No description available.

In this thesis, parametric studies have been performed for a soil-steel compositebridge to determine and investigate the most influential parameters on the dynamicresponse.High-speed railways are currently being planned in Sweden by the Swedish TransportAdministration with train speeds up to 320 km/h. According to the European designcodes, bridges must be verified with respect to dynamic resonance behaviour for trainspeeds exceeding 200 km/h. However, there are no guidelines or design criterion forperforming dynamic verifications of soil-steel composite bridges. The aim of thisthesis has therefore been to investigate the influence of the geometry and materialproperties of soil-steel composite bridges on their dynamic response.This thesis is based upon the frequency domain approach for dynamic analysis ofa soil-steel composite bridge using finite element software. In 2018, field measurementswere performed on a soil-steel composite bridge in Hårestorp, Sweden. Areference finite element model was developed based on previous research and wasverified against these field measurements. Parametric studies where performed byextrapolating the geometry of the reference model, focusing primarily on the crownheight, culvert span width and the location of the bedrock. Sensitivity analyses ofthe density- and stiffness of the soil was also performed.The parametric studies showed that the crown height was the most influential parameterwith respect to the amplitude of the resonance peak. Increasing it from 1 mto 3 m reduced the amplitude by approximately 70 %. An increased span width ofthe culvert was found to reduce the frequency and amplitude of the resonance peak,however increasing the stiffness of the culvert increased the resonance frequency.The position of the rock layer also reduced the amplitude of the resonance peak iflowered, likely because of lessened wave reflection. The lowest rock level investigatedshowed a significant decrease of more than 70 % in amplitude. However, the modelused to calculate this response was heavily extrapolated and thus difficult to verify.The sensitivity analyses showed that the soil density- and stiffness was negativelyand positively correlated with the resonance frequency, respectively. Additionally,the soil density lowered the amplitude of the resonance peak if increased.

The main objective of this research is to model and analyse complex electromagnetic problems by means of a new hybridised computational technique combining the frequency domain Method of Moments (MoM), Finite-Difference Time-Domain (FDTD) method and a subgridded Finite-Difference Time-Domain (SGFDTD) method. This facilitates a significant advance in the ability to predict electromagnetic absorption in inhomogeneous, anisotropic and lossy dielectric materials irradiated by geometrically intricate sources. The Method of Moments modelling employed a two-dimensional electric surface patch integral formulation solved by independent linear basis function methods in the circumferential and axial directions of the antenna wires. A similar orthogonal basis function is used on the end surface and appropriate attachments with the wire surface are employed to satisfy the requirements of current continuity. The surface current distributions on structures which may include closely spaced parallel wires, such as dipoles, loops and helical antennas are computed. The results are found to be stable and showed good agreement with less comprehensive earlier work by others. The work also investigated the interaction between overhead high voltage transmission lines and underground utility pipelines using the FDTD technique for the whole structure, combined with a subgridding method at points of interest, particularly the pipeline. The induced fields above the pipeline are investigated and analysed. FDTD is based on the solution of Maxwell¿s equations in differential form. It is very useful for modelling complex, inhomogeneous structures. Problems arise when open-region geometries are modelled. However, the Perfectly Matched Layer (PML) concept has been employed to circumvent this difficulty. The establishment of edge elements has greatly improved the performance of this method and the computational burden due to huge numbers of time steps, in the order of tens of millions, has been eased to tens of thousands by employing quasi-static methods. This thesis also illustrates the principle of the equivalent surface boundary employed close to the antenna for MoM-FDTD-SGFDTD hybridisation. It depicts the advantage of using hybrid techniques due to their ability to analyse a system of multiple discrete regions by employing the principle of equivalent sources to excite the coupling surfaces. The method has been applied for modelling human body interaction with a short range RFID antenna to investigate and analyse the near field and far field radiation pattern for which the cumulative distribution function of antenna radiation efficiency is presented. The field distributions of the simulated structures show reasonable and stable results at 900 MHz. This method facilitates deeper investigation of the phenomena in the interaction between electromagnetic fields and human tissues. / Ministry of Higher Education Malaysia and Universiti Tun Hussein Onn Malaysia (UTHM)

