Institutional Repository

The UDSpace Institutional Repository collects and disseminates research material from the University of Delaware.

  • Faculty, staff, and graduate students can deposit their research material directly into UDSpace. Faculty may use UDSpace to fulfill the University of Delaware Faculty Senate Open Access Resolution, and in many cases may use it to fulfill open access requirements from grant funding agencies.
  • Departments can use UDSpace to publish or distribute their working papers, technical reports, or other research material.
  • UDSpace also includes all doctoral dissertations from winter 2014 forward, and all master's theses from fall 2009 forward.

To learn more about UDSpace, and how you can make your research openly accessible to the public, visit our UDSpace Policies website.

 

Recent Submissions

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Disseminating Resources Online for Teaching Sex Education to People with Developmental Disabilities
(Sexuality and Disability, 2021-06-12) Curtiss, Sarah L.; Stoffers, Melissa
Sex education is important for individuals with developmental disabilities; however, it is difficult for educators to find resources to support them when teaching sex education. A website, asdsexed.org, was developed to disseminate sex education resources. Using analytic data from the website we explored how dissemination occurs online. We identified (1) how visitors were referred to the website; (2) what search terms were used to look for sex education resources; (3) what content was most frequently viewed; and (4) how visitors engaged with the content. Search engines were the top referrer. Variations on the phrase “body parts” were the most frequently recorded terms. Free lesson plans were the most viewed content. Privacy social stories were the most engaged with content. Online dissemination was a complex undertaking but did allow for potential sex educators to be connected with research-based resources.
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An analysis of U.S. state level cybersecurity plans and policies
(University of Delaware, 2022) Karakoç, Mesut
While technological developments and interconnected information networks have improved many aspects of individuals’ lives and increased the effectiveness of public and private services, they also have created cybersecurity challenges. To address these challenges, governments at federal, state, and local levels have started to plan and make policies that improve their cybersecurity posture. This research explores U.S. state governments’ cybersecurity planning documents and cybersecurity-related policies. Through the use of document analyses methods, the research reveals the landscape of 50 states’ cybersecurity planning documents and sample states’ cybersecurity-related legislative policies. In addition, this study combines issue-area specific criteria gained from the state and local government cybersecurity scholarship with overall guidelines from the 2018 NIST Cybersecurity Framework. By doing so, this research contributes to a Best Practices Framework, which is the most complete set of assessment criteria for state cybersecurity planning and policymaking. The research offers a categorization of states based on how well their planning documents and policies incorporate the Best Practices Framework. Findings show that while all states engage in cybersecurity planning and policy making to some extent, there is significant variation across states’ level of incorporation of the Best Practices Framework. ☐ For the sample states with high cyberattack figures, the research conducts a correspondence assessment between their planning documents and policies. The assessment finds that the majority of sample states present a high correspondence level between planning and legislative policy, and their policies incorporate the Best Practices Framework better than their plans. Overall, the research findings lead to the conclusion that cybersecurity policies often lead planning rather than the other way around. Besides contributing one of the first academic endeavors that systematically research state level cybersecurity governance, the research findings and conclusions offer lessons for state cybersecurity planners and policy makers. The research significance stems from the exploration of an emerging and understudied issue area for state governments, the development of an original framework for the assessment of cybersecurity planning documents and policies, and the generation of options for improving cybersecurity posture for state governments.
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Training a machine learning model for underwater chemical source localization in simulated turbulent flows
(University of Delaware, 2022) Phan, Hau
Underwater chemical source localization is challenging due to the dynamic and chaotic processes involved. Averaged across long time scales, the geometry of the chemical plume is determined by the mean water flow. At shorter time scales, the turbulence of water tends to create swirls, eddies, and vortices, preventing the observation of a smooth gradient of chemical concentration. Instead, the chemical concentration in the plume downstream from a source is intermittent with mostly low-level concentrations interspersed with short high-concentration segments. Various underwater platforms could deploy chemical sensors to sample the chemical concentration and measure the water flow as they move. By traveling with a predefined trajectory, the sensors can collect observations at different positions. However, these observations may consist of only a few non-zero chemical concentration measurements along the path through the turbulent plume. It is non-trivial to process these measurements to recognize the geometry of the chemical plume and predict the chemical source’s location. In order to predict the location of any chemical sources, we train recurrent neural networks to process the input time series jointly consisting of the chemical concentration observations, water flow measurements, and sensor platform movements. From there, the neural network model constructs a heatmap that represents the probability that the chemical source is located at different locations around the sensing platform. This heatmap is trained based on simulations where a sensor platform moves along different trajectories across numerous scenarios of various source locations, water flows, and turbulence characteristics. In each simulated trajectory, the heatmap at each time step (after an initial non-zero chemical concentration measurement) is compared to the true source location using a Wasserstein distance metric as the loss function. This encourages the heatmap to minimize the expected distance given the source localization predictions and the true source location, which is known during simulation. Since Wasserstein distance keeps the geometries of distributions in consideration and it does not require the support of distributions to be the same, it provides an additional advantage in comparison to the traditional cross-entropy-based loss functions. Thus, when