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Recent Submissions
You're Only as Good as Your Customs Holdings: Optimizing Interlibrary Loan Borrowing
(2024-06-29) Norton, Brynne; Weible, Cherié
Faster shipping, cheaper fees, and longer loan periods are the buzzwords when it comes to interlibrary borrowing, but how do you effectively gather data to identify lending libraries that meet these factors? One way is to take advantage of customization and automations in WorldShare ILL to leverage smart lender strings in combination with partner libraries. Information from lending libraries is available in the policies directory, however, sometimes other factors, such as shipping times, impact turnaround time. Reviewing real data periodically helps to identify misconceptions around which libraries meet that criteria. Two large academic libraries will show how they have effectively optimized their customs holdings and how you can replicate the process in order to meet these goals as applicable to your local environment. Graphs of turnaround times, maps, and screenshots showing customizations and workflows will demonstrate to the audience how easy it is to implement changes in their library setting and the positive impact these changes have on patron satisfaction as well as the reduction of staff time.
Supplementary material for machine learning and statistical analyses of sensor data reveal variability between repeated trials in Parkinson’s disease mobility assessments
(2024) 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 assess the test-retest reliability of these tasks in a cohort of 262 PD and 50 controls by evaluating the performance of machine learning models based on quantitative measures derived from wearable sensors, along with statistical measures. This evaluation examines 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 by a mean of 1.1% from the first to the second trial of our mobility tasks, suggesting that mobility testing can be simplified by not repeating tasks without losing diagnostic accuracy. Although the total duration remains relatively consistent between trials (intraclass correlation coefficient (ICC) = 0.62 to 0.95), there is more variability in subtask duration and sensor-derived measures, evident in the differences in machine learning model performance and statistical metrics. Our findings also show that the variability between trials differs not only between controls and participants with PD but also among groups with varying levels of PD severity. Relying solely on total task duration and conventional statistical metrics to gauge the reliability of mobility tasks may fail to reveal nuanced variations in movement captured by other quantitative measures. Additionally, the population studied should be carefully considered, as reliability results differ among and within groups based on disease severity.
A Smart, Connected, and Sustainable Campus Community: Using the Internet of Things (IoT) and low-cost sensors to improve stormwater management at UMD/Greater College Park
(2024) Hendricks, Marccus D.; Si, Qianyao; Alves, Priscila B. R.; Pavao-Zuckerman, Mitchell A.; Davis, Allen P.; Burke, Tara; Bonsignore, Elizabeth M.; Baer, Jason; Peterson, Kaitlyn; Cotting, Jennifer; Gaunaurd, Pierre; Clegg, Tamara; Loshin, David; Fellow, Andrew; Keen, Taylor; Knaap, Gerrit-Jan
This dataset is part of the research project titled “A Smart, Connected, and Sustainable Campus Community: Using the Internet of Things (IoT) and low-cost sensors to improve stormwater management at UMD/Greater College Park”. We use an Internet of Things (IoT) framework along with low-cost sensors to monitor and improve stormwater management on the University of Maryland Campus. This project provides real-time data that can inform both short term responses and longer-term adaptations to stormwater surface runoff. New buildings, the Purple Line, and other developments on the UMD campus will potentially increase the amount of impervious cover and thus increases the amount of surface runoff.
Furthermore, as a result of climate change, the region is expected to experience more frequent and intense rainfall events over shorter periods of time. These two factors have implications for higher quantities of water on campus, pooling water, and potential localized flooding. Stormwater issues can affect the movement of people, goods and services, campus infrastructure, and students as they walk across campus exposing their belongings, and particularly their feet to wetter conditions.
As part of more sustainable development, communities and campuses across the world, are beginning to plan for adaptations within the built campus environment to mitigate both larger scale stormwater issues as well as more practical everyday concerns, including wet pathways, and to meet and evaluate the effectiveness of stormwater permitting requirements.
The research objectives for this project are fourfold:
(1) Install low-cost stormwater sensors that measure water levels at a number of locations across campus that include high pedestrian traffic areas and major campus arterials;
(2) Develop an online database for campus water levels;
(3) Train students to install and read the stormwater sensors, manage the data platform, interpret the data
(4) Use the data to write adaptation plans and designs to better manage stormwater on campus and, perhaps subsequently, downstream from campus.
The dataset contains clean stormwater quality and quantity measurements collected from three different sites, along with processed data that describe runoff behavior during selected rainfall events and corresponding catchment characteristics (imperviousness, slope). The spatial data files provide location information for the outfall locations and the corresponding catchment boundaries. The R code provided includes data processing, statistical analysis, and visualization steps.
Quantized topological energy pumping and Weyl points in Floquet synthetic dimensions with a driven-dissipative photonic molecule
(Nature Physics, 2024-02-26) Sridhar, Sashank; Ghosh, Sayan; Dutt, Avik
Topological effects manifest in a wide range of physical systems, such as solid crystals, acoustic
waves, photonic materials and cold atoms. These effects are characterized by `topological invariants'
which are typically integer-valued, and lead to robust quantized channels of transport in space, time,
and other degrees of freedom. The temporal channel, in particular, allows one to achieve higher-
dimensional topological effects, by driving the system with multiple incommensurate frequencies.
However, dissipation is generally detrimental to such topological effects, particularly when the systems consist of quantum spins or qubits. Here we introduce a photonic molecule subjected to
multiple RF/optical drives and dissipation as a promising candidate system to observe quantized
transport along Floquet synthetic dimensions. Topological energy pumping in the incommensurately modulated photonic molecule is enhanced by the driven-dissipative nature of our platform.
Furthermore, we provide a path to realizing Weyl points and measuring the Berry curvature emanating from these reciprocal-space (k-space) magnetic monopoles, illustrating the capabilities for
higher-dimensional topological Hamiltonian simulation in this platform. Our approach enables direct k-space engineering of a wide variety of Hamiltonians using modulation bandwidths that are
well below the free-spectral range (FSR) of integrated photonic cavities.
Dataset for "Resistance of Boron Nitride Nanotubes to Radiation-Induced Oxidation" as published in The Journal of Physical Chemistry C
(2024) Chao, Hsin-Yun (Joy); Nolan, Adelaide M.; Hall, Alex T.; Golberg, Dmitri; Park, Cheol; Yang, Wei-Chang David; Mo, Yifei; Sharma, Renu; Cumings, John