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The increasing application of BIM processes and technologies has facilitated an increase in the use of 4D (3D+Time) simulations of construction projects. Numerous studies have acknowledged the benefit of 4D models in project planning and construction phases, enhancing communication between construction teams and avoiding unforeseen conflicts during the build process. The development of BIM has prompted a deeper understanding of the issue surrounding Level of Development (LODt), Level of Information (LOI) and Level of Detail (LOD) relating to the graphical detail and non-graphical information of the static geometric design model. However, up to now there is limited research methodically investigating the issue of LOD within 4D BIM applications. This research aims to develop a framework for specifying the LOD of 4D BIM to enhance communication and planning at various stages of the construction process. A 4D simulation needs more dynamic elements to alter the current 4D static image in order to provide more realistic simulation and more accurate results. A mixed research methods approach was developed to address the needs for successful framework development. A combination quantitative and qualitative survey was undertaken to gather data from professionals engaged in the development of 4D BIM simulations on construction projects. A framework was developed to provide professionals with an approach to develop LOD for 4D simulations (LOD4d) and following this the framework was validated through qualitative interview with experts in the field. The uniqueness of the work required the invention of new terminology. The developed framework incorporates terms for Level of Graphical Detail (LODg) the graphical information of the model. Level of Detail of object geometry “granulated” (LODgran) into segments showing how the object was constructed over the time. The framework comprises a time period required between state changes in the model during the simulation which is Temporal Level of Detail (LODti). The outcome of the work is the generation of a framework which supports the development of 4D simulations at a range of LOD. This can then be utilised as part of the BIM process to support the generation of 4D simulations at levels of detail suitable to the operations being undertaken. This could then lead to the development of an additional protocol within the BIM suite. Beside the construction industry specialists have provided suggestions to further support approach of communication during the construction process.

Disease resistance is critical in soft red winter wheat (Triticum aestivum L.) cultivars. Leaf rust caused by Puccinia triticina Eriks and stripe rust caused by Puccinia striiformis Westend. f.sp. tritici Eriks. are destructive pathogens of wheat. From 2014 to 2015 phenotypic data was collected at diverse locations for resistance to leaf rust (North Carolina, Texas, and Virginia) and stripe rust (Arkansas, North Carolina, Georgia, Texas, and Virginia) in a Pioneer ‘25R47’ /‘Jamestown’ (P47/JT) population composed of 186 F5:9 recombinant inbred lines (RILs). Analysis of the P47/JT population identified two quantitative trait loci (QTL) for leaf rust resistance on chromosome 5B and two QTL for stripe rust resistance on chromosomes 3B and 6A. Phenotypic variation (%) explained by the putative leaf rust resistance QTL of Jamestown on 5B was as high as 22.1%. Variation explained by the putative stripe rust resistance QTL of Jamestown on 3B and 6A was as high as 11.1 and 14.3%, respectively. Jamestown is postulated to contain gene Lr18. Seedlings of 186 F5:9 recombinant inbred lines from the P47/JT population and 200 F2 seedlings from eight other crosses including Jamestown and/or the Lr18 host differential line RL6009 (Thatcher*6/Africa 43) were screened with P. triticina race TNRJJ. Genetic analysis of the populations was conducted to validate the presence of Lr18 in Jamestown. Results of linkage analysis identified SNP maker IWB41960 linked within 5 cM of gene Lr18 in all three populations. From 2016 to 2017 phenotypic data was collected at diverse locations for resistance to leaf rust (Illinois, North Carolina, and Virginia) in a ‘2013412’ (PI 667644) / VA10W-21 (PI 676295) population (412/21) composed of 157 doubled haploid (DH) lines. The 412/21 DH lines were genotyped via genotyping by sequence (GBS). Analysis of the 412/21 population identified one quantitative trait loci (QTL) region associated with adult plant resistance to leaf rust on chromosome 1B. Phenotypic variation (%) explained by the putative leaf rust resistance QTL of 2013412 on 1B was as high as 40.1%. Kompetitive allele-specific (KASP) markers KASP_S1B_8414614 and KASP_S1B_8566239 were developed as markers for use in marker assisted selection.

