A static investigation of the thrust vectoring system of the F/A-18 high-alpha research vehicle

A static (wind-off) test was conducted in the static test facility of the Langley 16-foot Transonic Tunnel to evaluate the vectoring capability and isolated nozzle performance of the proposed thrust vectoring system of the F/A-18 high alpha research vehicle (HARV). The thrust vectoring system consisted of three asymmetrically spaced vanes installed externally on a single test nozzle. Two nozzle configurations were tested: A maximum afterburner-power nozzle and a military-power nozzle. Vane size and vane actuation geometry were investigated, and an extensive matrix of vane deflection angles was tested. The nozzle pressure ratios ranged from two to six. The results indicate that the three vane system can successfully generate multiaxis (pitch and yaw) thrust vectoring. However, large resultant vector angles incurred large thrust losses. Resultant vector angles were always lower than the vane deflection angles. The maximum thrust vectoring angles achieved for the military-power nozzle were larger than the angles achieved for the maximum afterburner-power nozzle

Qualitative Measures of Equity in Small Groups

We investigate the utility of two qualitative measures of equity. Our data are videos of groups of first-generation and Deaf or hard-of-hearing students in a pre-matriculation university program designed to help them persist in STEM fields by developing their metacognitive practices. We analyze video data of students in small groups trying to accomplish various tasks. We analyze how groups engage with proposed ideas (inchargeness) and create a space of open sharing (civility). By capturing different aspects of each group, these measures combine to help our understanding of what an equitable group could look like.Comment: Accepted to International Conference of the Learning Sciences (ICLS) 201

Using Social Network Analysis on classroom video data

We propose a novel application of Social Network Analysis (SNA) using classroom video data as a means of quantitatively and visually exploring the collaborations between students. The context for our study was a summer program that works with first generation students and deaf/hard-of-hearing students to engage in authentic science practice and develop a supportive community. We applied SNA to data from one activity during the two-week program to test our approach and as a means to begin to assess whether the goals of the program are being met. We used SNA to identify groups that were interacting in unexpected ways and then to highlight how individuals were contributing to the overall group behavior. We plan to expand our new use of SNA to video data on a larger scale

Development of the Global Engineering Programming Model: A Participatory, Mixed-Methods Approach

Over the past few decades, higher education institutions have emphasized global education as a core aspect of their strategic goals, yet a gap exists in implementation at the school level, particularly in engineering. As engineering schools invest in internationalizing their programs, research is needed regarding key strategic areas and their relationship to sustained programming efforts. This study uses a participatory, integrative mixed-methods approach to develop an operational framework for global strategies, policies, and programs. A thematic, qualitative analysis of semi-structured interviews followed by a group concept mapping activity was conducted with directors of study abroad and vice provosts of global education from nine universities regarding their global programming strategies, intended outcomes, and organizational resources. The results of this research provide both implicit and explicit engineering school-wide global programming strategies, their sustainable development, and future program evaluation plans

Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis

Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. We find that for low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on a combination of amplitude and phase contrast changes to identify nanoparticles, receptive field is a key parameter for increased performance, especially in images with minimal amplitude contrast. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.Comment: 11 pages, 8 figure

Social learning in nest-building birds : a role for familiarity

© 2016 The Author(s).It is becoming apparent that birds learn from their own experiences of nest building. What is not clear is whether birds can learn from watching conspecifics build. As social learning allows an animal to gain information without engaging in costly trial-and-error learning, first-time builders should exploit the successful habits of experienced builders. We presented first-time nest-building male zebra finches with either a familiar or an unfamiliar conspecific male building with material of a colour the observer did not like. When given the opportunity to build, males that had watched a familiar male build switched their material preference to that used by the familiar male. Males that observed unfamiliar birds did not. Thus, first-time nest builders use social information and copy the nest material choices when demonstrators are familiar but not when they are strangers. The relationships between individuals therefore influences how nest-building expertise is socially transmitted in zebra finches.Publisher PDFPeer reviewe

