Orientador: Camila Pires Cremasco Gabriel / Resumo: Para uma satisfatória produtividade, a cultura do rabanete (Raphanus sativus L.) exige principalmente boa qualidade do solo e grande disponibilidade de água. A irrigação é uma técnica artificial utilizada para disponibilizar água as plantas. Seu uso deve ser criterioso e para que se obtenha menores custos de produção, deve-se evitar o uso desnecessário de água, e consequentemente, energia elétrica. Formas de utilização da água são cada vez mais estudadas, entre elas, a água de irrigação tratada magneticamente, que tem mostrado aprimoramentos produtivos em diversas culturas. O objetivo deste trabalho foi o desenvolvimento de um conjunto de sistemas computacionais baseados em regras fuzzy para previsão de componentes de produção de culturas irrigadas. Para tanto, foram utilizados dados de um experimento conduzido com água de irrigação convencional ou tratada magneticamente, sendo avaliado variáveis biométricas, tais como: peso verde do bulbo, número de folhas, comprimento da raiz, diâmetro do bulbo, comprimento do bulbo, peso verde da raiz, peso verde da folha, peso seco da raiz, peso seco da folha e peso seco do bulbo. Como resultado, foi apresentado um conjunto de softwares com uma interface de simples uso e fácil compreensão, que poderá auxiliar os produtores na estimativa dos resultados das variáveis biométricas do rabaneteiro e de outras culturas irrigadas. / Abstract: For satisfactory productivity, the cultivation of radish (Raphanus sativus L.) mainly requires good soil quality and great availability of water. Irrigation is an artificial technique used to make water available to plants. Its use must be judicious and to obtain lower production costs, unnecessary use of water and, consequently, electric energy should be avoided. Ways of using water are increasingly studied, among them, magnetically treated irrigation water, which has shown productive improvements in several cultures. The objective of this work was the development of a set of computational systems based on fuzzy rules for forecasting production components of irrigated crops. For that, data from an experiment conducted with conventional irrigation water or magnetically treated water were used, and biometric variables were evaluated, such as green bulb weight, number of leaves, root length, bulb diameter, bulb length, green weight root weight, green leaf weight, dry root weight, dry leaf weight, and dry bulb weight. As a result, a set of software was presented with a simple to use and easy to understand interface, which can assist producers in estimating the results of the biometric variables of the radish feet and other irrigated crops. / Doutor

In this work, two differential equation models for smallpox are numerically solved to find the optimal intervention policy. In each model we look for the range of values of the parameters that give rise to the worst case scenarios. Since the scale of an epidemic is determined by the number of people infected, and eventually dead, as a result of infection, we attempt to quantify the scale of the epidemic and recommend the optimum intervention policy. In the first case study, we mimic a densely populated city with comparatively big tourist population, and heavily used mass transportation system. A mathematical model for the transmission of smallpox is formulated, and numerically solved. In the second case study, we incorporate five different stages of infection: (1) susceptible (2) infected but asymptomatic, non infectious, and vaccine-sensitive; (3) infected but asymptomatic, noninfectious, and vaccine-in-sensitive; (4) infected but asymptomatic, and infectious; and (5) symptomatic and isolated. Exponential probability distribution is used for modeling this case. We compare outcomes of mass vaccination and trace vaccination on the final size of the epidemic.

