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Membrane proteins are involved in vital cellular functions and have important implications in disease processes, drug design and therapy. However, it is difficult to obtain diffraction quality crystals to study transmembrane protein structure. Transmembrane protein topology prediction tools try to fill in the gap between abundant number of transmembrane proteins and scarce number of known membrane protein structures (3D structure and biochemically characterized topology). However, at present, the prediction accuracy is still far from perfect. TMHMM is the current state-of- the-art method for membrane protein topology prediction. In order to improve the prediction accuracy of TMHMM, based upon the method of GenomeScan, the author implemented AHMM (augmented HMM) by incorporating functional domain information externally to TMHMM. Results show that AHMM is better than TMHMM on both helix and sidedness prediction. This improvement is verified by both statistical tests as well as sensitivity and specificity studies. It is expected that when more and more functional domain predictors are available, the prediction accuracy will be further improved.

As the current rate of improvement in processor performance far exceeds the rate of memory performance, memory latency is the dominant overhead in many performance critical applications. In many cases, automatic compiler-based approaches to improving memory performance are limited and programmers frequently resort to manual optimisation techniques. However, this process is tedious and time-consuming. Furthermore, a diverse range of a rapidly evolving hardware makes the optimisation process even more complex. It is often hard to predict the potential benefits from different optimisations and there are no simple criteria to stop optimisations i.e. when optimal memory performance has been achieved or sufficiently approached. This thesis presents a platform independent optimisation approach for numerical applications based on iterative feedback-directed program restructuring using a new reasonably fast and accurate performance prediction technique for guiding optimisations. New strategies for searching the optimisation space, by means of profiling to find the best possible program variant, have been developed. These strategies have been evaluated using a range of kernels and programs on different platforms and operating systems. A significant performance improvement has been achieved using new approaches when compared to the state-of-the-art native static and platform-specific feedback directed compilers.

Spoken term detection (STD) is a fundamental task for multimedia information retrieval. A major challenge faced by an STD system is the serious performance reduction when detecting out-of-vocabulary (OOV) terms. The difficulties arise not only from the absence of pronunciations for such terms in the system dictionaries, but from intrinsic uncertainty in pronunciations, significant diversity in term properties and a high degree of weakness in acoustic and language modelling. To tackle the OOV issue, we first applied the joint-multigram model to predict pronunciations for OOV terms in a stochastic way. Based on this, we propose a stochastic pronunciation model that considers all possible pronunciations for OOV terms so that the high pronunciation uncertainty is compensated for. Furthermore, to deal with the diversity in term properties, we propose a termdependent discriminative decision strategy, which employs discriminative models to integrate multiple informative factors and confidence measures into a classification probability, which gives rise to minimum decision cost. In addition, to address the weakness in acoustic and language modelling, we propose a direct posterior confidence measure which replaces the generative models with a discriminative model, such as a multi-layer perceptron (MLP), to obtain a robust confidence for OOV term detection. With these novel techniques, the STD performance on OOV terms was improved substantially and significantly in our experiments set on meeting speech data.

The Navy's Impact Burial Model (IMPACT35) predicts the cylindrical mine trajectory in air and water columns and burial depth and orientation in sediment. Impact burial calculations are derived primarily from the sediment characteristics and from the mine's three-dimensional air and water phase trajectories. Accurate burial prediction requires that the model's water phase trajectory reasonably mimics the object's true trajectory. In order to determine what effect varying the shape to more closely match real-world mines has on the shape's water phase trajectory, Mine Drop Experiment II was conducted. The experiment consisted of dropping four separate types of scaled shapes (Sphere, Gumdrop, Manta, and Rockan) into a water column, and the resultant falls were filmed from two nearly orthogonal angles. Initial drop position, initial velocities, and the drop angle were controlled parameters. The Sphere and Gumdrop shapes tended to have smooth arcing drop paths. The Manta shape dropped much more slowly than the Sphere or Gumdrop shapes. The Manta had a tendency to either fall in a spiral with its bottom parallel to the bottom or on its side in a twisting motion. The Rockan tended to either flip or swoop as it entered the water but then settle in a slow spin with its primary length parallel to the bottom. The dispersion of all four shapes at the selected depth of 2.5 m was wide and variable. The data collected from the experiment can be used to develop and validate the mine Impact Burial Prediction Model with operational, non-cylindrical mine shapes.

Master of Science / Department of Agricultural Economics / Glynn T. Tonsor / The past several years has marked one of the most heightened periods of fed cattle basis volatility since the installment of live cattle futures contracts. Understanding basis, the difference between local cash price and the futures contract price, is imperative when making marketing and procurement decisions. In the face of increased volatility, the ability to produce accurate basis expectations is no simple task. The purpose of these analyses was to develop econometric models to determine the greatest influencers of fed cattle basis, to test the presence of structural changes in the determinants of fed cattle basis, and to compare out-of-sample forecasting performance. This study analyzed in-sample econometric models using monthly data from January 2003 through September 2016, then compared the results of the competing models. Using the same time period, we then identified the presence of structural breaks in the data. Furthermore, this study analyzed the out-of-sample forecasting performance for January 2012 through September 2016. The out-of-sample results were then compared to in-sample estimations and historical average basis models. The in-sample estimations indicated the important factors that influence fed cattle basis. The results indicate that there are multiple structural breaks present in the determinants of fed cattle basis examined during this study. We can robustly conclude that there was a market structural break present in the fourth quarter of 2013 and within the 2005-2006 time period. The results indicate that the out-of-sample regression estimations were outperformed by historical average models and did not improve our ability to accurately forecast basis. Overall, a 3 or 4 year historical average model should be preferred over econometric estimations when forecasting fed cattle basis.

The problem of this study was the differentiation of high schools in a metropolitan school district from the standpoint of the measures taken to prevent student dropouts.