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Middle and high school principals’ perceptions of movement as part of instruction in lecture-style classrooms
Get PDFResearch has reported multiple benefits when using movement in the classroom as part of the curriculum. These benefits have been shown to help students physically, emotionally, and mentally. Research on the benefits of movement on our bodies, specifically centered on brain growth and development in both medical and educational studies, supported that movement is beneficial for students’ learning and retaining information, along with developing positive lifelong habits (Holzschneider et al., 2012; Lin et al., 2012; Lu et al., 2016; Mahar et al., 2006; Stevens-Smith, 2016).
A primary objective of this study was to determine principals' perceptions of best practices for adding movement into middle and high school courses that are routinely identified as traditional lecture courses. As part of the learning environment, movement in the classroom helps students learn the content without the teacher providing extra materials or taking time to focus on this piece of the instruction in planning or implementation. There is a potential need for principals to provide professional development on best practices for implementing movement in the classroom, specifically those classrooms centered on lecture-style instructional methods.
This exploratory sequential mixed methods study incorporated an understanding of action-based learning theory (Madigan, 2004; Medina, 2008; Ratey, 2008) as a framework for understanding principals’ perspectives on movement in traditional lecture-style courses. A survey and interviews were designed to explore and discover patterns in principals' perspectives. One finding was principals understand the benefits of movement as part of learning, but do not have the resources to support teachers in this area. Second, testing demands, significant amounts of content, and time restrictions were barriers principals faced when trying to promote best instructional practices. Finally, the demand for students in both middle and high schools to be
prepared for lecture-style courses in college prevented many principals from being able to convince teachers and districts that movement as part of instruction would be a purposeful school initiative
Artificial Intelligence-based Network Modeling for enabling Zero Touch Automation in Next Generation Cellular Networks
Get PDFThe crux of contemporary network design involves mathematical models to describe the quantitative impact of system components on overall performance. However, these models often grapple with trade-offs between accuracy and complexity, and the burgeoning complexity of wireless networks compounds the problem. Artificial Intelligence (AI) and Machine Learning (ML) techniques present promising solutions, albeit with their own challenges, including integration of domain knowledge, sparsity of training data, and network environment dynamism. To address these challenges, the major contribution of this dissertation is to develop novel AI-based network behavior models, which are domain-aware, interpretable, and robust to sparsity and dynamicity.
It first proposes an interpretable AI/ML-based framework to create a large-scale 3D propagation model by leveraging domain knowledge to create a novel set of key predictors (features) that can characterize the physical and geometric structure of the environment traversed by a signal in its propagation path.
Subsequently, the dissertation introduces a novel deep transfer learning framework to create network behavior models when the available network data is extremely sparse and unrepresentative, especially for system-level network behavior modeling. By leveraging network data from similar (source) cells, the proposed novel framework boosts the performance of conventional deep transfer learning by harnessing the strengths of deep neural networks and extreme gradient boosting methods.
However, in a dynamic network environment (with continuously changing user mobility, traffic demand, etc.), selection of source cells with a similar network environment becomes challenging and can hinder the performance of transfer learning. Thus, the dissertation further presents a novel AI-based domain-aware cell similarity prediction framework, designed to identify similar cells in a spatio-temporally dynamic environment to enable transfer learning for cellular network modeling.
In conclusion, the dissertation paves the way for a future of zero-touch automation solutions in network design and optimization, using AI and ML, which could yield immense economic benefits for network operators and better quality of experience for users. It encapsulates novel frameworks for network behavior modeling and paves the way for future research in this field
Hip Brace for Improved Patient Outcomes During Athletic Activity
Get PDFIt is estimated that 30 to 40 percent of adults who participate in sports experience hip pain (Langhout et al., 2019; Thorborg et al., 2017). Additionally, roughly one in four people will be diagnosed with symptomatic osteoarthritis in their lifetime (Murphy et al., 2010). Due to the hip joint’s integral role in many everyday processes such as walking and bending, it is imperative that the health of the joint be maintained. While various treatments for hip pain exist currently, some patients require extra support while participating in athletic activities, even after surgical intervention such as hip arthroscopy. Current bracing methods available in the market include postoperative stabilization braces, joint unloading braces, and compression wraps (Kemker et al., 2021). However, none of these solutions contain all the desired properties for an athletic hip brace. This thesis sets out to develop a novel hip brace that can be worn during athletic activity, while still providing pain relief and alignment correction to the hip joint. The methods used to create said hip brace are outlined, and then the case study performed to investigate the brace’s efficacy at meeting certain design criteria and outcome measures is discussed. The case study shows that the brace has promising results in the comfort and task initiation categories, but needs improvement in the area of hip alignment correction. In conclusion, with a few modifications in future research, the novel hip brace designed in this thesis has the potential to help millions of people get back to participating in athletic activities even after hip injury
From the Graveyard Shift to the Benzene Plume: Visualizing Embodied Work Experience at the Former B.F. Goodrich Tire Factory in Miami, Oklahoma
Get PDFFrom the time of its construction in 1944, to its eventual closure on February 28, 1986, the B.F. Goodrich Tire Factory in Miami, Oklahoma has played an important role in the community. During its 40-year lifetime, it was one of the largest employers in the Ottawa County and Tri-State region (Oklahoma, Kansas, and Missouri), employing over 2,000 workers at its peak. As one community member has noted, “when people got a job at B.F. Goodrich, they thought they had a job for life.” However, this was not to be. On August 23, 1985, a company executive announced that the plant would close in six months, citing increasing competition from foreign producers. In the years following the closure, the site switched owners numerous times while becoming dilapidated. There are also concerns surrounding the use of various chemicals in the production process and an unclear situation regarding who is responsible for cleanup. As a result, the residents of Miami continue to face the consequences of lasting environmental degradation and health concerns.
