Desenvolve-se, neste trabalho, um modelo de simulação estocástica para o sistema de transporte por táxi com viagens exclusivas. Basicamente, o modelo simula a geração da demanda pelo serviço e as formas alternativas de oferta: ponto privativo, ponto livre e rádio táxi. A validação do modelo é realizada através da comparação entre o sistema de táxi observado na cidade de Bauru (SP) e o sistema simulado através da aplicação do modelo. A comparação é feita para a alternativa de oferta com pontos privativos, a única existente na cidade. Os resultados indicam que o modelo de simulação proposto reproduz bem a demanda e a oferta de viagens por táxi em cidades de porte médio. Apresenta-se, além disso, uma análise comparativa das simulações realizadas para as duas outras alternativas fictícias de operação na cidade de Bauru (SP); os resultados destas, quando comparados aos da situação existente, mostram uma significativa economia nos custos variáveis. Conclui-se que o modelo pode constituir-se num instrumento de análise e avaliação das diferentes políticas de operação de táxi e apontar a mais adequada para uma determinada cidade de porte médio. / In this work, a stochastic simulation model for the exclusive-ride taxi system is developed. The model simulates the demand generation for taxi service and the alternative forms of service supply: private pickup point, free pickup point, and radiotaxi. The model validation is carried out by comparing the taxi system observed in Bauru (SP) with that simulated using data set collected in it. The comparison is concerning to the operation with private pickup point because in Bauru there is only this type of operation. The results indicate that the proposed simulation model reproduces adequately the taxi demand and supply in medium sized cities. It is also presented a comparative analysis between existent system and other operation systems simulated. The analysis shows that a significative cost saving is obtained if the operation system is changed in Bauru. It is concluded that the proposed model may constitute an important tool to analyze and evaluate the different operational policy and indicate the most adequate one for the medium sized cities.

Se manipulado de forma eficiente, o crescimento compensatório pode ser uma alternativa para reduzir o custo com a alimentação. No presente trabalho objetivou-se analisar o efeito bioeconômico do crescimento compensatório sobre sistemas intensivos de recria e terminação de bovinos de corte. Por meio de simulação, em um Sistema de Apoio a Decisão, a produtividade (Pr) e a resposta econômica foram avaliadas em quatro sistemas. Caracterizados por diferentes períodos de restrição alimentar (sem restrição, CONT; 90 dias de restrição, COMP90; 120 dias de restrição, COMP120 e 150 dias de restrição, COMP150) no período pós-desmama. Além disso, foram realizadas análises de risco e de sensibilidade, por meio de simulação de Monte Carlo. Os sistemas com regime alimentar restrito necessitaram de maior período de engorda (14, 21 e 35 dias para COMP90, COMP120 e COMP150, respectivamente) que CONT. O sistema COMP90 teve Pr (434,2 kg/ha/ano) similar ao CONT (434,0 kg/ha/ano) e ambos maiores que COMP120 (395,0 kg/ha/ano) e COMP150 (394,0 kg/ha/ano). A margem bruta/ha foi de 608,98; 493,5; 366,96 e 304,23 R$/ha/ano para os sistemas COMP90, CONT, COMP120 e COMP150, respectivamente. Entretanto, na análise de risco o sistema menos estável economicamente foi o CONT e o com menor risco foi COMP90. A análise de sensibilidade demonstrou que as variáveis com maior efeito sobre a margem bruta foram o preço do boi gordo, do bezerro e do milho usado no confinamento. O uso do crescimento compensatório pode ser uma ferramenta para redução de custos com a alimentação em sistemas de recria e engorda de bovinos de corte, mas a sua eficácia é influenciada pelo período de restrição. / When used efficiently the compensatory growth can be an option to reduce feeding cost. The aim was to analyze the bioeconomic effect of compensatory growth on intensive growing and fattening beef cattle systems. By simulation using a Decision Support System the productivity and the economic return were evaluated in four different systems, characterized by different periods of feeding restriction (no restriction, CONT; 90 days restriction, COMP90; 120 days restriction, COMP120 and 150 days restriction, COMP150). Besides, the risk analysis and sensitivity analysis were performed using Monte Carlo simulation. The systems with restriction of feeding required longer fattening periods (14, 21 e 35 days for COMP90, COMP120 e COMP150 respectively) than the CONT system. The COM90 obtained higher productivity (434,2 kg/ha/year) close of CONT system (434kg/ ha/ year) and both were higher than COMP120 (395 kg/ha/year) and COMP150 (394 kg/ha/year). The highest gross margin/ha was obtained in COMP90 (608,98 R$/ha/year) that was more than CONT (493,5 R$/ha/year), COMP120 (366,96 R$/ha/year) and COMP150 (304,23 R$/ha/ year). However, the risk analysis resulted in a highest risk using CONT system and lowest risk with COMP90. The sensitivity analysis demonstrated that the variables with the most effect on gross margin are beef, calf and corn prices. The use of compensatory growth can be a tool to reduce feeding costs in beef cattle systems however its effectiveness is influenced by the restriction period.

