Exploring remote service provision in adult day centers during the COVID-19 pandemic

The COVID-19 pandemic profoundly impacted the long-term services and supports (LTSS) sector, necessitating a rapid shift from in-person services to remote. Adult day service centers (ADSCs) – a type of LTSS – offer in-person community-based programs comprised of health and wellness services to historically underserved populations, such as communities of color, low-income, and older adults. Based on data collected from 23 semi-structured interviews with 22 providers from eight ADSCs across a Mid-Atlantic state, this thesis explores the experiences of ADSC providers – such as directors, activity staff, and nurses – as they navigated pandemic-related closures. To ensure uninterrupted services, centers leveraged their existing infrastructure and adapted to a remote service model. An intricate interplay of technical (e.g., access to devices, internet) to non-technical (e.g., digital literacy, sociocultural context, limited staff) variables affected the overall success of remote services. Simultaneously, ADSCs grappled with limited reimbursement for remote services – which directly impacted their operations and the sustainability of remote services. These findings offer insights into the challenges and adaptations providers experienced amidst an unprecedented crisis, shedding light on the systemic issues throughout this period. The study seeks to inform future interventions that promote the sustainability of remote services in ADSCs, with a specific focus on preventing service disruptions for historically underserved populations

In Pursuit of Equity: The Politics of Desegregation in Howard County, Maryland

School desegregation policies aim to redistribute educational resources and opportunities more equitably, but they have not always done so. Evidence indicates that political factors, including resistance from White parents and legal constraints, have undermined desegregation policies’ potential to fulfill their aims. Yet, a few studies suggest that windows of opportunity to desegregate schools exist. Even so, these studies often focus on how a subset of political factors shape desegregation efforts, and some political factors remain understudied. Furthermore, school desegregation research tends to focus on either the political dynamics of advancing these policies or the effects these policies have on segregation. Thus, the extent to which political factors affect desegregation policies’ potential to reduce segregation and, eventually, to advance educational equity remains an open question. My dissertation addresses these gaps in the literature by using a race-conscious political framework and a qualitative-dominant, convergent parallel mixed methods design to explore the politics and outcomes of the Howard County Public School System’s (HCPSS) recent effort to desegregate by redistricting, or redrawing school attendance boundary lines. Howard County is an ideal setting to study desegregation because it possesses several favorable conditions for desegregating schools, including racial/ethnic diversity, espoused commitments to educational equity, and a history of racial/ethnic and socioeconomic integration. These favorable conditions allow me to “test” whether desegregation is a feasible policy goal for school districts and to provide policymakers with insights about how to advance desegregation policies in ways that maximize their potential to reduce segregation and promote educational equity. I find that school overcrowding, growing racial/ethnic and socioeconomic segregation, and resource inequities led the HCPSS Superintendent and the Howard County Board of Education to initiate redistricting. The superintendent proposed a redistricting plan that had the potential to reduce segregation in HCPSS. Yet, various political factors—including resistance from wealthy White and Asian parents and limitations from HCPSS’s formal attendance boundary adjustment policy—led the board to enact a redistricting plan that had relatively less potential to reduce segregation and would have increased it at some school levels. Upon implementation, the enacted redistricting plan appeared to reduce segregation in HCPSS, but those reductions likely resulted from enrollment changes in the district. Ultimately, findings suggest that, under favorable political conditions, desegregation policies do have the potential to reduce segregation. However, realizing these policies’ potential will require districts to either a) explicitly prioritize desegregation, rather than allowing policymakers to attempt to balance desegregation with other, often competing policy goals, or b) align desegregation with other policy goals, rather than pitting it against them