The length of a day on Earth (abbreviated LOD) is not exactly 24 hours. There is a smallexcess LOD that varies on timescales ranging from a few days to thousands of years, generallyon the order of milliseconds. One characteristic of LOD variations is a sinusoidal componentwith a period of ~6 years. The cause of the ~6-year signal is unknown, but is generally suspectedto be exchanges of angular momentum between the mantle and the core. This study aimed to testthe hypothesis that the ~6-year LOD signal is due to coupling between the mantle and fluid outercore. The flow of the core's fluid deforms the base of the mantle, leading to redistribution ofEarth's mass (causing changes in the gravitational field) and deformation of the overlying crust.Surface deformation data from a global network of high-precision Global Positioning System(GPS) stations was analyzed, and the component that acts on the ~6-year timescale was isolatedand inverted for the core's flow. Resulting angular momentum changes were computed for theouter core and compared to the LOD signal to search for evidence of core-mantle coupling.Outer core angular momentum changes obtained from GPS deformation data exhibit evidence ofthe suspected core-mantle coupling, but this result is sensitive to inversion parameters. Changesin the gravitational field were also modeled and found to be smaller than the errors in thecurrently available data.

Wellness sensor technology is an emerging diagnostic test research field, which mostly deal with the point of care of the patients in the recent days. Due to the lack of awareness from the patients, most diseases cannot be detected in due time. This led to worse conditions, such as diabetic and alcoholic syndrome. Therefore, many research groups have been working to develop portable sensor devices that can track serious diseases. These include diabetic and alcoholic biomarkers in breathing. These devices have very high selectivity and reliability. However, the major limitation of biomarkers is that it deals with the bio-molecular based sensing mechanism. Extensive challenges exist in the selectivity and reliability of breathing sensors. These require development of proper materials and effective detection methods. Thus, selection of proper materials, correct sensing parameters, effective device architecture and simple fabrication processing are substantially critical. The goal of this work is to develop graphene based breathing sensors with high selectivity and sensitivity using a novel molecular imprinted filtering technique. The sensors have various applications including Point of Care Testing (POCT) device for personalized home and clinical use in early detection of diabetic and alcoholic patients. Different fabrication procedures were used to optimize the sensor performance. The optimized results demonstrate that a proper biomarker molecule imprinting process could selectively detect diabetes and alcohol. The graphene layer was optimized by maintaining spray coating time, pattern and distance between the substrate and spray coater. Graphene adhesion to the substrate was also improved using polyvinyl pyrrolidone. The molecular imprinting filter made on top of the graphene layer improved the performance of acetone and ethanol molecule detection, indicated by the change of resistance in the graphene layer. The sensors showed poor performance for long-time exposure (> 10 second) due to ambient molecules and moisture. However, the sensor characteristics were significantly improved for short exposing time (3-4 second) due to the optimization in the thickness of the filtering layer and sensing layer.

Developing perennial grain crops is an effective and crucial way to prevent soil erosion caused by conventional agriculture of using annual crops while meeting the increasing need of global food demand. We hypothesized that the regrowth in Zea might be controlled by two dominant complementary genes. F1 hybrids were created by crossing Zea diploperennis Iltis, Doebley & R. Guzman with annual Zea mays L. ssp. mays inbred line B73. A Total of 134 F2 plants derived from nine F1 were planted and phenotyped. A subpopulation of 94 F2 plants were genotyped with Genotype-by- Sequencing (GBS) and called 10,431 SNPs after filtering. A total number of 946 SNPs were then found related to regrowth by chi-square goodness of fit test P(χ2) > 0.05. Two chromosomal regions, 24,244,192 to 28,975,747 on chromosome 2 and 2,862,253 to 6,681,861 on chromosome 7 with high LOD scores (P > 0.05) are considered as the candidate loci. Candidate genes found located within these loci have no known functions or not likely to be related with regrowth. Twenty-five pairs of PCR primers were then designed on the basis of 13 SNPs and only 4 of them showed polymorphism between the parental alleles. One of the polymorphic SNP marker targeting the 27,773,017 bp on chromosome 2 showed good fitness of genotype to regrowth (P(χ2) = 0.195), yet the value is still low. More effort is needed to identify the candidate genes by SNP markers.