Desenvolve-se, neste trabalho, um modelo de simulação estocástica para o sistema de transporte por táxi com viagens exclusivas. Basicamente, o modelo simula a geração da demanda pelo serviço e as formas alternativas de oferta: ponto privativo, ponto livre e rádio táxi. A validação do modelo é realizada através da comparação entre o sistema de táxi observado na cidade de Bauru (SP) e o sistema simulado através da aplicação do modelo. A comparação é feita para a alternativa de oferta com pontos privativos, a única existente na cidade. Os resultados indicam que o modelo de simulação proposto reproduz bem a demanda e a oferta de viagens por táxi em cidades de porte médio. Apresenta-se, além disso, uma análise comparativa das simulações realizadas para as duas outras alternativas fictícias de operação na cidade de Bauru (SP); os resultados destas, quando comparados aos da situação existente, mostram uma significativa economia nos custos variáveis. Conclui-se que o modelo pode constituir-se num instrumento de análise e avaliação das diferentes políticas de operação de táxi e apontar a mais adequada para uma determinada cidade de porte médio. / In this work, a stochastic simulation model for the exclusive-ride taxi system is developed. The model simulates the demand generation for taxi service and the alternative forms of service supply: private pickup point, free pickup point, and radiotaxi. The model validation is carried out by comparing the taxi system observed in Bauru (SP) with that simulated using data set collected in it. The comparison is concerning to the operation with private pickup point because in Bauru there is only this type of operation. The results indicate that the proposed simulation model reproduces adequately the taxi demand and supply in medium sized cities. It is also presented a comparative analysis between existent system and other operation systems simulated. The analysis shows that a significative cost saving is obtained if the operation system is changed in Bauru. It is concluded that the proposed model may constitute an important tool to analyze and evaluate the different operational policy and indicate the most adequate one for the medium sized cities.

