VictorJuiz/thesis huggingface dataset:
Effects of Flashing on Characteristics of Splashplate Nozzle Sprays
Keywords: Bubble
Droplet
Flashing
Phase Change
Sprays

Black liquor, the byproduct of karf process to produce pulp, is injected into kraft recovery boiler to recover its energy and valuable chemicals. The temperature of the black liquor is increased to reduce its viscosity. Above the boiling point, a part of water content of black liquor starts to evaporate and creates a two-phase spray. This is a common phenomenon in the sprays that work at high temperatures, called "flashing". Flashing can change spray pattern, breakup mechanism, droplets size, droplets velocity and mass distribution. In this thesis the objectives are first, to predict the conditions at which flashing can occur, and second, to predict the effects of flashing on spray characteristics such as droplets size, droplet velocity and spray pattern. Water was used to simulate black liquor and a small scale nozzle was used to inject it into a spray chamber. The experiments were performed below and above flashing point. Spray patterns and internal flow regimes were studied using hundreds of images using DSLR and high-speed cameras. A particle sizing technique in imaging was used to measure the droplets size. Phase Doppler Anemometry was another technique for droplets size measurement and all of the measurements in all of the conditions were repeated using this method.Then another set of images, which were taken from black liquor sprays in a real size system, were analyzed. These images were taken during a separate experimental work in Helsinki, Finland. Spray patterns were categorized based on nondimensional values and a correlation was proposed to predict the conditions at which flashing occurs. New image analysis techniques were developed to obtain all the information available in the images about the particles size and velocity. Then the results of droplet size measurement from water and black liquor were combined and a correlation that can predict the average droplet size was obtained. Using the findings of this study, the operators of the boiler can adjust the temperature and mass flow rate of the black liquor to avoid flashing conditions or make flashing occur in case it is required, without suffering from damages due to unknown circumstances.
Structural Abnormalities and Treatment-related Plasticity in Classical Trigeminal Neuralgia
Keywords: brain plasticity
MRI
neuroimaging
pain
trigeminal nerve
trigeminal neuralgia

Classical trigeminal neuralgia (TN) is a unique neuropathic pain disorder characterized by highly intense electric shock-like attacks of unilateral facial pain. While TN is commonly associated with neurovascular compression of the trigeminal nerve at the root entry zone (REZ) its pathophysiology is not well understood. In conjunction with trigeminal nerve abnormalities, central gray matter and white matter (GM, WM) structure may be affected and/or contribute to the maintenance of TN. Surgical treatment for TN may produce analgesia by effectively normalizing these structural abnormalities. The main aim of this thesis is to determine if there are structural neural abnormalities in patients with TN and whether effective neurosurgical treatment can reverse these abnormalities. The specific aims were to determine: 1) if patients with TN have brain GM abnormalities; 2) if patients with TN have trigeminal nerve and/or brain WM abnormalities based on multiple DTI-derived metrics; 3) if effective neurosurgical treatment for TN is associated with a reversal of the trigeminal REZ and cortical and subcortical GM abnormalities that we report in studies 1 and 2 of this thesis. In groups of TN patients with right-sided pain and healthy age- and sex-matched controls, magnetic resonance imaging revealed that patients had: 1) increased GM in the sensory thalamus, amygdala, periaqueductal gray, basal ganglia, contralateral primary somatosensory cortex, and frontal pole and less GM in the pregenual anterior cingulate cortex, insula, and orbitofrontal cortex; 2) abnormalities in the trigeminal REZ and cerebral WM tracts including the corpus callosum, cingulum, corona radiata, and superior longitudinal fasciculus; and 3) a reversal ventral anterior insula and trigeminal REZ abnormalities following effective treatment, with the degree of REZ normalization correlating with pain relief. Taken together, this thesis demonstrates for the first time both trigeminal nerve and brain abnormalities in patients with TN, and the impact of effective treatment on these abnormalities.
Investigation of the Interfaces between Cu(111)-based Electrodes and Water Using Density Functional Theory
Keywords: Density Functional Theory
Electrochemical Energy Storage
First principles
Interface
Modeling