The recent emergence of the “Linked Data” approach for publishing data represents a major step forward in realizing the original vision of a web that can "understand and satisfy the requests of people and machines to use the web content" i.e. the Semantic Web. This new approach has resulted in the Linked Open Data (LOD) Cloud, which includes more than 295 large datasets contributed by experts belonging to diverse communities such as geography, entertainment, and life sciences. However, the current interlinks between datasets in the LOD Cloud, as we will illustrate,are too shallow to realize much of the benefits promised. If this limitation is left unaddressed, then the LOD Cloud will merely be more data that suffers from the same kinds of problems, which plague the Web of Documents, and hence the vision of the Semantic Web will fall short.This thesis presents a comprehensive solution to address the issue of alignment and relationship identification using a bootstrapping based approach. By alignment we mean the process of determining correspondences between classes and properties of ontologies. We identify subsumption, equivalence and part-of relationship between classes. The work identifies part-of relationship between instances. Between properties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of being able to utilize the information which is contained within the datasets for improving the data within them. The work showcases use of bootstrapping based methods to identify and create richer relationships between LOD datasets. The BLOOMS project (http://wiki.knoesis.org/index.php/BLOOMS) and the PLATO project, both built as part of this research, have provided evidence to the feasibility and the applicability of the solution.

In this dissertation, Bayesian solutions have been given to the repeated measure model (chapter II), the nested random effects repeated measure model (chapter III), the mixture model consisting of a censored lognormal distribution and a point mass distribution (chapter IV), and the mixture model with random effects (chapter V), for the analyses of data with values below the Limit of Detection (LOD). The Markov chain Monte Carlo methods (Gibbs sampler and Metropolis-Hastings algorithm) were implemented to generate posterior samples for model parameters from the full conditional distributions. The Deviance Information Criterion (DIC) was calculated in MCMC in order to compare the fit and complexity between Bayesian models. SAS macros written in PROC IML were developed to output Bayesian estimates and the DICs for all the models discussed in this dissertation.

Mapping of complex traits has become the main challenge in the field of linkage analysis. To localize a disease gene in a whole-genome study, the traditional testing of linkage signals turns out to be lack of accurate assessment of statistical significance. In this dissertation, we propose to test every position on the chromosome for its possibility of being a disease locus and construct a confidence set for the disease locus by including those positions that are not rejected. Three test statistics are proposed to perform the test, including a LOD and two generalized likelihood ratio test statistics with or without model averaging (GLRT/MA and GLRT). Based on different test statistics, several approaches are developed to estimate the null distribution. Specifically, based on LOD, an integrated procedure is formed by an adaptive procedure and an importance sampling procedure. Asymptotic approaches based on GLRT and GLRT/MA are also proposed as alternatives that are much more efficient computationally but rely on the validity of the limiting distribution of approximation. An importance sampling procedure is also developed based on GLRT (GLRT/IS). Simulation studies show that the integrated procedure tends to give longer confidence intervals with right coverage probability. Both the GLRT asymptotic and the GLRT/IS approaches give actual coverage probability around the nominal one when there are moderate to strong linkage signals. When the signals are weak, the GLRT asymptotic approach tends to undercover the disease locus while the GLRT/IS has the correct coverage. Compared with the 1-LOD support interval, all our methods capture the disease loci to confidence intervals with closer to nominal coverage probability when there are weak to moderate linkage signals. However, when there is strong linkage information, the performance of the 1-LOD method is as good as that of the GLRT/IS. Finally, we apply all the methods to three sets of rheumatoid arthritis data to obtain confidence intervals of the putative disease loci. Both asymptotic methods and the integrated procedure recover the well recognized susceptibility locus, HLA*DRB1, to 99% confidence intervals of around 5-20 cM in length while the 3-LOD method either misses it or gets a longer confidence set.