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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Bowie State University

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Recent Submissions

Idle Space, Missed Opportunity: Evaluating the Vacant Buildings on Frostburg’s Campus
(2025-12-10) Hall, Niles; Department of English; English 330
This paper is a detailed analysis of all of the vacant buildings on Frostburg State University's campus. As you read, follow me through the many insightful interviews about the history of Frostburg and the future plans for its revitalization. In this essay, you will learn why these buildings are closed, what they plan to do with the extra space, and what faculty and staff think and are doing to save these buildings.
Fragility in a Togashi–Kaneko stochastic model with mutations
(2025) Fu, Yi; Kang, Hye-Won; Khudabukhsh, Wasiur R.; Popovic, Lea; Rempala, Grzegorz A.; Williams, Ruth J.
The Togashi–Kaneko (TK) stochastic model is a prototypical example of an autocatalytic reaction network exhibiting dramatic switching behavior. The desire to understand this unusual behavior has attracted considerable attention in recent years. In this paper, we study the TK model with additional mutations. We establish a rigorous stochastic averaging principle that describes slow dynamics in terms of certain ergodic means of fast variables. Beginning with two species, we demonstrate a sensitivity of the model to even slight departures from symmetry in the autocatalytic reactions. We accomplish this through a detailed analysis of the stationary distribution of the fast process when the state of the slow process is fixed. We call this high sensitivity property “fragility”. We give some examples of behavior that can occur when there are more than two species. These preliminary explorations for multiple species point to a wealth of open questions for future research. Relevance to Life Sciences. Autocatalysis or self-amplification plays a key role in many biochemical and biological processes, ranging from pattern formation and self-organization, through gene regulation and signaling cascades, to ecological interactions and evolutionary dynamics. Understanding the sensitivity of such stochastic systems to small parameter changes is important for the formulation of models from data and for drawing conclusions for real life systems. In this paper we explore the sensitivity of the prototypical Togashi–Kaneko model with additional mutations. We find a high sensitivity to even slight departures from symmetry in the autocatalytic reactions, which we call fragility. We believe that fragility is an important underappreciated and understudied phenomenon, that will affect the formulation and interpretation of autocatalytic models across a wide variety of applications in the life sciences. Mathematical Content. We develop tools for analyzing and understanding the dynamic stochastic behavior of autocatalytic reaction systems, especially in asymmetric situations, by considering an extension of the standard TK model with additional mutation reactions. We prove a rigorous stochastic averaging principle that links the slow population dynamics with fast autocatalytic reactions. Through analysis of the ergodic mean of the fast variables (when the slow variables are frozen at a given value) for the two-species model, we find a high sensitivity of the model (which we call fragility) to even a slight departure from symmetry in autocatalytic rates. Furthermore, our preliminary explorations for more than two species suggest that such a phenomenon can occur for four species but not for three. This rather surprising observation suggests a wealth of open problems for future research.
Security Compliance for Smart Manufacturing using Knowledgegraph based Digital Twin
(2020-12-11) Tamboli, Javed; Joshi, Karuna; Clark, Ommo
The combination of Information Technology (IT) and Operational Technology (OT) in smart manufacturing, driven by smart factory innovations and Internet of Things (IoT) devices, generates vast, diverse, and rapidly evolving Big Data, which in turn increases cybersecurity and compliance issues. Adherence to security standards, such as NIST SP 800-171, which requires rigorous access control and audit reporting, is currently obstructed by the resource-intensive and error-prone aspects of manual evaluations. We have developed a semantically rich knowledge graph-based digital twin to automate security compliance of the smart assembly line, specifically focusing on categories specified in NIST SP 800-171. We have used Semantic Web technologies like RDF, OWL, and SPARQL using the Jena Fuseki server to build our system. Our approach improves data integrity and structure identification in IT/OT systems by tackling the Big Data 5Vs. The qualitative assessment of our digital twin shows a scalable approach with reduced compliance violations and enhanced audit effectiveness. In this paper, we describe our design in detail along with the validation results. This study propels future investigations by integrating Knowledge graphs and reasoning with industrial compliance, establishing a basis for automated compliance in smart manufacturing.
Reconstructing hand gestures with synergies extracted from dance movements
(Springer Nature, 2025-11-24) Olikkal, Parthan Sathishkumar; Dollo, Chris; Ajendla, Akshara; Clemmensen, Ann Sofie; Vinjamuri, Ramana
Comprehending and replicating human hand gestures is crucial for advancements in robotics, sign language interpretation and human-computer interactions. While extensive research has focused on improving hand gesture recognition, the therapeutic benefits of dance movements have often been overlooked. This study introduces a novel approach to understanding hand gestures through structured movement primitives derived from hand gestures (mudras) used in Indian classical dance form known as Bharatanatyam. Hand gesture synergies were extracted using Gaussian-modeled joint angular velocities and represented as fundamental syllables of motion. These syllables were then employed to reconstruct 75 diverse hand gestures, including American Sign Language (ASL) postures, a dataset of natural hand grasps and traditional mudras. Comparative analysis between mudra-derived synergies achieved superior reconstruction accuracy (95.78% for natural grasps and 92.99% for mudras) compared to synergies derived from natural grasps (88.92% for natural grasps and 82.51% for mudras). The results suggest that the structured and intentional nature of Bharatanatyam mudras leads to much stronger representation of syllables of movements that have superior generalizability and precision. Additionally, the reconstructed gestures were successfully mapped onto Mitra, a humanoid robot with five degree of freedom hand using a continuous joint-mapping approach. This research highlights the potential of dance inspired structured learning in enhancing dexterity, rehabilitation, and motor control, paving the way for more efficient gesture-based interaction models in robotics, prosthetics and rehabilitation.
