Measuring User Satisfaction for the Natural Hazards Engineering Research Infrastructure Consortium

The User Forum is a Natural Hazards Engineering Research Infrastructure (NHERI)-wide group focused on providing the NHERI Council with independent advice on community user satisfaction, priorities, and needs relating to the use and capabilities of NHERI. The User Forum has representation across NHERI activities, including representatives working directly with the Network Coordination Office (NCO), Education and Community Outreach (ECO), Facilities Scheduling, and Technology Transfer efforts. The User forum also provides feedback on the NHERI Science Plan. As the community voice within the governance of NHERI, the User Forum is composed of members nominated and elected by the NHERI community for a specified term of 1–2 years. User Forum membership spans academia and industry, the full breadth of civil engineering and social science disciplines, and widespread hazard expertise including earthquakes, windstorms, and water events. One of the primary responsibilities of the User Forum is to conduct an annual community user satisfaction survey for NHERI users, and publish a subsequent Annual Community Report. Measuring user satisfaction and providing this feedback to the NHERI Council is critical to supporting the long-term sustainability of NHERI and its mission as a multidisciplinary and multi-hazard network. In this paper, the role and key activities of the User Forum are described, including User Forum member election procedures, User Forum member representation and roles across NHERI activities, and the processes for measuring and reporting user satisfaction. This paper shares the user satisfaction survey distributed to NHERI users, and discusses the challenges to measuring community user satisfaction based on the definition of user. Finally, this paper discusses the evolving approaches of measuring user satisfaction using other methods, including engaging with the twelve NHERI research infrastructures

Association between Self-Reported Prior Nights’ Sleep and Single-Task Gait in Healthy Young Adults: An Exploratory Study Using Machine Learning

Failure to obtain 7-9 hours of sleep has been associated with decreased gait speed in young adults. While Machine Learning (ML) has been used to identify sleep quality in young adults, there are no current studies that have used ML to identify prior night’s sleep in a sample of young adults. PURPOSE: To use ML to identify prior night’s sleep in healthy young adults using single-task walking gait. METHODS: Participants (n=126, age 24.3±4.0yrs; 65% female) completed a survey on their prior night’s sleep and performed a 2-minute walk around a 6m track. Gait data were collected using inertial sensors. Participants were split into 2 groups (\u3c7hs or \u3e9hs: poor sleepers; 7-9hs: good sleepers) and gait characteristics were used to classify participants into each group using ML models via a 10-fold cross validation. A post-hoc ANCOVA was used to assess gait differences. RESULTS: Using Random Forest Classifiers (RFC), top 9 features were extracted. Classification results suggest a 0.79 correlation between gait parameters and prior night’s sleep. The RFC models had a 65.03% mean classification accuracy rate. Top 0.3% of the models had 100% classification accuracy rate. The top 9 features were primarily characteristics that measured variance between lower limb movements. Post-hoc analyses suggest significantly greater variances between lower limb characteristics. CONCLUSION: Good sleepers had more asymmetrical gait patterns (faster gait speed, less trunk motion). Poor sleepers had trouble maintaining gait speed (increased variance in cadence, larger stride lengths, and less time spent in single leg support time). Although the mechanisms of these gait changes are unknown, these findings provide evidence that gait is different for individuals who not receive 7-9 hours of sleep the night before. As evidenced by the high correlation co-efficient of our classification models, gait may be a good way of identifying prior night’s sleep

word~river literary review (2009)

wordriver is a literary journal dedicated to the poetry, short fiction and creative nonfiction of adjuncts and part-time instructors teaching in our universities, colleges, and community colleges. Our premier issue was published in Spring 2009. We are always looking for work that demonstrates the creativity and craft of adjunct/part-time instructors in English and other disciplines. We reserve first publication rights and onetime anthology publication rights for all work published. We define adjunct instructors as anyone teaching part-time or full-time under a semester or yearly contract, nationwide and in any discipline. Graduate students teaching under part-time contracts during the summer or who have used up their teaching assistant time and are teaching with adjunct contracts for the remainder of their graduate program also are eligible.https://digitalscholarship.unlv.edu/word_river/1002/thumbnail.jp