As part of my MS thesis project, I am undertaking multi-faceted mixed-method analysis that seeks to answer the question: how can a Miami community-based organization’s archival materials be incorporated into a StoryMap to engage a community, illustrate the embodied work experience, and introduce the environmental impacts of the five key areas of concern stemming from the plant closure?
Through the use of a variety of quantitative and qualitative data and methods, this research showcases the stories of those who lived, worked, and interacted with the B.F. Goodrich Tire Factory (in the following referred to as “the plant"), where five key items contribute to environmental contamination concerns: Benzene, Asbestos, Carbon Black, Underground Storage Tanks (USTs), and a Solid Waste Disposal Site. To examine these materials and their impacts, I (and others) have conducted a series of interviews with community members about their experiences with environmental contamination and the slow violence resulting from lengthy clean-up endeavors, as well as important areas within the factory site. Additionally, I have organized and analyzed a series of related documents (primarily court proceedings, images, and corporate correspondence) and interviews. I used this qualitative data combined with point locations derived from on-site photographs, to create a community-based B.F. Goodrich ArcGIS Story Map that visualizes work experience at the plant and highlights the environmental and societal impacts stemming from the closure.
After conducting this analysis, I found that: 1. Contamination is a very slow process and slow remediation and observation efforts are needed to address this. 2. Deindustrialization in the United States creates lingering negative societal and environmental impacts. 3. Participatory research (including archival and interviews) that centers the voices of residents via the creation of a StoryMap is a promising strategy for visualizing the events of the Miami plant.
By highlighting community stories, we gain a better understanding of deindustrialization and its connections to places such as Miami across the United States
Relationship between volume of physical activity and sleep quality among college students
Get PDFPhysical activity (PA) and sleep quality (SQ) are crucial components of a healthy lifestyle. College students have a high prevalence of poor SQ. PA may be an important way to improve SQ. The main purpose of this study was to determine the relationship between volume of physical activity and sleep quality among college students. Participants (n=159) were college students who enrolled at a regional university. Recruitment was done by email blast with online survey link. The International Physical Activity Questionnaire (IPAQ) and Pittsburgh Sleep Quality Index (PSQI) were used to analyze PA and SQ. Total PA, vigorous PA, moderate PA, and light PA were calculated from the IPAQ in MET·min·wk-1. PSQI includes a global sleep quality index with seven component scores. Pearson correlation was used to analyze the relationship between volume of PA and SQ. Independent-t tests and one-way ANOVAs were used to compare differences in SQ in groups created based on volume of reported PA. No significant relationships were observed between any PA variable and SQ. The independent t-test comparing those reporting no vigorous PA and those reporting any vigorous PA showed significantly better SQ in those reporting any vigorous PA (t =-2.14, p=.03). When looking at SQ differences between groups formed based on reported volume of vigorous PA, a significant difference was found (F = 4.79, p=.01). Those reporting a low volume of vigorous PA had significantly better SQ than those reporting no vigorous PA (p = .01) and a trend toward better SQ when compared to those reporting a high volume of vigorous PA (p = .08). The conclusion of this study was that college students who reported small amounts of vigorous activity reported better SQ as compared to vigorously inactive college students. This study could help to understand the importance of PA to improve sleep quality among college students
Moral foundation violation effects on felt emotions, perceptions of moral intensity, and ethical decision-making
Get PDFMoral Foundation Theory (Graham et al., 2013) posits how individuals may decide what behaviors are morally transgressive and how to then make ethical decisions regarding situations containing ethical elements. Despite the inclusion of specific discrete emotions into MFT, relatively little research has empirically examined what discrete emotions are associated with various moral foundations or the differential effects of these emotions on related processes such as perceived moral intensity or ethical decision-making (Kligyte et al., 2013; Johnson & Connelly, 2016; Higgs et al., 2020). To test this, two studies were employed. The first study conceptually replicates previous research (Landmann & Hess, 2018) and establishes the emotional profiles that are elicited by experiencing violations to different moral foundations. This is done by experimentally manipulating moral violations present in an ethical dilemma between subjects, measuring an array of emotions felt by participants, and examining the patterns of felt emotions to establish the emotional profiles. Results showed partial support for the theoretical pattern of emotions elicited from moral foundation violations. However, there was little overlap in this pattern across studies. Violations to the care and fairness foundation resulted in greater perceptions of the moral intensity of a situation. Unique patterns of results between moral foundation violations and ethical decision-making emerged but receiving a violation to a moral foundation generally increased the usages of ethical sensemaking processes and resulted in more ethical decisions. Theoretical and practical implications, limitations, and future directions are discussed