<p>    </p> <p>Migration is a ubiquitous phenomenon in biology and is relevant to all scales ranging from bacteria to human beings. It is relevant to fundamental biological processes like bacterial chemotaxis, development, disease progression, etc. So, understanding migration is pivotal to addressing fundamental questions in biology. We address three broad questions relevant to cell migration using models from physics: (i) What are the critical features of cancer cell migration? (ii) Is it possible to explain complex cell migration data using minimal bio- chemical networks? And (iii) how does cell-to-cell communication affect its migration at the population level? To address these questions we performed (i) mathematical analysis using the Cellular Potts model, simulations using the Biased Persistent random walk model, and steady-state analysis of cell response to graded signals to explain cancer cell migration in response to single and multiple chemical and mechanical signals, (ii) rigorous network anal- ysis of ∼ 500,000 minimal networks having features of fundamental biochemical processes like regulation, conversion or molecular binding to understand the origin of antagonism in multiple cue cancer cell migration experiments and (iii) the steady-state analysis of Keller- Segel equations mimicking collective cell migration to understand the role of cell to cell communication on chemotaxis of a bacterial population. From our analysis, we found that (i) persistence and bias in cancer cell migration are decoupled from each other owing to a lack of memory about past movements and for any general cell migration they are inherently constrained to take only a fixed set of values. (ii) Bias in cancer cell migration in response to a combination of chemoattractant gradients can be less than the response to individual gradients (antagonism in bias) while the speed remains unaltered. This antagonism in bias and lack thereof in speed can be explained by several minimal networks having molecular regulation, conversion, or binding as its central feature and all these distinct mechanisms show convergence and saturation of an internal molecule common to both the chemoattrac- tants. (iii) By analyzing the role of cell-cell communication in bacterial chemotaxis using the Keller-Segel model we find that communication enhances chemotaxis only when it is adaptive to its external surroundings and cell-to-cell variability helps in increasing the chemotactic drift in the bacterial population. </p>

We present a mathematical model that describes how cyanobacterial communities use natural light as a source of energy and water as a source of electrons to perform photosynthesis and therefore, grow and co-survive together with other bacterial species. We apply our model to a phototrophic population of bacteria, namely, cyanobacteria. Our model involves the use of light as a source of energy and inorganic carbon as a source of nutrients. First, we study a single species model involving only cyanobacteria, then we include heterotrophs in the two species model. The model consists of ordinary differential equations describing bacteria and chemicals evolution in time. Stability analysis results show that adding heterotrophs to a population of cyanobacteria increases the level of inorganic carbon in the medium, which in turns allows cyanobacteria to perform more photosynthesis. This increase of cyanobacterial biomass agrees with experimental data obtained by collaborators at the Center for Biofilm Engineering at Montana State University. / Mathematics

In the present study, the problem of optimization of thermal cracker (pyrolysis) operation is discussed. The main objective in thermal cracker optimization is the estimation of the optimal flow rates of different feeds (such as, Gas-oil, Propane, Ethane and Debutanized natural gasoline) to the cracking furnace under the restriction on ethylene and propylene production. Thousands of combinations of feeds are possible. Hence the optimization needs an efficient strategy in searching for the global minimum. The optimization problem consists of maximizing the economic profit subject to a number of equality and inequality constraints. Modelling, simulation and optimal operation via optimization of the thermal cracking reactor has been carried out by gPROMS (general PROcess Modelling System) software. The optimization problem is posed as a Non-Linear Programming problem and using a Successive Quadratic Programming (SQP) method for solving constrained nonlinear optimization problem with high accuracy within gPROMS software. New results have been obtained for the control variables and optimal cost of the cracker in comparison with previous studies.

Yes / Naphtha isomerization is an important issue in petroleum industries and it has to be a simple and cost effective technology for producing clean fuel with high gasoline octane number. In this work, based on real industrial data, a detailed process model is developed for an existing naphtha isomerization reactor of Baiji North Refinery (BNR) of Iraq which involves estimation of the kinetic parameters of the reactor. The optimal values of the kinetic parameters are estimated via minimizing the sum of squared errors between the predicted and the experimental data of BNR. Finally, a new isomerization process (named as AJAM process) is proposed and using the reactor model developed earlier, the reactor condition is optimized which maximizes the yield and research octane number (RON) of the reactor.

Yes / Increasing the yield and research octane number (RON) of naphtha isomerization process are the most important issues in industries. There are many alternative industrial naphtha isomerization processes practiced around the world. In addition, AJAM is a new naphtha isomerization process proposed by the authors recently (Ahmed et al., 2018) where the isomerization reactor model was validated using real data of Baiji North Refinery (BNR) of Iraq. In this work, first, the performance of the AJAM Process is evaluated against 8 existing industrial isomerization processes in terms of RON, yield and the cost using model based optimisation techniques. To be consistent, we have used the same isomerization reactor model in all the industrial processes we evaluated here. Secondly, energy saving opportunity in the new AJAM process is studied using pinch technology.

