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Browsing by Author "Sharma, Ashish, author"

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    Differential gene expression in Escherichia coli following exposure to non-thermal atmospheric-pressure plasma
    (Colorado State University. Libraries, 2008) Sharma, Ashish, author; Collins, George, advisor; Pruden, Amy, advisor
    Plasma decontamination provides a low temperature and non-toxic means of treating objects where heating and exposure to poisonous compounds is not acceptable especially in applications relating to medical devices and food packaging. The effects of various plasma constituents (UV photons, reactive species, charged particles etc.) acting independently and/or synergistically on bacteria at the biomolecular level is not well understood. High-density oligonucleotide microarrays were used to explore the differential gene expression of the entire genome of E. coli following plasma treatment. The results indicate a significant induction of genes involved in DNA repair and recombination suggesting that plasma exposure caused substantial DNA damage in the cell. There was also evidence of oxidative stress and suppression of genes involved in housekeeping functions of energy metabolism and ion transport. Experiments were also carried out to optimize plasma operating parameters to achieve a higher rate of inactivation of microbes. Overall, the results of this study will help to further optimize non-thermal plasma applications for bacterial inactivation.
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    Dynamic mapping in a heterogeneous environment with tasks having priorities and multiple deadlines
    (Colorado State University. Libraries, 2003) Yallampalli, Siva Sankar, author; Vangari, Praveen, author; Sripada, Siddhartha, author; Sharma, Ashish, author; Kaul, Aditya, author; Joshi, Rohit S., author; Dilmaghani, Raheleh B., author; Ramakrishna, Chitta, author; Tideman, Sonja, author; Schneider, Myron, author; Bruan, Tracy D., author; Maciejewski, Anthony A., author; Siegel, Howard Jay, author; Shivle, Sameer, author; Kim, Jong-Kook, author; IEEE, publisher
    In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign resources to tasks (match) and order the execution of tasks on each resource (schedule) in a manner that exploits the heterogeneity of the resources and tasks. The mapping (defined as matching and scheduling) of tasks onto machines with varied computational capabilities has been shown, in general, to be an NP-complete problem. Therefore, heuristic techniques to find a near-optimal solution to this mapping problem are required. Dynamic mapping is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no communication among tasks), and tasks have priorities and multiple deadlines. This research proposes, evaluates, and compares eight dynamic heuristics. The performance of the best heuristics is 83% of an upper bound.
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    Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment
    (Colorado State University. Libraries, 2006) Yellampalli, Siva Sankar, author; Vangari, Praveen, author; Sripada, Siddhartha, author; Sharma, Ashish, author; Kaul, Aditya, author; Joshi, Rohit, author; Dilmaghani, Raheleh B., author; Chitta, Ramakrishna, author; Tideman, Sonja, author; Schneider, Myron, author; Braun, Tracy D., author; Maciejewski, Anthony A., author; Siegel, Howard Jay, author; Shivle, Sameer, author; Kim, Jong-Kook, author; Elsevier Inc., publisher
    In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.