UMD General Research Works

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Start date: 2020

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Now showing 1 - 10 of 23
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    Dataset for Revealing isotropic abundant low-energy excitations in UTe2 through complex microwave surface impedance
    (arXiv, 2025-02-11) Carlton-Jones, Arthur; Anlage, Steven
    This is the dataset used to perform all the analysis and create all the figures of the paper: Revealing isotropic abundant low-energy excitations in UTe2 through complex microwave surface impedance.
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    Dataset for Figures in Microwave Microscope Studies of Trapped Vortex Dynamics in Superconductors
    (2025) Chung-Yang Wang; Steven M. Anlage; Steven M. Anlage
    This is the dataset used to create figures in the paper: Microwave Microscope Studies of Trapped Vortex Dynamics in Superconductors.
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    Dataset for Figures in Robust Wave Splitters Based on Scattering Singularities in Complex non-Hermitian Systems
    (2025) Erb, Jared; Anlage, Steven
    This is the dataset used to create all the figures in the paper: Robust Wave Splitters Based on Scattering Singularities in Complex non-Hermitian Systems.
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    Centering relationships in collegiate leadership curricula
    (Wiley, 2024-02-08) Holder, Courtney; Pursley, Dana
    The relational leadership model and the five practices of exemplary leadership are widely used models that both emphasize a relational approach to leadership and center the collegiate context as a transformative environment for practicing and developing leadership. This article highlights two different applications of these models and provides important considerations for designing relational leadership curricula and programs for college students.
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    Including Campus Forest Carbon Estimates Into Climate Mitigation Planning -- Year 2
    (2022-05-01) Albee, Maddy; Hoffman Delett, Camille; Panday, Frances Marie; Patterson, Amelia; James, Jarrett; Hurtt, George C.; Lamb, Rachel
    Summary of project led by student researchers in the UMD Department of Geographical Sciences to integrate high-resolution forest carbon estimates into the University of Maryland's Climate Action Plan and GHG Inventory. Covers year 2 progress of a three-year project funded by the UMD Sustainability Fund.
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    Supplementary materials for statistical and machine learning analyses demonstrate test-retest reliability assessment is misled by focusing on total duration of mobility tasks in Parkinson's disease
    (2023) Khalil, Rana M.; Shulman, Lisa M.; Gruber-Baldini, Ann L.; Shakya, Sunita; Hausdorff, Jeffrey M.; von Coelln, Rainer; Cummings, Michael P.; Cummings, Michael P.
    Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insight into dynamic motor control, balance, and cognitive functions affected by Parkinson’s disease (PD). We evaluate the test-retest reliability of these tasks by assessing the performance of machine learning models based on quantitative sensor-derived measures, and statistical measures to examine total duration, subtask duration, and other quantitative measures across both trials. We show that the diagnostic accuracy of differentiating between PD and control participants decreases from the first to the second trial of our mobility tasks, suggesting that mobility testing can be simplified by not repeating tasks without losing relevant information. Although the total duration remains relatively consistent between trials, there is more variability in subtask duration and sensor-derived measures, evident in the differences in machine learning model performance and statistical metrics. Relying solely on total task duration and conventional statistical metrics to gauge the reliability of mobility tasks overlooks the nuanced variations in movement captured by other quantitative measures.
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    A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems
    (MDPI, 2020-03-12) Lewis, Austin D.; Groth, Katrina M.
    Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling framework. As a result, there has been growing interest in using DBNs for reliability engineering problems and applications in risk assessment. However, there are open questions about how they can be used to support diagnostics and prognostic health monitoring of a complex engineering system (CES), e.g., power plants, processing facilities and maritime vessels. These systems’ tightly integrated human, hardware, and software components and dynamic operational environments have previously been difficult to model. As part of the growing literature advancing the understanding of how DBNs can be used to improve the risk assessments and health monitoring of CESs, this paper shows the prognostic and diagnostic inference capabilities that are possible to encapsulate within a single DBN model. Using simulated accident sequence data from a model sodium fast nuclear reactor as a case study, a DBN is designed, quantified, and verified based on evidence associated with a transient overpower. The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent CES health. Such a model can therefore serve as another training tool for CES operators to better prepare for accident scenarios.
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    Pilot Study to Detect Genes Involved in DNA Damage and Cancer in Humans: Potential Biomarkers of Exposure to E-Cigarette Aerosols
    (MDPI, 2021-03-22) Hamad, Samera H.; Brinkman, Marielle C.; Tsai, Yi-Hsuan; Mellouk, Namya; Cross, Kandice; Jaspers, Ilona; Clark, Pamela I.; Granville, Courtney A.
