Metabolic channelling occurs when two enzymes that act on a common substrate pass that intermediate directly from one active site to the next without allowing it to diffuse into the surrounding aqueous medium. In this study, properties of channelling are investigated through the use of computational models and cell simulation tools. The effects of enzyme kinetics and thermodynamics on channelling are explored with the emphasis on validating the hypothesized roles of metabolic channelling in living cells. These simulations identify situations in which channelling can induce acceleration of reaction velocities and reduction in the free concentration of intermediate metabolites. Databases of biological information, including metabolic, thermodynamic, toxicity, inhibitory, gene fusion and physical protein interaction data are used to predict examples of potentially channelled enzyme pairs. The predictions are used both to support the hypothesized evolutionary motivations for channelling, and to propose potential enzyme interactions that may be worthy of future investigation.

Models are used widely within software engineering and have been studied from many perspectives. A perspective that has received little attention is the role of modeler intent in modeling. Knowing the intent of the modeler supports both model comprehension by providing the correct context for interpreting the model and model quality by clearly defining what information the model must contain. Furthermore, formal expressions of this intent allow automated support for this. Despite the value that the knowledge of modeler intent can provide, there are no adequate means in the current state of modeling practice for expressing this information. The focus of this thesis is to address this gap by providing mechanisms for expressing modeler intent both explicitly and formally. We approach this problem by recognizing the existence of a role level in modeling where the role each model plays defines what information it should contain and how this is related to the information in other models. The specification of these roles is what we refer to as the expression of modeler intent. We then present a framework that incorporates four aspects of modeler intent at the role level: the existential intent for a model that arises in response to the need for a set of information by stakeholders, the content criteria that express what information the model is intended to contain, model relationships that express how models are intended to constrain one another and the decomposition criteria that express the intent behind how a model is decomposed into a collection of models. A key contribution of this thesis is the specification of the macromodeling language as a new modeling language designed for the role level that supports the expression of all four aspects of modeler intent. We evaluate these techniques by applying them to two real-world modeling examples.

The goal of this thesis is to gain a deep understanding of inverse problems arising from 2D image and 3D surface reconstruction, and to design effective techniques for solving them. Both computational and theoretical issues are studied and efficient numerical algorithms are proposed. The first part of this thesis is concerned with the recovery of 2D images, e.g., de-noising and de-blurring. We first consider implicit methods that involve solving linear systems at each iteration. An adaptive Huber regularization functional is used to select the most reasonable model and a global convergence result for lagged diffusivity is proved. Two mechanisms---multilevel continuation and multigrid preconditioning---are proposed to improve efficiency for large-scale problems. Next, explicit methods involving the construction of an artificial time-dependent differential equation model followed by forward Euler discretization are analyzed. A rapid, adaptive scheme is then proposed, and additional hybrid algorithms are designed to improve the quality of such processes. We also devise methods for more challenging cases, such as recapturing texture from a noisy input and de-blurring an image in the presence of significant noise. It is well-known that extending image processing methods to 3D triangular surface meshes is far from trivial or automatic. In the second part of this thesis we discuss techniques for faithfully reconstructing such surface models with different features. Some models contain a lot of small yet visually meaningful details, and typically require very fine meshes to represent them well; others consist of large flat regions, long sharp edges (creases) and distinct corners, and the meshes required for their representation can often be much coarser. All of these models may be sampled very irregularly. For models of the first class, we methodically develop a fast multiscale anisotropic Laplacian (MSAL) smoothing algorithm. To reconstruct a piecewise smooth CAD-like model in the second class, we design an efficient hybrid algorithm based on specific vertex classification, which combines K-means clustering and geometric a priori information. Hence, we have a set of algorithms that efficiently handle smoothing and regularization of meshes large and small in a variety of situations.

Throughout a very long piping network crude oil in Saudi Arabia is sent to Gas Oil Separation Plant called GOSP. The main objectives of the GOSP are: - Separation of the associated gas through pressure drop in two series stages one to 120 psig and the other to 50 psig. - Separation of water by gravity separators called High Pressure Production Trap (HPPT), Dehydrator, Desalter and Water Oil Separator (WOSEP). - Reducing salt concentration to less than 10 PTB utilizing wash water and demulsifier. During the desalting process, the challenge is to overcome the existence of an emulsion layer at the interface between oil and water. In petroleum industry normally emulsions encountered are some kind of water droplets dispersed in a continuous phase of oil. In crude oil emulsions, emulsifying agents are present at the oil-water interface, hindering this coalescence process. Such agents include scale and clay particles, added chemicals or indigenous crude oil components like asphaltenes, resins, waxes and naphthenic acids. Many techniques made available to gas oil separation plant operators to minimize the effect of tight emulsions. These techniques include injection of demulsifier, increasing oil temperature, gravity separation in large vessels with high retention time as well as electrostatic voltage. From experience and studies these variables have been already optimized to a good extent; however, from the believe that knowledge never stop, this study is conducted targeting enhancing the demulsifier control and optimizing the wash water rate. The objective of this study is to design an Artificial Neural Network (ANN) trained on data set to cover wide operating range of all parameters effecting demulsifier dosage. This network will be used to work as a control black box inside the controller in which all effecting parameters are inputs and the demulsifier dosage is the controller output. Testing this control scheme showed an effective reduction in demulsifier consumption rate compared to the existing linear method. Results also, showed that the existing control strategy is highly conservative to prevent the salt from exceeding the limit. The generated function from the ANN was used also to optimize the amount of fresh water added to wash the salty crude oil. Finally, another ANN was developed to generate an online estimate of the salt content in the produced oil.