[pt] Em muitas aplicações reais de processamento de sinais, as observações do fenômeno em estudo chegam seqüencialmente no tempo. Consequentemente, a tarefa de análise destes dados envolve estimar quantidades desconhecidas em cada observação concebida do fenômeno. Na maioria destas aplicações, entretanto, algum conhecimento prévio sobre o fenômeno a ser modelado está disponível. Este conhecimento prévio permite formular modelos Bayesianos, isto é, uma distribuição a priori sobre as quantidades desconhecidas e uma função de verossimilhança relacionando estas quantidades com as observações do fenômeno. Dentro desta configuração, a inferência Bayesiana das quantidades desconhecidas é baseada na distribuição a posteriori, que é obtida através do teorema de Bayes. Infelizmente, nem sempre é possível obter uma solução analítica exata para esta distribuição a posteriori. Graças ao advento de um formidável poder computacional a baixo custo, em conjunto com os recentes desenvolvimentos na área de simulações estocásticas, este problema tem sido superado, uma vez que esta distribuição a posteriori pode ser aproximada numericamente através de uma distribuição discreta, formada por um conjunto de amostras. Neste contexto, este trabalho aborda o campo de simulações estocásticas sob a ótica da genética Mendeliana e do princípio evolucionário da sobrevivência dos mais aptos. Neste enfoque, o conjunto de amostras que aproxima a distribuição a posteriori pode ser visto como uma população de indivíduos que tentam sobreviver num ambiente Darwiniano, sendo o indivíduo mais forte, aquele que possui maior probabilidade. Com base nesta analogia, introduziu-se na área de simulações estocásticas (a) novas definições de núcleos de transição inspirados nos operadores genéticos de cruzamento e mutação e (b) novas definições para a probabilidade de aceitação, inspirados no esquema de seleção, presente nos Algoritmos Genéticos. Como contribuição deste trabalho está o estabelecimento de uma equivalência entre o teorema de Bayes e o princípio evolucionário, permitindo, assim, o desenvolvimento de um novo mecanismo de busca da solução ótima das quantidades desconhecidas, denominado de inferência evolucionária. Destacamse também: (a) o desenvolvimento do Filtro de Partículas Genéticas, que é um algoritmo de aprendizado online e (b) o Filtro Evolutivo, que é um algoritmo de aprendizado batch. Além disso, mostra-se que o Filtro Evolutivo, é em essência um Algoritmo Genético pois, além da sua capacidade de convergência a distribuições de probabilidade, o Filtro Evolutivo converge também a sua moda global. Em conseqüência, a fundamentação teórica do Filtro Evolutivo demonstra, analiticamente, a convergência dos Algoritmos Genéticos em espaços contínuos. Com base na análise teórica de convergência dos algoritmos de aprendizado baseados na inferência evolucionária e nos resultados dos experimentos numéricos, comprova-se que esta abordagem se aplica a problemas reais de processamento de sinais, uma vez que permite analisar sinais complexos caracterizados por comportamentos não-lineares, não- gaussianos e nãoestacionários. / [en] In many real-world signal processing applications, the phenomenon s observations arrive sequentially in time; consequently, the signal data analysis task involves estimating unknown quantities for each phenomenon observation. However, in most of these applications, prior knowledge about the phenomenon being modeled is available. This prior knowledge allows us to formulate a Bayesian model, which is a prior distribution for the unknown quantities and the likelihood functions relating these quantities to the observations. Within these settings, the Bayesian inference on the unknown quantities is based on the posterior distributions obtained from the Bayes theorem. Unfortunately, it is not always possible to obtain a closed-form analytical solution for this posterior distribution. By the advent of a cheap and formidable computational power, in conjunction with some recent developments in stochastic simulations, this problem has been overcome, since this posterior distribution can be obtained by numerical approximation. Within this context, this work studies the stochastic simulation field from the Mendelian genetic view, as well as the evolutionary principle of the survival of the fittest perspective. In this approach, the set of samples that approximate the posteriori distribution can be seen as a population of individuals which are trying to survival in a Darwinian environment, where the strongest individual is the one with the highest probability. Based in this analogy, we introduce into the stochastic simulation field: (a) new definitions for the transition kernel, inspired in the genetic operators of crossover and mutation and (b) new definitions for the acceptation probability, inspired in the selection scheme used in the Genetic Algorithms. The contribution of this work is the establishment of a relation between the Bayes theorem and the evolutionary principle, allowing the development of a new optimal solution search engine for the unknown quantities, called evolutionary inference. Other contributions: (a) the development of the Genetic Particle Filter, which is an evolutionary online learning algorithm and (b) the Evolution Filter, which is an evolutionary batch learning algorithm. Moreover, we show that the Evolution Filter is a Genetic algorithm, since, besides its capacity of convergence to probability distributions, it also converges to its global modal distribution. As a consequence, the theoretical foundation of the Evolution Filter demonstrates the convergence of Genetic Algorithms in continuous search space. Through the theoretical convergence analysis of the learning algorithms based on the evolutionary inference, as well as the numerical experiments results, we verify that this approach can be applied to real problems of signal processing, since it allows us to analyze complex signals characterized by non-linear, nongaussian and non-stationary behaviors.