    There is a paucity of data on how gene expression enables identification of individuals who are at risk of exposure to carcinogens from e-cigarette (e-cig) vaping; and how human vaping behaviors modify these exposures. This pilot study aimed to identify genes regulated from acute exposure to e-cig using RT-qPCR. Three subjects (2M and 1F) made three visits to the lab (nTOT = 9 visits); buccal and blood samples were collected before and immediately after scripted vaping 20 puffs (nTOT = 18 samples); vaping topography data were collected in each session. Subjects used their own e-cig containing 50:50 propylene glycol (PG):vegetable glycerine (VG) +3–6 mg/mL nicotine. The tumor suppressor TP53 was significantly upregulated in buccal samples. TP53 expression was puff volume and flow rate dependent in both tissues. In blood, the significant downregulation of N-methylpurine DNA glycosylase (MPG), a base excision repair gene, was consistent across all subjects. In addition to DNA repair pathway, cell cycle and cancer pathways were the most enriched pathways in buccal and blood samples, respectively. This pilot study demonstrates that vaping 20 puffs significantly alters expression of TP53 in human tissues; vaping behavior is an important modifier of this response. A larger study is needed to confirm these relationships.
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    Assessing Hydrologic Cycle Dynamics Using High-Resolution Satellite Imagery
    (2023-10-16) Bachhu, Ankith; Petković, Veljko; Berbery, Ernesto Hugo; Petković, Veljko; Berbery, Ernesto Hugo
    This study presents an investigation of the hydrologic cycle over a two-decade span (2000 – 2020) using high-resolution satellite products, in-situ measurements, and modeled data. The scope of this work encompasses an examination of the accuracy of satellite-based estimates in calculating the water budget, both on a global scale and within the Mississippi River Basin. The global assessment considers land areas spanning latitudes 90°S to 90°N, while the Mississippi River Basin includes the Lower Mississippi, Arkansas-Red, Missouri, Ohio, and North Central sub-basins. We utilize the IMERG version-6 and PERSIANN precipitation datasets to quantify water inflow over these regions. Correspondingly, water outflow estimates incorporate the GLEAM product for evaporation, G-RUN and ERA5 datasets for runoff, and SMOPS and SMAP estimates for changes in soil moisture. The assessment of water budget changes assesses the difference between Inflow (Precipitation) and Outflow (Runoff, Evaporation, Δ Soil Moisture) components. Our findings reveal discernible discrepancies in the global water budget over an annual cycle, indicating the presence of water “leaks”. These leaks, warranting further investigation, may be attributed to factors such as snow, ice, and groundwater dynamics, which fall outside the scope of this study. On a smaller basin scale, the closure of the water budget is estimated to fall within the combined products’ uncertainty. This provides additional validation for the suspected factors contributing to the global scale “leak.” Analyzing the annual water cycle components, we find the inherent variability and uncertainty associated with satellite-derived products. The study advances comprehension of hydrologic processes and underscores the imperative for enhanced accuracy in satellite-based measurements. Notably, our findings accentuate the importance of a closed water budget as a defining criterion for the accuracy of these satellite-derived products.
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    A Review of the Environmental Trigger and Transmission Components for Prediction of Cholera
    (MDPI, 2021-08-05) Usmani, Moiz; Brumfield, Kyle D.; Jamal, Yusuf; Huq, Anwar; Colwell, Rita R.; Jutla, Antarpreet
    Climate variables influence the occurrence, growth, and distribution of Vibrio cholerae in the aquatic environment. Together with socio-economic factors, these variables affect the incidence and intensity of cholera outbreaks. The current pandemic of cholera began in the 1960s, and millions of cholera cases are reported each year globally. Hence, cholera remains a significant health challenge, notably where human vulnerability intersects with changes in hydrological and environmental processes. Cholera outbreaks may be epidemic or endemic, the mode of which is governed by trigger and transmission components that control the outbreak and spread of the disease, respectively. Traditional cholera risk assessment models, namely compartmental susceptible-exposed-infected-recovered (SEIR) type models, have been used to determine the predictive spread of cholera through the fecal–oral route in human populations. However, these models often fail to capture modes of infection via indirect routes, such as pathogen movement in the environment and heterogeneities relevant to disease transmission. Conversely, other models that rely solely on variability of selected environmental factors (i.e., examine only triggers) have accomplished real-time outbreak prediction but fail to capture the transmission of cholera within impacted populations. Since the mode of cholera outbreaks can transition from epidemic to endemic, a comprehensive transmission model is needed to achieve timely and reliable prediction with respect to quantitative environmental risk. Here, we discuss progression of the trigger module associated with both epidemic and endemic cholera, in the context of the autochthonous aquatic nature of the causative agent of cholera, V. cholerae, as well as disease prediction.