Social learning in nest-building birds : a role for familiarity

© 2016 The Author(s).It is becoming apparent that birds learn from their own experiences of nest building. What is not clear is whether birds can learn from watching conspecifics build. As social learning allows an animal to gain information without engaging in costly trial-and-error learning, first-time builders should exploit the successful habits of experienced builders. We presented first-time nest-building male zebra finches with either a familiar or an unfamiliar conspecific male building with material of a colour the observer did not like. When given the opportunity to build, males that had watched a familiar male build switched their material preference to that used by the familiar male. Males that observed unfamiliar birds did not. Thus, first-time nest builders use social information and copy the nest material choices when demonstrators are familiar but not when they are strangers. The relationships between individuals therefore influences how nest-building expertise is socially transmitted in zebra finches.Publisher PDFPeer reviewe

Shoreline Evolution: City of Virginia Beach, Virginia, Chesapeake Bay, Lynnhaven River, Broad Bay, and Atlantic Ocean Shorelines

The City of Virginia Beach is situated along both the Atlantic Ocean and Chesapeake Bay (Figure 1). Through time, the City’s shoreline has evolved, and determining the rates and patterns of shore change provides the basis to know how a particular coast has changed through time and how it might proceed in the future.Along Chesapeake Bay’s estuarine shores, winds, waves, tides and currents shape and modify coastlines by eroding, transporting and depositing sediments.The purpose of this report is to document how the shore zone of the City of Virginia Beach has evolved since 1937. Aerial imagery was taken for most of the Bay region beginning that year and can be used to assess the geomorphic nature of shore change. Aerial photos show how the coast has changed, how beaches, dunes, bars, and spits have grown or decayed, how barriers have breached, how inlets have changed course, and how one shore type has displaced another or has not changed at all. Shore change is a natural process but, quite often, the impacts of man, through shore hardening or inlet stabilization, come to dominate a given shore reach. In addition to documenting historical shorelines, the change in shore positions along the rivers and larger creeks in the City of Virginia Beach will be quantified in this report. The shore lines of very irregular coasts, small creeks around inlets, and other complicated areas will be shown but not quantified. In addition to the Atlantic Ocean and Chesapeake Bay shorelines, the Lynnhaven River and Broad Bay shorelines were analyzed for change.Back Bay was not included

Shoreline Evolution: Prince William County, Virginia Potomac River, Occoquan Bay, and Occoquan River Shorelines

Prince William County is situated along the Potomac River (Figure 1). Through time, the County’s shoreline has evolved, and determining the rates and patterns of shore change provides the basis to know how a particular coast has changed through time and how it might proceed in the future. Along Chesapeake Bay’s estuarine shores, winds, waves, tides and currents shape and modify coastlines by eroding, transporting and depositing sediments. The purpose of this report is to document how the shore zone of Prince William County has evolved since 1937. Aerial imagery was taken for most of the Bay region beginning that year and can be used to assess the geomorphic nature of shore change. Aerial photos show how the coast has changed, how beaches, dunes, bars, and spits have grown or decayed, how barriers have breached, how inlets have changed course, and how one shore type has displaced another or has not changed at all. Shore change is a natural process but, quite often, the impacts of man, through shore hardening or inlet stabilization, come to dominate a given shore reach. In addition to documenting historical shorelines, the change in shore positions along the rivers and larger creeks in Prince William County will be quantified in this report. The shorelines of very irregular coasts, small creeks around inlets, and other complicated areas will be shown but not quantified

Shoreline Evolution Update: 1937/38-2009 End Point Rate Calculations Counties of Accomack, Gloucester, and York Cities of Newport News, Norfolk, and Poquoson

Through time, Chesapeake Bay’s shoreline has evolved, and determining the rates and patterns of shore change provides the basis to know how a particular coast has changed through time and how it might proceed in the future. Along Chesapeake Bay’s estuarine shores, winds, waves, tides and currents shape and modify coastlines by eroding, transporting and depositing sediments. The purpose of this report is to document how the shore zone of six Virginia localities, Accomack, Gloucester, York, Newport News, Norfolk, and Poquoson, have evolved since 1937/38 (Figure 1). Aerial imagery was taken for most of the Bay region beginning then and can be used to assess the geomorphic nature of shore change. Aerial photos show how the coast has changed, how beaches, dunes, bars, and spits have grown or decayed, how barriers have breached, how inlets have changed course, and how one shore type has displaced another or has not changed at all. Shore change is a natural process but, quite often, the impacts of man, through shore hardening or inlet stabilization, come to dominate a given shore reach. In addition to documenting historical shorelines, the change in shore positions along the rivers and larger creeks will be quantified in this report. The shorelines of very irregular coasts, small creeks around inlets, and other complicated areas will be shown but not quantified