Horizontal gene transfer (HGT), a mechanism that facilitates exchange of genetic material between organisms from different lineages, has a profound impact on prokaryotic evolution. To infer HGT, we first developed a comparative genomics-based tool, APP, which can perform phyletic pattern analysis using completely sequenced genomes to identify genes are unique to a genome or have sporadic distribution in its close relatives. Performance assessment against currently available tools on a manually created 18-genome dataset and 2 benchmarking datasets revealed the superior accuracy of APP over other methods. We then utilized a parametric method to construct a gene exchange network. The composition-based method, Jenson-Shannon Codon Bias (JS-CB), groups genes into clusters based on similar codon usage bias. These clusters were analyzed using APP and examined for the enrichment HGT associated marker genes, then annotated as of native or alien origin based on these multiple lines of evidence. Intergenome clustering enabled identification of genes mobilized across alien components of the genomes (alien-alien transfer) and from native components of donor genomes to the recipient genomes (native-alien transfer). Functional classification of alien gene clusters revealed that metabolism associated genes are most frequently mobilized, in concurrence with previous reports, and additionally, a large number of genes with yet unknown functions were found to have been horizontally transferred, a important finding that needs to be further investigated.

Graphs are a pervasive data structure in computer science, and algorithms for working with them are fundamental to the field. There are hundreds of interesting computational problems defined in terms of graphs. This time we’ll touch one of them – the problem of maximum net flow. One of the main problems in the theory of graphs is the problem of maximum flow. The purpose is to calculate the biggest amount of matter, which can be relayed from the source to the flow. This is one of the easier tasks, which can be solved with the help of algorithms. Beside that the basic algorithms of maximum flow can be used for solving other problems about net flows. The results of this work are: - created the program equipment, realizing four classic methods of calculating maximum flow – Ford-Falkerson, Edmonds-Karp, Dinic and Goldberg; - with the help of this program equipment is collected the statistic of these methods efficiency. Results showed, that every method can be realized within shorter time as was proved earlier.

Bio-structures owe their remarkable mechanical properties to their hierarchical geometrical arrangement as well as heterogeneous material properties. This dissertation presents an integrated, interdisciplinary approach that employs computational mechanics combined with flow network analysis to gain fundamental insights into the failure mechanisms of high performance, light-weight, structured composites by examining the stress flow patterns formed in the nascent stages of loading for the rostrum of the paddlefish. The data required for the flow network analysis was generated from the finite element analysis of the rostrum. The flow network was weighted based on the parameter of interest, which is stress in the current study. The changing kinematics of the structural system was provided as input to the algorithm that computes the minimum-cut of the flow network. The proposed approach was verified using two classical problems – three- and four-point bending of a simply-supported concrete beam. The current study also addresses the methodology used to prepare data in an appropriate format for a seamless transition from finite element binary database files to the abstract mathematical domain needed for the network flow analysis. A robust, platform-independent procedure was developed that efficiently handles the large datasets produced by the finite element simulations. Results from computational mechanics using Abaqus and complex network analysis are presented. The complex network strategy successfully identified failure mechanisms in the bio-structure by identifying strain localization in regions of tension, and buckling/crushing in regions of compression. The transdisciplinary strategy used in this study identified the failure mechanisms early, when the material was still in the linearly elastic regime, thereby tremendously reducing the computational time and cost as compared to running a finite element analysis to failure. This work also developed five proof-of-concept, bio-inspired models with varying lattice complexity based on the rostrum. Performance of these bio-inspired models was analyzed with respect to the stress and deformation. Numerical experiments were carried out on one of the bio-inspired model to demonstrate the application of newly developed similitude laws for blast loading. This research has laid the groundwork for an efficient design-test-build cycle for rapid prototyping of novel bio-inspired structures by using flow network analysis, finite element analysis, and similitude laws.