Examining the effects of nudging and education on trust: An experimental comparison of potable recycled water interventions
Get PDFWith increasing interest in applications and insights from decision sciences, it has become important to define ethical grounds governing ways to interact with those receiving behavioral interventions. Here, I seek to evaluate two interventions, libertarian paternalistic default nudges and educational decision aids, on their impact on trust (a core element of American Psychological Association’s Integrity principle) when making decisions regarding recycled water. In 3 studies, I show that while both educational interventions and default nudges could be used to influence individuals’ decision to use recycled water, education managed to maintain trust while some default nudges decreased it. Specifically, calculating difference scores for domain specific trust (pre and post experimental conditions) revealed that the education condition did not significantly impact participants’ trust (M=.02, p=.82; M=-.05, p=.58, M=.06, p=.49). The default-in condition, on the other hand, led to either a significant or near significant reductions in trust (M=-.28, p<.001; M=-.22, p<.001, M=-.11, p=.08). These results can have some potential practical implications, as they can provide water reuse professionals and policymakers with recommendations as to which intervention is more likely to maintain public trust. They can also have important ethical implications. By demonstrating that default nudges can decrease individuals’ trust, these results provide some of the first evidence that the implementation of default nudges might contradict what is recommended in the APA’s code of conduct (professionals have an obligation to maintain trust). This research is an important steppingstone toward the goal of being able to empirically quantify and evaluate ethical costs associated with different kinds of behavioral interventions
Computational Study and Manipulation of Local Chemical Environment in heterogeneous Catalysis and Separation.
No full textReactive separation is proposed as a less energy-intensive chemical process with functions of catalytic reaction and separation. It offers an active chemical conversion combined with selective separation to obtain the desirable products. Catalytic membranes are one of the successful examples and the design of it requires understanding the structure-activity relationship between catalyst and membranes.
To guide the process design, this thesis gives computational insight on the following aspects: catalyst design focused on selectivity and activity in aqueous environments, and a porous structure suitable for incorporating catalytic active sites and responsible for separation. For catalyst design, the aim is to understand the role of the local chemical environment on reaction activity and selectivity. It is promising to tune the reaction activity by manipulating its solvation environments and coordination environment for the catalyst. By understanding the interplay between catalytic performance and the local chemical environment, a catalytic active site with a well-defined structure is proposed for further catalytic membrane design. Additionally, a well-established membrane for gas separation is studied to characterize cavity formation with tunable membrane contents. The study encompasses fundamental aspects of catalysis, including electronic structure, kinetic principles, and temperature-dependent separation processes, while analyzing detailed polymer structures.
Firstly, the aldol condensation reaction of cyclopentanone (CPO) over metal oxide MgO is investigated. Functionalized groups grown on an oxide catalyst highlights the tunable kinetic performance and promotional role of water in association with functionalized groups. Density Functional Theory (DFT) calculations show the strong adsorbate-adsorbate interaction results in hindered C-C coupling step, which contributes to low reaction rates observed on the functionalized surface. The role of water is identified in facilitating CPO adsorption and reducing the kinetic barrier of the kinetic-relevant step. Water thereby promotes the reaction rate by alleviating serious adsorbate-adsorbate interactions on the functionalized surface. This study paves a way to understand the solvent effect on the heterogeneous catalyst.
With an emphasis on denitrification in wastewater treatment, palladium has been proposed as an active catalyst for wastewater treatment, but the detailed mechanism and the solvent effect are not well understood. To address this, DFT calculations were employed to investigate the detailed reaction mechanism and kinetics for nitrite reduction. This study revealed that the overall activity is primarily determined by the activity of NO and a comprehensive kinetic model is developed to explain the observed reaction order. Interestingly, water played a promotional role in the nitrite hydrogenation reaction over the palladium catalyst. DFT analysis demonstrates that water facilitates a lower-energy pathway for hydrogenation by shuttling surface hydrogen to the reactants. The tunned free energy pathway aligns with the experimental findings on the reaction order.
