Annual Site Environmental Report Calendar Year 2007

This report summarizes the environmental status of Ames Laboratory for calendar year 2007. It includes descriptions of the Laboratory site, its mission, the status of its compliance with applicable environmental regulations, its planning and activities to maintain compliance, and a comprehensive review of its environmental protection, surveillance and monitoring activities. Ames Laboratory is located on the campus of Iowa State University (ISU) and occupies 11 buildings owned by the Department of Energy (DOE). See the Laboratory's Web page at www.external.ameslab.gov for locations and Laboratory overview. The Laboratory also leases space in ISU owned buildings. In 2007, the Laboratory accumulated and disposed of waste under U.S. Environmental Protection Agency (EPA) issued generator numbers. All waste is handled according to all applicable EPA, State, Local and DOE Orders. In 2006 the Laboratory reduced its generator status from a Large Quantity Generator (LQG) to a Small Quantity Generator (SQG). EPA Region VII was notified of this change. The Laboratory's RCRA hazardous waste management program was inspected by EPA Region VII in April 2006. There were no notices of violations. The inspector was impressed with the improvements of the Laboratory's waste management program over the past ten years. The Laboratory was in compliance with all applicable federal, state, local and DOE regulations and orders in 2007. There were no radiological air emissions or exposures to the general public due to Laboratory activities in 2007. See U.S. Department of Energy Air Emissions Annual Report in Appendix B. As indicated in prior SERs, pollution awareness, waste minimization and recycling programs have been in practice since 1990, with improvements implemented most recently in 2003. Included in these efforts were battery and CRT recycling, waste white paper and green computer paper-recycling. Ames Laboratory also recycles/reuses salvageable metal, used oil, styrofoam peanuts, batteries, fluorescent lamps and telephone books. Ames Laboratory reported to DOE-Ames Site Office (AMSO), through the Laboratory's Self Assessment Report, on its Affirmative Procurement Performance Measure. A performance level of 'A' was achieved in 2007 for Integrated Safety, Health, and Environmental Protection. As reported in Site Environmental Reports for prior years, the Laboratory's Environmental Management System has been integrated into the Laboratory's Integrated Safety Management System since 2005. The integration of EMS into the way the Laboratory does business allows the Laboratory to systematically review, address and respond to the Laboratory's environmental impacts

Evaluation of Fixed Penalty Notices for Careless Driving

Fixed penalty notices for careless driving offences were introduced in August 2013. This evaluation explores the impact on those affected: the police; courts; road users; and offenders

HydroLearn: Facilitating the Development, Adaptation and Sharing of Active-Learning Resources in Hydrology Education

Lightning presentation and workshop presented at CUAHSI HydroInformatics Conference, 2019. https://www.cuahsi.org/community/cuahsi-science-meetings/. This workshop is offered for hydrology faculty interested in implementing or adapting active-learning, data-driven resources to their educational settings. The workshop aspires to create faculty networking and development opportunities with the overall goal of promoting and reducing barriers against adoption of active-learning resources in hydrology. The workshop will use the recently developed NSF-sponsored HydroLearn platform, along with resources from CUAHSI, HydroShare and other community platforms, to enable participating faculty to develop and share educational resources. The workshop will showcase existing seed modules and will cover best practices in developing student-centered learning activities, including the design of pedagogically-sound learning objectives and assessment rubrics. Faculty who currently teach hydrology-related courses are encouraged to participate, especially those who teach undergraduate or early-level graduate courses. Interested faculty may also be invited to participate in a follow-up funded fellowship program to engage in a semester-long adoption and field testing of the HydroLearn platform and its content. The workshop will be jointly conducted by hydrology faculty along with an expert in education research

A Resource Centric Approach For Advancing Collaboration Through Hydrologic Data And Model Sharing

HydroShare is an online, collaborative system being developed for open sharing of hydrologic data and models. The goal of HydroShare is to enable scientists to easily discover and access hydrologic data and models, retrieve them to their desktop or perform analyses in a distributed computing environment that may include grid, cloud or high performance computing model instances as necessary. Scientists may also publish outcomes (data, results or models) into HydroShare, using the system as a collaboration platform for sharing data, models and analyses. HydroShare is expanding the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated, creating new capability to share models and model components, and taking advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. One of the fundamental concepts in HydroShare is that of a Resource. All content is represented using a Resource Data Model that separates system and science metadata and has elements common to all resources as well as elements specific to the types of resources HydroShare will support. These will include different data types used in the hydrology community and models and workflows that require metadata on execution functionality. The HydroShare web interface and social media functions are being developed using the Drupal content management system. A geospatial visualization and analysis component enables searching, visualizing, and analyzing geographic datasets. The integrated Rule-Oriented Data System (iRODS) is being used to manage federated data content and perform rule-based background actions on data and model resources, including parsing to generate metadata catalog information and the execution of models and workflows. This presentation will introduce the HydroShare functionality developed to date, describe key elements of the Resource Data Model and outline the roadmap for future development

Progress Report on the Airborne Composition Standard Variable Name and Time Series Working Groups of the 2017 ESDSWG