This thesis presents a fundamental study of the interface between Cu(111)-based electrodes and water. Density functional theory is used to investigate the interaction between water and Cu(111) both in the absence and presence of an external electric field. To analyze the water-electrode interaction in depth, a monomeric adsorption of water is studied and compared with other similar types of molecules. The orientation of the adsorbed molecules is found to be influenced by the energy gap between the Fermi level of Cu(111) and the highest occupied molecular orbital and the lowest unoccupied molecular orbital of the molecules. A simple relationship is observed where a stronger dipole moment of the molecule and a lower adsorption energy lead to an interface which responds more sensitively to the external electric field. The dipole moment is a more critical parameter.The behaviour of a water overlayer is then investigated on Cu(111). A differential capacitance of the Cu(111)/water interface remains constant when the molecular movements of water is ignored. The change in the orientations of the water molecules causes the differential capacitance of the interface to be potential dependent. A sharp increase in the differential capacitance is found when the water layer undergoes a structural transition. A new method, namely a "constant field" method is devised to determine the potential of the electrode at a given external electric field.The impact of coating Cu(111) with a layer of graphene is also studied. A quantum capacitance, an intrinsic capacitive property of graphene, is found to influence the differential capacitance of the graphene-coated Cu(111). However, it was found that the electronic interaction between graphene and Cu(111) alters the differential capacitance of the interface at certain electrode potentials. The change in the differential capacitance cannot be predicted by the standard modelling approaches and thus, ab initio modelling of the electrified interface is necessary. The water layer does not heavily influence the interaction between graphene and Cu(111), which agrees with the conventional understanding that graphene-coating suppresses the oxidation of the Cu surfaces.
Sleep Disturbance in Family Caregivers of Children who Depend on Medical Technology Compared to Family Caregivers of Healthy Children: The CARE to SLEEP Study
Keywords: caregiver
chronic illness
family centred care
paediatrics
sleep
technology dependent

Background/Rationale: Society relies on family caregivers of children who depend on medical technology (e.g., home ventilation) to provide highly skilled and vigilant care up to 24 hours per day. Few studies exist that have measured sleep in family caregivers, and those that do have relied entirely on subjective measures. These data suggest that sleep disruption places family caregivers at risk for poor health and related outcomes that may impair their daytime function and long-term capacity for caregiving. Objectives/Research Questions: The primary aim of this study was to compare sleep and related outcomes in family caregivers of children who depend on medical technology to outcomes in family caregivers of healthy children.Methods: In a prospective cohort study (balanced per child's age), 42 family caregivers of children who depend on medical technology from a tertiary-level paediatric hospital and 43 controls from community-based paediatric clinics were recruited. Actigraphy was used for 6 days and 7 nights, and the Pittsburgh Sleep Quality Index (PSQI) was applied to collect sleep data. At home visits, sleep diaries were collected and measures of depression (CES-D), daytime sleepiness (ESS), fatigue (MAF), quality of life (SF-12TM), sleep hygiene (SHI), and child's sleep habits (CSHQ) were administered.Results: Family caregivers of children who depend on medical technology achieved 40 minutes less sleep per night (6.56 [1.4h] vs. 7.21 [0.6], p = .02), had more nocturnal awakenings (8.00 [3.9] vs.6.01 [3.7], p = .02), and had more sleep deprived (
Effect of Mobility on the Sensing and Communication Performance of Mobile Sensor Networks
Keywords: cooperative sensing
mobility diversity
mobility model
mobility sensing gain
sensor network

We study the effect of mobility on the sensing and communication performance of a sensor network with mobile nodes. Communicating the sensed data clearly necessitates that the sensor node is able to connect to adjacent (sensor or sink) nodes to be able to exchange its information. Same node to node links can also be used in the sensing phase if a cooperative sensing scheme is in place. Assuming that the phenomenon being sensed generates a sparse and spatially correlated signal across the sensors, the theory of distributed compressive sensing suggests that cooperative sensing can facilitate the sensing process. We study the effect of mobility on the probability that a sensor node can connect to its neighboring nodes either to communicate its sensed data or to initiate a cooperative sensing procedure. We show that, by increasing the probability of such connections, mobility enhances both communication and sensing performance of mobile sensor networks.We first study how mobility of the nodes affects the communication performance of a mobile network. Based on a Wiener process mobility model, and in a network with mobile sensor and sink nodes, we study a repetition based approach for sensor node to sink node communication, where sensor nodes attempt communicating their sensed data with sink nodes repeatedly between two sensing instants. Using a stochastic framework, we prove that the average number of successful communications is an increasing function of the sensor node's mobility.Then, we study the sensing performance of the above network based on a cooperative sensing scheme. In this scheme, sensors share their samples and perform the sensing cooperatively, reducing the required number of samples per sensor node. The reduction in the number of samples each sensor needs to obtain, namely the sensing gain, depends on the level of correlation between such samples. Adopting a joint sparsity model for the correlation between the samples of the sparse signal, we show that the mobility of the sensor nodes can increase the chances of higher correlation between the samples, leading to a reduction in the number of requires samples.
System for Interpretation of Personal Genomes
Keywords: big data
databases
genomics
high throughput sequencing
user interfaces
visualization

