Maryland Shared Open Access Repository

MD-SOAR is a shared digital repository platform for twelve colleges and universities in Maryland. It is currently funded by the University System of Maryland and Affiliated Institutions (USMAI) Library Consortium (usmai.org) and other participating partner institutions. MD-SOAR is jointly governed by all participating libraries, who have agreed to share policies and practices that are necessary and appropriate for the shared platform. Within this broad framework, each library provides customized repository services and collections that meet local institutional needs. Please follow the links below to learn more about each library's repository services and collections.

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Recent Submissions

  • Item type: Item ,
    Accredidation Pressures, Culture, and the Lived Experiences of Community College Administrators: A Case Study
    (2021-03-22) Burton, Khalilah; Gaulee, Uttam; School of Education and Urban Studies; Community College Leadership Program
    The purpose of this research was to investigate the experiences and perceptions of community college administrators who lead campus accreditation initiatives including the accreditation visit. The study investigated administrator perceptions of compulsory or psychological pressure when involved with accreditation activities. The study employs the conceptual framework of isomorphism through an analysis of coercive, normative, and mimetic isomorphism shown in participant responses. Coercive isomorphism is related to pressures from external agencies that an organization may depend on for compliance. Normative isomorphism occurs when organizations develop a normalized standard for compliance based on a leading group or agency. Mimetic isomorphism occurs when an institution copies the practices of another institution to ensure compliance. The population for this study was a sample of six community college administrators who are responsible for accreditation planning and visitations at their campuses. The participants provided a myriad of reasons and examples of pressures they experienced related to accreditation. Four themes emerged as a result of analysis of findings. The findings were consistent with the components of coercive, normative, and mimetic isomorphism. Each participant indicated in in their own words that they experienced coercive pressure from external agencies, normative pressure to comply with a set of standards evenly applied throughout the accreditation agency, and mimetic pressure from leading institutions with similar accreditation profiles. Participant responses revealed that community college administrators are aware of pressures placed on them by continuous improvement efforts for institutional effectiveness, campus leadership, the compliance report required by accreditors, and accountability measures enforced by the federal government, state agencies, and institutional accreditors. The researcher intends for this study to serve as a valuable tool for accreditation administrators and campus leadership to enrich the institution’s accreditation process, campus staff, and the institution’s effectiveness.
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    Variable bit rate GPU texture decompression
    (Wiley, 2011-07-19) Olano, Marc; Baker, Dan; Griffin, Wesley; Barczak, Joshua
    Variable bit rate compression can achieve better quality and compression rates than fixed bit rate methods. None the less, GPU texturing uses lossy fixed bit rate methods like DXT to allow random access and on-the-fly decompression during rendering. Changes in games and GPUs since DXT was developed make its compression artifacts less acceptable, and texture bandwidth less of an issue, but texture size is a serious and growing problem. Games use a large total volume of texture data, but have a much smaller active set. We present a new paradigm that separates GPU decompression from rendering. Rendering is from uncompressed data, avoiding the need for random access decompression. We demonstrate this paradigm with a new variable bit rate lossy texture compression algorithm that is well suited to the GPU, including a new GPU-friendly formulation of range decoding, and a new texture compression scheme averaging 12.4:1 lossy compression ratio on 471 real game textures with a quality level similar to traditional DXT compression. The total game texture set are stored in the GPU in compressed form, and decompressed for use in a fraction of a second per scene.
  • Item type: Item ,
    Edge States Effects in Quantum Work Statistics
    (2026-01-26) Cavalcante, M. F.
    Motivated by the objective of quantifying the energetic cost of accessing boundary phases through local control, we investigate here a simple, analytically tractable quantum impurity model. This model exhibits a rich boundary phase diagram, characterized by phases with different numbers of edge states. By considering a local quench protocol that drives the system out of equilibrium, we calculate exactly the resulting quantum work distribution across these phases. Our results show that the presence of edge states strongly alters this distribution. In particular, we analytically determine key fingerprints of these states both near the low-energy threshold and in the high-energy region.
  • Item type: Item ,
    Snowcrete 2026, Potomac Sewage Spill, ICE Warehouses, Closing Chinese Immersion Program
    (I Hate Politics Podcasts, 2026-02-03) Dasgupta, Sunil; Friedson, Andrew
    How do other jurisdictions and past snow events compare to Snowcrete 2026? Montgomery County Councilmember Andrew Friedson weighs in prior to oversight hearings. Leaders from Republican-leaning jurisdictions oppose the 287g ICE agreement ban because they say the arrangement preempts ICE violence. MD Delegate Vaughn Stewart explores legislative ideas to limit ICE presence after the federal agency buys two warehouses in the state to serve as detention centers. In Prince Geroge's County, a Chinese Immersion Program in a Title 1 school in College Park is being cut as part of budget woes. And more. Newly in public domain music by George Gershwin, Paul Whiteman band, and Marian Andersen.
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    Does Reasoning Help LLM Agents Play Dungeons and Dragons? A Prompt Engineering Experiment
    (Wordplay Workshop, 2025-11-09) Delafuente, Patricia J; Honraopatil, Arya; Martin, Lara J.
    This paper explores the application of Large Language Models (LLMs) and reasoning to predict Dungeons & Dragons (DnD) player actions and format them as Avrae Discord bot commands. Using the FIREBALL dataset, we evaluated a reasoning model, DeepSeek-R1-Distill-LLaMA-8B, and an instruct model, LLaMA-3.1-8B-Instruct, for command generation. Our findings highlight the importance of providing specific instructions to models, that even single sentence changes in prompts can greatly affect the output of models, and that instruct models are sufficient for this task compared to reasoning models.