The role of NASA's Earth Science Data Systems Working Groups (ESDSWG) is to make recommendations relevant to NASA's Earth science data systems from users' experiences and community insight. Each group works independently, focusing on a unique topic. Progress of two of the 2017 Working Groups will be presented. In a single airborne field campaign, there can be several different instruments and techniques that measure the same parameter on one or more aircraft platforms. Many of these same parameters are measured during different airborne campaigns using similar or different instruments and techniques. The Airborne Composition Standard Variable Name Working Group is working to create a list of variable standard names that can be used across all airborne field campaigns in order to assist in the transition to the ICARTT Version 2.0 file format. The overall goal is to enhance the usability of ICARTT files and the search ability of airborne field campaign data. The Time Series Working Group (TSWG) is a continuation of the 2015 and 2016 Time Series Working Groups. In 2015, we started TSWG with the intention of exploring the new OGC (Open Geospatial Consortium) WaterML 2 standards as a means for encoding point-based time series data from NASA satellites. In this working group, we realized that WaterML 2 might not be the best solution for this type of data, for a number of reasons. Our discussion with experts from other agencies, who have worked on similar issues, identified several challenges that we would need to address. As a result, we made the recommendation to study the new TimeseriesML 1.0 standard of OGC as a potential NASA time series standard. The 2016 TSWG examined closely the TimeseriesML 1.0 and, in coordination with the OGC TimeseriesML Standards Working Group, identified certain gaps in TimeseriesML 1.0 that would need to be addressed for the standard to be applicable to NASA time series data. An engineering report was drafted based on the OGC Engineering Report template, describing recommended changes to TimeseriesML 1.0, in the form of use cases. In 2017, we are conducting interoperability experiments to implement the use cases and demonstrate the feasibility and suitability of these modifications for NASA and related user communities. The results will be incorporated into the existing draft engineering report

Polar Chemoreceptor Clustering by Coupled Trimers of Dimers

Receptors of bacterial chemotaxis form clusters at the cell poles, where clusters act as "antennas" to amplify small changes in ligand concentration. Interestingly, chemoreceptors cluster at multiple length scales. At the smallest scale, receptors form dimers, which assemble into stable timers of dimers. At a large scale, trimers form large polar clusters composed of thousands of receptors. Although much is known about the signaling properties emerging from receptor clusters, it is unknown how receptors localize at the cell poles and what the cluster-size determining factors are. Here, we present a model of polar receptor clustering based on coupled trimers of dimers, where cluster size is determined as a minimum of the cluster-membrane free energy. This energy has contributions from the cluster-membrane elastic energy, penalizing large clusters due to their high intrinsic curvature, and receptor-receptor coupling favoring large clusters. We find that the reduced cluster-membrane curvature mismatch at the curved cell poles leads to large and robust polar clusters in line with experimental observation, while lateral clusters are efficiently suppressed.Comment: 11 pages, 6 figures, and 1 tabl

Classification of Wildfires from MODIS Data Using Neural Networks

Wildfires are destructive to both life and property, which necessitates an approach to quickly and autonomously detect these events from orbital observatories. This talk will introduce a neural network based approach for classifying wildfires in MODIS multispectral data, and will show how it could be applied to a constellation of low-cost CubeSats. The approach combines training a deep neural network on the ground using high performance consumer GPUs, with a highly optimized inference system running on a flight-proven embedded processor. Normally neural networks execute on hardware orders of magnitude more powerful than anything found in a space-based computer, therefore the inference system is designed to be performance even on the most modest of platforms. This implementation is able to be significantly more accurate than previous neural network implementations, while also approaching the accuracy of the state-of-the-art MODFIRE data products

An Architectural Overview Of HydroShare, A Next-Generation Hydrologic Information System

HydroShare is an online, open-source, collaborative system being developed for sharing hydrologic data and models as part of the NSF’s Software Infrastructure for Sustained Innovation (SI2) program. The goal of HydroShare is to enable scientists to easily discover and access hydrologic data and models, retrieve them to their desktop, or perform analyses in a distributed computing environment that may include grid, cloud, or high performance computing. Scientists may also publish outcomes (data, results or models) into HydroShare, using the system as a collaboration platform for sharing data, models, and analyses. HydroShare involves a large distributed software development effort requiring collaboration between domain scientists, software engineers, and software developers across eight U.S. universities, RENCI, and CUAHSI. HydroShare expands the data sharing capabilities of the Hydrologic Information System of the Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI): It broadens the classes of data accommodated, enables sharing of models and model components, and leverages social media functionality to enhance collaboration around hydrologic data and models. The HydroShare architecture is a stack of storage and computation, web services, and user applications. A content management system, Django+Mezzanine, provides user interface, search, social media functions, and services. A geospatial visualization and analysis component enables searching, visualizing, and analyzing geographic datasets. The integrated Rule-Oriented Data System (iRODS) is used to manage federated data content and perform rule-based background actions on data and model resources, including parsing to generate metadata catalog information and the distributed execution of models and workflows. A web browser is the main interface to HydroShare, however a web services applications programming interface (API) supports access through HydroDesktop and other hydrologic modeling systems, and the architecture separates the interface layer and services layer exposing all functionality through these web services. This presentation will describe key components of HydroShare and discuss how HydroShare is designedto enable better hydrologic science concomitant with sustainable open-source software practices

Adult Out of Court Disposal Pilot Evaluation - Final Report

This report combines the findings from the process and impact evaluations of the Adult Out of Court Disposal (OOCD) pilot which aimed to: assess whether (a) the pilot achieved the requirements of greater simplicity and transparency, with (b) acceptable wider implications for Criminal Justice Partners (i.e., police, HMCTS, CPS, NOMS)