91-100 of 238 results
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Resolving Physical Conditions of Diffuse Ionized Gas throughout the Milky Way-Magellanic System
PI Lawrence Haffner
CO-I Edwin Mierkiewicz
We use a dedicated, sensitive spectroscopic facility in Chile, the Wisconsin H-Alpha Mapper (WHAM), to study the physical conditions of the diffuse ionized gas (DIG) in the Milky Way and Magellanic System.
WHAM can reveal emission nearly a 100-million times fainter than the Orion Nebula, making it unsurpassed for collecting high-resolution, optical-line spectra from faint, diffuse sources. Here, we embark on a diverse observational program using multiple optical emission lines with this powerful, remotely-controlled, Fabry-Perot instrument to substantially advance our understanding of interstellar matter and processes that shape it. In previous work, we released the first spectral survey of the Galaxy's DIG with observations of the Balmer-alpha optical emission line of hydrogen. This effort, the WHAM Sky Survey (WHAM-SS), complements neutral gas surveys of the 21-cm radio emission line. The WHAM-SS reveals ionized gas that can be seen in every direction from our location inside the Galaxy and offers a comprehensive view of the distribution and dynamics of the Milky Way's ionized gas. Using different instrument configurations, we are now surveying the southern sky in other emission lines, allowing us to measure physical conditions within the same ionized component.
NSF AST-2009276
Categories: Faculty-Staff
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Information Systems (IS) and Information Security & Assurance (ISA) Curriculum Development and Design: A DACUM Approach.
PI Leila Halawi
PI Wendi Kappers
PI Aaron Glassman
Issues associated with information security are numerous and diverse. Since the majority of organizational actions rely greatly on information and communication technologies, Information Systems (IS) security and Information Security & Assurance (ISA) is now a main concern for firms, governments, institutes, and society as a whole. As a result, a plethora of graduate programs have been created, covering nearly every aspect of IS security. The purpose of this project is to document the findings for using a particularly inventive and extremely efficient technique of job skill analysis known as a DACUM, which stands for “developing a curriculum.” A DACUM begins with an identification of an industry pool that is further reduced to an expert panel, culminating in a daylong workshop to identify new job skill statements and skill needs.
Due to limited DACUM application within the Information Systems (IS) and Information Security & Assurance (ISA) fields of study, DACUM curriculum development milestones and outcomes experienced during the process will be shared to benefit future industry and academic collaborations with regard to curriculum development. Additionally, within the workshop process, the identification of job skill need versus curriculum assessment and activity inclusion is planned and will be documented to produce an end of process expert field survey that expects to support future IS and ISA course development to best support employment opportunities. Furthermore, a discussion of Embry-Riddle Aeronautical University and its unique relationship with Microsoft will also be included as a possible model for future university-industry partnerships.
Categories: Faculty-Staff
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Implementing Active Learning Techniques in an Undergraduate Aviation Meteorology Course
PI Daniel Halperin
PI Joseph Keebler
CO-I Robert Eicher
CO-I Thomas Guinn
CO-I Kim Chambers
Student feedback from end-of-course evaluations repeatedly indicated a desire to change the format of the course by de-emphasizing the PowerPoint-based lectures. The goal of the present study was to determine whether including a set of new active-learning techniques in an Aviation Weather course would result in better student understanding (as measured by exam scores) and make the course more engaging (as measured by end-of-course evaluations). During 2018-19, three instructors implemented five different active-learning techniques into their classes (i.e., the experimental group), while two instructors continued to use the unrevised course materials (i.e., the control group). The new active-learning techniques, described below, included daily quizzes, polling questions, flipped classroom sessions, in-class activities, and assertion-evidence-based lectures. All sections used the same assignments and exams, allowing for direct assessment of the effectiveness of the active-learning techniques. Analyses of Variance (ANOVA) tables were used to determine the statistical significance of the differences in exam scores. Indirect assessments in the form of end-of-course evaluations were also examined.
Categories: Faculty-Staff
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Adding Tropical Cyclone Verification Capabilities to the Model Evaluation Tools – Tropical Cyclone (MET-TC) Software
PI Daniel Halperin
Producing reliable tropical cyclone (TC) genesis forecasts is an operational priority. The National Hurricane Center uses several TC genesis guidance products for their Tropical Weather Outlook. Furthermore, global model output is used in many TC genesis guidance products and is considered an important source of deterministic TC genesis forecast guidance. This project creates a standard framework for verifying deterministic and probabilistic TC genesis forecasts using the TC-Gen tool in the Model Evaluation Tools software package.
