a murine PK model, which supports model based dosage selection. However, the traditional PK/PD index based dose selection was predicted to have poor extrapolation properties from pre-clinical to clinical settings, and across patient populations. The precision of the model parameters, and hence the model predictions, is dependent on the experimental design. A limited study design is dictated by cost and, for in vivo studies, ethical reasons. In this work optimal design (OD) was demonstrated to be able to reduce the experimental effort in time-kill curve experiments and was utilized to suggest the experimental design for identification and estimation of an interaction between antibiotics. OD methods to handle inter occasion variability (IOV) in optimization of individual PK parameter estimates were proposed. The strategy was applied in the design of a sparse sampling schedule that aim to estimate individual exposures of colistin in a multi-centre clinical study. Plasma concentration samples from the first 100 patients have been analysed and indicate that the performance of the design is close to the predicted. The methods described in this thesis holds promise to facilitate the development of new antibiotics and to improve the use of existing antibiotics.

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Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / Diversas formas de geração de energia vêm sendo desenvolvidas com o objetivo de oferecer alternativas ecologicamente corretas. Neste contexto, a energia eólica vem se destacando na região Nordeste do Brasil, devido ao grande potencial dos ventos da região. As torres, que representam parcela significativa do custo total do sistema, tendem a crescer buscando ventos mais fortes e permitindo assim a utilização de aerogeradores com maior capacidade de geração de energia. Este trabalho tem como objetivo formular um modelo de otimização de torres tubulares de aço, para aerogeradores eólicos. Busca-se minimizar o volume total (custo, indiretamente), tendo como variáveis de projeto as espessuras da parede da torre. São impostas restrições relativas à frequência natural e ao comportamento estrutural (tensão e deslocamento máximo de acordo com recomendações da norma Europeia). A estrutura da torre é modelada com base no Método dos Elementos Finitos e o carregamento atuante na estrutura inclui os pesos da torre, do conjunto de equipamentos instalados no topo (aerogerador), e o efeito estático da ação do vento sobre a torre. Para verificação das tensões, deslocamentos e frequências naturais, foram utilizados elementos finitos de casca disponíveis na biblioteca do programa de análise ANSYS. Os modelos de otimização foram também implementados no modulo de otimização do programa ANSYS (design optimization), que utiliza técnicas matemáticas em um processo iterativo computadorizado até que um projeto considerado ótimo seja alcançado. Nas aplicações foram usados os métodos de aproximação por subproblemas e o método de primeira ordem. Os resultados obtidos revelam que torres para aerogeradores merecem atenção especial, em relação à concepção do projeto estrutural, sendo que seu desempenho deve ser verificado através de metodologias completas que englobem além das análises clássicas (estáticas e dinâmicas), incluam também as análises de otimização. / Different forms energy generation have been developed with the main goal of offering correct ecological alternatives. In this context, wind energy is highly prominent in the northeast Brazil, due to the potential wind in the region. The towers, which represent a significant portion of the total cost of the system tends to find strong winds and permit them to utilize wind turbines as a medium for the generating energy. This work shows an objective way to formulate an model of towers with steel turbines, for wind generation. The work seeks to minimize the total volume (cost, indirectly) with the wall thickness of the tower as design variables. Natural frequencies and structural behavior (allowable stresses and maximum displacement according to the European code) are the design constraints. The tower structure is modeled using the Finite Element Method and the applied loads are the total weight of the structure (including the tower and the wind turbine installed on the top) and the static effect of the wind on the tower. To determine the stresses, displacements and natural frequencies shell elements available in the ANSYS analysis program were used. Optimization models were also implemented using the ANSYS design optimization modulus which uses mathematical programming techniques to find the optimum structure. The first order and sub problem methods were used in the developed applications. The obtained results show that the towers for wind generators deserve special attention related to the conception of the structural project. Its performance has to be verified by a complete methodology that embodies the classical analysis (static and dynamics) and also the optimization analysis.