A theoretical study has been carried out on different methods available to analyze complex mufflers. Segmentation methods have been discussed in detail. The latest two port segmentation method has been discussed and employed for a few common muffler configurations, describing its implications and limitations. A new transfer matrix based method has been developed in view of the lacunae of the available approaches. This Integrated Transfer Matrix (ITM) method has been developed particularly to analyze complex mufflers. An Integrated transfer matrix relates the state variables across the entire cross-section of the muffler shell, as one moves along the axis of the muffler, and can be partitioned appropriately in order to relate the state variables of different tubes constituting the cross-section. The method presents a 1-D approach, using transfer matrices of simple acoustic elements which are available in the literature. Results from the present approach have been validated through comparisons with the available experimental and three dimensional FEM based results. The total pressure drop across perforated muffler elements has been measured experimentally and generalized expressions have been developed for the pressure loss across cross-flow expansion, cross-flow contraction elements, etc. These have then been used to derive empirical expressions for flow-acoustic resistance for use in the Integrated Transfer Matrix Method in order to predict the flow-acoustic performance of commercial mufflers. A flow resistance model has been developed to analytically determine the flow distribution and thereby pressure drop of mufflers. Generalized expressions for resistance across the perforated elements have been derived by means of flow experiments as mentioned above. The derived expressions have been implemented in a flow resistance network that has been developed to determine the pressure drop across any given complex muffler. The results have been validated with experimental data.

Thermal analysis is an important topic that can affect the electrical machine performance, reliability, lifetime and efficiency. In order to predict the electrical machine thermal performance accurately, thermal analysis of electrical machines must include fluid flow modelling. One of the technologies which may be used to estimate the flow distribution and pressure losses in throughflow ventilated machines is flow network analysis, but suitable correlations that can be used to estimate the pressure losses in rotor ducts due to fluid shock is not available. The aim of this work is to investigate how the rotation affects the pressure losses in rotor ducts by performing a dimensional analysis. Apart from the additional friction loss due to the effects of rotation, other rotational pressure losses that appear in a rotor-stator system are: duct entrance loss due to fluid shock and combining flow loss at the exit of the rotor-stator gap. These losses are analysed using computational fluid dynamics (CFD) methods. The CFD simulations use the Reynolds-averaged Navier Stokes (RANS) approach. An experimental test rig is built to validate the CFD findings. The investigation showed that the CFD results are consistent with the experimental results and the rotational pressure losses correlate well with the rotation ratio (a dimensionless parameter). It shows that the rotational pressure loss generally increases with the increase in the rotation ratio. At certain operating conditions, the rotational pressure loss can contribute over 50 % of the total system loss. The investigation leads to an original set of correlations for the pressure losses in air ducts in the rotor due to fluid shock which are more suitable to be applied to fluid flow modelling of throughflow ventilated machines. Such correlations provide a significant contribution to the field of thermal modelling of electrical machines. They are incorporated into the air flow modelling tool that has been programmed in Portunus by the present author. The modelling tool can be integrated with the existing thermal modelling method, lumped-parameter thermal network (LPTN) to form a complete analytical thermal-fluid modelling method.

<p>Many biological processes are effectively modeled as networks, but a frequent assumption is that these networks do not change during data collection. However, that assumption does not hold for many phenomena, such as neural growth during learning or changes in genetic regulation during cell differentiation. Approaches are needed that explicitly model networks as they change in time and that characterize the nature of those changes.</p><p>In this work, we develop a new class of graphical models in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. We first present the model, explain how to derive it from Bayesian networks, and develop an efficient MCMC sampling algorithm that easily generalizes under varying levels of uncertainty about the data generation process. We then characterize the nature of evolving networks in several biological datasets.</p><p>We initially focus on learning how neural information flow networks change in songbirds with implanted electrodes. We characterize how they change in response to different sound stimuli and during the process of habituation. We continue to explore the neurobiology of songbirds by identifying changes in neural information flow in another habituation experiment using fMRI data. Finally, we briefly examine evolving genetic regulatory networks involved in Drosophila muscle differentiation during development.</p><p>We conclude by suggesting new experimental directions and statistical extensions to the model for predicting novel neural flow results.</p> / Dissertation

In the first two chapters of this thesis, the novel method, developed by Mathews and Richards1, for predicting the thermal performance of buildings is introduced. The further enhancement and theoretical clarification of this method is the objective of this thesis. The method is based on a very simple thermo-flow network which models only the most important aspects of heat-flow in buildings. While Mathews and Richards based their network on analysis of the primary aspects of heat-flow in buildings, this thesis derives the simplified model by reduction from a comprehensive model. In this way, the assumptions and limitations is illuminated and the theoretical foundation of the method can be established. As a result of the investigation, a new simplified model with certain theoretical benefits is suggested. In later chapters, the method is extended and refined. Also, a new calculation procedure for finding solutions of the model is presented. In particular the method is extended to include multi-zone heat-flow, structural storage- and variable thermal systems. The new solution method is powerful, simple and efficient. This thesis contributes to the establishment of a viable tool for thermal analysis of buildings. / Dissertation (MEng)--University of Pretoria, 1990. / Mechanical and Aeronautical Engineering / unrestricted

