3,851 research outputs found
Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange
Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy.
In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur.
In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease
Hybrid BEM-FEM for 2D and 3D dynamic soil-structure interaction considering arbitrary layered half-space and nonlinearities
Experiences and studies have shown that soil-structure interaction (SSI) effect has a vital role in the dynamic behaviour of a soil-structure system. Despite this, analyses involving dynamic SSI are still challenging for practicing engineers due to their complexity and accessibility. In this thesis, the hybrid BEM-FEM implementation is aimed at practicality by combining commercial software and an in-house code. The pre-processing task can be performed under one graphical environment, and it is enhanced with the capability to compute different types of dynamic sources and other improvements to increase its efficiency, accuracy, and modeling flexibility. Further, the underlying soil is commonly a layered profile with arbitrary geometries. Most existing solutions solve the problem through simplification of the geometry and pattern. One of the main contributions in this thesis is the development of layer-wise condensation method to solve these cases using hybrid BEM-FEM. The method significantly reduces the computational memory requirement. Another challenge in the dynamic SSI addressed in this work is the consideration of secondary nonlinearities. Existing solutions using the time domain BEM and iterative hybrid method are computationally costly, and implementation of such a hybrid method on commercial software is tedious. The solution to address this case using a sequential frequency-time domain procedure is presented. The relatively simple approach makes it possible to consider the nonlinearities in the simulation without using the time domain BEM and without requiring additional iterations. Case studies demonstrating the application of the enhanced hybrid method are presented including cases of bridges, containment structures, and a 3D multi-storey structure under point source and double-couple sources. These case studies illustrate the role of following critical factors such as the site effect, inhomogeneity, and nonlinearities
Markov field models of molecular kinetics
Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions
Emerging Power Electronics Technologies for Sustainable Energy Conversion
This Special Issue summarizes, in a single reference, timely emerging topics related to power electronics for sustainable energy conversion. Furthermore, at the same time, it provides the reader with valuable information related to open research opportunity niches
Bending fluctuations in semiflexible, inextensible, slender filaments in Stokes flow: towards a spectral discretization
Semiflexible slender filaments are ubiquitous in nature and cell biology,
including in the cytoskeleton, where reorganization of actin filaments allows
the cell to move and divide. Most methods for simulating semiflexible
inextensible fibers/polymers are based on discrete (bead-link or blob-link)
models, which become prohibitively expensive in the slender limit when
hydrodynamics is accounted for. In this paper, we develop a novel
coarse-grained approach for simulating fluctuating slender filaments with
hydrodynamic interactions. Our approach is tailored to relatively stiff fibers
whose persistence length is comparable to or larger than their length, and is
based on three major contributions. First, we discretize the filament
centerline using a coarse non-uniform Chebyshev grid, on which we formulate a
discrete constrained Gibbs-Boltzmann equilibrium distribution and overdamped
Langevin equation. Second, we define the hydrodynamic mobility at each point on
the filament as an integral of the Rotne-Prager-Yamakawa kernel along the
centerline, and apply a spectrally-accurate quadrature to accurately resolve
the hydrodynamics. Third, we propose a novel midpoint temporal integrator which
can correctly capture the Ito drift terms that arise in the overdamped Langevin
equation. We verify that the equilibrium distribution for the Chebyshev grid is
a good approximation of the blob-link one, and that our temporal integrator
samples the equilibrium distribution for sufficiently small time steps. We also
study the dynamics of relaxation of an initially straight filament, and find
that as few as 12 Chebyshev nodes provides a good approximation to the dynamics
while allowing a time step size two orders of magnitude larger than a resolved
blob-link simulation. We conclude by studying how bending fluctuations aid the
process of bundling in cross-linked networks of semiflexible fibers
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Multivolume devices, kits and related methods for quantification and detection of nucleic acids and other analytes
Provided are devices comprising multivolume analysis regions, the devices being capable of supporting amplification, detection, and other processes. Also provided are related methods of detecting or estimating the presence nucleic acids, viral levels, and other biological markers of interest
Numerical Analysis of Lithium-ion Battery Thermal Management System Towards Fire Safety Improvement
The development of alternative energy sources aims to tackle the energy crisis and climate change. Due to the intermittent nature of renewable energy, energy storage systems find antidotes to the current flaws for ensuring a stable and consistent power supply and reducing our reliance on fossil fuels. Lithium-ion batteries are the most used energy storage unit and have been applied in many fields, such as portable devices, building infrastructure, automotive industries, etc. Nevertheless, there remain significant safety concerns and fire risks. Thus, this has created much interest particularly in developing a comprehensive numerical tool to effectively assess the thermal behaviour and safety performance of battery thermal management systems (BTMs).
In this thesis, a modelling framework was built by integrating the artificial neural network model with the computational fluid dynamics analysis. This includes (i) a comparison of natural ventilation and forced air cooling under various ambient pressures; (ii) an analysis of thermal behaviour and cooling performance with different ambient temperatures and ventilation velocities; and (iii) optimisation of battery pack layout for enhancing the cooling efficiency and reducing the risks of thermal runaway and fire outbreak. The optimal battery design achieved a 1.9% decrease in maximum temperature and a 4.5% drop in temperature difference. Moreover, this thesis delivered an overall review of BTMs employing machine learning (ML) techniques and the application of various ML models in battery fire diagnosis and early warning, which brings new insights into BTMs design and anticipates further smart battery systems. In addition, the battery thermal propagation effect under various abnormal heat generation locations was demonstrated to investigate several stipulating thermal propagation scenarios for enhancing battery thermal performances. The results indicated that various abnormal heat locations disperse heat to the surrounding coolant and other cells, affecting the cooling performance of the battery pack.
The feasibility of compiling all pertinent information, including battery parameters and operation conditions, was studied in this thesis since ML models can build non-related factors relationships. The integrated numerical model offers a promising and efficient tool for simultaneously optimising multiple factors in battery design and facilitates a constructive understanding of battery performance and potential risks
Modeling and Simulation in Engineering
The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering
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