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No / Software rejuvenation is a preventive and proactive fault management technique that is particularly useful for counteracting the phenomenon of software aging, aimed at cleaning up the system internal state to prevent the occurrence of future failure. The increasing interest in combing software rejuvenation with cluster systems has given rise to a prolific research activity in recent years. However, so far there have been few reports on the dependency between nodes in cluster systems when software rejuvenation is applied. This paper investigates the software rejuvenation policy for cluster computing systems with dependency between nodes, and reconstructs an stochastic reward net model of the software rejuvenation in such cluster systems. Simulation experiments and results reveal that the software rejuvenation strategy can decrease the failure rate and increase the availability of the cluster system. It also shows that the dependency between nodes affects software rejuvenation policy. Based on the theoretic analysis of the software rejuvenation model, a prototype is implemented on the Smart Platform cluster computing system. Performance measurement is carried out on this prototype, and experimental results reveal that software rejuvenation can effectively prevent systems from entering into disabled states, and thereby improving the ability of software fault-tolerance and the availability of cluster computing systems. / National Natural Science Foundation of China under the grant No. 60872044, 71133006, and Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China.

Net blotch is one of the most devastating diseases of barley (<i>Hordeum vulgare</i> L.) and occurs in two distinct forms, net-type net blotch (NTNB) and spot-type net blotch (STNB), caused by the fungal pathogens <i>Pyrenophora teres</i> f. sp. <i>teres</i> Smedeg. and <i>P</i>. <i>teres</i> f. sp. <i>maculata</i> Smedeg., respectively. Several sources of resistance have been previously reported, however, few barley cultivars with high levels of resistance have been developed from these sources. Efficient utilization of available resistance sources is dependent upon successful characterization of genes governing resistance in each resistant parent. Five net blotch resistant parents and one susceptible parent were crossed to identify novel resistance genes, postulate gene number and mode of inheritance, and conduct linkage mapping of novel genes for net blotch resistance. Results indicate that the highly resistant spring barley lines CIho 2291 and CIho 5098, and the winter barley cultivar Nomini each have single dominant genes for NTNB resistance. Resistance to NTNB in CIho 5098 is controlled by the same dominant gene conferring resistance in Nomini. Resistance to NTNB in CIho 2291 is controlled by one dominant gene which putatively is the same gene conferring resistance in ND B112, but differs from the resistance genes carried by the other parents in this study. An F2 population of 238 individuals derived from a cross between Nomini and the susceptible parent "Hector", and an F2 population of 193 individuals derived from a cross between CIho 2291 and Hector were used to map the genes governing NTNB resistance in Nomini and CIho 2291. The dominant gene governing resistance in Nomini, temporarily designated <i>Rpt-Nomini</i>, was mapped to a 9.2 cM region near the centromere of barley chromosome 6H between the flanking microsatellite markers Bmag0344a (r2=0.70) and Bmag0103a (r2=0.90), which were 6.8 cM and 2.4 cM away from <i>Rpt-Nomini</i>, respectively. The dominant gene governing resistance in CIho 2291, temporarily designated <i>Rpt-CIho2291</i>, was mapped to the distal region of barley chromosome 6H between the flanking microsatellite markers Bmag0173 (r2=0.65) and Bmag0500 (r2=0.26), which were 9.9 cM and 24.4 cM from <i>Rpt-CIho2291</i>, respectively. Previous studies have reported genes governing net blotch resistance in this region; however, allelism tests have not been conducted to determine the relationship between these genes. Identification of the chromosomal location of <i>Rpt-Nomini</i> and <i>Rpt-CIho2291</i> will facilitate future efforts in pyramiding multiple independent genes for net blotch resistance. / Ph. D.

Critical Infrastructures (CIs), such as power, transportation, healthcare, etc., refer to systems, facilities, technologies, and networks vital to national security, public health, and socio-economic well-being of people. CIs play a crucial role in emergency management. For example, the recent Hurricane Ida, Texas Winter storm, colonial cyber-attack that occurred during 2021 in the US, shows the CIs are highly inter-dependent with complex interactions. Hence power system failures and shutdown of natural gas pipelines, in turn, led to debilitating impacts on communication, waste systems, public health, etc. Consider power failures during a disaster, such as a hurricane. Subject Matter Experts (SMEs) such as emergency management authorities may be interested in several decision-making tasks. Can we identify disaster phases in terms of the severity of damage from analyzing changes in power failures? Can we tell the SMEs which power grids or regions are the most affected during each disaster phase and need immediate action to recover? Answering these questions can help SMEs to respond quickly and send resources for fast recovery from damage. Can we systematically provide how the failure of different power grids may impact the whole CIs due to inter-dependencies? This can help SMEs to better prepare and mitigate the risks by improving system resiliency. In this thesis, we explore problems to efficiently operate decision-making tasks during a disaster for emergency management authorities. Our research has two primary directions, guide decision-making in resource allocation and plans to improve system resiliency. Our work is done in collaboration with the Oak Ridge National Laboratory to contribute impactful research in real-life CIs and disaster power failure data. 1. Explainable resource allocation: In contrast to the current interpretable or explainable model that provides answers to understand a model output, we view explanations as answers to guide resource allocation decision-making. In this thesis, we focus on developing a novel model and algorithm to identify disaster phases from changes in power failures. Also, pinpoint the regions which can get most affected at each disaster phase so the SMEs can send resources for fast recovery. 2. Networks for improving system resiliency: We view CIs as a large heterogeneous network with nodes as infrastructure components and dependencies as edges. Our goal is to construct a visual analytic tool and develop a domain-inspired model to identify the important components and connections to which the SMEs need to focus and better prepare to mitigate the risk of a disaster. / Doctor of Philosophy / Critical Infrastructure Systems (CIs) entitle multiple infrastructures valuable for maintaining public life and national security, e.g., power, water, transportation. US Federal Emergency Management Agency (FEMA) aims to protect the nation and citizens by mitigating all hazards during natural or man-made disasters. For this, they aim to adopt different decision-making strategies efficiently. E.g., During an ongoing disaster, when to quickly send resources, which regions to send resources first, etc. Besides, they also need to plan how to prepare for a future disaster and which CIs need maintenance to improve system resiliency. We explore several data-mining problems which can guide FEMA towards developing efficient decision-making strategies. Our thesis emphasizes explainable and network-based models and algorithms to help decision-making operations for emergency management experts by leveraging critical infrastructures data.

