Best Practices for Training the Structures Flight Test Engineer

The Structures Flight from 412 Test Wing and the Aerostructures Branch at NASA Armstrong Flight Research Center at Edwards Air Force Base have developed a set of best practices for training a structures flight test engineer. These practices represent the hard-won lessons learned and best practices from training generations of engineers to perform high risk envelope expansion and developmental flight test

Omaha Area Retail Trade Analysis

The retail trade industry is a critical sector of the economy, and is an important source of tax revenue for cities. This study examines retail sales trends in the Omaha area as well as factors that determine retail sales including total population, minority population, commuting, income, wages, and total employment. We examine trends during the 1990s, and in particular during the 1997 to 2005 period. The focus is on Douglas County and a group of surrounding Nebraska counties. We found that Douglas County remains the dominant retail center in Nebraska, but its market share is slowly eroding as competition from suburban retailers increases. Suburbanization of the Omaha area’s retail sector is likely to be a long term trend. If the Douglas County retail community is to maintain its position of dominance, it will need to pay close attention to both its competitors and to emerging market opportunities. The concentration of minority populations in Douglas County represents one such opportunity for niche oriented retail activity. Another is found in retail clusters built around Douglas County’s strongest retail sectors such as furniture, home furnishings, recreational vehicles, specialty foods and luxury items such as jewelry

X-Plane Structures Challenges Lessons Learned

This presentation will showcase the unique place flight research plays in technology development

An Overview of Normal Theory Structural Measurement Error Models

Summary This paper gives an introduction and overview to the often under-used measurement error model. The purpose is to provide a simple summary of problems that arise from measurement error and of the solutions that have been proposed. We start by describing how measurement error models occur in real-world situations. Then we proceed with defining the measurement error model, initially introducing the multivariate form of the model, and then, starting with the simplest form of the model thoroughly discuss its features and solutions to the problems introduced due to measurement error. We discuss higher-dimensional and more advanced forms of the model and give a brief numerical illustration

Performance of AI Methods in Detecting Melanoma

This research has shown that features extracted from color skin tumor images by computer vision methods can be reliable discriminators of malignant tumors from benign ones. Reliability was demonstrated by the monotonically increasing success ratios with increasing training set size and by the small standard deviations from the mean success rates. An average success rate of 70 percent in diagnosing melanoma was attained for a training set size of 60 percent. The presence or absence of atypical moles in the training and test sets was shown to have a dramatic impact on the effectiveness of the generated classification rules. This was the case with both AIM and lst-Class, and indicates a high potential for success if a method can be found for discriminating between atypical moles and melanom

High-efficiency, single-stage 7-kHz high-average-power ultrafast laser system

Includes bibliographical references (page 467).We demonstrate a simple and practical single-stage ultrafast laser amplifier system that operates at a repetition frequency from 1 to 10 kHz, with millijoule pulse energy and as much as 13 W of average power. The repetition rate can be adjusted continuously from 1 to 10 kHz by new all-solid-state pump laser technology. This is to our knowledge the highest average power ever obtained from a single-stage ultrafast laser amplifier system. This laser will significantly increase the average power and the repetition rate that is easily accessible for high-field experiments such as coherent x-ray generation or for laser-synchrotron studies

IVOA Recommendation: Simple Spectral Access Protocol Version 1.1

The Simple Spectral Access (SSA) Protocol (SSAP) defines a uniform interface to remotely discover and access one dimensional spectra. SSA is a member of an integrated family of data access interfaces altogether comprising the Data Access Layer (DAL) of the IVOA. SSA is based on a more general data model capable of describing most tabular spectrophotometric data, including time series and spectral energy distributions (SEDs) as well as 1-D spectra; however the scope of the SSA interface as specified in this document is limited to simple 1-D spectra, including simple aggregations of 1-D spectra. The form of the SSA interface is simple: clients first query the global resource registry to find services of interest and then issue a data discovery query to selected services to determine what relevant data is available from each service; the candidate datasets available are described uniformly in a VOTable format document which is returned in response to the query. Finally, the client may retrieve selected datasets for analysis. Spectrum datasets returned by an SSA spectrum service may be either precomputed, archival datasets, or they may be virtual data which is computed on the fly to respond to a client request. Spectrum datasets may conform to a standard data model defined by SSA, or may be native spectra with custom project-defined content. Spectra may be returned in any of a number of standard data formats. Spectral data is generally stored externally to the VO in a format specific to each spectral data collection; currently there is no standard way to represent astronomical spectra, and virtually every project does it differently. Hence spectra may be actively mediated to the standard SSA-defined data model at access time by the service, so that client analysis programs do not have to be familiar with the idiosyncratic details of each data collection to be accessed

Iris: an Extensible Application for Building and Analyzing Spectral Energy Distributions

Iris is an extensible application that provides astronomers with a user-friendly interface capable of ingesting broad-band data from many different sources in order to build, explore, and model spectral energy distributions (SEDs). Iris takes advantage of the standards defined by the International Virtual Observatory Alliance, but hides the technicalities of such standards by implementing different layers of abstraction on top of them. Such intermediate layers provide hooks that users and developers can exploit in order to extend the capabilities provided by Iris. For instance, custom Python models can be combined in arbitrary ways with the Iris built-in models or with other custom functions. As such, Iris offers a platform for the development and integration of SED data, services, and applications, either from the user's system or from the web. In this paper we describe the built-in features provided by Iris for building and analyzing SEDs. We also explore in some detail the Iris framework and software development kit, showing how astronomers and software developers can plug their code into an integrated SED analysis environment.Comment: 18 pages, 8 figures, accepted for publication in Astronomy & Computin

Aerostructures X-Plane Airworthiness Guidelines and Best Practices

This presentation describes aerostructures lessons learned and best practices developed over the years at AFRC

Initiating technological and pedagogical shifts in low achieving urban minority classrooms

This study explored the introduction of multi-modal teaching strategies alongside technology implementation in high poverty schools. Teachers were provided with scientific tools, simulations, and teaching stations and provided with training and opportunities to practice teaching strategies developed in conjunction with special education and literacy experts. Teacher interviews, classroom observations, and student focus groups comprised the bulk of the sited data supplementing the student achievement scores and pre and post content tests administered for each unit. Findings suggested not all teachers incorporated multi-modal strategies into lessons incorporating technology and that lower achieving students interpreted lessons quite differently than their teachers in these contexts. Implications for ways technology implementations may explicate emerging literacies are discussed