Entry Systems Panel deliberations

The Entry Systems Panel was chaired by Don Rummler, LaRC and Dan Rasky, ARC. As requested, each panel participant prior to the workshop prepared and delivered presentations to: (1) identify technology needs; (2) assess current programs; (3) identify technology gaps; and (4) identify highest payoff areas R&D. Participants presented background on the entry systems R&D efforts and operations experiences for the Space Shuttle Orbiter. These participants represented NASA Centers involved in research (Ames Research Center), development (Johnson Space Center) and operations (Kennedy Space Center) and the Shuttle Orbiter prime contractor. The presentations lead to the discovery of several lessons learned

A CR-hydro-NEI Model of Multi-wavelength Emission from the Vela Jr. Supernova Remnant (SNR RX J0852.0-4622)

Based largely on energy budget considerations and the observed cosmic-ray (CR) ionic composition, supernova remnant (SNR) blast waves are the most likely sources of CR ions with energies at least up to the "knee" near 3 PeV. Shocks in young shell-type TeV-bright SNRs are surely producing TeV particles, but the emission could be dominated by ions producing neutral pion-decay emission or electrons producing inverse-Compton gamma-rays. Unambiguously identifying the GeV-TeV emission process in a particular SNR will not only help pin down the origin of CRs, it will add significantly to our understanding of the diffusive shock acceleration (DSA) mechanism and improve our understanding of supernovae and the impact SNRs have on the circumstellar medium. In this study, we investigate the Vela Jr. SNR, an example of TeV-bright non-thermal SNRs. We perform hydrodynamic simulations coupled with non-linear DSA and non-equilibrium ionization near the forward shock (FS) to confront currently available multi-wavelength data. We find, with an analysis similar to that used earlier for SNR RX J1713.7-3946, that self-consistently modeling the thermal X-ray line emission with the non-thermal continuum in our one-dimensional model strongly constrains the fitting parameters, and this leads convincingly to a leptonic origin for the GeV-TeV emission for Vela Jr. This conclusion is further supported by applying additional constraints from observation, including the radial brightness profiles of the SNR shell in TeV gamma-rays, and the spatial variation of the X-ray synchrotron spectral index. We will discuss implications of our models on future observations by the next-generation telescopes.Comment: 12 pages, 10 figures, to appear at the Astrophysical Journa

Environmental Law for the 21st Century

In this issue, Professors Elliott and Esty expand on their original proposal and respond to critics.2 They apply their perspectives as practitioners, as well as academics, to develop their vision for environmental law in the 21st century. They establish three legal duties that should apply to entities that release potentially harmful materials into the environment. Professors Elliott and Esty contend that such entities have a duty (1) of research and disclosure to assure the public that any environmental releases are not harmful, (2) to minimize harm if they fail to demonstrate the releases are harmless, and (3) to compensate those at risk of environmental harm financially. They point out that employees have all three of these elements in the workplace, and argue that the public should be no less protected

Subsumption Reduces Dataset Dimensionality Without Decreasing Performance of a Machine Learning Classifier

When Features in a High Dimension Dataset Are Organized Hierarchically, There is an Inherent Opportunity to Reduce Dimensionality. Since More Specific Concepts Are Subsumed by More General Concepts, Subsumption Can Be Applied Successively to Reduce Dimensionality. We Tested Whether Sub-Sumption Could Reduce the Dimensionality of a Disease Dataset Without Impairing Classification Accuracy. We Started with a Dataset that Had 168 Neurological Patients, 14 Diagnoses, and 293 Unique Features. We Applied Subsumption Repeatedly to Create Eight Successively Smaller Datasets, Ranging from 293 Dimensions in the Largest Dataset to 11 Dimensions in the Smallest Dataset. We Tested a MLP Classifier on All Eight Datasets. Precision, Recall, Accuracy, and Validation Declined Only at the Lowest Dimensionality. Our Preliminary Results Suggest that When Features in a High Dimension Dataset Are Derived from a Hierarchical Ontology, Subsumption is a Viable Strategy to Reduce Dimensionality.Clinical Relevance - Datasets Derived from Electronic Health Records Are Often of High Dimensionality. If Features in the Dataset Are based on Concepts from a Hierarchical Ontology, Subsumption Can Reduce Dimensionality

Estimating the Impacts of Storage Dry Matter Losses on Switchgrass Production

This poster estimates dry matter losses as a function of harvest method, storage treatment, and time in storage. We then calculate the cost to store switchgrass bales under alternate harvest method and storage treatment scenarios; and determine the breakeven harvest method and storage treatment as a function of biomass price and time in storage.Biomass, bioenergy crops, function form, sustainable systems, Farm Management, Production Economics, Q10, Q42,

Preface to Computational Intelligence Applications in Medicine and Biology

This special edition of the European Science Journal is devoted to applying computational intelligence methods to solving complex problems in medicine and biology