VictorJuiz/thesis huggingface dataset:
Coherent Control of Molecular Processes in the Condensed Phase: Theory and Applications
Keywords: Coherent control
Nonlinear spectroscopy
Quantum control
Quantum dynamics
Quantum optics
Ultrafast

Coherent control schemes are interference-based procedures for the control of quantum mechanical systems informed by a careful analysis of the physical conditions on interference. In this thesis, the coherent control approach is applied to experimental phase control in the condensed phase. Specifically, coherent control schemes are proposed as mechanisms for experimental manifestations of phase interference. First, a simulation of a ``black box'' feedback control experiment using realistic pulse shaping is shown to automatically find a specific, previously devised coherent control scheme. Qualitative features of experiments performed in this way are reproduced. Second, an analysis of interference in one-photon phase control yields an upper bound on the duration of phase effects. One-photon phase control was previously shown to be possible only over the transient (as opposed to steady-state) value of observables. The bound described here quantifies this statement and provide a rigorous definition of ``transient'' control. We propose that phase control beyond this bound, as reported in a number of weak-field experimental and computational studies, is the result of previously unaccounted for multiphoton effects. Third, we analyze a recent experiment showing phase control of the macroscopic current emanating from living brain cells photostimulated by two-photon absorption. We propose mechanisms for the phase dependence of the current that operate over fifteen orders of magnitude in time, from the femtosecond dynamics of light-matter interactions to the macroscopic dynamics of neuron current. Significantly, coherent control is extended to the case of systems repeatedly interacting with pulses of light; we show that macroscopic control is obtained, in this case, from the multi-pulse accumulation of small phase effects. Research into multiphoton control led to the development of a novel algorithm to compute higher order perturbation theory integrals, presented in the final chapter of this thesis. The Fourier-Laplace Inversion of the Perturbation Theory (FLIPT) method is a highly efficient, numerically exact, ``black box'' integration algorithm. The FLIPT method exploits the specific structure of the multidimensional perturbation theory integrals to obtain an exponential improvement in performance over standard quadrature.
Effect of Fly Ash Composition and Flue Gas Conditions on Removal Efficiency of Electrostatic Precipitators in Kraft Recovery Boilers
Keywords: Air quality
Ash
Electrostatic Precipitator ESP
Kraft Recovery Boiler
Pulp and Paper

In the chemical recovery process of kraft pulp mills, electrostatic precipitators (ESPs) are used to remove ash particles from the recovery boiler flue gas. High ash collection efficiency is necessary to minimize particulate emissions. A slight decrease in ESP performance could cause kraft mills to exceed their environmental emissions limits, which in severe cases, can force mills to curtail production until the issue is resolved. Therefore, understanding factors affecting ESP performance is of vital importance to the productivity and sustainability of kraft mills. This thesis work consists of two parts: 1) sensitivity analysis of factors affecting the particle collection efficiency of ESP using the conventional Deutsch-Anderson equation and 2) experimental investigation the effects of various parameters on ESP performance, including flue gas temperature, moisture content and ash chemical composition. Three types of ash were considered: ash with low carbonate and low chloride contents (typical ash), ash with low chloride but high carbonate contents, ash with low carbonate but high chloride contents. Results of sensitivity studies show that increasing the gas temperature decreases the ESP collection efficiency. Of the three types of ash examined, the high carbonate ash is the easiest to capture, followed by high chloride ash and then typical ash. Results of experiments using a bench scale ESP show that 1) increasing the gas temperature increases efficiency, 2) typical ash is the easiest to capture, followed by high carbonate ash and then high chloride ash, and 3) increasing moisture content in the gas improves the collection efficiency. The results also show that ash dielectric constant is as important as ash electrical resistivity in predicting how ash may affect the ESP collection efficiency, and that the sensitivity analysis predictions are improved when more accurate values of dielectric constant are used. From the results of the experiments, equations were derived for estimating the particle migration velocity for a given ESP design. The experimental results imply that the ESP collection efficiency decreases with an increase in black liquor firing load, increases as a result of the deposits that form on the heat exchanger tubes, and is not affected by sootblowing.
Flow and Mass Transport for Polymer Electrolyte Membrane Electrolyzers
Keywords: Electrolysis
Microfluidics
Multiphase Flow
Neutron
Porous Media

