Mass spectrometry has increasingly become the method of choice for the analysis of complex biological samples, including proteins and lipids. This thesis describes the development of MS-based analytical methodologies for the analysis of complex proteomic and lipidomic samples. Chapter 3 describes the development of microfluidic proteomic reactors, in the formats of SCX reactor, SCX 96-well plate reactor, and SAX reactor, for the enzymatic digestion of complex proteomic samples for subsequent LC-MS/MS analysis. These microfluidic proteomic reactors greatly simplified the enzymatic digestion of complex proteomic samples by combining multiple processing steps, such as rapid extraction and enrichment of proteins. Furthermore, chemical and enzymatic treatments of proteins were all performed in a few nanoliters effective volume, resulting in an increased protein digestion efficacy. After the protein digestion process, the resulting peptides were eluted in buffers that were compatible with HPLC-MS/MS analysis. In chapter 4, a methodology based on nano-HPLC-ESI-MS/MS for the analysis of PAF and LPC lipid species is described. In this method, lipid extracts from biological samples were separated by nano-flow HPLC prior to being introduced into a Q-TRAP 2000 mass spectrometer, where the lipid species of interest were detected using a precursor ion scan at m/z 184. Absolute quantitation of PAF family lipid species were performed with standard addition method, where 5 standard solutions containing 0.2-1 ng each of C16:0, C18:0 PAF and C16:0, C18:0 lyso-PAF were used in the experiment. Further, the spiking of identical amount of non-endogenous C13:0 LPC at time of extraction allow the relative comparisons of other LPC lipid species of interest between different samples. The developed methods were employed to analyze the changes of PAF and LPC lipid species in NGFdifferentiated PC12 cells, in the posterior/entorhinal cortex of AD patients and TgCRND8 transgenic mice, and over the course of 24 hour exposure of human hNT neurons to Aβ42 treatment, respectively, in comparison to controls. iii Chapter 5 describes the development of a novel shotgun lipidomic methodology for the determination of stereospecificity of diacyl glycerophospholipids including glycerophosphatidic acids (PA), glycerophosphoserines (PS), glycerophosphoglycerols (PG), glycerophosphoinositols(PI), and glycerophosphoethanolamines (PE), which can be conventionally ionized under negative ion mode. The stereospecificity of diacyl glycerophospholipids was determined based on the relative abundance of the lyso-form fragment ions, attributed to the neutral loss of fatty acyl moieties. The fragmentation patterns of a variety of diacyl glycerophospholipid standards were first fully examined over a wide range of collision energy. We observed that lyso-form fragment ions corresponding to the neutral loss of fatty acyl moieties attached to the sn2 position as free fatty acids ([M-Sn2]-) and as ketenes ([M-(Sn2-H2O)]-) exhibited consistently higher intensity than their counter part ions due to the neutral loss of fatty acyl moieties attached to the sn1 position ([M-Sn1]- and [M-(Sn1-H2O)]-). We then examined the product ion spectra of diacyl glycerophospholipids recorded from lipid extracts of rat hepatoma cells, where the stereospecific information of these lipids was conclusively determined.

Construction delays are one of the biggest issues facing the construction industry and affecting delivery in terms of time, budget and the required quality. The characteristics of delay factors and their level of impact vary from project to project, ranging from a few days to years. They have significant financial, environmental and social impacts in construction projects; therefore, it is vital to investigate the causes of delay and analyse their impact. In this context, the research study was initiated to develop a new methodology for analysing and quantifying the impacts of delay factors on construction projects. A comprehensive literature survey was conducted to build up general background knowledge of delay factors in construction projects and particular attention was paid to identifying the potential differences in delay factors between Libya and the UK. A construction industry survey was conducted through a semi-structured questionnaire amongst contractors, consultants and owners. A total of 116 out of 300 responses (38.66%) were received from both countries. Statistical tests including T-test and Wilkinson rank test were executed to analyse the responses and present the findings from the survey. Following the findings from the literature review and an industry survey, a framework of Delay Analysis System (DAS) augmented with simulation model was developed by integrating the importance weight (IW) of each delay factor associated with critical activities using @risk tool. The key function of the system is the flexibility to analyse and quantify the impact in project duration, considering the IW of each delay factor independently. The system was evaluated through two case studies from building projects in Libya using the developed system. The analysis of case study 1 using DAS found that the building project might be delayed by 97 to 103 days when considering the delay factors identified from Libya whereas the project might be delayed by 80 to 85 days when considering the delay factors identified from the UK. The evaluation results from the case study revealed that the impact of delay factors in Libya is higher than in the UK. This confirms that the impact of delay in construction projects is higher in developing countries than in developed ones and varies from project to project in the same country. Finally, it is concluded that the system is a decision-supporting tool that helps to make government departments and decision-makers aware of the significance of delays in construction projects in terms of economic growth and the development processes. The key contribution of this study is the development of a strategy (delay analysis system) for analysing the impact of delay factors in the Libyan construction industry through better investigated, understood and documented reports. The system is expected to help policymakers, decision-makers and others stakeholders within the construction industry to gain a fuller understanding of the industry and to formulate short- and long-term construction strategies and policies that aim to improve the industry’s processes and operations.