Accurately predicting tropical cyclone (TC) genesis is an important component of providing the public with the information they need to protect life and property in the event of a landfalling storm. The National Hurricane Center (NHC) issues probabilistic forecasts of TC genesis within two and five days in their Tropical Weather Outlook product. There are several guidance products available to the forecasters, many of which rely at least in part on global forecast models. It is important to understand how accurate these guidance products and the global models are at forecasting TC genesis.
This project seeks to create a standard framework for verifying TC genesis forecasts from various sources. This will allow a straightforward comparison between official forecasts, the experimental guidance products, and global model forecasts. These verification capabilities are developed within the existing Model Evaluation Tools (MET) software package from the National Center for Atmospheric Research (NCAR). As such, the user will have considerable flexibility when setting up a verification task. They will also be provided numerous output statistics for deterministic and probabilistic forecasts.
Categories: Faculty-Staff
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An Exploratory Study of General Aviation Visual to Instrument Meteorological Condition Contextual Factors
PI James Hartman
CO-I Mark Friend
The purpose of this dissertation was to bridge the existing literature gap of outdated contextual factor (CF) research through examination and determination of current General Aviation (GA) Title 14 Code of Federal Regulations (CFR) Part 91 visual flight rules (VFR)-into-instrument meteorological condition (IMC) contextual factors. Contextual factors are a multifaceted arrangement of pertinent events or occurrences contributing to pilot accidents in weather-related decision-making errors.
A total of 46 contextual factors were identified and examined from the reviewed research literature. The study examined and determined the presence of the 46 contextual factors, frequencies, and manifestations in the GA VFR-into-IMC Aviation Accident Reports (AARs) archived in the National Transportation Safety Board (NTSB) online safety database. Significant relationships were identified among the contextual factors and pilot age, flight experience, weather, flight conditions, time of day, and certification level using point biserial and phi correlations. Contextual factor significant effects on the crash distance from departure and crash distance from the planned destination were revealed using multiple regression. A qualitative methodology was used on secondary data. Three subject matter experts (SMEs) for the main study analyzed a sample of 85 accidents for the presence of the 46 contextual factors. Raters then reported the presence of the contextual factors and provided opinions on how the contextual factors were manifested. Qualitative analysis revealed the presence of 37 out of 46 contextual factors. Highest frequency factors included number of passengers on board (CF29), accident time of day (CF1), crash distance from the planned destination (CF15), not filing of a flight plan (CF21), and underestimating risk (CF43). Raters described numerous manifestations of the contextual factors including 62% of the accident flights had passengers on board the aircraft (CF29). Quantitative analysis discovered several significantly weak to moderate relationships among pilot age, flight experience, weather, flight conditions, time of day, certification level, and the contextual factors. Several contextual factors had significant effects on the crash distance from departure and crash distance from the planned destination. Findings indicated the contextual factors were extensive in GA accidents. Additional research should focus on all flight domains, including further study of GA Part 91 VFR-into-IMC accidents. It is recommended the GA Part 91 pilot community be trained on the contextual factors assessed.
Categories: Graduate
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Demonstration of an Electrostatic Dust Shield on the Lunar Surface
PI Troy Henderson
This project will demonstrate the capability of an electrostatic dust shield, developed by NASA/KSC engineers, to remove dust from the lens of a camera after impact on the lunar surface.
This project, which is funded by NASA Kennedy Space Center, will demonstrate the capability of an electrostatic dust shield, developed by NASA/KSC engineers, to remove dust from the lens of a camera after impact on the lunar surface. Laboratory tests will confirm the experiment design, followed by a flight to the lunar surface in early 2022
Categories: Faculty-Staff
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Hazard Detection and Avoidance for Lunar Landing
PI Troy Henderson
This project develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander
This project, funded by Intuitive Machines, develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander. Preliminary work uses an optical camera and future work will include a lidar sensor. These algorithms will be tested in simulation, tested in laboratory experiments and demonstrated on a lunar lander flight mission.
Categories: Faculty-Staff
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Improved Image Processing for Orbit Estimation
PI Troy Henderson
This project seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes.