ANTIBACTERIAL MECHANISM OF PLANT-DERIVED PHENOLICS AGAINST SALMONELLA ENTERICA SEROVAR TYPHIMURIUM

Salmonella enterica serovar Typhimurium (ST) remain one of the main bacterial pathogens responsible for illnesses, hospitalizations, and deaths in the USA. Its ubiquitous prevalence in nature, invasive pattern and increasing antibiotic resistance make it a public health threat, warranting the discovery of novel antimicrobials that can be implemented as either treatments or as forms of control. Plant-derived compounds have been proposed as potential antimicrobials that can be used against gram-negative pathogens, with phenolic acids being of interest for their prevalence in nature and bioactivity. This research studied the effects of gallic acid (GA), protocatechuic acid (PA) and vanillic acid (VA) against ST. Findings showed these compounds to be able to inhibit bacterial growth in vitro, while also showing a reduction in the expression of key virulence genes, without inducing resistance over multiple passages. Further studies using a human epithelial cell line for studying host-pathogen interactions, showed their capability to reduce the number of ST that were able to invade the host cells. Further studies were performed in cecal fluid to test their potency in more complex environments and assess their effects on the microbiome. When in cecal fluid, compounds showed a reduced inhibitory potency compared to in vitro, but still exerted antimicrobial pressure against ST. When analyzing relative abundance of other bacteria through 16S-rRNA gene sequencing, there was an overall decrease in the Protobacteria phylum, while no significant negative effect was seen for other phyla like that of the Firmicutes and Actinobacteria. Experiments to determine the mechanism of action against ST showed these phenolic acids to permeabilize the cell plasma membrane, in addition to reducing cell wall synthesis. Scanning electron microscopy showed treated bacteria to have dents at the polar ends of the cell, while others were found in a duplet formation, suggesting further disruption of specific bacterial functions associated to cell division and structure. These findings suggest that despite their similarities, these compounds are capable of exerting different types of antimicrobial pressure against ST that could better inform their future use as control measures against ST, and their potential use case based on the desired outcome

Through Their Eyes and In Their Shoes: Providing Group Awareness During Collaboration Across Virtual Reality and Desktop Platforms

Many collaborative data analysis situations benefit from collaborators utilizing different platforms. However, maintaining group awareness between team members using diverging devices is difficult, not least because common ground diminishes. A person using head-mounted VR cannot physically see a user on a desktop computer even while co-located, and the desktop user cannot easily relate to the VR user’s 3D workspace. To address this, we propose the “eyes-and-shoes” principles for group awareness and abstract them into four levels of techniques. Furthermore, we evaluate these principles with a qualitative user study of 6 participant pairs synchronously collaborating across distributed desktop and VR head-mounted devices. In this study, we vary the group awareness techniques between participants and explore two visualization contexts within participants. The results of this study indicate that the more visual metaphors and views of participants diverge, the greater the level of group awareness is needed.https://doi.org/10.1145/3544548.358109

Example code and data for "Identifying physical structures in our Galaxy with Gaussian Mixture Models: An unsupervised machine learning technique"

See README.md.We present a python software repository implementing the PyGMMis (Melchior & Goudling 2018) method to astronomical data cubes of velocity resolved line observations. This implementation is described extensively in Tiwari et al. 2023, ApJ. An example is included in /example/ containing the SOFIA data of RCW120 used in Tiwari et al. 2023, ApJ, along with example scripts describing the full implementation of our code. The majority of parameter tweaking can be performed within 'rcw120-params.txt' which is continuously called during the procedure. A full description of the code and how to use it is in README.md (markdown file).This study was based on observations made with the NASA/DLR Stratospheric Observatory for Infrared Astronomy (SOFIA). SOFIA is jointly operated by the Universities Space Research Association Inc. (USRA), under NASA contract NNA17BF53C, and the Deutsches SOFIA Institut (DSI), under DLR contract 50OK0901 to the University of Stuttgart. upGREAT is a development by the MPI für Radioastronomie and the KOSMA/University of Cologne, in cooperation with the DLR Institut für Optische Sensorsysteme. N.S. acknowledges support from the FEEDBACK-plus project that is supported by the BMWI via DLR, Project number 50OR2217 (FEEDBACK-plus). S.K. acknowledges support from the Orion-Legacy project that is is supported by the BMWI via DLR, project number 50OR2311. Publication costs were provided by NASA through the award SOF070077 issued by USRA

Critical Montessori Education: Centering BIPOC Montessori Educators and their Anti-Racist Teaching Practices

