High pressure x-ray diffraction studies on ZrFe2: A potential hydrogen absorption medium

The potential application of intermetallic compounds (IMC) under high hydrogen pressure in studies of hydrogen sorption properties is defined by two important properties. Intermetallics of Laves phases have a suitable binding energy for hydrogen which allows its absorption or desorption near room temperature and atmospheric pressure. High pressures allow to efficiently interact hydrogen with intermetallics, which were considered nonhydride forming [1,2]. For example, ZrFe2, ZrCo2, and ZrFe2 possess fairly high hydrogen absorption capacity at high pressures [3]. A nonactivated ZrFe2 sample starts to interact with hydrogen only at 80 MPa, while equilibrium absorption and desorption pressures of the activated alloy on a plateau are 69 and 32.5 MPa, respectively. Even though ZrFe2 and related Laves phases are subjected only to moderate hydrogen pressures during absorption and desorption, it is essential to understand the structural phase stability under variable pressure-temperature conditions. The present investigation is aimed to study the pressure induced structural changes in ZrFe2 using synchrotron powder x-ray diffraction. High pressure structural studies were performed up to 50 GPa using a diamond anvil cell in the angle dispersion geometry

Tracing the last breath: Movements in Anlong Veng

Anlong Veng was the last stronghold of the Khmer Rouge until the organization's ultimate collapse and defeat in 1999. This dissertation argues that recent moves by the Cambodian government to transform this site into an "historical-tourist area" is overwhelmingly dominated by commercial priorities. However, the tourism project simultaneously effects an historical narrative that inherits but transforms the government's historiographic endeavors that immediately followed Democratic Kampuchea's 1979 ousting. The work moves between personal encounters with the historical, academic presentations of the country's recent past, and government efforts to pursue a museum agenda in the context of "development through tourism" policies

A step in the right direction: streambank restoration efforts at the Botanical Garden of the Ozarks

The Botanical Garden of the Ozarks (BGO) is a unique destination in Northwest Arkansas that draws more than 80,000 visitors a year. While the BGO manages low-input practices, run-off from pesticide application and synthetic fertilizers containing phosphorus and nitrogen are of concern to water quality, habitat, and overall ecological interactions of the BGO streambanks and adjacent Hilton Creek, which flows directly into Lake Fayetteville. One way to reduce pollution to waterbodies is through the use of riparian buffers. This project sought to establish a riparian buffer immediately adjacent to a portion of Hilton Creek in an effort to improve ecological functions and water quality. The hypothesis of this study is that the streambank restoration will increase plant abundance and diversity and improve riparian habitat quality, thus enhancing ecological functions of the Hilton Creek streambank. Pre- and post-restoration assessments were conducted to test this hypothesis. A streambank riparian habitat quality assessment was adapted from the Qualitat del Bosc de Ribera’ (in English, ‘Riparian Habitat Quality’, (QBR)) index and species diversity values based from on-site plant species inventories were analyzed using a Shannon–Wiener Index of diversity. Overall, the pre-restoration QBR index value was calculated as 55 out of 100 and post-restoration QBR index value was calculated as 65 out of 100, suggesting an immediate improvement in riparian habitat quality. Inventoried plant species equated to a pre-restoration Shannon–Wiener Index of diversity value of 2.13, while the post-restoration Shannon–Wiener Index of diversity equaled 2.91, indicating an increase in species diversity. Water quality parameters were recorded to establish baseline values for Hilton Creek to encourage future monitoring of the project site as the streambank restoration matures

Ensemble Concerts: University Band, December 6, 2023

Center for the Performing ArtsDecember 6, 2023Wednesday Evening8:00 p.m

Harnessing Modern Web Application Technology to Create Intuitive and Efficient Data Visualization and Sharing Tools

Neuroscientists increasingly need to work with big data in order to derive meaningful results in their field. Collecting, organizing and analyzing this data can be a major hurdle on the road to scientific discovery. This hurdle can be lowered using the same technologies that are currently revolutionizing the way that cultural and social media sites represent and share information with their users. Web application technologies and standards such as RESTful webservices, HTML5 and high-performance in-browser JavaScript engines are being utilized to vastly improve the way that the world accesses and shares information. The neuroscience community can also benefit tremendously from these technologies. We present here a web application that allows users to explore and request the complex datasets that need to be shared among the neuroimaging community. The COINS (Collaborative Informatics and Neuroimaging Suite) Data Exchange uses web application technologies to facilitate data sharing in three phases: Exploration, Request/Communication, and Download. This paper will focus on the first phase, and how intuitive exploration of large and complex datasets is achieved using a framework that centers around asynchronous client-server communication (AJAX) and also exposes a powerful API that can be utilized by other applications to explore available data. First opened to the neuroscience community in August 2012, the Data Exchange has already provided researchers with over 2500 GB of data

IoT Resources for Outdoor Play

This position paper argues that IoT tool-kit resources can enable children to be experts of their own experience and create new kinds of outdoor play. We advocate for physical-digital designs and present various ways that children may be enabled to create digital interactions in play. In turn, we suggest that in-the-moment adaptions and direct-control can support social negotiation and emerging opportunities in open-ended play. Finally, we suggest that future studies should investigate the role of shared ownership and community resources in enabling sustained engagement with such tool-kit resources

A Diffusion-Model of Joint Interactive Navigation

Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios expensive. In this paper, we present DJINN - a diffusion based method of generating traffic scenarios. Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future. On popular trajectory forecasting datasets, we report state of the art performance on joint trajectory metrics. In addition, we demonstrate how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions including goal-based sampling, behavior-class sampling, and scenario editing.Comment: 10 pages, 4 figure

Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images

The development of algorithms that learn behavioral driving models using human demonstrations has led to increasingly realistic simulations. In general, such models learn to jointly predict trajectories for all controlled agents by exploiting road context information such as drivable lanes obtained from manually annotated high-definition (HD) maps. Recent studies show that these models can greatly benefit from increasing the amount of human data available for training. However, the manual annotation of HD maps which is necessary for every new location puts a bottleneck on efficiently scaling up human traffic datasets. We propose a drone birdview image-based map (DBM) representation that requires minimal annotation and provides rich road context information. We evaluate multi-agent trajectory prediction using the DBM by incorporating it into a differentiable driving simulator as an image-texture-based differentiable rendering module. Our results demonstrate competitive multi-agent trajectory prediction performance when using our DBM representation as compared to models trained with rasterized HD maps