Thesis (Ph. D. ) -- McMaster University, 1998. / Includes bibliographical references. Also available via World Wide Web.

Orientador: Andriana Susana Lopes de Oliveira Campanharo / Resumo: Os Modelos Baseados em Indivíduos (MBI’s) têm sido crescentemente empregados na modelagem de processos infecciosos. Um MBI consiste de uma estrutura na qual ocorrem interações entre um certo número de indivíduos, cujo comportamento é determinado por um conjunto de características que evoluem estocasticamente no tempo. Estudos recentes têm mostrado que as redes complexas constituem um suporte natural para o estudo da propagação de uma doença. Redes complexas são descritas por um conjunto de vértices (nós), arestas (conexões, ligações ou links) e algum tipo de interação entre os mesmos. Na formulação original do MBI e em modelos SIR (Suscetível, Infectado e Recuperado) e SEI (Suscetível, Exposto e Infectado), as relações entre os indivíduos são representadas por grafos completos, ou seja, todos os indivíduos estão conectados entre si. Como a topologia de uma rede real não pode ser descrita por uma rede puramente aleatória, nesse trabalho o MBI foi implementado de forma a incorporar modelos mais realísticos de redes de contato na propagação de uma doença infecciosa. De maneira geral, observou-se que redes complexas com diferentes topologias resultam em curvas de indivíduos suscetíveis, infectados e recuperados (ou suscetíveis, expostos e infectados) com diferentes comportamentos, e desta forma, que a evolução de uma dada doença, em particular a tuberculose, é altamente sensível à topologia de rede utilizada. Mais especificamente, observou-se que quanto maior o valor do comprimen... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Individual-Based Models have been increasingly employed in the modeling of an infectious process. An IBM consists of a structure in which interactions occur between a certain number of individuals, whose behavior is determined by a set of characteristics that evolve stochastically in time. Recent studies have shown that complex networks are a natural framework for the study of a disease spread. Complex networks are described by a set of vertices (or nodes), edges (connections or links) and some type of interactions between them. In the original IBM approach and in SIR (Susceptible, Infected, Recovered) and SEI (Susceptible, Exposed and Infected) models, the relations between individuals are represented by complete graphs, that is, all individuals are connected to each other. Since the topology of a real network can not be described by a purely random network, in this work an IBM has been implemented in order to incorporate some realistic contact networks xvii models. In general, it was observed that complex networks with different topologies correspond to curves of susceptible, infected and recovered individuals (or susceptible, exposed and infected) with different behaviors, and thus, that the evolution of a given disease, in particular tuberculosis, is highly sensitive to a network topology. More specifically, it was observed that the higher the value of the mean jump length is, the faster the disease spreads and consequently, the higher is the number of infected individual... (Complete abstract click electronic access below) / Mestre