L'objectif de ce travail était de développer des méthodes pour permettre une évaluation in vivo plus robuste du réseau vasculaire dans la tumeur par imagerie de contraste ultrasonore. Trois aspects de l'analyse de donnée ont été abordé: 1) la régression des modèles paramétriques de flux sur les données de puissance linéaire, 2) la compensation du mouvement 3) l’évaluation d’une méthode de clustering pour identifier les hétérogénéités dans les tumeurs. Un modèle multiplicatif est proposé pour décrire le signal DCE-US. Une méthode de régression en est dérivée. La caractérisation du signal permet la mise au point d’une méthode de simulation de séquences 2D+T. La méthode de régression permet une diminution de la variabilité des paramètres de flux fonctionnels extraits, sur données simulées expérimentales. La méthode de simulation est appliquée pour évaluer une méthode combinant estimation du mouvement et estimations des paramètres micro-vasculaires dans un unique problème mathématique d'optimisation. Cette nouvelle méthode présente en plus l'avantage d'être indépendante de l'opérateur. Il est montré que dans une large majorité des cas l'estimation du mouvement est meilleure avec la nouvelle méthode qu'avec une méthode de références. Une méthode de clustering est adaptée et évaluée sur données DCE-US simulées et in-vivo. Elle permet de détecter des hétérogénéités dans la structure vasculaire des tumeurs. Les méthodes développées permettent d'améliorer l’évaluation du réseau microvasculaire par DCE-US grâce à une description rigoureuse du signal, à la mise au point d'outil diminuant l'intervention de l'opérateur et la prise en compte de l'hétérogénéité du réseau vasculaire. / This work aimed to develop methods to robustly evaluate in vivo functional flow within the tumor vascular network with Dynamic contrast-enhanced ultrasound (DCE-US). Three aspects of data analysis were addressed: 1) insuring best fit between parametric flow models and the experimentally acquired echo-power curves, 2) compensating sequences for motion and 3) evaluating a method to discriminate between tissues with different functional flow. A multiplicative model is proposed to describe the DCE-US signal. Based on this model, a new parametric regression method of the signal is derived. Characterization of the statistical properties of the noise and signal is also used to develop a new method simulating contrast-enhanced ultrasound 2D+t sequences. A significant decrease in the variability of the functional flow parameters extracted according to the new multiplicative-noise fitting method is demonstrated using both simulated and experimentally-acquired sequences. The new sequence simulations are applied to test a method combining motion estimation and flow-parameter estimation within a single mathematical framework. Because this new method does not require the selection of a reference image, it reduces operator intervention. Tests of the method on both simulations and clinical data and demonstrate in a majority of sequences a more accurate motion estimation than the commonly used image registration method. Finally, a non-parametric method for perfusion curve clustering is evaluated on 2D+t sequences. The aim of this method is to regroup similar filling patterns without a priori knowledge about the patterns. The method is tested on simulated and on pre-clinical data.