This dissertation considers sheltering network planning and operations for natural disaster preparedness and responses with a two-stage stochastic program. The first phase of the network design decides the locations, capacities and held resources of new permanent shelters. Both fixed costs for building a new permanent shelter and variable costs based on capacity are considered. Under each disaster scenario featured by the evacuee demand and transportation network condition, the flows of evacuees and resources to shelters, including permanent and temporary ones, are determined in the second stage to minimize the transportation and shortage/surplus costs. Typically, a large number of scenarios are involved in the problem and cause a huge computational burden. The L-shaped algorithm is applied to decompose the problem into the scenario level with each sub-problem as a linear program. The Sheltering Network Planning and Operation Problem considered in this dissertation also has a special structure in the second-stage sub-problem that is a minimum cost network flow problem with equal flow side constraints. Therefore, the dissertation also takes advantages of the network simplex method to solve the response part of the problem in order to solve the problem more efficiently. This dissertation investigates the extending application of special minimum cost equal flow problem. A case study for preparedness and response to hurricanes in the Gulf Coast region of the United States is conducted to demonstrate the usage of the model including how to define scenarios and cost structures. The numerical experiment results also verify the fast convergence of the L-shaped algorithm for the model.

No description available.

Digital elevation models (DEMs) and their derivatives such as slope, flow direction and flow accumulation maps, are used frequently as inputs to hydrologic and nonpoint source modeling. The depressions which are frequently present in DEMs may represent the actual topography, but are often the result of errors. Creating a depression-free surface is commonly required prior to deriving flow direction, flow accumulation, flow network, and watershed boundary maps. The objectives of this study were: 1) characterize the occurrence of depressions in 30m USGS DEMs and assess correlations to watershed topographic characteristics, and 2) compare the performance of two algorithms used to remove depressions and delineate flow networks from DEMs. Sixty-six watersheds were selected to represent a range of topographic conditions characteristic of the Piedmont and Mountain and Valley regions of Virginia. Analysis was based on USGS 30m DEMs with elevations in integer meters. With few exceptions watersheds fell on single 7.5minute USGS quadrangle sheets, ranged in size from 450 to 3000 hectares, and had average slopes ranging from 3 to 20 percent. ArcView (3.1) with the Spatial Analyst (1.1) extension was used to summarize characteristics of each watershed including slope, elevation range, elevation standard deviation, curvature, channel slope, and drainage density. TOPAZ (ver 1.2) and ArcView were each used to generate a depression-free surface, flow network and watershed area. Characteristics of the areas 'cut' and 'filled' by the algorithms were compared to topographic characteristics of the watersheds. Blue line streams were digitized from scanned USGS 7.5minute topographic maps (DRGs) then rasterized at 30 m for analysis of distance from the derived flow networks. The removal of depressions resulted in changes in elevation values in 0 - 11% of the cells in the watersheds. The percentage of area changed was higher in flatter watersheds. Changed elevation cells resulted in changes in two to three times as many cells in derivative flow direction, flow accumulation and slope grids. Mean fill depth by watershed ranged from 0 to 10 m, with maximum fill depths up to 40 m. In comparison with ArcView, TOPAZ, on average affected 30% fewer cells with less change in elevation. The significance of the difference between ArcView and TOPAZ decreased as watershed slope increased. A spatial assessment of the modified elevation and slope cells showed that depressions in the DEMs occur predominantly on or along the flow network. Flow networks derived by ArcView and TOPAZ were not significantly different from blue line streams digitized from the USGS quadrangles as indicated by a paired t test. Watershed area delineated by ArcView and TOPAZ was different for almost all watersheds, but was generally within 1%. Conclusions from this study are: 1) The depressions in 30 m DEMs can make up a significant portion of the area especially for flatter watersheds; 2) The TOPAZ algorithm performed better than ArcView in minimizing the area modified in the process of creating a depressionless surface, particularly in flatter topography; 3) Areas affected by removing depressions are predominantly adjacent to the stream network; 4) For every elevation cell changed, slopes are changed for two to three cells, on average; and 5) ArcView and TOPAZ derived flow networks closely matched the blue line streams. / Master of Science