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Texas ScholarWorks was established to provide open, online access to the products of the University's research and scholarship, to preserve these works for future generations, to promote new models of scholarly communication, and to help deepen community understanding of the value of higher education.
UT Tower and campus image credit: Earl McGehee, CC-BY, https://www.flickr.com/photos/ejmc/7452145850
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Recent Submissions
Structural and dynamical properties of H₂O and D₂O under confinement
(2023-08) Liang, Chenxing; Aluru, Narayana Rao
Water (H₂O) is of great societal importance and there has been a significant amount of research on its fundamental properties and related physical phenomena. Deuterium dioxide (D₂O), known as heavy water, also draws much interest as an important medium for medical imaging, nuclear reactors, etc. Although many experimental studies on the fundamental properties of H₂O and D₂O have been conducted, they have been primarily limited to understanding the differences between H₂O and D₂O in the bulk state. In this report, using path integral molecular dynamics simulations, the structural and dynamical properties of H₂O and D₂O in bulk and under nanoscale confinement in a (14,0) carbon nanotube are studied. We find that in bulk, the dipole moment of D₂O tends to be 4% higher than that of H₂O and the hydrogen bonding of D₂O is also stronger than H₂O. Under nanoscale confinement in a (14,0) carbon nanotube, H₂O and D₂O exhibit a smaller bond length and bond angle. The hydrogen bond number decreases, which demonstrates weakened hydrogen bond interaction. Moreover, confinement results in a lower libration frequency, and higher OH(OD) bond stretching frequency with an almost unchanged HOH(DOD) bending frequency. The D₂O-filled (14,0) carbon nanotube is found to have a smaller radial breathing mode than the H₂O-filled (14,0) carbon nanotube.
Characterizing the onset and offset of motor imagery during passive arm movements to control an upper-body exoskeleton
(2023-08) Mitra, Kanishka; Millán, José del R., 1962-; Deshpande, Ashish D.
In recent decades, two distinct technological advances have been made to understand and improve motor rehabilitation: human-robot interaction and brain-machine interfaces (BMIs). While the introduction of robots has been shown to increase the dosage and intensity of therapy, robot-mediated rehabilitation has failed to achieve clinically relevant levels of improvement over conventional therapy due to its limited influence on neural recovery. On the other hand, BMI-driven therapies have been able to engage neural activity, but the amount and extent of proprioceptive feedback elicited by passive movements were not sufficiently rich to boost activity-dependent plasticity. Harnessing their combined efforts could open up unprecedented opportunities for connecting neural commands to motor output, which may be the missing link to achieving clinically relevant recovery. However, a significant challenge is whether motor intentions from the user can be accurately detected using non-invasive BMIs in the presence of instrumental noise and passive movements induced by the rehabilitation exoskeleton. Therefore, this study aims to characterize the onset and offset of motor imagery during passive arm movements induced by an upper-body exoskeleton to allow for the natural control (initiation and termination) of functional movements. Ten participants were recruited to perform kinesthetic motor imagery (MI) of the right arm while attached to the robot, simultaneously cued with LEDs indicating the initiation and termination of a goal-oriented reaching task. Using electroencephalogram (EEG) signals, we built a decoder to detect the transition between i) rest and beginning MI and ii) maintaining and ending MI. Offline decoder evaluation achieved group average onset accuracy of 60.7% and 66.6% for offset accuracy, revealing that the start and stop of MI could be identified while attached to the robot. Furthermore, pseudo-online evaluation could replicate this performance, simulating reliable online exoskeleton control. Finally, results from a pilot study indicate the feasibility of the onset and offset of MI to control passive arm movements induced by an upper-body exoskeleton using a novel real-time streaming hierarchical machine-learning approach. Our method showed that participants could produce strong and reliable sensorimotor rhythms regardless of noise or passive arm movements induced by wearing the exoskeleton, which opens new possibilities for BMI control of assistive devices and could have further implications for novel neurorehabilitation strategies.