Genomics is undergoing a revolution sparked by higher throughput and cost effective DNA sequencing technologies. Sequencing has become a ubiquitous tool with varied inputs, scopes, techniques, technologies, and purposes. There is potential for the development of software systems that assist in translating raw sequence data into actionable information that helps improve disease assessment, detection, and treatment so that individuals who are or may be affected by genetic conditions are treated with an unprecedented level of precision and predictiveness. This new kind of medicine informed by personal genomic interpretation promises to have immense medical and economic benefits. Despite the capacity of new sequencing technologies to generate huge volumes of raw sequence data, it remains a substantial informatics challenge to efficiently analyze it. HTS technologies produce data at a rate that exceeds Moore's Law, creating enormous technical and usability issues. Freely available tools that are both powerful enough to be efficient and user-friendly enough to be used by genomic researchers without informatics expertise are scarce. Notwithstanding a few exceptions, users are often forced to choose between powerful, specialized software that needs to be run on the command-line or alternatives that are less specialized but are graphical and user-friendly. This thesis presents two software platforms that combine techniques from various domains of computer science, most notably data structures, databases, algorithm design, data visualization, user interface design, and user experience design, that together form a highly integrated system for interpretation of personal genomes that is both powerful and easy to use.
Low Dose Computed Tomography Reconstruction Methods
Keywords: Compressed Sensing
Image Reconstruction
X-ray Computed Tomography

This study examines potential methods of achieving a reduction in X-ray radiation dose of Computer Tomography (CT) without significantly degrading the quality of the CT images. Assuming that the effective X-ray dose in CT is proportional to the product of the exposure area, the number of projections and the dose for each projection, four approaches are proposed to lower the radiation dose. Each of the proposed approaches controls one of these factors. Increasing the slice thickness increases the number of detected X-ray photons, thereby reducing the image noise. However, it also reduces the detectability and measurement accuracy of the small structures. An optimum slice thickness/overlap for low dose thoracic-CT images is proposed to compromise between the detectability and characterization accuracy of small pulmonary nodules.Compressed Sensing (CS) based CT reconstruction methods have shown significant potential for low dose CT imaging by reducing the number of projections. The connection between the statistical iterative CT reconstruction and compressed sensing is explored, based on maximum a posteriori formulation of CT reconstruction. It is shown that this formulation can be transformed into a weighted CS problem, the weights of which are direct consequences of the geometry and the CT statistics. A computationally efficient solution is proposed, enabling lower dose CT scans, while substantially reducing the computational load in comparison to conventional CS-based CT reconstruction methods.An iterative denoising algorithm is proposed to improve the CNR of the low dose CT images. To achieve this goal, a noise confidence region evaluation algorithm is proposed to adaptively update the regularization parameters, by validating the statistical properties of the denoised images at the end of each iteration. The algorithm stops when the statistical properties of the denoising residual resemble those of the considered noise. Finally, an innovative combination of a newly developed accelerated wavelet inverse Radon transform and non-convex CS formulation is proposed to recover the wavelet subbands of CT images from a reduced number of locally scanned projections. The proposed method reduces the radiation dose by simultaneous reduction of the number of projections and the area of exposure.
Development and Preclinical Evaulation of Novel Enzyme-targeting Radiotracers for Positron Emission Tomography
Keywords: carbon-11
CNS enzymes
fluorine-18
positron emission tomography
radiotracer development

This work sought to radiolabel novel positron-emitting radiotracers and evaluate their utility for quantifying enzymes relevant to neurological and psychiatric disorders via position emission tomography (PET) in preclinical studies. Four enzymes were targeted: glycogen synthase kinase 3 (GSK-3), fatty acid amide hydrolase (FAAH), monoacylglycerol lipase (MAGL), and monoamine oxidase B (MAO-B). By adapting a [11C]CO2 fixation method, a GSK-3 inhibitor with a 1-aryl-3-benzyl-[11C-carbonyl]urea scaffold was synthesized and represents a technique for radiolabeling unsymmetrical ureas bearing aromatic substrates. Using a similar method, we were able to prepare a 11C isotopologue of PF-04457845, a new investigation agent targeting FAAH in phase 2 trials. [11C]PF-04457845 was prepared in 5% radiochemical yield (uncorrected, based on [11C]CO2) and high radiochemical purity (>98%) with a specific activity of 74 GBq/µmol. Following ex vivo biodistribution and specificity experiments in rats, it was determined that [11C]PF-04457845 was a promising candidate which should be assessed in higher species. Further expanding the [11C]CO2 fixation method, five candidate radiotracers targeting MAGL were prepared in high radiochemical purity with sufficient yield and specific activity for conducting preclinical evaluations. Although none of these sufficiently penetrated the rodent brain, they represent rare examples for attempting to image this important neurological target with PET. Furthermore, the low nucleophilicity of the alcohol and amines used to prepare the [11C]carbamate- and [11C]urea-based radiotracers broadened the scope of the [11C]CO2 fixation method. Lastly, a [18F]fluoropropyl derivative of a reversibly-binding MAO-B inhibitor, SL25.1188, was synthesized in 52% radiochemical yield (uncorrected, based on [18F]F-) with a specific activity of 109 GBq/µmol and had high radiochemical purity (>98%); however, low MAO-B affinity and rapid metabolism in rat plasma led to poor brain uptake. Our efforts to expand the basic knowledge of new 11C- and 18F-labeled compounds to non-invasively probe important neurological enzyme targets are discussed herein.
Pore Network Modeling and Synchrotron Imaging of Liquid Water in the Gas Diffusion Layer of Polymer Electrolyte Membrane Fuel Cells
Keywords: Fuel Cells
Gas Diffusion Layer
Pore Network Modeling
Simulations
Stochastic Modeling
Synchrotron Radiography