The solvent effect is associated with the micro-solvation environments created by a polymer (n-isopropyl acrylamide) overlayer that shows temperature-dependent behavior. Growing this NIPAM overlayer on the catalyst support alters the selectivity and activity of palladium along with the tuned solvation environment. Molecular dynamics simulations propose a lower activation barrier introduced by NIPAM due to polymer-water interactions. The presence of polymer in the aqueous phase forms a structured water layer on the palladium, favoring the reaction pathway through the proton-shuttling mechanism. These findings enhance our understanding of designing and manipulating the micro-solvation environments for a tunable solvent effect.
As revealed on metal catalysts, the binding energy of NO provides crucial insights to promote reaction activity and selectivity. The binding energy determines the activation of NO bond hydrogenation is highly associated with the structure of the active sites. The investigation extends to the activity of single atoms in nitrite reduction powered by renewably generated electricity and examines the denitrification mechanisms across dispersed Co and La-based catalysts. The enhanced charge transfer is identified on NO bonded with La. and different mechanisms are investigated including entropic stabilization and the solvent effect. The study suggests that atom-dispersed metal is one of the promising catalysts toward a high activity and selectivity of NO reductions which dominates the denitrification reaction.
The catalytic membrane requires a stable polymer structure to selectively filter out the ions. We conducted a simulation on membrane synthesis using a triptycene-based membrane (TPBO) with tunable cavity distribution based on the content of triptycene. By manipulating the content of the triptycene unit, the free volume distribution corresponding to the triptycene molecule content is obtained. These structure-properties relationships reveal the flexibility of TPBO-based polymers for gas separation. Our study on polymer structure provides a foundation acknowledge for further membrane design which is critical to catalytic membranes.
Overall, this dissertation suggests a synergetic combination that takes advantage of polymer structures and single-atom catalysis to achieve enhanced activity and selectivity in reactive separation. This approach holds great promise for advancing catalytic technologies and addressing challenges in fields such as separation science and environmental sustainability. This study on catalysis research has been extensively focused on understanding the fundamental principles governing catalyst performance, with a growing recognition of the significance of the local chemical environment. Fine-tuning and manipulating this environment presents novel avenues for catalyst design
Between the leaves: cannabis policy, medicalization, and user communities in the Sooner State
No full textThis anthropological study uses an ethnographic approach to contextualize and explore the policy behind Oklahoma’s expansive medical cannabis industry and the lived experiences of user communities in the state. First, I conduct a history of cannabis policy in the United States to reveal the roots of the negative racial bias surrounding perceptions of cannabis. This bias was used as a tool by politicians throughout the 20th century to get votes by implementing anti-cannabis legislation. Next, I explore how politicians navigate the creation of cannabis policy in a conservative state where there is still a negative bias toward cannabis. In order to keep getting votes, politicians aim to appease their constituents and create ample opportunities for the state. By allowing cannabis to become medicinally legal in the state, politicians are able to satisfy conservative constituents by maintaining the stigma toward recreational cannabis while opening the state up to the lucrative economic opportunities offered by the medical cannabis industry. However, this medicalization is not rooted in biomedical practices. Given the state’s lenient regulations on medical cannabis, cannabis use on the ground occurs in both medical and recreational contexts. As a result, cannabis users and those employed in the industry are still subject to the negative stigma toward cannabis. Lastly, I explore how user communities navigate this stigma toward cannabis by normalizing its use through interactions and participation
Forecasting the COVID-19 pandemic in the United States and Peru using ARIMA, LSTM, GRU, CNN, and a hybrid approach
Get PDFThe COVID-19 outbreak spread swiftly and infected many individuals resulting in overwhelmed and overfilled hospitals causing an immense loss of life globally. Identifying the number of infected individuals preemptively provides critical time for governmental and health officials to implement a strategy to respond to the pandemic such as requiring masking, reducing public gatherings, closing restaurants, as well as additional time to prepare hospitals and medical staff for surges in infections. The work explores implementing convolutional neural network models (CNN), long short-term network models (LSTM), gated recurrent unit models (GRU), the combination of encoding CNN layers and decoding LSTM and/or GRU layers in a hybrid model, and Auto-Regressive Integrated Moving Average (ARIMA) models to predict COVID-19 case count in the United States and Peru for 7, 15 or 30 days in the future using 30 days of case counts. The study evaluates predictions from January 23, 2020 through March 9, 2023 for the United States and March 6, 2020 through April 2, 2023 for Peru. For each model, the forecasting results are displayed visually and presented statistically using RMSE and MAPE. The hybrid model performed as well as or better than any other model when predicting 7 days, 15 days, or 30 days into the future. These results demonstrate models that potentially assist healthcare providers and policymakers’ response to the spread of COVID-19