Data for this study were age-adjusted linear scores on all cows scored between 1980 and 1993. Lifetime production information on these cows and their herdmates was used to calculate Relative Net Income adjusted for opportunity cost (RNIOC) for the 2,249,835 cows with an 84 month herdlife opportunity. The effect of inbreeding was analyzed using both a fixed and animal model, with little difference in results. Inbreeding depressed RNIOC by $12.69 in a fluid market and $11.53 in a manufacturing market per 1% increase in inbreeding. Addition of somatic cell information in the profit function had little effect. Heritabilities of profit functions were .16 and .14 for a fluid and manufacturing market, respectively. Animal model estimates of inbreeding depression were +.16 days, -6.7 days and -5.1 days for age at first freshening (AFF), days of productive life (DPL) and days in milk (TDIM), respectively. Inbreeding decreased first lactation mature equivalent milk, fat, and protein by 23.7 kg, .85 kg, and .76 kg, respectively and lifetime milk, fat, and protein production by 176.9 kg, 6.4 kg, 5.6 kg, respectively per 1% increase in inbreeding. Inbreeding had little effect on conformation traits. Effects of inbreeding were cumulative, exacting a larger effect on lifetime profit functions than on individual traits, when expressed as a percent of additive standard deviation. This study gives evidence that though not alarming, inbreeding has a deleterious effect on the lifetime performance of dairy cattle. / Master of Science

Currently, the design model for flexural rupture of an eccentrically loaded bracket plate is based on the material tensile rupture strength times the net elastic section modulus. Different bolt and plate sizes were tested to determine if this model is correct. It was found that the current model is conservative and that the material tensile rupture strength times the net plastic section modulus is a superior design model. Also, limited finite element modeling was performed to predict the elastic stiffness of such connections. The resulting data correlates well with test results, and confirms that most of the connection ductility comes from bolt plowing. These results can be used for splice plate connections in cantilever construction, as well. / Master of Science

Life Cycle Cost Analysis (LCCA) forms an important part of asset management practices and provides an informed decision support. The holistic nature of LCCA includes life cycle assessment (LCA) as an important component alongside economic life cycle cost analysis. The drinking water industry is right now lacking a reliable cost data structure which will ensure that all the utilities capture the same set of cost data. Also, models and tools currently available in the academia and industry are purely deterministic in nature and do not cater to uncertainty in the data. This study provides a framework for a holistic life cycle cost analysis tool which will help drinking water utilities to prioritize the activities and optimize the cost spending of the utility. The methodology includes the development of a cost data structure, a life cycle cost analysis and a life cycle assessment model in the form of an excel spreadsheet. The LCCA model has the capability to compare different pipe materials, installation, condition assessment, rehabilitation and replacement technologies. Whereas, LCA model can compare different pipe materials based on greenhouse gas emissions calculations. The final step of the methodology includes piloting the model with data from utility A. The analysis has been shown in the form of three case studies - comparison of two pipe materials, two pipe installation technologies and two pipe rehabilitation technologies. The case studies provide results in the form of comparison of total life cycle costs for different alternatives and hence a better alternative can be chosen. / Master of Science / The drinking water industry is right now lacking a reliable cost data structure which will ensure that all the utilities capture the same set of cost data. Also, models and tools currently available in the academia and industry do not cater to uncertainty in the data. This study provides a framework for a holistic life cycle cost analysis tool which will help drinking water utilities to prioritize the activities and optimize the cost spending of the utility. The methodology includes the development of a cost data structure, a life cycle cost analysis and a life cycle assessment model in the form of an excel spreadsheet. The LCCA model has the capability to compare different pipe materials, installation, condition assessment, rehabilitation and replacement technologies. Whereas, LCA model can compare different pipe materials based on greenhouse gas emissions calculations. The final step of the methodology includes piloting the model with data from utility A. The analysis has been shown in the form of three case studies - comparison of two pipe materials, two pipe installation technologies and two pipe rehabilitation technologies. The case studies provide results in the form of comparison of total life cycle costs for different alternatives and hence a better alternative can be chosen.