Paleo data-model comparison is the process of comparing output from model simulations of past periods with paleoenvironmental data. It enables us to understand both the paleoclimate mechanism and responses of the earth environment to the climate and to evaluate how models work. This dissertation has two parts that each involve the development and application of approaches for data-model comparisons. In part 1, which is focused on the understanding of both past and future climatic changes/variations, I compare paleoclimate and historical simulations with future climate projections exploiting the fact that climate-model configurations are exactly the same in the paleo and future simulations in the Coupled Model Intercomparison Project Phase 5. In practice, I investigated large-scale temperature responses (land-ocean contrast, high-latitude amplification, and change in temperature seasonality) in paleo and future simulations, found broadly consistent relationships across the climate states, and validated the responses using modern observations and paleoclimate reconstructions. Furthermore, I examined the possibility that a small set of common mechanisms controls the large-scale temperature responses using a simple energy-balance model to decompose the temperature changes shown in warm and cold climate simulations and found that the clear-sky longwave downward radiation is a key control of the robust responses. In part 2, I applied the equilibrium terrestrial biosphere models, BIOME4 and BIOME5 (developed from BIOME4 herein), for reconstructing paleoclimate. I applied inverse modeling through the iterative forward-modeling (IMIFM) approach that uses the North American vegetation data to infer the mid-Holocene (MH, 6000 years ago) and the Last Glacial Maximum (LGM, 21,000 years ago) climates that control vegetation distributions. The IMIFM approach has the potential to provide more accurate quantitative climate estimates from pollen records than statistical approaches. Reconstructed North American MH and LGM climate anomaly patterns are coherent and consistent between variables and between BIOME4 and BIOME5, and these patterns are also consistent with previous data synthesis. This dissertation includes previously published and unpublished coauthored material.

Master of Science / Department of Diagnostic Medicine/Pathobiology / Alison P. Adams / Bovine respiratory disease (BRD) is the most common and costly disease affecting cattle in the world today. The disease was first described in the late 1800s and is one of the most extensively studied diseases of livestock. BRD accounts for 65 - 80% of the morbidity and 45 - 75% of the mortality in some feedlots. Outbreaks typically occur around 10 days after transportation with the majority of deaths occurring within the first 45 days of arrival. Bacterial pathogens, physiologic stressors, and concurrent viral infections are all important factors causing BRD; other factors include seasonality, heritability, and breed tolerance. Diagnostic and treatment measures are continually being critiqued and researched. Even with continued research and the administration of antibiotics, BRD still continues to be a problem for the beef industry. Remote early detection and previous calf history are two resources that can help feedlots diagnose the disease earlier, or prevent it entirely. Feeding behavior and physical exams of the calves can also aid in early detection. New antibiotics and treatment methods have been developed, but the BRD problem still exists. Since the disease is most problematic in feedlot cattle, treatment of a large number of cattle in this setting can be costly, and often, performance and carcass traits are also affected. New preventative measures will be crucial to the industry with the continued problems and consequences of BRD. Improved treatment options and enhanced diagnostic tools will also be imperative for the control and treatment of BRD in the future.

abstract: In-situ fatigue damage diagnosis and prognosis is a challenging problem for both metallic and composite materials and structures. There are various uncertainties arising from material properties, component geometries, measurement noise, feature extraction techniques, and modeling errors. It is essential to manage and incorporate these uncertainties in order to achieve accurate damage detection and remaining useful life (RUL) prediction. The aim of this study is to develop an integrated fatigue damage diagnosis and prognosis framework for both metallic and composite materials. First, Lamb waves are used as the in-situ damage detection technique to interrogate the damaged structures. Both experimental and numerical analysis for the Lamb wave propagation within aluminum are conducted. The RUL of lap joints under variable and constant fatigue loading is predicted using the Bayesian updating by incorporating damage detection information and various sources of uncertainties. Following this, the effect of matrix cracking and delamination in composite laminates on the Lamb wave propagation is investigated and a generalized probabilistic delamination size and location detection framework using Bayesian imaging method (BIM) is proposed and validated using the composite fatigue testing data. The RUL of the open-hole specimen is predicted using the overall stiffness degradation under fatigue loading. Next, the adjoint method-based damage detection framework is proposed considering the physics of heat conduction or elastic wave propagation. Different from the classical wave propagation-based method, the received signal under pristine condition is not necessary for estimating the damage information. This method can be successfully used for arbitrary damage location and shape profiling for any materials with higher accuracy and resolution. Finally, some conclusions and future work are generated based on the current investigation. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2016

Includes abstract. / Includes bibliographical references (p. 127-129). / Faults and inefficiencies in air conditioning installations account for between 2% and 11% of allenergy consumed by commercial buildings in the United States each year. Diagnostics systems havebeen proven to improve the performance of air conditioning plants but the high costs of purchasing,retrofitting and maintaining such a system results in limited market adoption of such systems.This thesis discusses the design, implementation and results of low-cost remote monitoring anddiagnostic platform for use in air conditioning installations. The design of the various hardwarecomponents is presented along with the structure of the framework developed for each device. The thesis also contains information regarding the selection, integration and installation of the various types of sensors required on the various installations. A specially-designed protocol was also developed to handle communication between the hardware devices. Both the physical configuration and details of the protocol structure are presented in detail in this thesis. The mechanism through which the device uploads data to a server is also described in this thesis and includes details on both the hardware and the server technologies used in the upload process. The system has been installed on two different sites in Cape Town, South Africa and has produced meaningful diagnostic information since November 2007.

