Review of assessment, design, and mitigation of multiple hazards

Large parts of the world are subjected to one or more natural hazards, such as earthquakes, tsunamis, landslides, tropical storms (hurricanes, cyclones and typhoons), costal inundation and flooding. Virtually the entire world is at risk of man-made hazards. In recent decades, rapid population growth and economic development in hazard-prone areas have greatly increased the potential of multiple hazards to cause damage and destruction of buildings, bridges, power plants, and other infrastructure; thus posing a grave danger to the community and disruption of economic and societal activities. Although an individual hazard is significant in many parts of the United States (U.S.), in certain areas more than one hazard may pose a threat to the constructed environment. In such areas, structural design and construction practices should address multiple hazards in an integrated manner to achieve structural performance that is consistent with owner expectations and general societal objectives. The growing interest and importance of multiple-hazard engineering has been recognized recently. This has spurred the evolution of multiple-hazard risk-assessment frameworks and development of design approaches which have paved way for future research towards sustainable construction of new and improved structures and retrofitting of the existing structures. This report provides a review of literature and the current state of practice for assessment, design and mitigation of the impact of multiple hazards on structural infrastructure. It also presents an overview of future research needs related to multiple-hazard performance of constructed facilities.

Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.

Federal and state policy influence on woody biomass utilization

A considerable portion of public lands in the United States is at risk of uncharacteristically severe wildfires due to a history of fire suppression. Wildfires already have detrimental impacts on the landscape and on communities in the wildland-urban interface (WUI) due to unnatural and overstocked forests. Strategies to mitigate wildfire risk include mechanical thinning and prescribed burning in areas with high wildfire risk. The material removed is often of little or no economic value. Woody biomass utilization (WBU) could offset the costs of hazardous fuel treatments if removed material could be used for wood products, heat, or electricity production. However, barriers due to transportation costs, removal costs, and physical constraints (such as steep slopes) hinder woody biomass utilization. Various federal and state policies attempt to overcome these barriers. WBU has the potential to aid in wildfire mitigation and meet growing state mandates for renewable energy. This research utilizes interview data from individuals involved with on-the-ground woody biomass removal and utilization to determine how federal and state policies influence woody biomass utilization. Results suggest that there is not one over-arching policy that hinders or promotes woody biomass utilization, but rather woody biomass utilization is hindered by organizational constraints related to time, cost, and quality of land management agencies’ actions. However, the use of stewardship contracting (a hybrid timber sale and service contract) shows promise for increased WBU, especially in states with favorable tax policies and renewable energy mandates. Policy recommendations to promote WBU include renewal of stewardship contracting legislations and a re-evaluation of land cover types suited for WBU. Potential future policies to consider include the indirect role of carbon dioxide emission reduction activities to promote wood energy and future impacts of air quality regulations.

Orbits design for Leo space based solar power satellite system

Space Based Solar Power satellites use solar arrays to generate clean, green, and renewable electricity in space and transmit it to earth via microwave, radiowave or laser beams to corresponding receivers (ground stations). These traditionally are large structures orbiting around earth at the geo-synchronous altitude. This thesis introduces a new architecture for a Space Based Solar Power satellite constellation. The proposed concept reduces the high cost involved in the construction of the space satellite and in the multiple launches to the geo-synchronous altitude. The proposed concept is a constellation of Low Earth Orbit satellites that are smaller in size than the conventional system. For this application a Repeated Sun-Synchronous Track Circular Orbit is considered (RSSTO). In these orbits, the spacecraft re-visits the same locations on earth periodically every given desired number of days with the line of nodes of the spacecraft’s orbit fixed relative to the Sun. A wide range of solutions are studied, and, in this thesis, a two-orbit constellation design is chosen and simulated. The number of satellites is chosen based on the electric power demands in a given set of global cities. The orbits of the satellites are designed such that their ground tracks visit a maximum number of ground stations during the revisit period. In the simulation, the locations of the ground stations are chosen close to big cities, in USA and worldwide, so that the space power constellation beams down power directly to locations of high electric power demands. The j2 perturbations are included in the mathematical model used in orbit design. The Coverage time of each spacecraft over a ground site and the gap time between two consecutive spacecrafts visiting a ground site are simulated in order to evaluate the coverage continuity of the proposed solar power constellation. It has been observed from simulations that there always periods in which s spacecraft does not communicate with any ground station. For this reason, it is suggested that each satellite in the constellation be equipped with power storage components so that it can store power for later transmission. This thesis presents a method for designing the solar power constellation orbits such that the number of ground stations visited during the given revisit period is maximized. This leads to maximizing the power transmission to ground stations.

Through the back door : Melungeon literacies and 21st century technologies

The Melungeons, a minority recognized in Southern Appalachia where they settled in the early 1800s, have mixed heritage—European, Mediterranean, Native American, and Sub-Saharan African. Their dark skin and distinctive features have marked them and been the cause of racial persecution both by custom and by law in Appalachia for two centuries. Their marginalization has led to an insider mentality, which I call a “literacy” of Melungeon-ness that affects every facet of their lives. Just a century ago, while specialized practices such as farming, preserving food, hunting, gathering, and distilling insured survival in the unforgiving mountain environment, few Melungeons could read or write. Required to pay property taxes and render military service, they were denied education, suffrage, and other legal rights. In the late 1890s visionary Melungeon leader Batey Collins invited Presbyterian homemissionaries to settle in one Tennessee Melungeon community where they established a church and built a school of unparalleled excellence. Educator-ministers Mary Rankin and Chester Leonard creatively reified the theories of Dewey, Montessori, and Rauschenbusch, but, despite their efforts, school literacy did not neutralize difference. Now, taking reading and writing for granted, Melungeons are exploring their identity by creating websites and participating in listserv discussions. These online expressions, which provide texts for rhetorical, semiotic, and socio-linguistic analysis, illustrate not solidarity but fragmentation on issues of origins and legitimacy. Armed with literacies of difference stemming from both nature and nurture, Melungeons are using literacy practices to embrace the difference they cannot escape.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

SCIENTIFIC MANAGEMENT SYSTEMS AS ENFRAMING OR EMPOWERING: HOW LEAN AND SIX SIGMA DEFINE TECHNICAL COMMUNICATION

In this project, I examine current forms of scientific management systems, Lean and Six Sigma, as they relate to technical communication. With the goal of breaking work up into standardized processes in order to cut costs and increase efficiency, Lean, Six Sigma and Lean Six Sigma hybrid systems are increasingly applied beyond manufacturing operations to service and other types of organizational work, including technical communication. By consulting scholarship from fields such as business, management, and engineering, and analyzing government Lean Six Sigma documentation, I investigate how these systems influence technical communication knowledge and practice in the workplace. I draw out the consequences of system-generated power structures as they affect knowledge work, like technical communication practice, when it is reduced to process. In pointing out the problems these systems have in managing knowledge work, I also ask how technical communication might shape them.