Naturally fractured reservoirs are generally extremely complex. The aim of characterisation and modelling of such reservoirs is to construct numerical models of rock and fractures, preparing input data for reliable stimulation and fluid flow simulation analyses. This requires the knowledge of different fracture heterogeneities and their correlations at well locations and inter-well regions. This study addresses the issues of how to integrate different information from various field data sources and construct comprehensive discrete fracture networks for naturally fractured reservoir. The methodology combines several mathematical and artificial intelligent techniques, which include statistics, geostatistics, fuzzy neural network, stochastic simulation and simulated annealing global optimisation. The study has contributed to knowledge in characterisation and modelling of naturally fractured reservoirs in several ways. It has developed: .An effective and data-dependant fracture characterisation procedure. It examines all the conventional reservoir data sources and their roles towards characterisation of different fracture properties. The procedure has the advantage of being both comprehensive and flexible. It is able to integrate all multi-scaled and diverse fracture information from the different data sources. .An improved hybrid stochastic generation algorithm for modelling discrete fracture networks. The stochastic simulation is able to utilise both discrete and continuum fracture information. It could simulate not only complicated distributions for fracture properties (e.g. multimodal circular statistics and non-parametric distributions) but also their correlations. In addition, with the incorporation of artificial fuzzy neural simulation, discrete multifractal geometry of fracture size and fracture density distribution map could be evaluated and modelled. Compared to most of the previous fracture modelling approach, this model is more flexible and comprehensive. .An improved conditional global optimisation model for modelling discrete fracture networks. The hybrid model takes full advantages of the advanced fracture characterisation using geostatistical and fuzzy neural analyses. Discrete fractures are treated individually and yet continuum information could be modelled. Compared to the stochastic simulation approach, this model produces more representative fracture networks. Compared to the conventional optimisation programs, this model is more versatile and contains superior objective function.

Quantitative descriptions of reaction kinetics formulated at the stochastic mesoscopic level are frequently used to study various aspects of regulation and control in models of cellular control systems. For this type of systems, numerical simulation offers a variety of challenges caused by the high dimensionality of the problem and the multiscale properties often displayed by the biochemical model. In this thesis I have studied several aspects of stochastic simulation of both well-stirred and spatially heterogenous systems. In the well-stirred case, a hybrid method is proposed that reduces the dimension and stiffness of a model. We also demonstrate how both a high performance implementation and a variance reduction technique based on quasi-Monte Carlo can reduce the computational cost to estimate the probability density of the system. In the spatially dependent case, the use of unstructured, tetrahedral meshes to sample realizations of the stochastic process is proposed. Using such meshes, we then extend the reaction-diffusion framework to incorporate active transport of cellular cargo in a seamless manner. Finally, two multilevel methods for spatial stochastic simulation are considered. One of them is a space-time adaptive method combining exact stochastic, approximate stochastic and macroscopic modeling levels to reduce the simualation cost. The other method blends together mesoscale and microscale simulation methods to locally increase modeling resolution. / eSSENCE

Hvordan kan en omkostningskalkulationsmodel udformes under moderne og fleksible produktionsforudsætninger, og hvordan påvirker stokastikken fra produktionen en given kalkulationsmodel, når der tages højde for samtlige indsatte ressourcer fra produktionen? Disse forhold er diskuteret med udgangspunkt i den existerende kalkulationsteori på området og i relation till to konkrete case-virksomheder. For at kunne gøre konkrete beregninger af stokastikkens virkninger, er der udformet en model baseret på et FMS-system, som har været testet via stokastisk simulering. Resultatet heraf viste, at variationer i processerne, transport og leadtid kan have relativ stor effekt på stykomkostningerne sammenliget med det deterministiske tilfælde. Med en stokastisk omkostningsmodel er der også mulighed for, at estimere effekten fra Design For Manufacturability (DFM) via standardafvigelsen. Dermed bliver det muligt att søge efter at minimere stokastikken og variationen fra produktionen. / Diss. Stockholm : Handelshögskolan

