Interaction Between Roughages and Corn Milling Byproducts in Finishing Cattle Diets

During ethanol production, starch is the primary nutrient fermented and the remaining byproducts are excellent sources of fiber and protein. In addition, inclusion of byproducts in finishing diets may reduce the incidence of acidosis. As a result, roughage level and quality could potentially be reduced in finishing diets containing byproducts. Three experiments were conducted to examine the effects of roughage and wet corn gluten feed (WCGF) in finishing cattle diets containing corn distillers grains plus solubles. Cattle fed finishing diets containing wet distillers grains plus solubles (WDGS) with no roughage had decreased DMI and ADG compared to cattle fed roughage. Within roughage level, ADG was similar for cattle fed alfalfa hay, corn silage or corn stalks when included on an equal NDF basis. Apparent total tract digestibility of OM, NDF, and CP linearly decreased and ruminal pH variables increased linearly due to increasing roughage levels. Roughage sources can be exchanged on an equal NDF basis in beef finishing diets containing 30% WDGS (DM basis). In finishing diets containing modified distillers grains plus solubles (MDGS), DMI linearly increased due to increasing roughage levels but ADG responded quadratically and was lowest for cattle fed diets without roughage. There was also a quadratic response for DMI and ADG due to WCGF inclusion level. Gain:feed decreased linearly with increasing roughage and WCGF inclusion levels. Feeding 15% WCGF resulted in similar cattle performance and carcass traits to cattle fed no WCGF in diets containing 30% MDGS, but cattle fed diets with 60% total byproduct inclusion made up of 30% WCGF and 30% MDGS had reduced performance (DM basis). Additionally, reducing corn silage inclusion level to 7.5% resulted in similar finishing cattle performance and carcass traits to cattle fed 15% corn silage in diets containing 30% MDGS with or without inclusion of WCGF. Elimination of roughage in diets containing either WDGS or MDGS resulted in negative impacts on finishing cattle performance, ruminal metabolism, and carcass traits.

Managing Large Data Sets Using Support Vector Machines

Hundreds of Terabytes of CMS (Compact Muon Solenoid) data are being accumulated for storage day by day at the University of Nebraska-Lincoln, which is one of the eight US CMS Tier-2 sites. Managing this data includes retaining useful CMS data sets and clearing storage space for newly arriving data by deleting less useful data sets. This is an important task that is currently being done manually and it requires a large amount of time. The overall objective of this study was to develop a methodology to help identify the data sets to be deleted when there is a requirement for storage space. CMS data is stored using HDFS (Hadoop Distributed File System). HDFS logs give information regarding file access operations. Hadoop MapReduce was used to feed information in these logs to Support Vector Machines (SVMs), a machine learning algorithm applicable to classification and regression which is used in this Thesis to develop a classifier. Time elapsed in data set classification by this method is dependent on the size of the input HDFS log file since the algorithmic complexities of Hadoop MapReduce algorithms here are O(n). The SVM methodology produces a list of data sets for deletion along with their respective sizes. This methodology was also compared with a heuristic called Retention Cost which was calculated using size of the data set and the time since its last access to help decide how useful a data set is. Accuracies of both were compared by calculating the percentage of data sets predicted for deletion which were accessed at a later instance of time. Our methodology using SVMs proved to be more accurate than using the Retention Cost heuristic. This methodology could be used to solve similar problems involving other large data sets.