Purebred Beefmaster steers (n = 160) from five owners were fed at a commercial feedlot in South Texas beginning in November of 2004; 68 steers possessed pedigree information. Beginning in mid-November, steers were individually weighed and evaluated for ultrasound body composition at 56-d intervals by a certified technician. Feeder calf frame (FRM) and muscle (MUS) scores were assigned at initial ultrasound evaluation. Steers were fed and marketed through a lean-based, branded beef program and were harvested in two groups in May and June of 2005 at a commercial beef plant. Analyses were conducted to investigate the ability to predict carcass traits from the different sources of information available on these cattle. Evaluation of carcass traits were investigated using four sets of independent variables referred to as sources A, B, C, or D and ultrasound scan session (1 – 4). An analysis included initial weight at first scan session (IWT), FRM and MUS as independent variables through GLM procedures. B analyses utilized ultrasound measures of the longissimus area, intramuscular fat, fat thickness, rump fat, and gluteus medius depth along with IWT as independent variables. Multiple regression was performed on each carcass trait using IWT and ultrasound traits at each scan session. Mallow’s CP was used to select a model that best described each carcass trait. C analyses (GLM) utilized variables from A and B analyses combined plus ranch. D analyses (GLM) included variables from C analyses plus sire nested within ranch. Respective R-square values (scan 1 – 4) for marbling score were .02, .04, .05, and .10 using A information, .14, .17, .42, and .54, using B information, .35, .35, .47, and .55 using C information, and .56, .59, .65, and .76 using D information. R-square values ranged from .34 to .86 for carcass weight, .11 to .77 for fat thickness, .06 to .82 for ribeye area, and .10 to .81 for yield grade. Ultrasound data obtained closer to harvest and increasing amount of data related to genetic and management background showed increased R-square values, but may be best utilized in conjunction with one another to predict carcass traits and final carcass value.

The reliability of electronic component plays an important role in proper functioning of the electronic devices. The manufacturer tests electronic components before they are used by end users. Still at times electronic devices fail due to undue stresses existing inside the microelectronic components such as memory chips, microcontrollers, resistors etc. The stresses can be caused by variation in the operating voltage, variation in the usage frequency of the particular chip and other factors. This variation leads to variation in chip temperature, which can be made evident from thermal profiles of these chips. In this thesis, effort was made to study two different kind of stress existing in the electronic board, namely signal stress based on variation in duty cycle/frequency of chip usage and the voltage stress. Memory chips were stressed using these stresses causing change in heating rates, which were captured by infrared camera. This data was then extracted and plotted to obtain different curves for the heating rate. The same experiment was done time and again for a large number of chips to get heating rate data. This data consisting of average heating rate for large number of chips was used to build Neural Network model (NN). Back Propagation algorithm was used for modeling because of its advantage of converging to solution faster compared to other algorithms. To develop a prediction model, data sets were divided into two-third and one-third parts. This two-thirds of the data was used to build the prediction model and remaining one third was used to evaluate the model. The designed model would predict the stress levels existing in the chips based on the heating rates of the chips. Results obtained suggested 1. There is difference in heating rate for chips stressed at different stress levels. 2. Accuracy of the model to predict the stress is high (greater than 90 %). 3. Model is robust enough that is it can yield efficient results even if there is presence of noise in the data. 4. Generic methodology can be proposed based on the experiments. This work is a progress in direction of making predictive model, for a complete electronic device, which can predict the stress level existing on any component in the device and will provide an opportunity to either protect the data or removal of the defected components timely before it even fails.

The all-atom protein backbone reconstruction problem (PBRP) is to rebuild the 3D coordinates of all atoms on the backbone which includes N, C, and O atoms. In the previous work, we find that the prediction accuracy of the 3D positions of the O atoms is not so good, compared with the other two atoms N and C. Thus, our goal is to refine the positions of the O atoms after the initial prediction of N, C, and O atoms on the backbone has been done by the previous work. Based on the AMBER force field, we modify the energy function to a simplified one with the statistical data on the bond lengths and bond angles of the 21 distinct amino acids (including the nonstandard one). Then, we propose a two-phase refinement method (TPRM) to find the position of each O atom independently that optimizes the modified energy function. We perform our method on two test sets of proteins. The experimental results show that the reconstruction accuracy of our method is better than the previous ones. The solution of our method is also more stable than most of the previous work. Besides, our method runs much faster than the famous prediction tool, SABBAC.

Thesis (M. Land Res. Sc.)--University of Queensland, 2002. / Includes bibliographical references.

Thesis (Ph. D.)--Columbia University, 1924. / Vita. Published also as Contributions to education, Teachers College, Columbia University, no. 140. eContent provider-neutral record in process. Description based on print version record.

Thesis--Marquette University. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 146-151).

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 104-111). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Queensland, 2002. / Includes bibliographical references.

Thesis (M.S.) -- Central Connecticut State University, 2009. / Thesis advisor: Stephen M. Cox. "... in partial fulfillment of the requirements for the degree of Master of Science in Criminal Justice." Includes bibliographical references (leaves 40-41). Also available via the World Wide Web.