Effective two-phase transport is a prerequisite for achieving high efficiency operation for polymer electrolyte membrane electrolyzers since the undesired accumulation of gaseous oxygen leads to mass transport losses. In this thesis, two-phase flow in the PTL and flow channels were investigated via experimental and numerical techniques to inform new designs and optimize operating conditions. The effect of gas compressibility on displacement behaviour in the PTL was elucidated by studying gas invasion in liquid-saturated microfluidic cells. Smaller pore throat sizes led to larger pore burst velocities, which inspired a new PTL design with engineered pore throat sizes for enhanced gas removal. Next, the gas transport behaviour in the PTL was investigated via a custom microfluidic cell that was based on a realistic PTL microstructure. A unique pore throat was identified where gas snap-off occurred, and the location of this pore throat governed the average gas saturation in the PTL. Next, the temperature-dependent gas saturation in the PTL was investigated using in operando neutron imaging. Increasing the operating temperature led to lower gas saturation near the catalyst layer and PTL interface, resulting in a decrease in mass transport overpotential. To increase the accuracy of anode flow channel visualizations, the nitrogen purging rate was optimized to minimize the impact of purging on cell performance. Excessive cathode purging led to undesired changes in performance, and a minimal purge rate was recommended to achieve accurate through-plane imaging. Lastly, temperature effects on two-phase quality in the anode flow channels were investigated via neutron imaging. A more uniform reactant distribution across the flow channels was observed at higher temperatures, enabling a uniform operating current density across the active area. The main findings in this thesis inform the design of novel PTL microstructure and explain the benefits of higher operating temperature for enhanced mass transport in PEM electrolyzers.
Neural and Cognitive Mechanisms Underlying Human Categorization
Keywords: Categorization
fMRI
learning
Prefrontal cortex

Categorization is one of the primary mechanisms underlying human perception and cognition, but how humans categorize complex multimodal sensory inputs from the real-world is still an open question. In this dissertation, I present three studies that examine how the human brain processes and represents categorical information of natural scenes, and how such categorical information shapes cognition, influencing what humans can learn from sensory inputs. Chapter 2 presents a study that shows scene information from either visual or auditory cues is represented in the prefrontal cortex independently of sensory modality, suggesting that conceptual aspects of scene category information are represented in this brain area. Chapter 3 describes a study that examined how scene representations in the prefrontal cortex are modulated by task context. This study demonstrated that scene category information is represented in the prefrontal cortex regardless of task context, unlike the global properties of scenes which were only represented in the prefrontal cortex when behaviorally relevant. These findings suggest that category information is automatically, without deliberate effort, processed and represented in the brain. In Chapter 4, we demonstrated that category information that observers extract from sensory inputs broadens the scope of learning, allowing humans to show the learning effect to novel stimuli based on category-level information. Taken together, these findings demonstrate that category information is automatically processed and represented across different areas in the brain, both sensory-specific and modality-independent brain regions, and interacts with such as learning and memory.
Characterization of Potential Mechanisms that Render the Combination of First Trimester Umbilical Cord-Derived Perivascular Cells (FTM HUCPVC) and Endothelial Progenitor Cells (EPC) a Promising Cellular Therapeutic Strategy Following Ischemic Cardiac Injury
Keywords: Angiogenesis
Cellular-based Therapy
Human Umbilical Cord Perivascular Cells
Mesenchymal Stem Cells
Myocardial Infarction

Heart disease is the main cause of mortality worldwide. The available treatments are limited, high risk and often inefficient. Cell-based therapies are being developed to provide an efficient and safe option to improve quality of life, prevent future mortality and the need for heart transplantation. Aged and ill individuals often have impaired endogenous regenerative cells. Instead, healthy, young cells can be delivered into the damaged heart to decrease inflammation, restore blood flow, preserve the myocardium and increase cardiac output. Pre-clinically and clinically tested therapeutic cell types achieved some of these goals, but no single-cell type seems to be versatile enough, to heal the heart on its own. Herein, I propose the combined use of two regenerative cell types as a treatment after myocardial infarction (MI). The first cell type is the endothelial progenitor cell (EPC), which are the origin of new blood vessels, second is the first trimester human umbilical cord-derived perivascular cell (FTM HUCPVC). FTM HUCPVC can support developing blood vessels, control inflammation and release growth factors. We hypothesized that combined administration of these cells can achieve clinically significant healing in a damaged heart. We previously found that FTM HUCPVC have greater regenerative potential following cardiac injury compared to term HUCPVC and bone marrow-derived mesenchymal stem cells. Optimal cell ratios were determined by a mouse model of subcutaneous angiogenesis and an immunocompromised rat model of MI. The combination of FTM HUCPVC and EPC significantly improved vasculogenesis compared to single cell-type treatments partly through the platelet-derived growth factor beta (PDGF-B) signalling cascade. Both FTM HUCPVC and EPC in combination support perivascular niches in the myocardium by homing to vascular structures and secreting pro-angiogenic factors. The resultant cardiac perfusion is likely required for cardiac muscle preservation, scar tissue reduction, and ultimately the improved cardiac function. To our knowledge, this study is the first to show significant structural and functional cardiac improvements following combination cell therapy compared to single cell-type therapies. Currently our group is working on large animal studies to test this novel therapeutic approach as we plan for a phase I clinical trial to provide a novel treatment option for patients following MI.
Organic Frontiers: The Politics of Agricultural Market-making in Jumla, Nepal
Keywords: Food Sovereignty
Governmentality
International Development
Nepal
Organic Agriculture
Political Ecology