Dengue viral infection is a mosquito-borne disease with four distinct strains, where the interactions between these strains have implications on the severity of the disease outcomes. The two competing hypotheses for the increased severity during secondary infections are antibody dependent enhancement and original antigenic sin. Antibody dependent enhancement suggests that long-lived antibodies from primary infection remain during secondary infection but do not neutralize the virus. Original antigenic sin proposes that T cells specific to primary infection dominate cellular immune responses during secondary infections, but are inefficient at clearing cells infected with non-specific strains. To analyze these hypotheses, we developed within-host mathematical models. In previous work, we predicted a decreased non-neutralizing antibody effect during secondary infection. Since this effect accounts for decreased viral clearance and the virus is in quasi-equilibrium with infected cells, we could be accounting for reduced cell killing and the original antigenic sin hypothesis. To further understand these interactions, we develop a model of T cell responses to primary and secondary dengue virus infections that considers the effect of T cell cross-reactivity in disease enhancement. We fit the models to published patient data and show that the overall infected cell killing is similar in dengue heterologous infections, resulting in dengue fever and dengue hemorrhagic fever. The contribution to overall killing, however, is dominated by non-specific T cell responses during the majority of secondary dengue hemorrhagic fever cases. By contrast, more than half of secondary dengue fever cases have predominant strain-specific T cell responses. These results support the hypothesis that cross-reactive T cell responses occur mainly during severe disease cases of heterologous dengue virus infections. Finally, using the results from our within-host models, we develop a multiscale model of dengue viral infection which couples the within-host virus dynamics to the population level dynamics through a system of partial differential equations. We analytically determine the relationship between the model parameters and the characteristics of the solutions, and find thresholds under which infections persist in the population. Furthermore, we develop and implement a full numerical scheme for our model. / Ph. D.

Mathematical modeling of biological systems has improved the knowledge of scientists for many years. In virology, particularly in the study of hepatitis B virus, mathematical models were used to explain interactions between hepatitis B virus and the human host in the absence and presence of interventions such as drug therapy and vaccines. This thesis seeks to explain the role of e-Antigen, a particle produced by hepatitis B virus, in the pathogenesis of hepatitis B infection. To accomplish this goal, I will provide biological background as well as previous modeling work on the role of e-Antigen in hepatitis B virus infection, before finally developing a new model adapted specifically for connecting hepatitis B progression with e-Antigen and drug therapy. I will analyze the model both analytically and numerically, fit it to virus data from humans chronically infected with hepatitis B that undergo drug therapy, and draw conclusions about the relation between drugs, immune activation, and loss of e-Antigen. / Master of Science

In recent years, dengue viral infection has become one of the most widely-spread mosquito-borne diseases in the world, with an estimated 50-100 million cases annually, resulting in 500,000 hospitalizations. Due to the nature of the immune response to each of the four serotypes of dengue virus, secondary infections of dengue put patients at higher risk for more severe infection as opposed to primary infections. The current hypothesis for this phenomenon is antibody-dependent enhancement, where strain-specific antibodies from the primary infection enhance infection by a heterologous serotype. To determine the mechanisms responsible for the increase in disease severity, we develop mathematical models of within-host virus-cell interaction, epidemiological models of virus transmission, and a combination of the within-host and between-host models. The main results of this thesis focus on the within-host model. We model the effects of antibody responses against primary and secondary virus strains. We find that secondary infections lead to a reduction of virus removal. This is slightly different than the current antibody-dependent enhancement hypothesis, which suggests that the rate of virus infectivity is higher during secondary infections due to antibody failure to neutralize the virus. We use the results from the within-host model in an epidemiological multi-scale model. We start by constructing a two-strain SIR model and vary the parameters to account for the effect of antibody-dependent enhancement. / Master of Science