Submitted by EDUARDO RIBEIRO PINTO (eduribeiro2@bol.com.br) on 2018-05-03T15:47:28Z No. of bitstreams: 1 dissertacao_Eduardo.pdf: 6068904 bytes, checksum: 4ff00adcd4667c6d7ed4bcfb5db2321a (MD5) / Approved for entry into archive by Sulamita Selma C Colnago null (sulamita@btu.unesp.br) on 2018-05-03T19:01:49Z (GMT) No. of bitstreams: 1 pinto_er_me_bot_int.pdf: 6068904 bytes, checksum: 4ff00adcd4667c6d7ed4bcfb5db2321a (MD5) / Made available in DSpace on 2018-05-03T19:01:49Z (GMT). No. of bitstreams: 1 pinto_er_me_bot_int.pdf: 6068904 bytes, checksum: 4ff00adcd4667c6d7ed4bcfb5db2321a (MD5) Previous issue date: 2018-02-23 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Os Modelos Baseados em Indivíduos (MBI’s) têm sido crescentemente empregados na modelagem de processos infecciosos. Um MBI consiste de uma estrutura na qual ocorrem interações entre um certo número de indivíduos, cujo comportamento é determinado por um conjunto de características que evoluem estocasticamente no tempo. Estudos recentes têm mostrado que as redes complexas constituem um suporte natural para o estudo da propagação de uma doença. Redes complexas são descritas por um conjunto de vértices (nós), arestas (conexões, ligações ou links) e algum tipo de interação entre os mesmos. Na formulação original do MBI e em modelos SIR (Suscetível, Infectado e Recuperado) e SEI (Suscetível, Exposto e Infectado), as relações entre os indivíduos são representadas por grafos completos, ou seja, todos os indivíduos estão conectados entre si. Como a topologia de uma rede real não pode ser descrita por uma rede puramente aleatória, nesse trabalho o MBI foi implementado de forma a incorporar modelos mais realísticos de redes de contato na propagação de uma doença infecciosa. De maneira geral, observou-se que redes complexas com diferentes topologias resultam em curvas de indivíduos suscetíveis, infectados e recuperados (ou suscetíveis, expostos e infectados) com diferentes comportamentos, e desta forma, que a evolução de uma dada doença, em particular a tuberculose, é altamente sensível à topologia de rede utilizada. Mais especificamente, observou-se que quanto maior o valor do comprimento do salto médio, mais rápida será a propagação da doença e, consequentemente, maior será o número de indivíduos infectados. / Individual-Based Models have been increasingly employed in the modeling of an infectious process. An IBM consists of a structure in which interactions occur between a certain number of individuals, whose behavior is determined by a set of characteristics that evolve stochastically in time. Recent studies have shown that complex networks are a natural framework for the study of a disease spread. Complex networks are described by a set of vertices (or nodes), edges (connections or links) and some type of interactions between them. In the original IBM approach and in SIR (Susceptible, Infected, Recovered) and SEI (Susceptible, Exposed and Infected) models, the relations between individuals are represented by complete graphs, that is, all individuals are connected to each other. Since the topology of a real network can not be described by a purely random network, in this work an IBM has been implemented in order to incorporate some realistic contact networks xvii models. In general, it was observed that complex networks with different topologies correspond to curves of susceptible, infected and recovered individuals (or susceptible, exposed and infected) with different behaviors, and thus, that the evolution of a given disease, in particular tuberculosis, is highly sensitive to a network topology. More specifically, it was observed that the higher the value of the mean jump length is, the faster the disease spreads and consequently, the higher is the number of infected individuals.

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The main goal of this project is to investigate the behaviors of parameters used when modeling an epidemic. A stochastic SIHDRe model is used to simulate how an epidemic evolves over time. The SIHDRe model has nine parameters, and this project focuses on the infection rate (β) and the fraction of infections leading to deaths (FID), with all other parameters being considered known. Both parameters are time dependent. To estimate the two chosen parameters, this project uses synthetic data so that comparisons between estimations with true parameters are possible. A dynamic optimization procedure inspired by Model Predictive Control is utilized for the predictions. Using synthesized data from hospitalizations and deaths, a cost function is minimized to obtain estimations of the parameters. Only a subset of the time span, called a window, is considered for every parameter optimization. The parameters within the window are optimized and the window then moves forward in time defined by a time step until the parameters are optimized over the whole time span. To obtain error estimations of the parameters, synthetic bootstrapping is used, using optimized parameters to simulate new epidemics of which the parameters are optimized. The square difference between the new estimations compared to the original estimations can be used to obtain the standard deviation of the estimated parameters. This project also discusses how regularization parameters within the cost functions are chosen so that the estimated parameters will be most similar to the real parameter values, and end-of-data effects, i.e. increased uncertainty towards the end of a window, is also discussed. / Projektet undersöker hur olika parametrar till en epidemisk modell kan skattas. En stokastisk SIHDRe modell (Susceptible, Infected, Hospitalizalized, Dead, Recovered) används för att simulera hur en epidemi utvecklas över tid. SIDHRe modellen delar in populationen i olika grupper baserat på hur epidemin har påverkat dem, till exempel om de har blivit smittade eller om de har hamnat på sjukhus på grund av sjukdomen. Personer kan flyttas mellan olika grupper beroende på en rad parametrar samt storleken på de olika grupperna. Detta projekt fokuserar på att skatta två parameterar: β, som påverkar hur personer med risk för infektion blir smittade, samt FID som påverkar hur många infekterade som dör av sjukdomen. Modellen har nio parametrar totalt och alla andra parametrar anses kända. Projektet använder syntetisk data, som gör det möjligt att jämföra skattningar av parametrarna med deras sanna värden. Båda okända parametrarna är tidsberoende. För att bestämma parametervärdena används en dynamisk optimiseringsmetod. Data från antal individer inlagda på sjukhus samt antal döda anses känt och kan användas för att minimera en kostfunktion som har de okända parametrarna som inmatningsvärden genom att ändra dessa. Tidsspannet begränsas till en mindre del, det sägs att man ser ett fönster av hela tidsspannet. Fönstret startar vid den första tidspunkten och kostfunktionen minimiseras för inmatningsvärden inom fönstret. När detta är gjort flyttas fönstret ett kort tidsteg fram i tiden och optimiseringsprocessen återupprepas tills fönstret når slutet av hela tidsserien och alla parametervärden har uppskattats. Dessa skattade parametervärden kan sen jämföras med de sanna värdena. För att kunna uppskatta felet när parametervärdena bestäms används en metod kallad ”Synthetic Bootstrap”. Grundidén är att parameterna uppskattas en gång ochdenna uppskattning används sen som inmatningsvärde till epidemimodellen. Nya epidemier simuleras och baserat på dessa simuleringar, kan nya parametervärden estimeras. Dessa kommer att skilja i värde på grund av att modellen är stokastisk. De nya uppskattningarna jämförs sedan med de första uppskattningarna och en uppfattning om skillnaden mellan dessa kan sedan beskrivas som en standardavvikelse mellan de nya skattningarna och den första skattningen. Projektet diskuterar också val av olika regulariseringsparametrar för kostfunktionen. Dessa kontrollerar hur mycket de uppskattade värdena kan ändras från tidpunkt till tidpunkt genom att ett stort värde minskar möjliga ändringar och ett litet värde ökar dem. Ett fenomen som kallas ”end-of-data effects” diskuteras också och handlar om att osäkerheten växer i ett fönster ju längre in i fönstret man är.