This work aims to further the understanding of the trajectory and velocity profile of curved motion. Two competing theories, the two-thirds power law and the minimum jerk velocity profile, were tested. A set of two experiments was run that had the subjects generate curved motion. The first experiment had subjects move along a bounded oval and the second experiment had subjects move is a less constrained manner inducing a curved path. The study shows evidence for the expected effects of distance travel and allowable room for error. The evidence for the two movement profiles explaining the data is minimal.

Throughout a very long piping network crude oil in Saudi Arabia is sent to Gas Oil Separation Plant called GOSP. The main objectives of the GOSP are: - Separation of the associated gas through pressure drop in two series stages one to 120 psig and the other to 50 psig. - Separation of water by gravity separators called High Pressure Production Trap (HPPT), Dehydrator, Desalter and Water Oil Separator (WOSEP). - Reducing salt concentration to less than 10 PTB utilizing wash water and demulsifier. During the desalting process, the challenge is to overcome the existence of an emulsion layer at the interface between oil and water. In petroleum industry normally emulsions encountered are some kind of water droplets dispersed in a continuous phase of oil. In crude oil emulsions, emulsifying agents are present at the oil-water interface, hindering this coalescence process. Such agents include scale and clay particles, added chemicals or indigenous crude oil components like asphaltenes, resins, waxes and naphthenic acids. Many techniques made available to gas oil separation plant operators to minimize the effect of tight emulsions. These techniques include injection of demulsifier, increasing oil temperature, gravity separation in large vessels with high retention time as well as electrostatic voltage. From experience and studies these variables have been already optimized to a good extent; however, from the believe that knowledge never stop, this study is conducted targeting enhancing the demulsifier control and optimizing the wash water rate. The objective of this study is to design an Artificial Neural Network (ANN) trained on data set to cover wide operating range of all parameters effecting demulsifier dosage. This network will be used to work as a control black box inside the controller in which all effecting parameters are inputs and the demulsifier dosage is the controller output. Testing this control scheme showed an effective reduction in demulsifier consumption rate compared to the existing linear method. Results also, showed that the existing control strategy is highly conservative to prevent the salt from exceeding the limit. The generated function from the ANN was used also to optimize the amount of fresh water added to wash the salty crude oil. Finally, another ANN was developed to generate an online estimate of the salt content in the produced oil.

My aim is to find out how to create a 3d character model for a pre-existing live action film, give this character characteristics that match the intellectual property and follow the visual style of the chosen film. For my example in this degree project, I chose Disney's adaption of John Carter of Mars. I used my own pipeline, which is a collection of work methods from different artists, for the creation of the example 3d character model. Though with a limit of bringing the model through the first two steps, as I focus on the constraint of this thesis work. In order to create this model, I researched the universe of John Carter, and the visual style of the film, and from that knowledge I designed a character to create a 3d model of. The finished 3d character model of this degree project was then compared to models from the production of John Carter of Mars, both by the author and through a survey to evaluate the result.

In this paper, we study the influence of the National Telecom Business Volume by the data in 2008 that have been published in China Statistical Yearbook of Statistics. We illustrate the procedure of modeling “National Telecom Business Volume” on the following eight variables, GDP, Consumption Levels, Retail Sales of Social Consumer Goods Total Renovation Investment, the Local Telephone Exchange Capacity, Mobile Telephone Exchange Capacity, Mobile Phone End Users, and the Local Telephone End Users. The testing of heteroscedasticity and multicollinearity for model evaluation is included. We also consider AIC and BIC criterion to select independent variables, and conclude the result of the factors which are the optimal regression model for the amount of telecommunications business and the relation between independent variables and dependent variable. Based on the final results, we propose several recommendations about how to improve telecommunication services and promote the economic development.

An accurate and fast behavior modeling procedure is presented for codesign of active optical device with circuitry. The developed method is based upon defining partial elements (PEs) and their measurement-based partial element equivalent circuits (M-PEEC), associating design rules with them, and characterizing them through the use of test structures. The test structures are designed such that they can include only sensitive combinations of predefined building blocks, and they are measured over a wide band of frequencies using network analysis techniques. Measurement-based partial element equivalent circuits of the building blocks are derived from the measured s-parameters of the test structures by nonlinear optimization methods. The method has been experimentally verified using metal-semiconductor-metal photodetectors. The method has also been verified with circuits using simulations, with good results obtained.

This study uses a conductance-based computer simulation to test the feasibility of a mechanism underlying a newly-described dynamic form of neuromodulation, called spike-timing dependent neuromodulation (STDN). In the mollusc, Tritonia diomedea, it was recently found that a serotonergic neuron (called DSI) alters the synaptic strength of another neuron (VSI-B) in a temporally biphasic-bidirectional manner, with an initial potentiation followed by prolonged synaptic depression (Sakurai and Katz 2003). Physiological evidence suggested that the depression phase is due to serotonin enhancing the A-current in VSI-B, thereby causing spike-narrowing or a decrease in spike amplitude, and thus a decrease in transmitter release. We sought to test the feasibility of this mechanism by developing a conductance-based model of VSI-B using a Hodgkin-Huxley style simulation with a minimal number of ion conductances: A-current, delayed rectifier potassium, fast sodium, and leak channels. From our model, we conducted simulations in order to study how the spike shape of the VSI-B action potential changes as the A-current conductance is enhanced, from which we are able to predict the amount of depression in the post-synaptic cell. Our model indicates that the depression due to the narrowing of the spike with A-current enhancement is sufficient to account for the empirically observed depression during STDN, although it suggests a greater effect of serotonin at the terminals than is observed in the soma. Additionally, the model suggested that the slow inactivation kinetics of the A-current cannot explain the dynamics of the depression phase of STDN. These modeling results suggest that serotonergic modulation of the A-current plays a role in STDN but does not account for its dynamics.