Understanding reacting flows in conditions relevant to practical combustion devices is a challenging but critically important task. In such devices, combustion nearly always occurs in a turbulent flow field and at high pressure. The formation of soot is highly sensitive to these parameters. However, little research has been conducted in conditions that replicate the complex physics of such devices in simplified configurations. This body of work focuses on the development of a rig suitable for investigating turbulence-chemistry interactions in simple jet flames at high pressure and high Reynolds numbers and discusses results from the initial experiments in that rig. First, the flame structure of syngas flames at pressures up to 12 bar and at Reynolds numbers up to 83,500 is investigated using OH-PLIF. A corrugation factor is used to characterize the wrinkling of the flame fronts and PDFs of this factor show that the corrugation of the flame front is a very strong function of the Reynolds number, but in most cases, the pressure has no effect. Separations in the OH layers become less probable as the pressure increases if the Reynolds number remains constant. Next, the flame structure of nitrogen-diluted ethylene flames at pressures up to 5 bar and Reynolds numbers up to 50,000 are examined using OH-PLIF. Again, the corrugation factor is used to show that the flame fronts become more wrinkled as the Reynolds number increases. Further analysis shows that the extent of wrinkling is limited and further increases in turbulence result in more frequent breaks in the OH layer. Lastly, two soot studies on the ethylene flames are presented. The soot particle size distribution is characterized in two flames at atmospheric pressure. The time-averaged, mean particle diameter on the centerline increases as the distance from the nozzle increases. Soot volume fraction measurements are made with LII in three flames at different pressures and Reynolds numbers. Soot production is found to be much more sensitive to changes in pressure than changes in Reynolds number. Increases in the mean soot volume fraction as the pressure increases are due to higher instantaneous soot concentrations and lower soot intermittency.

Magister Scientiae (Biodiversity and Conservation Biology) - MSc (Biodiv and Cons Biol) / Ichthyophonus hoferi is a highly pathogenic histozoic parasite that has low host specificity capable of producing mass mortalities of epizootic proportions in marine commercial fish populations. Currently in Southern Africa, I. hoferi has been reported from flathead mullet (Mugil cephalus) from the Kowie lagoon and from multiple species on exhibit at the Two Oceans Aquarium. Since epizootiologists rely on accurate assessments of prevalence to establish patterns of morbidity and mortality within populations, using the most accurate diagnostic techniques for accurate assessments of infection is imperative. Currently, several diagnostic techniques have been employed to detect I. hoferi in infected fish hosts. These include macroscopic examination of tissues, microscopic examinations of wet-mount squash preparations of tissue, histological examination of tissue sections, in vitro culture of tissue explants, the polymerase chain reaction (PCR) using I. hoferi-specific primers and real-time quantitative PCR (qPCR) using I. hoferi-specific primers and a hydrolysis probe.

The aim of this thesis was to get acquainted with the electrical properties of distribution transformers, which are used in the area of high voltage technology. Further aim was to describe the existing diagnostic methods according to the standards and to introduce the measuring workstation. Finally, the diagnostic of transformer was performed, and the results were evaluated.

In-vitro electronic devices are promising to dynamically monitor minute-changes in biological systems. Electronic devices based on conducting polymers such as poly(3,4- ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) provide suitable and attractive substrates for biointerfacing. The soft polymer surface acts as a cushion for the living systems to interface while electronically detecting their properties. However, to this date, most bioelectronics devices have been fabricated via multi-step lithography techniques, which do not allow for mass fabrication and hence high throughput biosensing. Inkjet printing presents an alternative to fabricate organic bioelectronic devices. Besides being low-cost, inkjet printing allows to fabricate several devices in a short time with flexible design patterns and minimal material waste. Here, using inkjet printing, we fabricated PEDOT:PSS based organic electrochemical transistors (OECTs) for biomembrane interfacing. We optimized the deposition of various inks (silver nanoparticles (AgNPs), PEDOT:PSS, and the dielectric SU-8) used during the fabrication of these devices. We characterized the electrical characteristics of all-printed OECTs with various geometries and identified the high-performing ones. Due to the flexibility of ink optimization and design patterns, these all inkjet-printed electronic devices provide an alternative for mass production of biointerfacing platforms.