Caspases are important players in programmed cell death. Normal activities of caspases are critical for the cell life cycle and dysfunction of caspases may lead to the development of cancer and neurodegenerative diseases. They have become a popular target for drug design against abnormal cell death. In this study, the recognition of P5 position in substrates by caspase-3, -6 and -7 has been investigated by kinetics, modeling and crystallography. Crystal structures of caspase-3 and -7 in complexes with substrate analog inhibitor Ac-LDESD-CHO have been determined at resolutions of 1.61 and 2.45 Å, respectively, while a model of caspase-6/LDESD is constructed. Enzymatic study and structural analysis have revealed that Caspase-3 and -6 recognize P5 in pentapeptides, while caspase-7 lacks P5-binding residues. D-arginine dehydrogenase catalyzes the flavin-dependent oxidative deamination of D-amino acids to the corresponding imino acids and ammonia. The X-ray crystal structures of DADH and its complexes with several imino acids were determined at 1.03-1.30 Å resolution. The DADH crystal structure comprises a product-free conformation and a product-bound conformation. A flexible loop near the active site forms an “active site lid” and may play an essential role in substrate recognition. The DADH Glu87 forms an ionic interaction with the side chain of iminoarginine, suggesting its importance for DADH preference of positively charged D-amino acids. Comparison of the kinetic data of DADH activity on different D-amino acids and the crystal structures demonstrated that this enzyme is characterized by relatively broad substrate specificity, being able to oxidize positively charged and large hydrophobic D-amino acids bound within a flask-like cavity. Understanding biology at the system level has gained much more attention recently due to the rapid development in genome sequencing and high-throughput measurements. Current simulation methods include deterministic method and stochastic method. Both have their own advantages and disadvantages. Our group has developed a deterministic-stochastic crossover algorithm for simulating biochemical networks. Simulation studies have been performed on biological systems like auto-regulatory gene network and glycolysis system. The new system retains the high efficiency of deterministic method while still reflects the random fluctuations at lower concentration.

Simulation of animal disease spread is essential for understanding and controlling the outbreak of disease among herds of livestock (in particular cattle and poultry). Using a computerized system or simulator, animal health professionals or epidemiologists often spend many hours determining the set of input parameters that most accurately represent a disease spread or an outbreak scenario. A parameter can be a simple boolean value, or a scientific or often hypothetically derived range of real numbers. Many times, an epidemiologist chooses a value provisionally in a random fashion and repeats the simulation until a viable solution is achieved. This tedious process is inefficient and lengthy. To assist and improve this laborious practice in a concise and timely manner, a Genetic Algorithm is employed to determine a population based solution consisting of input parameters using the North American Animal Disease Spread Model (NAADSM).

Closed depressions, low elevation features in a landscape with no outlet point, play an important role in both surface and subsurface hydrology. These depressions, which are common in hummocky morainal landscapes, pool incoming surface flow, promoting infiltration and facilitating leaching of surface pollutants into vital groundwater resources. Due to the cost of ground based identification in large areas and difficulties with the identification of irregular depressions, remote identification using digital elevation models (DEMs) stands as a practical and effective tool for the mapping of these closed depressions. A modified stochastic depression identification algorithm was used in this study to characterize depressions in the Waterloo and Paris-Galt-Guelph moraines of Southwestern Ontario. The simulation output was a map of depressions in the study area. Depressions were corroborated using GRCA Wetlands data, Google Street View imagery, SWOOP 2006 orthophotos and field validation. Depression corroboration showed that the algorithm was able to accurately identify the location of closed depressions containing wetlands and closed depressions that are dry (largely due to wetland draining) in the hummocky topography of the study site. This research has implications for depression mapping in the field of digital terrain analysis as it enables the identification of real depressions in large study areas with a moderate resolution DEM. Providing a means of efficiently mapping closed depressions is important because of the role closed depressions play in the recharge of important groundwater stores. / Natural Resources Canada