Français interactif (Fourth Edition)
(Center for Open Educational Resources and Language Learning, 2019) Kelton, Karen; Guilloteau, Nancy; Blyth, Carl
Français interactif, (www.laits.utexas.edu/fi), the web-based French program developed and in use at the University of Texas since 2004, and its companion site, Tex’s French Grammar (2000) (www.laits.utexas.edu/tex/) are free open educational multimedia resources (OER), which require neither password nor fees. OER promote learning and scholarship for everyone, everywhere! Français interactif, used increasingly by students, teachers, and institutions throughout the world, includes 320 videos (American students in France, native French interviews, vocabulary and culture presentation videos) recorded vocabulary lists, phonetic lessons, online grammar lessons (600 pages) with self-correcting exercises and audio dialogues, online grammar tools (verb conjugation reference, verb practice), and diagnostic grammar tests.
Yorùbá Yé Mi
(Center for Open Educational Resources and Language Learning, 2012) Mosadomi, Fehintola
Yorùbá Yémi (http://www.coerll.utexas.edu/yemi/) is a multi-media program designed to enliven classroom activities. It promotes and enhances the learning of Yorùbá by incorporating the four language learning skills: listening, speaking, reading, and writing.
US walking distances and pedestrian safety outcomes + integrating autonomous vehicles into Texas’ Statewide Analysis Model
(2023-08) Vellimana, Maithreyi; Kockelman, Kara
This thesis consists of two, very distinct parts. The first part focuses on pedestrian safety, while the second investigates Texas travel impacts of autonomous cars and trucks.
Part 1 examines walk distances across the United States by time of day and year, using data from the National Household Travel Survey 2016/2017, with the aim to understand factors contributing to higher pedestrian deaths at night across various states. Using hurdle regression to predict daily walk-miles traveled (WMT) and nighttime WMT across the US, the study finds that the decision to walk and distances walked on each survey day and night vary significantly with demographic attributes (like race, income, worker status and education), time of year, latitude, state of residence, and other factors. Longer daylight hours and more nighttime walking do not appear to be the reasons for some states’ much higher pedestrian fatality rates. Additionally, there is no evidence from traffic fatality rates due to alcohol consumption or overall alcohol consumption per capita to support this result. Differences in built environments, law enforcement, and aggressive driving may be key factors for much higher pedestrian death rates in southern settings.
Part 2 focuses on predicting travel patterns for the year 2040 by integrating mode options for autonomous vehicles (AVs), shared autonomous vehicles (SAVs), and automated trucks (ATrucks) into the Texas Statewide Analysis Model (SAM). Initial results suggest that for long-distance passenger trips, human-driven vehicles (HVs) are estimated to remain more popular than AVs and SAVs, with roughly 10% increases (for both business and non-business trips) for the shared ride 3+ (SR3+) mode option on trips below 400 miles (drawing primarily from air and intercity-rail trips). The airline mode remains preferred choice for trips over 400 miles (80% of all >400 mile person-trips and 18% of total PMT, which is 11.6% of all person-trips). Introduction of ATrucks had its biggest impacts on freight movements for the oil, gas, and mining sectors. Major roadways for freight-truck movement (under both the before and after-AVs scenarios) are US 60, US 99, I-40, I-35, US 87, US 287 and US 57. While the model applications provide some valuable insights, SAM model limitations (such as exclusion of bus as a mode in long-distance passenger model, and fixed mode splits for most passenger trips) highlight the need for future research and improvements. Another limitation in this study is that only the mode choice step of a model has been updated to account for introduction of these new modes. So this model will predict the effect on mode splits (and trip distribution when feedback loops are included), but it cannot reflect the change in trips due introduction of these new modes. Future wok will focus on updating trip generation to reflect effect on trip production. Addressing these limitations and calibrating the model mode choice parameters will enhance the study and the scope of the SAM model, enabling a more comprehensive analysis of the impacts of AVs across the nation and Texas.