京都大学 / 新制・課程博士 / 博士(農学) / 甲第23950号 / 農博第2499号 / 新制||農||1091(附属図書館) / 学位論文||R4||N5385(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)准教授 岡田 直紀, 教授 井鷺 裕司, 教授 柴田 昌三 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

The biogeochemical processes responsible for production and emission of ammonia from stored liquid dairy manure are governed by environmental factors (e.g. manure temperature, moisture) and manure characteristics (e.g. total ammoniacal nitrogen concentration, pH). These environmental factors and manure characteristics vary spatially as a result of spatially heterogeneous physical, chemical, and biological properties of manure. Existing process-based models used for estimating ammonia emission consider stored manure as a homogeneous system and do not consider these spatial variations leading to inaccurate estimations. In this study, a one-dimensional compartmental biogeochemical model was developed to (i) estimate spatial variation of temperature and substrate concentration (ii) estimate spatial variations and rates of biogeochemical processes, and (iii) estimate production and emission of ammonia from stored scraped liquid dairy manure. A one-dimension compartmentalized modeling approach was used whereby manure storage is partitioned into several sections in vertical domain assuming that the conditions are spatially uniform within the horizontal domain. Spatial variation of temperature and substrate concentration were estimated using established principles of heat and mass transfer. Pertinent biogeochemical processes were assigned to each compartment to estimate the production and emission of ammonia. Model performance was conducted using experimental data obtained from National Air Emissions Monitoring Study conducted by the United States Environmental Protection Agency. A sensitivity analysis was performed and air temperature, manure pH, wind speed, and manure total ammoniacal nitrogen concentration were identified as the most sensitive model inputs. The model was used to estimate ammonia emission from a liquid dairy manure storage of a dairy farm located in Rockingham and Franklin counties in Virginia. Ammonia emission was estimated under different management and weather scenarios: two different manure storage periods from November to April and May to October using historical weather data of the two counties. Results suggest greater ammonia emissions and manure nitrogen loss for the manure storage period in warm season from May to October compared to the storage period in cold season from November to April. / Ph. D.

