This thesis proposes several new predictors for the compression of shapes, volumes and animations. To compress frames in triangle-mesh animations with fixed connectivity, we introduce the ELP (Extended Lorenzo Predictor) and the Replica predictors that extrapolate the position of each vertex in frame $i$ from the position of each vertex in frame $i-1$ and from the position of its neighbors in both frames. For lossy compression we have combined these predictors with a segmentation of the animation into clips and a synchronized simplification of all frames in a clip. To compress 2D and 3D static or animated scalar fields sampled on a regular grid, we introduce the Lorenzo predictor well suited for scanline traversal and the family of Spectral predictors that accommodate any traversal and predict a sample value from known samples in a small neighborhood. Finally, to support the compressed streaming of isosurface animations, we have developed an approach that identifies all node-values needed to compute a given isosurface and encodes the unknown values using our Spectral predictor.

There are many studies have been devoted to solve the all-atom protein back- bone reconstruction problem (PBRP), such as Adcock¡¦s method, MaxSprout, SAB- BAC and Chang¡¦s method. In the previous work, Wang et al. tried to solve this problem by homology modeling. Then, Chang et al. improved Wang¡¦s result by refining the positions of oxygen based on the AMBER force field. We compare the results in CASP7 and 8 from Chang et al. and SABBAC v1.2 and find that some proteins get better predicting results by Chang¡¦s method and others do better in SABBAC. Based on SVM, we propose a tool preference classification method for determining which tool is potentially the better one for predicting the structure of a target protein. We design a series of steps to select the better feature sets for SVM. Our method is tested on the proteins with standard amino acids in CASP7 and 8 dataset, which contains 30 and 24 protein sequences, respectively. The experimen- tal results show that our method has 7.39% and 2.94% RMSD improvement against Chang¡¦s result in CASP7 and 8, respectively. Our method can also be applied to other effective prediction methods, even if they will be developed in the future.

The biological function of a protein is mainly maintained by its three-dimensional structure. Protein folds support the three-dimensional structure of a protein, and then the inter-residue contacts in the protein impact the formation of protein folds and the stability of its protein structure. Therefore, the protein contact plays a critical role in building protein structures and analyzing biological functions. In this thesis, we propose a methodology to predict the residue-residue contacts of a target protein and develop a new measurement to evaluate the accuracy of prediction. With three prediction tools, the support vector machine (SVM), the k-nearest neighbor algorithm (KNN), and the penalized discriminant analysis (PDA), we compare these classifiers based on the self-testing of the training set, which are derived from representative protein chains from PDB (PDB-REPRDB), and apply the best (SVM) to predict a testing set of 173 protein chains derived from previous study. The experimental results show that the accuracy of our prediction achieves 24.84%,15.68%, and 8.23% for three categories of different contacts, which greatly improves the result of random exploration (5.31%, 3.33%, and 1.12%, respectively).

Gas hydrates are a well-known problem in the oil and gas industry and cost millions of dollars in production and transmission pipelines. To prevent this problem, it is important to predict the temperature and pressure under which gas hydrates will form. Of the thermodynamic models in the literature, only a couple can predict the hydrate-formation temperature or pressure for complex systems including inhibitors. I developed two simple correlations for calculating the hydrate-formation pressure or temperature for single components or gas mixtures. These correlations are based on over 1,100 published data points of gas-hydrate formation temperatures and pressures with and without inhibitors. The data include samples ranging from pure-hydrate formers such as methane, ethane, propane, carbon dioxide and hydrogen sulfide to binary, ternary, and natural gas mixtures. I used the Statistical Analysis Software (SAS) to find the best correlations among variables such as specific gravity and pseudoreduced pressure and temperature of gas mixtures, vapor pressure and liquid viscosity of water, and concentrations of electrolytes and thermodynamic inhibitors. These correlations are applicable to temperatures up to 90ºF and pressures up to 12,000 psi. I tested the capability of the correlations for aqueous solutions containing electrolytes such as sodium, potassium, and calcium chlorides less than 20 wt% and inhibitors such as methanol less than 20 wt%, ethylene glycol, triethylene glycol, and glycerol less than 40 wt%. The results show an average absolute percentage deviation of 15.93 in pressure and an average absolute temperature difference of 2.97ºF. Portability and simplicity are other advantages of these correlations since they are applicable even with a simple calculator. The results are in excellent agreement with the experimental data in most cases and even better than the results from commercial simulators in some cases. These correlations provide guidelines to help users forecast gas-hydrate forming conditions for most systems of hydrate formers with and without inhibitors and to design remediation schemes such as: · Increasing the operating temperature by insulating the pipelines or applying heat. · Decreasing the operating pressure when possible. · Adding a required amount of appropriate inhibitor to reduce the hydrateformation temperature and/or increase the hydrate-formation pressure.

Thesis (B.A.) Magna Cum Laude --Butler University, 2009. / Includes bibliographical references (leaves 43-48).

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International.

Thesis (Ph. D.)--Columbia University, 1917. / Vita. Published also as Teachers college, Columbia university. Contributions to education, no. 89.

Thesis (M.A.)--California State University, Chico. / Includes abstract. "Located in the Chico Digital Repository." Includes bibliographical references (p. 44 - 49).

Thesis--PlanB (M.S.)--University of Wisconsin--Stout, 2001. / Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 149-167). Available also in a digital version from Dissertation Abstracts.