Agronomic and economic viability of Miscanthus x giganteus as a novel biofuel in the Maryland climate

Gemstone Team Biofuels

Automated Software Debugging Using Hybrid Static/Dynamic Analysis

With the increasing complexity of today's software, the software development process is becoming highly time and resource consuming. The increasing number of software configurations, input parameters, usage scenarios, supporting platforms, external dependencies, and versions plays an important role in expanding the costs of maintaining and repairing unforeseeable software faults. To repair software faults, developers spend considerable time in identifying the scenarios leading to those faults and root-causing the problems. While software debugging remains largely manual, it is not the case with software testing and verification. The goal of this research is to improve the software development process in general, and software debugging process in particular, by devising techniques and methods for automated software debugging, which leverage the advances in automatic test case generation and replay. In this research, novel algorithms are devised to discover faulty execution paths in programs by utilizing already existing software test cases, which can be either automatically or manually generated. The execution traces, or alternatively, the sequence covers of the failing test cases are extracted. Afterwards, commonalities between these test case sequence covers are extracted, processed, analyzed, and then presented to the developers in the form of subsequences that may be causing the fault. The hypothesis is that code sequences that are shared between a number of faulty test cases for the same reason resemble the faulty execution path, and hence, the search space for the faulty execution path can be narrowed down by using a large number of test cases. To achieve this goal, an efficient algorithm is implemented for finding common subsequences among a set of code sequence covers. Optimization techniques are devised to generate shorter and more logical sequence covers, and to select subsequences with high likelihood of containing the root cause among the set of all possible common subsequences. A hybrid static/dynamic analysis approach is designed to trace back the common subsequences from the end to the root cause. A debugging tool is created to enable developers to use the approach, and integrate it with an existing Integrated Development Environment. The tool is also integrated with the environment's program editors so that developers can benefit from both the tool suggestions, and their source code counterparts. Finally, a comparison between the developed approach and the state-of-the-art techniques shows that developers need only to inspect a small number of lines in order to find the root cause of the fault. Furthermore, experimental evaluation shows that the algorithm optimizations lead to better results in terms of both the algorithm running time and the output subsequence length.

STRUCTURAL ANALYSIS OF RIBOSOME BINDING ELEMENTS OF THE 3´ UTR IN TWO POSITIVE-STRAND PLANT RNA VIRUSES

Turnip crinkle virus (TCV) and Pea enation mosaic virus (PEMV) are two positive (+)-strand RNA viruses that are used to investigate the regulation of translation and replication due to their small size and simple genomes. Both viruses contain cap-independent translation elements (CITEs) within their 3´ untranslated regions (UTRs) that fold into tRNA-shaped structures (TSS) according to nuclear magnetic resonance and small angle x-ray scattering analysis (TCV) and computational prediction (PEMV). Specifically, the TCV TSS can directly associate with ribosomes and participates in RNA-dependent RNA polymerase (RdRp) binding. The PEMV kissing-loop TSS (kl-TSS) can simultaneously bind to ribosomes and associate with the 5´ UTR of the viral genome. Mutational analysis and chemical structure probing methods provide great insight into the function and secondary structure of the two 3´ CITEs. However, lack of 3-D structural information has limited our understanding of their functional dynamics. Here, I report the folding dynamics for the TCV TSS using optical tweezers (OT), a single molecule technique. My study of the unfolding/folding pathways for the TCV TSS has provided an unexpected unfolding pathway, confirmed the presence of Ψ3 and hairpin elements, and suggested an interconnection between the hairpins and pseudoknots. In addition, this study has demonstrated the importance of the adjacent upstream adenylate-rich sequence for the formation of H4a/Ψ3 along with the contribution of magnesium to the stability of the TCV TSS. In my second project, I report on the structural analysis of the PEMV kl-TSS using NMR and SAXS. This study has re-confirmed the base-pair pattern for the PEMV kl-TSS and the proposed interaction of the PEMV kl-TSS with its interacting partner, hairpin 5H2. The molecular envelope of the kl-TSS built from SAXS analysis suggests the kl-TSS has two functional conformations, one of which has a different shape from the previously predicted tRNA-shaped form. Along with applying biophysical methods to study the structural folding dynamics of RNAs, I have also developed a technique that improves the production of large quantities of recombinant RNAs in vivo for NMR study. In this project, I report using the wild-type and mutant E.coli strains to produce cost-effective, site-specific labeled, recombinant RNAs. This technique was validated with four representative RNAs of different sizes and complexity to produce milligram amounts of RNAs. The benefit of using site-specific labeled RNAs made from E.coli was demonstrated with several NMR techniques.

MULTI-UNIT ACCIDENT CONTRIBUTIONS TO U.S. NUCLEAR REGULATORY COMMISSION QUANTITATIVE HEALTH OBJECTIVES: A SAFETY GOAL POLICY ANALYSIS USING MODELS FROM STATE-OF-THE-ART REACTOR CONSEQUENCE ANALYSES

The U.S. Nuclear Regulatory Commission implemented a safety goal policy in response to the 1979 Three Mile Island accident. This policy addresses the question “How safe is safe enough?” by specifying quantitative health objectives (QHOs) for comparison with results from nuclear power plant (NPP) probabilistic risk analyses (PRAs) to determine whether proposed regulatory actions are justified based on potential safety benefit. Lessons learned from recent operating experience—including the 2011 Fukushima accident—indicate that accidents involving multiple units at a shared site can occur with non-negligible frequency. Yet risk contributions from such scenarios are excluded by policy from safety goal evaluations—even for the nearly 60% of U.S. NPP sites that include multiple units. This research develops and applies methods for estimating risk metrics for comparison with safety goal QHOs using models from state-of-the-art consequence analyses to evaluate the effect of including multi-unit accident risk contributions in safety goal evaluations.