This project, funded by Air Force Research Laboratory, seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes. Additional work uses RF signals to estimate orbits of transmitting spacecraft.
Categories: Faculty-Staff
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Using Machine Learning to Improve Forecasting of Deep Convection
PI Christopher Hennon
CO-I Ronny Schroeder
CO-I Curtis James
CO-I Abd AlRahman AlMomani
We are working to train a neural network to forecast the initiation time, location, and intensity of thunderstorms. These results will support operations during the proposed CONVECT project and could ultimately aid operational forecasting during the North American Monsoon (NAM).
This research, funded through an NSF EAGER grant, seeks to improve forecast accuracy of monsoon thunderstorm activity and precipitation amounts in the Southwest. The project creates an innovative machine learning tool trained using regional numerical weather model output and satellite remote sensing data (the predictors) with respect to known thunderstorm cell locations and intensities detected by radar (the targets). The tool will be designed to extract important fundamental relationships between the predictors and targets that help explain the development and evolution of thunderstorms. After an intense training, validation and testing phase, the relationships will then be leveraged to generate better forecasts of the timing, severity and location of future thunderstorm events in the Southwest. The tool will be shared with the National Weather Service tto help forecasters predict thunderstorm-related hazards such as large hail, flash flooding or wildfire ignition. This innovative approach will also provide a framework for improving operational meteorological and geophysical prediction systems and for guiding scientific field studies.
The project develops a probabilistic model to predict convective initiation, rain rates, and convective cell tracks during the wet phase of the North American Monsoon (NAM). Predictors of convection (e.g., relative humidity, convective available potential energy, precipitable water) will be collected from dynamic mesoscale model (High Resolution Rapid Refresh, University of Arizona-Weather Research Forecast model) analyses and forecasts and combined with new satellite-derived observations of soil moisture and surface temperature to produce a unique prediction tool. A novel machine learning approach – causality informed learning – will be applied to identify the most suitable predictors for further training in a neural network and to gain insight into the processes governing convective initiation and evolution. Hourly forecasts of precipitation occurrence, nature, and categorical rain rates will be produced operationally to guide forecasters and field research.
Categories: Faculty-Staff
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Data-enabled Science & Engineering Education (IUSE 1626602, 2016-2020),
PI Matthew Ikle
CO-I Hong Liu
CO-I Michael Wolyniak
CO-I Raphael Isokpehi
This project will develop a virtual department across four partner campuses to provide computer science education to students at campuses that are individually too small to support this kind of department. The new department will focus on the analysis of "big data" - large sets of computational and observational data - that are becoming increasingly prevalent in STEM. Cyber-learning techniques such as recorded lectures, archived materials, blog participation, and active learning approaches will be combined to offer a set of classes in big data science spanning meteorology, environmental science, biology and chemistry. By combining students from different campuses into the same courses, problems with minimal resources and limited potential enrollments on the individual campuses can be overcome. In particular, the project will focus on developing courses in biology and earth science, areas where students are not attracted by traditional computer science classes.
The project will develop a flexible, blended learning model and effective learning assessment tools that can be implemented across multiple disciplines and institutions. The major goals and corresponding objectives of the project are to:
1) Develop and implement high quality and relevant Computational and Data-Enabled Science and Engineering (CDSE) courses in mathematical modeling, data mining, genomics and bioinformatics, and problems in atmospheric and hydrospheric science using active learning and research-based teaching methodologies that promote inter-institutional and interdisciplinary collaboration.
2) Use innovative web-based technologies, to develop and implement learning assessment tools to gauge achievement of students from diverse backgrounds and contexts.
3) Develop, implement, and test an expanded CDSE pedagogical network in which resource sharing allows institutions of all sizes and types to consistently and sustainably offer CDSE coursework.
Instructors from different campuses will be paired in a peer teaching/peer review model for course design and implementation. Including pairs of instructors from different institutions ensures that (1) each instructor will gain the knowledge and experience to teach a new course that is originally developed by the other instructor; and (2) the courses are thoroughly reviewed and revised by peers. The coalition will share its discoveries in building inter-institutional teaching efficiency, undergraduate research opportunities, and learning assessment via online networks, new coalition partners, conferences, and publications.Categories: Faculty-Staff
91-100 of 238 results