In 2008, Jumla District, located in northwest Nepal, declared itself “organic.” The declaration was made in the context of a rise in identity-based territorial claims and reinvigorated state territorialisation following the end of a decade-long civil conflict. The declaration was also broadly aligned with a diverse range of socially inclusive and ecologically sustainable market-making initiatives targeting Nepal’s agrarian frontiers. Such initiatives have aimed to capitalize on the “quality turn” in globalized agro-food markets and expert re-assessments of the socio-ecological value of bio-diverse and chemical-free agro-ecologies. These market development initiatives are framed as having the potential to conserve vulnerable frontier ecologies by rendering them economically valuable, while also extending market access into marginalized regions where farmers have been unable to compete with large-scale, industrial producers on the basis of price alone. This dissertation builds on critical development, agrarian and governmentality studies and contributes to a growing body of critical scholarship studying alternative agro-food initiatives outside of the Global North. Drawing on an analysis of state and donor archives, semi-structured interviews and ethnographic participant observation, I examine how changes in globalized food markets and expert understandings of frontier ecologies are transforming governmental projects of market-making. Using Jumla’s organic district declaration and donor-led value chain development projects as examples, I demonstrate how eco-governmental discourses and development strategies are assembled within specific territorial logics and political agendas, resulting in multiple—and at times contradictory—priorities and approaches. The research further examines how ostensibly technical projects intended to develop socially inclusive and ecologically sustainable markets deliberately blur public-private boundaries. I argue that the new sites of state-society-market encounter created by these projects can bring markets into view as targets of political practice, particularly as development agents and subjects negotiate the contradictions between and within different market-making initiatives. While the politicization of markets can contribute to processes of accumulation and dispossession, it can also present new opportunities for rights-based claims-making.
Discrimination of Neural Sources using Spatiotemporal Signatures in Nerve Cuff Electrode Recordings
Keywords: Machine Learning
Nerve Cuff Electrode
Peripheral Nerve Recordings
Rat Sciatic Nerve
Simulations
Spatiotemporal Signatures

Recording and stimulating from the peripheral nervous system are becoming important components in new generations of bioelectronic systems. Neurostimulation has seen a long history of success in chronic application but robust neural recordings remain a challenge. Nerve cuff electrodes are an option that have demonstrated chronic viability in humans but lack the required recording selectivity. The objective of this thesis was to increase the recording selectivity achievable using multi-contact nerve cuff electrodes. Neural activity recorded from multi-contact nerve cuffs for a given neural pathway will have a distinct spatiotemporal signature. This signature is determined by the location of the bioelectric sources in the nerve combined with the type of fiber, which governs the propagation properties of the action potentials. Both the spatial and temporal information are needed to interpret the physiological meaning of the recorded activity, but have not been adequately combined in signal processing techniques to date. We investigated selective recording approaches based on spatiotemporal characterizations of neural pathways. First, we performed a simulation study to investigate the effects of the spatial and temporal information on recording selectivity. A matched filter approach was developed and results showed that neural pathways that had different fiber types were highly distinguishable, whereas neural pathways with similar fiber types showed varying distinguishability results depending on pathway locations. Next, we performed a validation study in vivo in a rat sciatic nerve model. We tested our matched filter approach and machine learning algorithms in distinguishing the neural pathways. The results of the matched filter approach were comparable to the simulation study, validating our simulations, and the machine learning algorithms showed improvements in recording selectivity. These results demonstrated classification of individual naturally evoked compound action potentials (nCAPs) for the first time. Lastly, we demonstrated that the use of a convolutional neural network (CNN) led to further improvements in recording selectivity. With the help of a recurrent neural network, joint angles could be tracked accurately based on the classified nCAPs. These findings suggest that nerve cuff electrodes can provide robust recordings and be beneficial in many neuroprosthetic and neuromodulation application.
Protein-protein Interactions Provide a Platform for G-protein-coupled Receptors to Exert Physiological and Pathological Functions in Central Nervous System: Therapeutic Potential?
Keywords: Animal Studies
GPCR
Peptide
Protein-Protein Interaction
Therapeutic