IXPE observation of the low-synchrotron peaked blazar S4 0954+65 during an optical-X-ray flare
(EDP, 2025-03-01) Kouch, Pouya M.; Liodakis, Ioannis; Fenu, Francesco; Zhang, Haocheng; Boula, Stella; Middei, Riccardo; Gesu, Laura Di; Paraschos, Georgios F.; Agudo, Iván; Jorstad, Svetlana G.; Lindfors, Elina; Marscher, Alan P.; Krawczynski, Henric; Negro, Michela; Hu, Kun; Kim, Dawoon E.; Cavazzuti, Elisabetta; Errando, Manel; Blinov, Dmitry; Gourni, Anastasia; Kiehlmann, Sebastian; Kourtidis, Angelos; Mandarakas, Nikos; Triantafyllou, Nikolaos; Vervelaki, Anna; Borman, George A.; Kopatskaya, Evgenia N.; Larionova, Elena G.; Morozova, Daria A.; Savchenko, Sergey S.; Vasilyev, Andrey A.; Troitskiy, Ivan S.; Grishina, Tatiana S.; Shishkina, Ekaterina V.; Zhovtan, Alexey V.; Aceituno, Francisco José; Bonnoli, Giacomo; Casanova, Víctor; Escudero, Juan; Agís-González, Beatriz; Husillos, César; Otero-Santos, Jorge; Piirola, Vilppu; Sota, Alfredo; Myserlis, Ioannis; Gurwell, Mark; Keating, Garrett K.; Rao, Ramprasad; Angelakis, Emmanouil; Kraus, Alexander; Antonelli, Lucio Angelo; Bachetti, Matteo; Baldini, Luca; Baumgartner, Wayne H.; Bellazzini, Ronaldo; Bianchi, Stefano; Bongiorno, Stephen D.; Bonino, Raffaella; Brez, Alessandro; Bucciantini, Niccolò; Capitanio, Fiamma; Castellano, Simone; Chen, Chien-Ting; Ciprini, Stefano; Costa, Enrico; Rosa, Alessandra De; Monte, Ettore Del; Lalla, Niccolò Di; Marco, Alessandro Di; Donnarumma, Immacolata; Doroshenko, Victor; Dovčiak, Michal; Ehlert, Steven R.; Enoto, Teruaki; Evangelista, Yuri; Fabiani, Sergio; Ferrazzoli, Riccardo; Garcia, Javier A.; Gunji, Shuichi; Hayashida, Kiyoshi; Heyl, Jeremy; Iwakiri, Wataru; Kaaret, Philip; Karas, Vladimir; Kislat, Fabian; Kitaguchi, Takao; Kolodziejczak, Jeffery J.; Monaca, Fabio La; Latronico, Luca; Maldera, Simone; Manfreda, Alberto; Marin, Frédéric; Marinucci, Andrea; Marshall, Herman L.; Massaro, Francesco; Matt, Giorgio; Mitsuishi, Ikuyuki; Mizuno, Tsunefumi; Muleri, Fabio; Ng, Chi-Yung; O’Dell, Stephen L.; Omodei, Nicola; Oppedisano, Chiara; Papitto, Alessandro; Pavlov, George G.; Peirson, Abel Lawrence; Perri, Matteo; Pesce-Rollins, Melissa; Petrucci, Pierre-Olivier; Pilia, Maura; Possenti, Andrea; Poutanen, Juri; Puccetti, Simonetta; Ramsey, Brian D.; Rankin, John; Ratheesh, Ajay; Roberts, Oliver J.; Sgrò, Carmelo; Slane, Patrick; Soffitta, Paolo; Spandre, Gloria; Swartz, Douglas A.; Tamagawa, Toru; Tavecchio, Fabrizio; Taverna, Roberto; Tawara, Yuzuru; Tennant, Allyn F.; Thomas, Nicholas E.; Tombesi, Francesco; Trois, Alessio; Tsygankov, Sergey S.; Turolla, Roberto; Romani, Roger W.; Vink, Jacco; Weisskopf, Martin C.; Wu, Kinwah; Xie, Fei; Zane, Silvia
The X-ray polarization observations, made possible with the Imaging X-ray Polarimetry Explorer (IXPE), offer new ways of probing high-energy emission processes in astrophysical jets from blazars. Here, we report the first X-ray polarization observation of the blazar S4 0954+65 in a high optical and X-ray state. During our multi-wavelength (MWL) campaign of the source, we detected an optical flare whose peak coincided with the peak of an X-ray flare. This optical-X-ray flare most likely took place in a feature moving along the parsec-scale jet, imaged at 43 GHz by the Very Long Baseline Array (VLBA). The 43 GHz polarization angle of the moving component underwent a rotation near the time of the flare. In the optical band, prior to the IXPE observation, we measured the polarization angle to be aligned with the jet axis. In contrast, during the optical flare, the optical polarization angle was perpendicular to the jet axis; after the flare, it reverted to being parallel to the jet axis. Due to the smooth behavior of the optical polarization angle during the flare, we favor shocks as the main acceleration mechanism. We also infer that the ambient magnetic field lines in the jet were parallel to the jet position angle. The average degree of optical polarization during the IXPE observation was (14.3 ± 4.1)%. Despite the flare, we only detected an upper limit of 14% (at 3σ level) on the X-ray polarization degree; however, a reasonable assumption on the X-ray polarization angle results in an upper limit of 8.8% (3σ). We modeled the spectral energy distribution (SED) and spectral polarization distribution (SPD) of S4 0954+65 with leptonic (synchrotron self-Compton) and hadronic (proton and pair synchrotron) models. Our combined MWL polarization observations and SED modeling tentatively disfavor the use of hadronic models for the X-ray emission in S4 0954+65.