Raman spectroscopy is a powerful molecular fingerprinting method which measures the vibrational modes of molecules to identify and quantify chemical species. In biomedical spectroscopy, where samples are usually complex mixtures of many molecules, Raman spectra give a biochemical “portrait” that can be used to discriminate between distinct samples. One major technical challenge in implementing Raman spectrometer sensors is the technique’s low intrinsic signal to noise ratio. To amplify the Raman signal, a number of different approaches can be applied. In this thesis two techniques are used; surface enhanced Raman scattering (SERS) from metal nanoparticles along with light-matter interaction enhancement from co-coupling light and sample to a liquid core waveguide. In order to process the complex spectral data arising from these sensors, a robust signal processing method is required. To this end, we have developed and validated a machine learning spectral analysis platform based on genetically optimized support vector machines (GA-SVM). This work is the subject of Chapter 3. We found that the GA-SVM significantly outperformed the standard statistical based modelling approach, partial least squared, in regression tasks for several different biomedical Raman applications. Furthermore, we found that the use of more complex kernel functions in the SVM yielded superior results. The genetic optimization algorithm was necessary to use these more complex kernel functions because its computation time scales linearly with complexity, whereas the standard brute force approach scales exponentially. Chapter 4 concerns the development of a Raman sensor used to quantify and identify pathogenic bacteria. This device centres on a microfluidic flow cell which forces bacteria to flow through a hollow-core photonic crystal fiber (HC-PCF) to which the Raman excitation laser is also coupled. The bacteria are also mixed with silver nanoparticles to simultaneously achieve SERS and light-matter interaction enhancement in the sensor. Overall, the fiber and nanoparticles yield a bulk enhancement of 400x for the Raman spectrum. Bacteria are quantified in this system by counting the number of “spectral events” that occur as cells flow through the HC-PCF in a 15-minute window. This approach achieved very high linearity, as well as an average detection limit of 3.7 CFU/mL. In addition, bacteria are identified by using the same GA-SVM algorithm developed in the preceding chapter. These machine learning models achieved a discrimination accuracy of ~92% when comparing the spectra of the bacteria S. aureus, P. aeruginosa, and E. coli. In mixed samples of bacteria, the error of quantification increased significantly to 13.3 CFU/mL, but the output of the sensor was highly correlated with the ground-truth bacterial load. In Chapter 5 we outline the development of a diagnostic scheme for chemoresistance in ovarian cancer based on SERS measurements from cysteine-capped gold nanoparticles. Resistance to chemotherapy was determined based on three factors: the concentration of tumor derived exosomes, the chemical composition of the exosomes, and the concentration of exosome-derived cisplatin. Cisplatin is the drug of interest for this problem, as it is the most basic chemotherapy agent. The system works by first incubating the gold nanoparticles with tumor derived exosomes. The cisplatin therein causes the particles to destabilize slightly, resulting in the aggregation rate of the nanoparticles being proportional to the drug concentration. At steady state aggregation, the magnitude of the Raman spectrum is proportional to the exosome concentration, and the spectrum contains its chemical identity. Using in vitro cancer cell lines, we found that resistant cells tend to produce more exosomes and excrete a higher concentration of cisplatin within them. Overall, this sensor exhibited good diagnostic power for chemoresistance particularly in the most common subtype in ovarian cancer.

Early-onset diagnostics, or the detection of disease before clinical symptoms arise, has gained traction for its potential to improve patient quality of life and health outcomes. Biosensors, found in point-of-care (POC) devices, facilitate early-onset diagnostics and disease monitoring by addressing the limitations of current diagnostics strategies, which include timeliness, cost-effectiveness, and accessibility. Biosensors often incorporate microarrays within their design to allow for the detection of disease-associated biomolecules, known as biomarkers. Microarrays are composed of capture biomolecules, such as monoclonal antibodies, that are immobilized through either contact or non-contact printing techniques. In the following thesis, we investigated microarray designs within novel biosensing platforms for diagnostic and disease monitoring applications. First, we highlighted the advantages and challenges of implementing different types of biosensors, detection methods, and biomolecule immobilization strategies. Additionally, we proposed a novel 3D microarray incorporating hydrogels composed purely of crosslinked bovine serum albumin (BSA) proteins decorated with capture antibodies (CAbs). Utilizing industry-standard inkjet printing, we developed and optimized a two-step fabrication protocol, by which BSA proteins and CAbs are printed first, followed by the crosslinking agent, 1-Ethyl-3-[3-dimethylaminopropyl]carbodiimide (EDC). Characterization of the unique three-dimensional (3D) microstructure and hydrogel parameters and conducting comparisons with standard two-dimensional (2D) microdots, showed that increasing biosensor surface area led to a 3X increase in signal amplification. The limits of detection (LODs) for cytokine biomarkers were 0.3pg/mL for interleukin-6 (IL-6) and 1pg/mL for tumor necrosis factor receptor I (TNF RI), which were highly sensitive compared to reported LODs from literature. Alongside the investigation of novel printing protocols, proof-of-concepts for multiplex detection and distinguishing clinical patient samples from healthy donors were also presented. Overall, this thesis demonstrated the fabrication and optimization of microarray development shows promise in improving current biosensor designs, allowing for enhanced early-onset disease detection and monitoring. / Thesis / Master of Applied Science (MASc)