Functional analyses have gained more interest as we have easier access to massive data sets. However, such data sets often contain large heterogeneities, noise, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model, or developed from a more generic one by changing the prior distributions. Hence, this dissertation focuses on the development of Bayesian approaches for functional analyses due to their flexibilities. A nonparametric Bayesian approach, such as the Dirichlet process mixture (DPM) model, has a nonparametric distribution as the prior. This approach provides flexibility and reduces assumptions, especially for functional clustering, because the DPM model has an automatic clustering property, so the number of clusters does not need to be specified in advance. Furthermore, a weighted Dirichlet process mixture (WDPM) model allows for more heterogeneities from the data by assuming more than one unknown prior distribution. It also gathers more information from the data by introducing a weight function that assigns different candidate priors, such that the less similar observations are more separated. Thus, the WDPM model will improve the clustering and model estimation results. In this dissertation, we used an advanced nonparametric Bayesian approach to study functional variable selection and functional clustering methods. We proposed 1) a stochastic search functional selection method with application to 1-M matched case-crossover studies for aseptic meningitis, to examine the time-varying unknown relationship and find out important covariates affecting disease contractions; 2) a functional clustering method via the WDPM model, with application to three pathways related to genetic diabetes data, to identify essential genes distinguishing between normal and disease groups; and 3) a combined functional clustering, with the WDPM model, and variable selection approach with application to high-frequency spectral data, to select wavelengths associated with breast cancer racial disparities. / Doctor of Philosophy / As we have easier access to massive data sets, functional analyses have gained more interest to analyze data providing information about curves, surfaces, or others varying over a continuum. However, such data sets often contain large heterogeneities and noise. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this dissertation focuses on the development of nonparametric Bayesian approaches for functional analyses. Our proposed methods can be applied in various applications: the epidemiological studies on aseptic meningitis with clustered binary data, the genetic diabetes data, and breast cancer racial disparities.

Perfectly matched layer (PML) is a commonly used method of absorbing waves at a computational boundary for partial differential equation (PDE) problems. In this thesis, methods for improving the usability of implementations in Comsol Multiphysics is addressed. The study looks at complex coordinate stretching PMLs in the context of Helmholtz equation using the finite element method (FEM). For a PML to work it has to be set up properly with parameters that takes into account the properties of the problem. It is not always straight forward. Some theory behind PMLs is presented and experimentation on PML properties performed. Methods for PML optimization and adaption is presented. Currently, the way PMLs is applied in COMSOL Multiphysics requires the user to perform many tasks; setting up a geometry, meshing and choosing a suitable complex coordinate stretching. Using a so-called extra-dimension implementation it is possible to attach PMLs as boundary conditions in COMSOL Multiphysics. This simplifies for the user since the geometry and mesh is handled by the software. / Perfectly matched layer (PML) är en metod som ofta används för vågabsorbering vid randen för problem med partiella differentialekvationer (PDE). I det här examensarbetet presenteras metoder som förenklar användingen av PMLer i COMSOL Multiphysics. Studien kollar på PMLer baserade på komplex-koordinatsträckning med fokus på Helmholtz ekvation och finita elementmetoden (FEM). För att en PML ska fungera måste den sättas upp på rätt sätt med parametrar anpassade efter det givna problemet. Att göra detta är inte alltid enkelt. Teori presenteras och experiment på PMLer görs. Flera metoder för optimisering och adaption av PMLer presenteras. I nuläget kräver appliceringen av PMLer i COMSOL Multiphysics att användaren sätter upp en geometri, ett beräkningsnät och väljer den komplexa koordinatsträckningen. Genom att använda COMSOLs implementation av extra dimensioner är det möjligt att applicera PMLer som randvilkor. I en sådan implementation kan geometri och beräkningsnät skötas av mjukvaran vilket underlättar för användaren.

