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    Harvesting Energy And Water From Fertilizer Osmosis
    (2022-04-13) Pourmovahed, Pouyan; Maisonneuve, Jonathan; Guessous, Laila; Hansen, Fay; Lefsrud, Mark; Wang, Xia
    The potential for concentrated fertilizer to drive water treatment, nutrient recovery, and power generation has received increased attention. Large amounts of energy are wasted in agricultural systems each time concentrated fertilizers are diluted in water for fertigation, such as is common in hydroponic cultivation. This energy can be harnessed and converted to mechanical work or electricity to take a considerable load off specific farm subsystems, such as pumping and ventilation, or can directly drive desalination and filtration of non-potable waters such as seawater and wastewater. This thesis analyzes membrane processes for converting fertilizer energy to useful work. First, the novel concept of using fertilizer to generate power via pressure retarded osmosis (PRO) is introduced. Second, the concept of fertilizer PRO is experimentally validated, and power generation and energy recovery are shown for a range of common fertilizers. Third, the thermodynamic and practical limitations of recovering energy from fertilizer are established using a number of new analytical, numerical, and experimental methods. Finally, an alternative to energy recovery is examined, namely the possibility of using fertilizer to drive forward osmosis (FO) to recover clean irrigation water from wastewater feed sources. The limitations of fertilizer FO are also established, again using a number of new analytical, numerical, and experimental methods. Results indicate that up to 1200 l of water and 125 Wh of energy may theoretically be recovered per kg of fertilizer, when low-concentration municipal wastewater is available. Given typical nutrient requirements for hydroponic plant cultivation, such values approach nearly 500 % of necessary irrigation water and 5 % of the electricity consumed by a typical greenhouse. However, practical limitations and non-ideal transport dynamics reduce these values and must be overcome in future research, so that fertilizer energy can be economically deployed to farm systems. To conclude, other applications of fertilizer energy are introduced and pathways for future research and development are discussed. This research may contribute to the future of sustainable agriculture by opening up new possibilities for energy efficiency, water security, and food productivity.
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    Crowding Reduction And Waiting Time Analysis In Health-Care System Using Machine Learning
    (2022-05-20) Hijry, Hassan Mohmmed; Olawoyin, Richard; McDonald, Gary; Edward, William; Debnath, Debatosh
    In the hospital setting, the emergency room (ER) offers timely emergency care for patients and is considered the busiest department because of the urgency of cases. Emergency rooms have the highest number of patients overcrowding within any hospital; more than 50% of the patients admitted to the hospital come through the ER. Healthcare management is continuously trying to minimize wait times and optimize the hospital's allocated resources, but most ERs still suffer from the overcrowding crisis due to the stochastic arrival and random arrival distribution. Advanced techniques, such as machine learning algorithms, are useful for determining real life queue scenarios and patient flow (e.g., waiting time in queue and length of stay), which are considered measures of ER overcrowding. As such, we began by building a model to predict patient length of stay through predictive input factors such as patient age, mode of arrival, and patientÕs type of condition using three machine learning algorithms (e.g., artificial neural networks (ANN), linear regression, and logistic regression). The best model accuracy ANN resulted in an increase of 19.5% compared to the performance from previous studies. Then, the Deep Learning Model was applied for historical queueing variables to vi predict patient waiting time in a system alongside, or in place of, queueing theory (QT). Four optimization algorithms (SGD, Adam, RMSprop, and AdaGrade) were applied and compared to find the best model with the lowest mean absolute error. The results showed that the SGD algorithm achieved better prediction accuracy than the traditional approach and reduced the use of assumptions. Moreover, the model decreased the error reduction by 24% when compared to prior literature. Lastly, we proposed a model to predict the patient waiting time based on the lab test results. Multi-algorithms were implemented by using real-life COVID-19 test results data recorded during the pandemic. Among the eight proposed models, the results showed that decision tree regression performed better for predicting waiting times. Based on experiments performed in the research, this dissertation provides a guideline for waiting time analysis in the queue--not only in healthcare, but also in other sectors, considering model understandability and the feature extraction process.