the source is out of the prediction range, the heatmap can still be useful to predict the direction of the chemical source location which respects the sensor platform’s current location. Additionally, we show that the expected Wasserstein distance for cases where no chemical is detected leads to a regularization term that shrinks the variance of source localization predictions. In order to train and test our methodology, we created a particle-based turbulence simulation based on prior work. The simulation models the Spatio-temporal variation in water flow along with the diffusion of the dissolved chemical. In every simulation episode, the source location is randomized radially symmetric around the sensor platform. At each time step, the sensor platform moves at a fixed speed for a predefined number of steps. To assess performance, we measure the resolution-accuracy tradeoff of the heatmap prediction under various water flow characteristics. The results indicate the potential for predicting chemical source locations from chemical sensor readings from limited observations in turbulent environments.
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Evaluating the properties and uses of metal-organic framework (MOF) and polymer nanoparticles for applications in vaccines and pulmonary drug delivery
(University of Delaware, 2022) Stillman, Zachary
Pulmonary administration offers many advantages for biomedical applications because of the ability to deliver cargo locally and systemically. For drug delivery, the pulmonary route of administration generally has high bioavailability of delivered therapeutics and can avoid off-target effects for pulmonary diseases alongside avoidance of first-pass clearance. In particular, nanomedicine approaches to pulmonary drug delivery using biomaterials have risen in prominence because of the ability to have ready cellular internalization of nanomaterials and high loading capacity of therapeutic cargo. However, identifying nanomaterials that have tunable applications and ideal suitability for pulmonary drug delivery remains a challenge. ☐ In this work, we evaluate two types of nanomaterials for pulmonary therapeutic delivery applications: poly(ethylene glycol) (PEG) hydrogel and metal- organic framework (MOF) nanoparticles. PEG has impressive biocompatibility and can lead to sustained release of cargo; however, the properties of PEG-based nanoparticles (NPs) are often conflated with macroscopic gels. In this dissertation, we characterize the degradation rate and products of PEG-based NPs to better understand the effects of these characteristics for tuning interactions with pulmonary innate immune cells, macrophages. We discover that a variety of PEG-based NP formulations, particularly those with degradable crosslinkers, increase the survival of macrophages in a tunable manner. These results could affect a number of cell-based therapies that rely on survival of macrophages such cancer vaccines. ☐ The second class of NPs evaluated is that of MOFs. MOFs have advantages for drug delivery and vaccine applications because of their high porosity, variable chemistry in their organic linkers and metal clusters, and tunable physiochemical properties. In this dissertation, we first explore the use of UiO-66, a zirconium-based MOF, as a pulmonary drug delivery vehicle. Through the modulation of the synthesis of UiO-66 via precise modulation of water and linker amounts, we present a strategy to control NP size as well as missing linker extents and report the discovery of inherent UiO-66 fluorescence, a huge boon for theranostic applications. We demonstrate that the aerodynamic properties of UiO-66 are of the range leading to efficient aerosol delivery and the NP framework shows biocompatible both in vitro and in vivo. Moreover, UiO-66 NPs can successfully be loaded with cargo that is selectively released in environments that mimic intracellular pH. Building from these successes, we evaluate a series of aluminum-based MOFs for pulmonary vaccination, given their structural similarities to alum, a vaccine adjuvant that elicits strong humoral immune responses. In vitro and in vivo examinations reveal that the Al-based MOF NPs activated macrophages more effectively than alum and were able to generate robust mucosal IgA and serum IgG antibodies, specifically IgG2a, following a murine pulmonary vaccination study that indicates creation of effective local and cellular immune responses. Furthermore, many of the Al-based MOF NPs fell into the ideal aerodynamic size range for alveolar deposition, unlike alum. The results demonstrate great potential for use of Al-based MOFs for pulmonary vaccination and highlight overall the potential for molecularly-defined NPs as novel pulmonary delivery strategies.
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Monitoring arsenic mobilization in variably flooded rice fields with Fe and Mn indicator of reduction in soil (IRIS) films
(University of Delaware, 2022) Hanrahan, Rebekah
Rice is a vital food source for millions of people, but it is susceptible to arsenic (As) uptake because it is grown in flooded paddy conditions that lead to As mobilization and because rice has an efficient As uptake pathway. The use of alternating wetting and drying (AWD) can help to immobilize As through water drainage, but is difficult to manage. We posit that indicators of reduction in soil (IRIS) films can be used an easy-to-use passive sensor to inform farmers of soil reducing conditions and risk of As mobilization in rice paddies. We used Fe or Mn coated IRIS films to determine when, during the life cycle of rice, these films would best predict the mobilization and uptake of As. We hypothesized that Mn films would have faster rates of paint removal than Fe films, but that Fe films would better predict As mobilization and accumulation in rice grain. We found that Mn films had a more rapid response (i.e., faster paint removal from films) than Fe films and both were predictive of soil redox potential (ORP) when in contact with the soil for 3 and 8 days, respectively. We then installed and removed Fe and Mn IRIS films every 8 and 3 days, respectively, in rice paddies subjected to 6 water managements (flooded, nonflooded and 4 different AWD severities) to determine when IRIS films show the strongest relationship with porewater and grain As. We found that both Fe and Mn IRIS films were equally as predictive as porewater Fe and ORP in predicting porewater and grain As concentrations. Synchrotron X-ray fluorescence imaging also revealed co-localization of As onto the synthesized Fe oxides on Fe IRIS films, as well as the neoformed Fe oxides on Mn films. This finding suggest that IRIS films may also serve as passive As samplers in reducing environments. Moreover, we recommend that IRIS paint removal >70% be used as a marker for risk of As mobilization and rice uptake; this recommendation must be verified in other soils. While future work is still needed to understand their wider application to other soils, this work shows that IRIS films can be an affordable and effective tool for farmers to manage water and mitigate As mobilization