This thesis concerns dynamic Soil-Structure Interaction (SSI) analysis of portal framerailway bridges. Dynamic problems are common for bridges used for high speedrailway traffic. The passing trains induce harmonic loads on the bridges causingvibration amplitudes that may cause damage to the bridge structures and userdiscomfort.Previous studies have shown that the effects of SSI are substantial for short spanportal frame bridges. The damping ratio of the system is greatly increased due to theenergy dissipation properties of the surrounding soil causing significant changes in thedynamic response of the structure. Therefore, it is of interest to investigate the effectsof SSI for portal frame bridges with longer spans.Two case study bridges with span lengths of approximately 16m have been investigatedin detail in this study. Dynamic analyses of the bridges and train passage simulationshave been performed. The results show that SSI significantly increases the dampingratio which leads to lower vibration amplitudes. It is also possible to draw theconclusion that more accurate results are achieved when modeling fixed foundationsrather than using static spring foundations to replicate the stiffness of the subsoil.Moreover, a simplified modeling approach accounting for the effects of SSI is proposed.The proposed method provides satisfactory results, but more future work may increasethe quality of the results further. To validate the conclusions from this study, a proposalfor experimental validation is presented. Performing full-scale dynamic tests on thestudied bridges would enable further comparison and validation of the results.

A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing those complex data in this dissertation. We have then provided several contributions to semiparametric and nonparametric methods for dealing with the following problems: the first is to propose a method for testing the significance of a functional association under the matched study; the second is to develop a method to simultaneously identify important variables and build a network in HDHC data; the third is to propose a multi-class dynamic model for recognizing a pattern in the time-trend analysis. For the first topic, we propose a semiparametric omnibus test for testing the significance of a functional association between the clustered binary outcomes and covariates with measurement error by taking into account the effect modification of matching covariates. We develop a flexible omnibus test for testing purposes without a specific alternative form of a hypothesis. The advantages of our omnibus test are demonstrated through simulation studies and 1-4 bidirectional matched data analyses from an epidemiology study. For the second topic, we propose a joint semiparametric kernel machine network approach to provide a connection between variable selection and network estimation. Our approach is a unified and integrated method that can simultaneously identify important variables and build a network among them. We develop our approach under a semiparametric kernel machine regression framework, which can allow for the possibility that each variable might be nonlinear and is likely to interact with each other in a complicated way. We demonstrate our approach using simulation studies and real application on genetic pathway analysis. Lastly, for the third project, we propose a Bayesian focal-area detection method for a multi-class dynamic model under a Bayesian hierarchical framework. Two-step Bayesian sequential procedures are developed to estimate patterns and detect focal intervals, which can be used for gas chromatography. We demonstrate the performance of our proposed method using a simulation study and real application on gas chromatography on Fast Odor Chromatographic Sniffer (FOX) system. / Doctor of Philosophy / A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing the following three types of data: (1) matched case-crossover data, (2) HCHD data, and (3) Time-series data. We contribute to the development of statistical methods to deal with such complex data. First, under the matched study, we discuss an idea about hypothesis testing to effectively determine the association between observed factors and risk of interested disease. Because, in practice, we do not know the specific form of the association, it might be challenging to set a specific alternative hypothesis. By reflecting the reality, we consider the possibility that some observations are measured with errors. By considering these measurement errors, we develop a testing procedure under the matched case-crossover framework. This testing procedure has the flexibility to make inferences on various hypothesis settings. Second, we consider the data where the number of variables is very large compared to the sample size, and the variables are correlated to each other. In this case, our goal is to identify important variables for outcome among a large amount of the variables and build their network. For example, identifying few genes among whole genomics associated with diabetes can be used to develop biomarkers. By our proposed approach in the second project, we can identify differentially expressed and important genes and their network structure with consideration for the outcome. Lastly, we consider the scenario of changing patterns of interest over time with application to gas chromatography. We propose an efficient detection method to effectively distinguish the patterns of multi-level subjects in time-trend analysis. We suggest that our proposed method can give precious information on efficient search for the distinguishable patterns so as to reduce the burden of examining all observations in the data.