word~river literary review (2011)

wordriver is a literary journal dedicated to the poetry, short fiction and creative nonfiction of adjuncts and part-time instructors teaching in our universities, colleges, and community colleges. Our premier issue was published in Spring 2009. We are always looking for work that demonstrates the creativity and craft of adjunct/part-time instructors in English and other disciplines. We reserve first publication rights and onetime anthology publication rights for all work published. We define adjunct instructors as anyone teaching part-time or full-time under a semester or yearly contract, nationwide and in any discipline. Graduate students teaching under part-time contracts during the summer or who have used up their teaching assistant time and are teaching with adjunct contracts for the remainder of their graduate program also are eligible.https://digitalscholarship.unlv.edu/word_river/1001/thumbnail.jp

Studying the Evolution of Warm Dust Encircling BD +20 307 Using SOFIA

© 2019. The American Astronomical Society. All rights reserved. The small class of known stars with unusually warm, dusty debris disks is a key sample to probe in order to understand cascade models and the extreme collisions that likely lead to the final configurations of planetary systems. Because of its extreme dustiness and small radius, the disk of BD +20 307 has a short predicted collision time and is therefore an interesting target in which to look for changes in dust quantity and composition over time. To compare with previous ground and Spitzer Space Telescope data, Stratospheric Observatory for Infrared Astronomy (SOFIA) photometry and spectroscopy were obtained. The system\u27s 8.8-12.5 μm infrared emission increased by 10 ± 2% over nine years between the SOFIA and earlier Spitzer measurements. In addition to an overall increase in infrared excess, there is a suggestion of a greater increase in flux at shorter wavelengths (less than 10.6 μm) compared to longer wavelengths (greater than 10.6 μm). Steady-state collisional cascade models cannot explain the increase in BD +20 307\u27s disk flux over such short timescales. A catastrophic collision between planetary-scale bodies is still the most likely origin for the system\u27s extreme dust; however, the cause for its recent variation requires further investigation

Cuticular profiling of insecticide resistant Aedes aegypti

Abstract Insecticides have made great strides in reducing the global burden of vector-borne disease. Nonetheless, serious public health concerns remain because insecticide-resistant vector populations continue to spread globally. To circumvent insecticide resistance, it is essential to understand all contributing mechanisms. Contact-based insecticides are absorbed through the insect cuticle, which is comprised mainly of chitin polysaccharides, cuticular proteins, hydrocarbons, and phenolic biopolymers sclerotin and melanin. Cuticle interface alterations can slow or prevent insecticide penetration in a phenomenon referred to as cuticular resistance. Cuticular resistance characterization of the yellow fever mosquito, Aedes aegypti, is lacking. In the current study, we utilized solid-state nuclear magnetic resonance spectroscopy, gas chromatography/mass spectrometry, and transmission electron microscopy to gain insights into the cuticle composition of congenic cytochrome P450 monooxygenase insecticide resistant and susceptible Ae. aegypti. No differences in cuticular hydrocarbon content or phenolic biopolymer deposition were found. In contrast, we observed cuticle thickness of insecticide resistant Ae. aegypti increased over time and exhibited higher polysaccharide abundance. Moreover, we found these local cuticular changes correlated with global metabolic differences in the whole mosquito, suggesting the existence of novel cuticular resistance mechanisms in this major disease vector

Association between Self-Reported Prior Night’s Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning

Failure to obtain the recommended 7–9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night’s sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7–9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night’s sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night’s sleep using single-task gait

Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning

Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed

Yellen v. Confederated Tribes of the Chehalis Reservation: Brief of Professors and Historians as Amici Curiae Supporting Respondents

Amici curiae are law professors who teach and write in the area of federal Indian law and Native American legal history. They file this brief to explain the history of the federal government’s practice of “recognizing” Indian tribes generally, as well as the specific history of recognition of Alaska Native tribes