On construit de nouvelles connexions quantiques intégrables dans fibrés vectoriels au-dessus d'espaces de modules de surfaces de Riemann et de leurs généralisations sauvages, en utilisant deux approches différentes. Premièrement, on utilise la quantification par déformation pour construire de nouvelles connexions intégrables à partir d'Hamiltoniennes d'isomonodromie irrégulières, dans l'esprit de Reshetikhin de la dérivation de la connexion de Knizhnik-Zamolodchikov à partir des Hamiltoniennes de Schlesinger. Deuxièmement, on construit une version complexe de la connexion de Hitchin pour la quantification géométrique de l'espace de modules de Hitchin sur une surface de genre un, par rapport au groupe SL(2,C) et à des polarisations Kähleriennes, en complémentant l'approche par polarisations réelles de Witten. Finalement, on utilise la transformée de Bargmann pour dériver une formule pour la connexion de Hitchin-Witten dans le fibré vectoriel des sections holomorphes, et pour transformer l'action de Hitchin en une transformée sur l'espace de Segal--Bargmann, basée sur les états cohérents. / We construct new flat quantum connections on vector bundles over moduli spaces of Riemann surfaces and their wild generalisations, using two different approaches. Firstly, we use deformation quantisation to construct new flat connections from irregular isomonodromy Hamiltonians, in the spirit of Reshetikhin's derivation of the Knizhnik-Zamolodchikov connection from the Schlesinger Hamiltonians. Secondly, we construct a complex version of the Hitchin connection for the geometric quantisation of the Hitchin moduli space over a surface of genus one, with respect to the group SL(2,C) and to Kähler polarisations, complementing Witten's real polarisation approach. Finally, we use the Bargmann transform to derive a formula for the connection of Hitchin-Witten on the vector bundle of holomorphic sections, and to turn Hitchin's action into a transform on the Segal--Bargmann space, which relies on coherent states.

abstract: This study investigates several students’ interpretations and meanings for negations of various mathematical statements with quantifiers, and how their meanings for quantified variables impact their interpretations and denials of these quantified statements. Eight students participated in three separate exploratory teaching interviews and were selected from Transition-to-Proof and advanced mathematics courses beyond Transition-to-Proof. In the first interview, students were asked to interpret mathematical statements from Calculus contexts and provide justifications and refutations for why these statements are true or false in particular situations. In the second interview, students were asked to negate the same set of mathematical statements. Both sets of interviews were analyzed to determine students’ meanings for the quantified variables in the statements, and then these meanings were used to determine how students’ quantifications influenced their interpretations, denials, and evaluations for the quantified statements. In the final interview, students were also be asked to interpret and negation statements from different mathematical contexts. All three interviews were used to determine what meanings comprised students’ interpretations and denials for the given statements. Additionally, students’ interpretations and negations across different statements in the interviews were analyzed and then compared within students and across students to determine if there were differences in student denials across different moments. / Dissertation/Thesis / Doctoral Dissertation Mathematics Education 2020

Master of Science

Reservoir history matching is becoming increasingly important with the growing demand for higher quality formation characterization and forecasting and the increased complexity and expenses for modern hydrocarbon exploration projects. History matching has long been dominated by adjusting reservoir parameters based solely on well data whose spatial sparse sampling has been a challenge for characterizing the flow properties in areas away from the wells. Geophysical data are widely collected nowadays for reservoir monitoring purposes, but has not yet been fully integrated into history matching and forecasting fluid flow. In this thesis, I present a pioneering approach towards incorporating different time-lapse geophysical data together for enhancing reservoir history matching and uncertainty quantification. The thesis provides several approaches to efficiently integrate multiple geophysical data, analyze the sensitivity of the history matches to observation noise, and examine the framework’s performance in several settings, such as the Norne field in Norway. The results demonstrate the significant improvements in reservoir forecasting and characterization and the synergy effects encountered between the different geophysical data. In particular, the joint use of electromagnetic and seismic data improves the accuracy of forecasting fluid properties, and the usage of electromagnetic data has led to considerably better estimates of hydrocarbon fluid components. For volatile oil and gas reservoirs the joint integration of gravimetric and InSAR data has shown to be beneficial in detecting the influx of water and thereby improving the recovery rate. Summarizing, this thesis makes an important contribution towards integrated reservoir management and multiphysics integration for reservoir history matching.