Polymer electrolyte membrane (PEM) fuel cells operate at levels of high humidity, leading to condensation throughout the cell components. The porous gas diffusion layer (GDL) must not become over-saturated with liquid water, due to its responsibility in providing diffusion pathways to and from the embedded catalyst sites. Due to the opaque and microscale nature of the GDL, a current challenge of the fuel cell industry is to identify the characteristics that make the GDL more or less robust against flooding. Modeling the system as a pore network is an attractive investigative strategy; however, for flooding simulations to provide meaningful material comparisons, accurate GDL topology and condensation distributions must be provided. The focus of this research is to provide the foundational tools with which to capture both of these requirements. The method of pore network modeling on topologically representative pore networks is demonstrated to describe flooding phenomena within GDL materials. A stochastic modeling algorithm is then developed to create pore spaces with the relevant features of GDL materials. Then, synchrotron based X-ray visualization experiments are developed and conducted to provide insight into condensation conditions.It was found that through-plane porosity distributions have significant effects on the GDL saturation levels. Some GDL manufacturing processes result in high porosity regions which are predicted to become heavily saturated with water if they are positioned between the condensation sites and the exhaust channels. Additionally, it was found that fiber diameter and the volume fraction of binding material applied to the GDL have significant impacts on the GDL heterogeneity and pore size distribution. Representative stochastic models must accurately describe these three material characteristics. In situ, dynamic liquid water behavior was visualized at the Canadian Light source, Inc. synchrotron using imaging and image processing techniques developed for this work. Liquid water primarily originated beneath the flow field landings, sometimes spreading laterally into the less compressed regions of the GDL beneath the flow field channels. Independent water clusters were tightly packed within the GDL, rarely occupying more than 1 mm2 of planar area. These tools and observations provide the capability to predictively design high performance GDL materials.
Decision Support Tools for Sustainable Water Distribution Systems
Keywords: data mining
energy efficiency
optimization
performance characteristics
stakeholder feedback

Sustainability relies on the balance between economic, social, and environmental objectives. Although seemingly divergent at times, the connections between them elucidate opportunities for improving different components simultaneously. In water distribution systems, aging infrastructure, insufficient funds, water scarcity, leakage, and high energy use have prompted the pursuit of sustainability through different strategies. These conditions demand improvement and create the opportunity to rethink the system. The present study proposes five separate decision support approaches that underlie a systems approach. These address current issues of North American water distribution systems and lessen the gap between research and application.The first two focus on the interface between users and networks. The relation between user characteristics and demand is studied by building an integrated database to support planning, especially demand management. Data from three Ontario municipalities are used to define metrics, benchmarks, conservation targets, and user clusters. The relation between users and service requirements is further explored qualitatively through a survey on water user expectations, addressing the disconnection between utilities and stakeholders. Applied to the City of Guelph, it informs the utility of knowledge gaps and preferred solutions.While these approaches concentrate on the needs of the stakeholders, inputs to more thorough planning, the next two assess the resulting systems. A set of energy metrics addresses the issue of inefficient energy use and provides indicators of system capacity, costs, and greenhouse gas emissions. The metrics are applied to the Toronto water network, and allow for the identification of specific areas for improvement. In order to further evaluate how complex networks respond to variations, a more comprehensive set of performance metrics are proposed and applied to two example networks.The final approach seeks to improve the design process by reducing the computational intensity of optimization procedures and thereby allowing for the analysis of more system objectives and uncertainties. The procedure, applied to Anytown, uses shorter time cycles to approximate full costs of systems with varying loads. Overall, the approaches facilitate current-day decision-making by applying systems thinking to the development of new solutions that collect, integrate, analyse, and re-interpret readily available data and models.