abstract: In mixture-process variable experiments, it is common that the number of runs is greater than in mixture-only or process-variable experiments. These experiments have to estimate the parameters from the mixture components, process variables, and interactions of both variables. In some of these experiments there are variables that are hard to change or cannot be controlled under normal operating conditions. These situations often prohibit a complete randomization for the experimental runs due to practical and economical considerations. Furthermore, the process variables can be categorized into two types: variables that are controllable and directly affect the response, and variables that are uncontrollable and primarily affect the variability of the response. These uncontrollable variables are called noise factors and assumed controllable in a laboratory environment for the purpose of conducting experiments. The model containing both noise variables and control factors can be used to determine factor settings for the control factor that makes the response "robust" to the variability transmitted from the noise factors. These types of experiments can be analyzed in a model for the mean response and a model for the slope of the response within a split-plot structure. When considering the experimental designs, low prediction variances for the mean and slope model are desirable. The methods for the mixture-process variable designs with noise variables considering a restricted randomization are demonstrated and some mixture-process variable designs that are robust to the coefficients of interaction with noise variables are evaluated using fraction design space plots with the respect to the prediction variance properties. Finally, the G-optimal design that minimizes the maximum prediction variance over the entire design region is created using a genetic algorithm. / Dissertation/Thesis / Ph.D. Industrial Engineering 2010

Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / Diversas formas de geração de energia vêm sendo desenvolvidas com o objetivo de oferecer alternativas ecologicamente corretas. Neste contexto, a energia eólica vem se destacando na região Nordeste do Brasil, devido ao grande potencial dos ventos da região. As torres, que representam parcela significativa do custo total do sistema, tendem a crescer buscando ventos mais fortes e permitindo assim a utilização de aerogeradores com maior capacidade de geração de energia. Este trabalho tem como objetivo formular um modelo de otimização de torres tubulares de aço, para aerogeradores eólicos. Busca-se minimizar o volume total (custo, indiretamente), tendo como variáveis de projeto as espessuras da parede da torre. São impostas restrições relativas à frequência natural e ao comportamento estrutural (tensão e deslocamento máximo de acordo com recomendações da norma Europeia). A estrutura da torre é modelada com base no Método dos Elementos Finitos e o carregamento atuante na estrutura inclui os pesos da torre, do conjunto de equipamentos instalados no topo (aerogerador), e o efeito estático da ação do vento sobre a torre. Para verificação das tensões, deslocamentos e frequências naturais, foram utilizados elementos finitos de casca disponíveis na biblioteca do programa de análise ANSYS. Os modelos de otimização foram também implementados no modulo de otimização do programa ANSYS (design optimization), que utiliza técnicas matemáticas em um processo iterativo computadorizado até que um projeto considerado ótimo seja alcançado. Nas aplicações foram usados os métodos de aproximação por subproblemas e o método de primeira ordem. Os resultados obtidos revelam que torres para aerogeradores merecem atenção especial, em relação à concepção do projeto estrutural, sendo que seu desempenho deve ser verificado através de metodologias completas que englobem além das análises clássicas (estáticas e dinâmicas), incluam também as análises de otimização. / Different forms energy generation have been developed with the main goal of offering correct ecological alternatives. In this context, wind energy is highly prominent in the northeast Brazil, due to the potential wind in the region. The towers, which represent a significant portion of the total cost of the system tends to find strong winds and permit them to utilize wind turbines as a medium for the generating energy. This work shows an objective way to formulate an model of towers with steel turbines, for wind generation. The work seeks to minimize the total volume (cost, indirectly) with the wall thickness of the tower as design variables. Natural frequencies and structural behavior (allowable stresses and maximum displacement according to the European code) are the design constraints. The tower structure is modeled using the Finite Element Method and the applied loads are the total weight of the structure (including the tower and the wind turbine installed on the top) and the static effect of the wind on the tower. To determine the stresses, displacements and natural frequencies shell elements available in the ANSYS analysis program were used. Optimization models were also implemented using the ANSYS design optimization modulus which uses mathematical programming techniques to find the optimum structure. The first order and sub problem methods were used in the developed applications. The obtained results show that the towers for wind generators deserve special attention related to the conception of the structural project. Its performance has to be verified by a complete methodology that embodies the classical analysis (static and dynamics) and also the optimization analysis.

