954 resultados para Eigenfunctions and fundamental solution
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Includes bibliographical references.
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Mode of access: Internet.
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Mode of access: Internet.
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Includes bibliographical references.
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Mode of access: Internet.
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Translation of Apres la mort.
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Mode of access: Internet.
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Includes index and bibliographical footnotes.
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Thesis (Ph.D.)--University of Washington, 2016-06
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Niobium pentoxide reacts actively with concentrate NaOH solution under hydrothermal conditions at as low as 120 degrees C. The reaction ruptures the corner-sharing of NbO7 decahedra and NbO6 octahedra in the reactant Nb2O5, yielding various niobates, and the structure and composition of the niobates depend on the reaction temperature and time. The morphological evolution of the solid products in the reaction at 180 degrees C is monitored via SEM: the fine Nb2O5 powder aggregates first to irregular bars, and then niobate fibers with an aspect ratio of hundreds form. The fibers are microporous molecular sieve with a monoclinic lattice, Na2Nb2O6 center dot(2)/3H2O. The fibers are a metastable intermediate of this reaction, and they completely convert to the final product NaNbO3 Cubes in the prolonged reaction of 1 h. This study demonstrates that by carefully optimizing the reaction condition, we can selectively fabricate niobate structures of high purity, including the delicate microporous fibers, through a direct reaction between concentrated NaOH solution and Nb2O5. This synthesis route is simple and suitable for the large-scale production of the fibers. The reaction first yields poorly crystallized niobates consisting of edge-sharing NbO6 octahedra, and then the microporous fibers crystallize and grow by assembling NbO6 octahedra or clusters of NbO6 octahedra and NaO6 units. Thus, the selection of the fibril or cubic product is achieved by control of reaction kinetics. Finally, niobates with different structures exhibit remarkable differences in light absorption and photoluminescence properties. Therefore, this study is of importance for developing new functional materials by the wet-chemistry process.
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The paper has been presented at the 12th International Conference on Applications of Computer Algebra, Varna, Bulgaria, June, 2006
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In the world, scientific studies increase day by day and computer programs facilitate the human’s life. Scientists examine the human’s brain’s neural structure and they try to be model in the computer and they give the name of artificial neural network. For this reason, they think to develop more complex problem’s solution. The purpose of this study is to estimate fuel economy of an automobile engine by using artificial neural network (ANN) algorithm. Engine characteristics were simulated by using “Neuro Solution” software. The same data is used in MATLAB to compare the performance of MATLAB is such a problem and show its validity. The cylinder, displacement, power, weight, acceleration and vehicle production year are used as input data and miles per gallon (MPG) are used as target data. An Artificial Neural Network model was developed and 70% of data were used as training data, 15% of data were used as testing data and 15% of data is used as validation data. In creating our model, proper neuron number is carefully selected to increase the speed of the network. Since the problem has a nonlinear structure, multi layer are used in our model.
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The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. ^ For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver.^ The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. ^ The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.^