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    Efficacy Of Telerehabilitation In Improving Grip Strength
    (2022-11-13) James, Sam Prasanna Rajkumar; Sengupta, Sankar; Conrad, Megan; Dean, Brian; McDonald, Gary
    Handgrip strength is essential to perform day-to-day tasks. People lose handgrip strength due to aging, diseases, and other conditions. According to neuroplasticity principles, grip strength can be improved using repetitive tasks and exercises. People often are not motivated enough to adhere to meaningless repeated movements to improve grip strength exercises. This study describes developing an innovative smartphone-based telerehabilitation system that includes an innovatively designed grip strength device (eGripper) and a phone application to play games. This telerehabilitation system encourages patients to play a game while improving grip strength.eGripper was a repurposed dynamometer that sends grip strength data to an android phone. The raw grip strength data stream was used as a control variable to play games. In this study, the grippyBird game was designed, where customizations can be done from a remote therapist dashboard. Thirty-four participants participated in validity and reliability experiments to measure this device against the “gold” standard Jamar dynamometer. The test results substantiate that eGripper has acceptable concurrent validity and inter-instrumental reliability. A randomized clinical trial with an experimental and control group measured efficacy and compliance. Findings from the clinical trial showed significant improvements in grip strength and compliance between groups. A formative and summative usability testing was performed. Formative usability used focus groups and informal interviews with a few therapists and patients during the design stage. Four experimental participants did a summative usability experiment with two surveys. An eGripper telerehabilitation system to resolve the issues of HEP compliance was developed for this study. The use of a game instead of repetitive exercises motivated participants to be compliant in performing their HEP more regularly. Future research is needed to continue developing both the eGripper and associated games to help patients with poor hand strength improve their ability to grip.
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    Stochastic Planning And Scheduling For Reconfigurable Job Shops And Flow Lines
    (2022-11-11) Imseitif, Jad Taysir; Nezamoddini, Nasim; Aydas, Osman; Pandey, Vijitashwa; Cheok, Ka C
    The uncertain and competitive market is leading manufacturers to look for fast and effective technological solutions to manage their production systems and make them highly responsive to market needs. Moreover, customers are requesting customized, high-quality products quickly and at low costs. Utilizing rigid manufacturing systems such as dedicated manufacturing systems (DMSs) or flexible manufacturing systems (FMSs) limits manufacturers’ responsiveness. Reconfigurable manufacturing systems (RMSs) were introduced to cope with these challenges. These systems are built around modularity and reconfigurability and use reconfigurable machine tools (RMTs) as their main component. The adjustable structure of RMT allows the system to adapt to market requirements. However, production management in RMSs is a particularly challenging task compared to traditional systems, which makes manufacturers skeptical about adopting these systems. To address this issue, this dissertation presents novel methodologies to manage production activities within RMSs regarding planning, scheduling, and control. The research was conducted in two main parts based on the system type (i.e., job shop or flow line). A novel mixed-integer linear programming (MILP) model for planning and scheduling is formulated for the former. Then, it was extended to a two-stage stochastic (TSS) formulation to incorporate the uncertainties in volume and machines’ productivity. A data-driven controller with predictive capabilities was developed for the latter. It collects real-time data to reschedule raw material injection time and control the inner-stage movement of work-in-process (WIP) units to optimize their levels. The applicability of the proposed models was validated using case studies adopted from the literature. The result of this dissertation showed the cost-benefits of utilizing RMSs and the effectiveness of adopting the proposed methodologies to manage RMSs.
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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.