While many BIPOC Montessori educators engage in anti-racist and culturally responsive teaching, Montessori education remains predominantly race-evasive. As a philosophy, it is rooted in colorblind perspectives in its focus on "all children" and lack of explicit centering of BIPOC students’ experiences. Teaching must account for race and racial lived realities in order to better support BIPOC students’ ways of knowing in culturally relevant and sustaining ways. This study seeks to center the voices of BIPOC Montessori educators and disrupt the pattern of Montessori research conducted without a critical racial lens. Framed by Critical Race Theory, this study focuses on the strengths, assets, and anti-racist teaching practices that one BIPOC educator brings to her classroom. I use critical ethnographic methods to better understand how a BIPOC Montessori teacher at a public charter Montessori school interprets and enacts the Montessori method to support BIPOC students. I consider how her racial identity informs her practices, and the structural barriers she faces at her school when enacting anti-racist and strength-based approaches. The guiding research questions of this study are: How does a Black Montessori teacher interpret the Montessori philosophy to more relevantly support her BIPOC students? How does she practice the Montessori method through culturally relevant and sustaining practices? What are the structural barriers that continue to challenge her as a Black educator doing her work? My analysis suggests that the teacher maintains her classroom space as a tangible and intangible cultural space that reflects and maintains her students' identities; that her own identity as a Black woman deeply contribute to the school's work around anti-racism and culturally responsive pedagogy; and that there are external barriers that both the teacher and the school face, that prevent them both from fully achieving culturally responsive teaching practices. At the core of the study, I seek to understand the possibilities and challenges of Montessori education from the perspective of BIPOC Montessori educators, and how we could learn from them to better support BIPOC students. I hope to begin a path toward more counter-stories in the Montessori community to specifically support BIPOC Montessori educators and understand the structural barriers they face to anti-racist teaching in Montessori programs in the United States

Purple Line Corridor Coalition GIS Field Maps Application for Walking Routes

An intermediate GIS class in the School of Community Planning developed an app using ArcGIS Field Maps to help record data from the field and then produced six walking tour maps for the Purple Line Corridor Coalition.The Purple Line Corridor Coalition (PLCC) is actively working to help keep the areas around the future stations inclusive for all income levels while still encouraging investment and density. This has important distinctions for zoning in Montgomery and Prince George's Counties separately and across some specific stations. Students in the course developed an app using ArcGIS Field Maps to help the PLCC record data from the field and during asset mapping walking tours with community members. The maps show designated areas with routes for the PLCC walking tours

Can A Relationship Optimism and Perfectionism Intervention Increase Romantic Relationship Well-being?

A key predictor for romantic relationship quality is relationship optimism (also known as positive relationship expectations). However, relationship expectations can negatively affect relationship well-being when they become unrealistic and rigid, also known as dyadic perfectionism. The current research tests the effectiveness of a novel intervention that aims to improve relationship optimism and decrease dyadic perfectionism among couples. The four components of the intervention were based on previously validated social-psychological interventions that target relevant traits such as personal optimism and growth mindset. There were three intervention conditions. The first condition paired relationship optimism and dyadic perfectionism components together. The second condition only had the relationship optimism component. The third was the control condition. Results showed that despite a significant effect of the intervention on lowering immediate dyadic perfectionism, the intervention failed to have enduring effects on relationship optimism or dyadic perfectionism. Additionally, this intervention did not improve relationship quality on other indicators. Implications, broader impacts, and future directions based on the current results are discussed

SPRAY STRATEGIES AND SELECTION FOR FUNGICIDE RESISTANCE: FENHEXAMID RESISTANCE IN BOTRYTIS CINEREA AS A CASE STUDY

Fungicide resistance is a limiting factor in sustainable crop production. Despite the wide adoption of general resistance management strategies by growers, the recent rate of resistance development in important fungal pathogens is concerning. In this study, Botrytis cinerea and the high-risk fungicide fenhexamid were used to determine the effects of fungicide dose, tank mixture, and application timing on resistance selection across varied frequencies of resistance via both detached fruit assays and greenhouse trials. The results showed that application of doses lower than the fungicide label dose, mixture with the low-risk fungicide captan, and application post-infection seem to be the most effective management strategies in our experimental settings. In addition, even a small resistant B. cinerea population can lead to a dramatic reduction of disease control efficacy. Our findings were largely consistent with the recent modeling studies which favored the use of the lowest possible fungicide dose for improved resistance management

Deep Thinking Systems: Logical Extrapolation with Recurrent Neural Networks

Deep neural networks are powerful machines for visual pattern recognition, but reasoning tasks that are easy for humans are still be difficult for neural models. Humans possess the ability to extrapolate reasoning strategies learned on simple problems to solve harder examples, often by thinking for longer. We study neural networks that have exactly this capability. By employing recurrence, we build neural networks that can expend more computation when needed. Using several datasets designed specifically for studying generalization from easy problems to harder test samples, we show that our recurrent networks can extrapolate from easy training data to much harder examples at test time, and they do so with many more iterations of a recurrent block of layers than are used during training