UMD General Research Works
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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 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.Item 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.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 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, AntarpreetClimate 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.Item The Evolution of Research on Abundances of Solar Energetic Particles(MDPI, 2021-08-08) Reames, Donald V.Sixty years of study of energetic particle abundances have made a major contribution to our understanding of the physics of solar energetic particles (SEPs) or solar cosmic rays. An early surprise was the observation in small SEP events of huge enhancements in the isotope 3He from resonant wave–particle interactions, and the subsequent observation of accompanying enhancements of heavy ions, later found to increase 1000-fold as a steep power of the mass-to-charge ratio A/Q, right across the elements from H to Pb. These “impulsive” SEP events have been related to magnetic reconnection on open field lines in solar jets; similar processes occur on closed loops in flares, but those SEPs are trapped and dissipate their energy in heat and light. After early controversy, it was established that particles in the large “gradual” SEP events are accelerated at shock waves driven by wide, fast coronal mass ejections (CMEs) that expand broadly. On average, gradual SEP events give us a measure of element abundances in the solar corona, which differ from those in the photosphere as a classic function of the first ionization potential (FIP) of the elements, distinguishing ions and neutrals. Departures from the average in gradual SEPs are also power laws in A/Q, and fits of this dependence can determine Q values and thus estimate source plasma temperatures. Complications arise when shock waves reaccelerate residual ions from the impulsive events, but excess protons and the extent of abundance variations help to resolve these processes. Yet, specific questions about SEP abundances remain.Item Environmental Injustice and Industrial Chicken Farming in Maryland(MDPI, 2021-10-20) Hall, Jonathan; Galarraga, Joseph; Berman, Isabelle; Edwards, Camryn; Khanjar, Niya; Kavi, Lucy; Murray, Rianna; Burwell-Naney, Kristen; Jiang, Chengsheng; Wilson, SacobyMaryland’s growing chicken industry, including concentrated animal feeding operations (CAFOs) and meat processing plants, raises a number of concerns regarding public health and environmental justice. Using hot spot analysis, we analyzed the totality of Maryland’s CAFOs and meat processing plants and those restricted to the Eastern Shore to assess whether communities of color and/or low socioeconomic status communities disproportionately hosted these types of facilities at the census tract level. We used zero-inflated regression modeling to determine the strength of the associations between environmental justice variables and the location of CAFOs and meatpacking facilities at the State level and on the Eastern Shore. Hot spot analyses demonstrated that CAFO hot spots on the Eastern Shore were located in counties with some of the lowest wealth in the State, including the lowest ranking county—Somerset. Zero-inflated regression models demonstrated that increases in median household income across the state were associated with a 0.04-unit reduction in CAFOs. For every unit increase in the percentage of people of color (POC), there was a 0.02-unit increase in meat processing facilities across the state. The distribution of CAFOs and meat processing plants across Maryland may contribute to poor health outcomes in areas affected by such production, and contribute to health disparities and health inequity.Item Limits to Perception by Quantum Monitoring with Finite Efficiency(MDPI, 2021-11-17) García-Pintos, Luis Pedro; del Campo, AdolfoWe formulate limits to perception under continuous quantum measurements by comparing the quantum states assigned by agents that have partial access to measurement outcomes. To this end, we provide bounds on the trace distance and the relative entropy between the assigned state and the actual state of the system. These bounds are expressed solely in terms of the purity and von Neumann entropy of the state assigned by the agent, and are shown to characterize how an agent’s perception of the system is altered by access to additional information. We apply our results to Gaussian states and to the dynamics of a system embedded in an environment illustrated on a quantum Ising chain.