In this thesis, a thorough study of the batch top-spray fluidized bed granulation was carried out including experimental study, population balance model (PBM), computational fluid dynamic (CFD) study and control strategy development. For the experimental study, the influence variables of pulsed frequency, binder spray rate and atomization pressure of a batch top-spray fluidized bed granulation process were studied using the Box-Behnken experimental design method. Different mathematical models were developed to predict the mean size of granules, yield, relative width of granule distribution, Hausner ratio and final granule moisture content. Validation experiments have shown the reliability and effectiveness of using the Box-Behnken experimental design method to study a fluidized bed granulation process. The one-dimensional population balance models (ODPBMs) have been developed to model a pulsed top-spray fluidized bed granulation, linking the operating factors of the pulsed frequency, the binder spray rate, and atomization air pressure with the granule properties to predict granule growth behavior at different operating conditions. A multi-stage open optimal control strategy based on the developed ODPBMs was proposed to reduce the model and process mismatch through adjusting the trajectory of the evolution of the granule size distribution at predefined sample intervals. The effectiveness of the proposed modeling and multi-stage open optimal control strategy has been validated by experimental and simulation tests. In addition, an Eulerian-Eulerian two-fluid model (EETFM) was developed to describe the gas-particle two-phase flow in the fluidized bed granulator. By computational fluid dynamic analysis, it has been proven that the fluidized bed granulation system is not homogeneous, based on which a two-compartmental population balance model (TCPBM) was developed to describe the particle growth in the fluidized bed granulation. Validation experiments have shown the effectiveness and superior accuracy of the TCPBM comparing with the ODPBM in predicting the final particle size distribution.

Recentes estudos sugerem a existência de sinapses elétricas mediadas por junções gap entre fotorreceptores na retina de vertebrados. Neste trabalho, descrevemos um modelo computacional dos circuitos primário e secundário mediados pelos bastonetes da retina de vertebrados. O modelo é composto pelas seguintes populações de células: bastonetes, cones, células bipolares dos bastonetes, células bipolares dos cones, células amácrinas do tipo AII e células ganglionares. As células do modelo estão acopladas entre si por sinapses químicas e elétricas segundo padrões realísticos de convergência e divergência. As sinapses elétricas ocorrem entre os bastonetes, entre os bastonetes e os cones, entre as células amácrinas AII e entre as células bipolares dos cones e a células amácrinas AII. O modelo assume que um estímulo luminoso de baixa intensidade, simulando condições escotópicas, atinge todos os bastonetes da camada receptora, porém menos da metade deles é excitada. A resposta dos bastonetes excitados é controlada por uma fotocorrente cuja amplitude pode ser alterada para simular estímulos de diferentes intensidades dentro da faixa escotópica. O modelo é utilizado para investigar os efeitos dos diferentes graus de acoplamento elétrico entre as células receptoras e entre as células amácrinas AII, além do efeito de diferentes valores de condutância do canal Ih ativado pela hiperpolarização nos bastonetes, sobre a faixa dinâmica da retina. Os resultados das simulações mostram que, para valores realísticos da condutância do canal Ih, a faixa dinâmica medida na camada receptora é maximizada para o índice de conectividade crítico para que haja percolação de ligação. No entanto, quando a faixa dinâmica é medida para as células bipolares ou ganglionares o valor máximo é obtido para um índice de conectividade subcrítico. Este resultado é conseqüência da alta convergência de sinapses químicas entre os bastonetes e células bipolares. / Recent studies suggest the existence of electrical synapses (gap junctions) connecting photoreceptors in the vertebrate retina. In this work we describe a computer model of the primary and secondary rod pathways in the vertebrate retina. The model is composed of the following cell populations: rods, cones, rod bipolar cells, cone bipolar cells, AII amacrine cells and ganglion cells. Cells of the model are connected via chemical as well as electrical synapses according to realistic convergence and divergence factors. There are electrical synapses between rods, rods and cones, AII amacrine cells, and cone bipolar cells and AII amacrine cells. The model assumes that low intensity stimuli simulating scotopic conditions reach all rods in the receptor array but less than half of them are excited. The excited rods response is controlled by a photocurrent waveform whose amplitude can be manipulated to simulate stimuli of different intensities within the scotopic range. The model is used to investigate the effects of different degrees of coupling among photoreceptors and among AII amacrine cells, as well as values of rod hyperpolarization activated current Ih on the dynamic range of the retina. Results show that for realistic values of Ih conductance the dynamic range of the rod array is maximized at the critical connectivity degree for bond percolation. However, the dynamic range of the rod bipolar and ganglion cells is maximized for a photoreceptor connectivity degree below the critical value. The latter result is a consequence of the high convergence of chemical synapses from rods to rod bipolar cells.