abstract: Functional or dynamic responses are prevalent in experiments in the fields of engineering, medicine, and the sciences, but proposals for optimal designs are still sparse for this type of response. Experiments with dynamic responses result in multiple responses taken over a spectrum variable, so the design matrix for a dynamic response have more complicated structures. In the literature, the optimal design problem for some functional responses has been solved using genetic algorithm (GA) and approximate design methods. The goal of this dissertation is to develop fast computer algorithms for calculating exact D-optimal designs. First, we demonstrated how the traditional exchange methods could be improved to generate a computationally efficient algorithm for finding G-optimal designs. The proposed two-stage algorithm, which is called the cCEA, uses a clustering-based approach to restrict the set of possible candidates for PEA, and then improves the G-efficiency using CEA. The second major contribution of this dissertation is the development of fast algorithms for constructing D-optimal designs that determine the optimal sequence of stimuli in fMRI studies. The update formula for the determinant of the information matrix was improved by exploiting the sparseness of the information matrix, leading to faster computation times. The proposed algorithm outperforms genetic algorithm with respect to computational efficiency and D-efficiency. The third contribution is a study of optimal experimental designs for more general functional response models. First, the B-spline system is proposed to be used as the non-parametric smoother of response function and an algorithm is developed to determine D-optimal sampling points of a spectrum variable. Second, we proposed a two-step algorithm for finding the optimal design for both sampling points and experimental settings. In the first step, the matrix of experimental settings is held fixed while the algorithm optimizes the determinant of the information matrix for a mixed effects model to find the optimal sampling times. In the second step, the optimal sampling times obtained from the first step is held fixed while the algorithm iterates on the information matrix to find the optimal experimental settings. The designs constructed by this approach yield superior performance over other designs found in literature. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2015

Forest quantification methods have evolved from a simple graphical approach to complex regression models with stochastic structural components. Currently, mixed effects models methodology is receiving attention in the forestry literature. However, the review work (Paper I) indicates a tendency to overlook appropriate covariance structures in the NLME modeling process. A nonlinear mixed effects modeling process is demonstrated in Paper II using Cupressus lustanica tree merchantable volume data and compared several models with and without covariance structures. For simplicity and clarity of the nonlinear mixed effects modeling, four phases of modeling were introduced. The nonlinear mixed effects model for C. lustanica tree merchantable volume with the covariance structures for both the random effects and within group errors has shown a significant improvement over the model with simplified covariance matrix. However, this statistical significance has little to explain in the prediction performance of the model. In Paper III, using several performance indicator statistics, tree taper models were compared in an effort to propose the best model for the forest management and planning purpose of the C. lustanica plantations. Kozak's (1988) tree taper model was found to be the best for estimating C. lustanica taper profile. Based on the Kozak (1988) tree taper model, a Ds optimal experimental design study is carried out in Paper IV. In this study, a Ds-optimal (sub) replication free design is suggested for the Kozak (1988) tree taper model.

In Canada, more than 1.11 TWh of energy per year is generated by diesel generators in off-grid remote areas. Delivering energy to these territories always has a high cost for the local and federal governments both financially and environmentally. Substituting fossil fuels with clean energies is the solution. However, the unreliability and intermittency of renewable energies (RE) are always challenging issues that need to be solved. Zinc air flow battery (ZAFB) with decoupled power and energy capacity can bring sustainability and reliability for microgrids. In this study, an efficient model was developed for ZAFB, which is applicable for large-scale modeling, and incorporated in microgrid modeling. A bilevel optimization approach was implemented in the microgrid model to find the optimal size and control of the microgrid simultaneously over the project lifetime. Using model predictive control (MPC) and based on user-defined foresight horizon and known information like energy demand and RE resources, the control model decides the future changes in microgrid components. This tool is used to propose the best microgrid design for these communities to reduce or eliminate their dependency on fossil fuels. The functionality of this tool was evaluated by three case studies in British Columbia: Blind Channel, Hot Springs Cove and Moresby Island. Zero CO2 emission and zero fuel consumption were achieved by a 100% RE microgrid consisting of wind and tidal turbines and large ZAFB. The net present cost (NPC) of this system and cost of energy are 39 – 46 % and 55 – 60 % less than the base case costs in which diesel is the main energy source. ZAFB with a longer storage duration (50 – 60 hours) satisfies 17 – 23% of annual energy demand in these case studies. / Graduate

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