UMD General Research Works
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Item Dataset for Revealing isotropic abundant low-energy excitations in UTe2 through complex microwave surface impedance(arXiv, 2025-02-11) Carlton-Jones, Arthur; Anlage, StevenThis 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.Item Dataset for Figures in Microwave Microscope Studies of Trapped Vortex Dynamics in Superconductors(2025) Chung-Yang Wang; Steven M. Anlage; Steven M. AnlageThis is the dataset used to create figures in the paper: Microwave Microscope Studies of Trapped Vortex Dynamics in Superconductors.Item Against Service Shaming(Inside Higher Ed, 2025-03-17) Merritt, Cullen C.Item Dataset for Figures in Robust Wave Splitters Based on Scattering Singularities in Complex non-Hermitian Systems(2025) Erb, Jared; Anlage, StevenThis is the dataset used to create all the figures in the paper: Robust Wave Splitters Based on Scattering Singularities in Complex non-Hermitian Systems.Item Centering relationships in collegiate leadership curricula(Wiley, 2024-02-08) Holder, Courtney; Pursley, DanaThe 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.Item 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, RachelSummary 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.Item 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.Item Assessing Hydrologic Cycle Dynamics Using High-Resolution Satellite Imagery(2023-10-16) Bachhu, Ankith; Petković, Veljko; Berbery, Ernesto Hugo; Petković, Veljko; Berbery, Ernesto HugoThis 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.Item Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery(MDPI, 2022-02-02) Khemani, Varun; Azarian, Michael H.; Pecht, Michael G.Analog circuits are a critical part of industrial electronics and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit fault diagnosis and isolation can be a valuable means of ensuring the reliability of circuits. This paper introduces a novel technique of learning time–frequency representations, using learnable wavelet scattering networks, for the fault diagnosis of circuits and rotating machinery. Wavelet scattering networks, which are fixed time–frequency representations based on existing wavelets, are modified to be learnable so that they can learn features that are optimal for fault diagnosis. The learnable wavelet scattering networks are developed using the genetic algorithm-based optimization of second-generation wavelet transform operators. The simulation and experimental results for the diagnosis of analog circuit faults demonstrates that the developed diagnosis scheme achieves greater fault diagnosis accuracy than other methods in the literature, even while considering a larger number of fault classes. The performance of the diagnosis scheme on benchmark datasets of bearing faults and gear faults shows that the developed method generalizes well to fault diagnosis in multiple domains and has good transfer learning performance, too.Item Evaluation of SNPP and NOAA-20 VIIRS Datasets Using RadCalNet and Landsat 8/OLI Data(MDPI, 2022-08-12) Jing, Xin; Uprety, Sirish; Liu, Tung-Chang; Zhang, Bin; Shao, XiIn this study, we used RVUS data from RadCalNet as a benchmark to verify the radiometric accuracy and stability of operational and reprocessed SNPP/VIIRS data and the accuracy of NOAA-20/VIIRS data, as well as to assess the efficiency of the SNPP/VIIRS reprocessing algorithm. In addition, to remove the uncertainty of the RVUS site itself, we used Landsat 8/OLI as another benchmark with which to validate the accuracy and stability of VIIRS data through the RUVS site. The radiometric biases of the operational and reprocessed SNPP VIIRS bands were within ±4% and ±2%, respectively, as compared with the RUVS site and OLI, except for the M10 and M11 bands. In particular, the biases of the M5 and M7 bands were reduced by ~2% in this study. NOAA-20 VIIRS, on the other hand, was consistently lower than SNPP by ~2 to ~4% for all the bands. For the equivalent bands, the drift differences between operational and reprocessed SNPP/VIIRS and OLI were no larger than 0.24%/year and 0.1%/year, respectively. The reprocessing algorithm of SNPP VIIRS efficiently improved the radiometric accuracy and stability of the SNPP/VIIRS dataset to meet its specifications.