In the central nervous system membrane receptors play an important role in neuronal communication. Since neurons mainly communicate through the release of neurotransmitters, membrane receptors are responsible for binding neurotransmitters and transducing their message into an intracellular response. In general, there are two types of membrane receptors: ionotropic and metabotropic. Ionotropic receptors are ligand-gated ion channels, which are responsible for mediating fast synaptic responses. On the other hand, metabotropic receptors act through their downstream signaling cascades to trigger intracellular responses. A prominent example of metabotropic receptors is the G-protein-coupled receptor (GPCR) superfamily. In the human body this receptor superfamily consists of approximately 799 receptors. Conventionally, these GPCRs are seven-transmembrane-domain receptors which act through their coupled G-proteins to elicit their physiological actions. There is also a growing body of evidence that these GPCRs can physically interact with each other as well as with other types of membrane receptors. This present study focuses on examining whether protein-protein interactions, other than coupled G-protein signaling cascades, are another platform for GPCRs to exert their effects or to regulate the functions of other proteins. We also investigate whether these protein-protein interactions involving GPCRs present any physiological or pathological implications.
The Investigation of Aerobic Interval Training in Women with Coronary Artery Disease in Cardiac Rehabilitation
Keywords: aerobic exercise capacity
cardiac rehabilitation
exercise
interval training
women

The generalizability of the potential superiority of aerobic interval training (AIT) over moderate intensity continuous exercise (MICE) in improving aerobic exercise capacity (VO2peak) remains elusive in women with coronary artery disease (CAD), as they have been underrepresented in the majority of studies to date. The overarching aim of this doctoral thesis was to optimize exercise training modalities for outpatient, cardiac rehabilitation (CR) programs to maximize the potential gains in quality of life, and survival. The central hypothesis was that a six-month CR program of AIT would significantly improve VO2peak more than six-months of usual care CR involving MICE, in postmenopausal women with CAD. The first of three studies sought to retrospectively determine the effectiveness of progressive AIT in the form of walk/jog intervals in individuals with CAD. Treatment effect analysis revealed a 3.84 mL·kg -1·min -1 superior improvement in VO2peak in response to AIT compared to usual care MICE (p
Predicting the Thermal Dependencies of Infectious Disease
Keywords: daphnia
disease
experimental
metabolic theory
modeling
temperature

Climate change is affecting infectious disease around the world. These effects can be complicated, as disease spread depends on characteristics of the host-parasite system in question, including host mortality rates, parasite contact rates, and how quickly the parasite kills its host. Importantly, these key parameters often vary with temperature. Combining mechanistic models of disease spread with thermal performance curves can be a powerful approach to predicting temperature’s effects on disease; however, most host-parasite systems are data poor and it is therefore difficult to parameterize their phenomenological thermal functions. The Metabolic Theory of Ecology (MTE) provides a potential solution to these data limitations. The MTE framework suggests that since metabolism scales with body mass and temperature, metabolic rate can be used to predict temperature’s effects on physiological rates, thereby generating models for the thermal dependencies of disease traits. Since MTE is predicated on first principles of chemistry and physics, it should be generalizable across biological systems and its functions may be parameterized a priori. Recent work has proposed combining MTE with host-parasite models, though we lack empirical tests of how well this approach can describe the thermal dependencies of disease across different host-parasite scales. Here, I use a Daphnia magna – microsporidian experimental host-parasite system to test this approach’s performance. In Chapter 1, I show that MTE is able to capture the thermal dependencies of within-host disease, including parameters that describe host aging and mortality, and parasite growth and equilibrium abundance. In Chapter 2, I provide evidence that MTE can accurately describe the rates that constitute disease transmission (contact and parasite infectivity) across temperature. Finally, in Chapter 3 I use the models described above to parameterize a population-level epidemiological model that predicts disease dynamics under warming or constant temperatures. I test the independent model predictions by driving experimental populations through constant or slowly warming conditions and show that MTE is able to accurately forecast warming-driven disease emergence. This thesis serves as a proof of principle that linking simple metabolic models with a mechanistic host–parasite framework can be used to predict temperature responses of within-host disease dynamics, disease transmission, and overall epidemics.
Semi-infinite Cohomology, Quantum Group Cohomology, and the Kazhdan-Lusztig Equivalence
Keywords: affine Lie algebra
BRST
chiral algebra
quantum algebra
vertex algebra

The Kazhdan-Lusztig tensor equivalence is a monoidal functor which sends modules over an affine Lie algebra at negative level to modules over the associated quantum group. A positive level Kazhdan-Lusztig functor is defined using Arkhipov-Gaitsgory's duality between positive and negative level affine Lie algebras. Our main result proves that under the positive level Kazhdan-Lusztig functor, the semi-infinite cohomology functor corresponds to the quantum group cohomology functor with respect to the positive part of Lusztig's quantum group. Monoidal structures of a category can be interpreted as factorization data on the associated global category. We describe a conjectural reformulation of the Kazhdan-Lusztig tensor equivalence in factorization terms. In this reformulation, the semi-infinite cohomology functor at positive level is naturally factorizable, and it is conjectured that the factorizable semi-infinite cohomology functor is essentially the positive level Kazhdan-Lusztig tensor functor modulo the Riemann-Hilbert correspondence. Our main result provides an important technical tool in a proposed approach to a proof of this conjecture.