Energy use around the world will rise in the coming decades. Renewable energy sources will help meet this demand, but renewable sources suffer from intermittency, uncontrollable power supply, geographic limitations, and other issues. Many of these issues can be mitigated by introducing energy storage technologies. These technologies facilitate load following and can effectively time-shift power. This analysis compares dedicated and synergistic energy storage technologies using energy efficiency as the primary metric. Energy storage will help renewable sources come to the grid, but fossil fuels still dominate energy sources for decades to come in nearly all projections. Carbon capture technologies can significantly reduce the negative environmental impact of these power plants. There are many carbon capture technologies under development. This analysis considers both the innovative and relatively new cryogenic carbon capture™ (CCC) process and more traditional solvent-based systems. The CCC process requires less energy than other leading technologies while simultaneously providing a means of energy storage for the power plant. This analysis shows CCC is effective as a means to capture CO2 from coal-fired power plants, natural-gas-fired power plants, and syngas production plants. Statistical analysis includes two carbon capture technologies and illustrates how uncertainty quantification (UQ) provides error bars for simulations. UQ provides information on data gaps, uncertainties for property models, and distributions for model predictions. In addition, UQ results provide a discrepancy function that can be introduced into the model to provide a better fit to data and better accuracy overall.

Ignition delay time, τ_ign, is a key quantity of interest that is used to assess the predictability of a chemical kinetic model. This dissertation explores the sensitivity of τ_ign to uncertainties in: 1. rate-rule kinetic rates parameters and 2. enthalpies and entropies of fuel and fuel radicals using global and local sensitivity approaches. We begin by considering variability in τ_ign to uncertainty in rate parameters. We consider a 30-dimensional stochastic germ in which each random variable is associated with one reaction class, and build a surrogate model for τ_ign using polynomial chaos expansions. The adaptive pseudo-spectral projection technique is used for this purpose. First-order and total-order sensitivity indices characterizing the dependence of τ_ign on uncertain inputs are estimated. Results indicate that τ_ign is mostly sensitive to variations in four dominant reaction classes. Next, we develop a thermodynamic class approach to study variability in τ_ign of n-butanol due to uncertainty in thermodynamic properties of species of interest, and to define associated uncertainty ranges. A global sensitivity analysis is performed, again using surrogates constructed using an adaptive pseudo-spectral method. Results indicate that the variability of τ_ign is dominated by uncertainties in the classes associated with peroxy and hydroperoxide radicals. We also perform a combined sensitivity analysis of uncertainty in kinetic rates and thermodynamic properties which revealed that uncertainties in thermodynamic properties can induce variabilities in ignition delay time that are as large as those associated with kinetic rate uncertainties. In the last part, we develop a tangent linear approximation (TLA) to estimate the sensitivity of τ_ign with respect to individual rate parameters and thermodynamic properties in detailed chemical mechanisms. Attention is focused on a gas mixture reacting under adiabatic, constant-volume conditions. The proposed approach is based on integrating the linearized system of equations governing the evolution of the partial derivatives of the state vector with respect to individual random variables, and a linearized approximation is developed to relate ignition delay sensitivity to scaled partial derivatives of temperature. The computations indicate that TLA leads to robust local sensitivity predictions at a computational cost that is order-of-magnitude smaller than that incurred by finite-difference approaches.

<p> </p> <p>In the mechanical design stage, engineers always meet with uncertainty, such as random</p> <p>variables, stochastic processes, and random processes. Due to the uncertainty, products may</p> <p>behave randomly with respect to time and space, and this may result in a high probability of failure,</p> <p>low lifetime, and low robustness. Although extensive research has been conducted on the</p> <p>component reliability methods, time- and space-dependent system reliability methods are still</p> <p>limited. This dissertation is motivated by the need of efficient and accurate methods for addressing</p> <p>time- and space-dependent system reliability and probabilistic design problems.</p> <p>The objective of this dissertation is to develop efficient and accurate methods for reliability</p> <p>analysis and design. There are five research tasks for this objective. The first research task develops</p> <p>a surrogate model with an active learning method to predict the time- and space-independent</p> <p>system reliability. In the second research task, the time- and space-independent system reliability</p> <p>is estimated by the second order saddlepoint approximation method. In the third research task, the</p> <p>time-dependent system reliability is addressed by an envelope method with efficient global</p> <p>optimization. In the fourth research task, a general time- and space-dependent problem is</p> <p>investigated. The envelope method converts the time- and space-dependent problem into time- and</p> <p>space-independent one, and the second order approximation is used to predict results. The last task</p> <p>proposes a new sequential reliability-based design with the envelope method for time- and spacedependent</p> <p>reliability. The accuracy and efficiency of our proposed methods are demonstrated</p> <p>through a wide range of mathematics problems and engineering problems.</p>