Background: Non-disclosure of HIV positive status to a partner threatens to reverse gains made in prevention of mother-to-child transmission (PMTCT) in resource limited settings. Determining the association between non-disclosure and infant HIV acquisition is important to justify focussing on disclosure as a strategy in PMTCT programmes. Objective: To determine the association between non-disclosure of HIV positive status to a partner and mother-to-child transmission (MTCT). Methods: Using a matched case-control design, we compared 34 HIV positive infants to 146 HIV negative infants and evaluated whether the mothers had disclosed their HIV status to their partner. Results: Non-disclosure was more frequent among cases (overall, 16.7%; cases, 52.8%; controls 7.6%), p<0.001 and significantly associated with MTCT (aOR 8.9 (3.0-26.3); p<0.0001), with male partner involvement partially mediating the effect of non-disclosure on MTCT. Conclusions: There is a need for PMTCT programs to focus on strategies to improve male partner involvement and partner disclosure without compromising the woman’s safety. / Health Studies / M. (Public Health)

We discuss a full-waveform based material profile reconstruction in two-dimensional heterogeneous semi-infinite domains. In particular, we try to image the spatial variation of shear moduli/wave velocities, directly in the time-domain, from scant surficial measurements of the domain's response to prescribed dynamic excitation. In addition, in one-dimensional media, we try to image the spatial variability of elastic and attenuation properties simultaneously. To deal with the semi-infinite extent of the physical domains, we introduce truncation boundaries, and adopt perfectly-matched-layers (PMLs) as the boundary wave absorbers. Within this framework we develop a new mixed displacement-stress (or stress memory) finite element formulation based on unsplit-field PMLs for transient scalar wave simulations in heterogeneous semi-infinite domains. We use, as is typically done, complex-coordinate stretching transformations in the frequency-domain, and recover the governing PDEs in the time-domain through the inverse Fourier transform. Upon spatial discretization, the resulting equations lead to a mixed semi-discrete form, where both displacements and stresses (or stress histories/memories) are treated as independent unknowns. We propose approximant pairs, which numerically, are shown to be stable. The resulting mixed finite element scheme is relatively simple and straightforward to implement, when compared against split-field PML techniques. It also bypasses the need for complicated time integration schemes that arise when recent displacement-based formulations are used. We report numerical results for 1D and 2D scalar wave propagation in semi-infinite domains truncated by PMLs. We also conduct parametric studies and report on the effect the various PML parameter choices have on the simulation error. To tackle the inversion, we adopt a PDE-constrained optimization approach, that formally leads to a classic KKT (Karush-Kuhn-Tucker) system comprising an initial-value state, a final-value adjoint, and a time-invariant control problem. We iteratively update the velocity profile by solving the KKT system via a reduced space approach. To narrow the feasibility space and alleviate the inherent solution multiplicity of the inverse problem, Tikhonov and Total Variation (TV) regularization schemes are used, endowed with a regularization factor continuation algorithm. We use a source frequency continuation scheme to make successive iterates remain within the basin of attraction of the global minimum. We also limit the total observation time to optimally account for the domain's heterogeneity during inversion iterations. We report on both one- and two-dimensional examples, including the Marmousi benchmark problem, that lead efficiently to the reconstruction of heterogeneous profiles involving both horizontal and inclined layers, as well as of inclusions within layered systems. / text

This thesis explores the links between different ways of modelling the physical world. Finite difference numerical simulations may be used to encode the behaviour of physical systems, allowing us to gain insight into their workings and even to predict their behaviour. Similarly, one can investigate the properties of gravitational black holes through the use of analogue black holes, physical systems which share at least some part of the physics of the astronomical objects. Concentrating on black hole analogues using Bose-Einstein condensates, I show how simulations of these systems may be greatly assisted through the use of a proper absorbing boundary condition, the Perfectly Matched Layer. Such a boundary condition allows the effcient truncation of the computational domain, both saving computational time and increasing accuracy. I then apply this technique to the simulation of the supersonic flow of a Bose-Einstein condensate through a Laval nozzle, a black hole analogue, showing that such a flow should be stable and observable in the laboratory. Moving to a related system, I investigate the optical analogue of the Iordanskii force - the friction resulting from interaction between excitations in a superfluid's normal component and a superfluid vortex - through the simulation of such a vortex in a Bose-Einstein condensate illuminated by slow light, which is light whose group velocity is on the order of metres per second. The interaction of the slow light with the vortex should produce a momentum transfer due to the optical Aharonov-Bohm effect, exerting a force on the vortex. The coupled system of equations describing the condensate-slow light system is simulated, giving some surprising results.