81-90 of 204 results
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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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Multiscale Computational and Experimental Framework to Elucidate the Biomechanics of Infant Growth
PI Victor Huayamave
There is currently a lack of biomechanical quantification of growth and development because: (1) there is no generic musculoskeletal infant model, and (2) the lack of infant data in the literature.
There is currently a lack of biomechanical quantification of growth and development because: (1) there is no generic musculoskeletal infant model, and (2) the lack of infant data in the literature. An infant’s spontaneous movements generate forces that are constantly acting on the joints and can affect the morphology and development of soft bone. Using experimental motion capture data, statistical shape modeling, and multi-scale musculoskeletal mechanobiological models, we will be able to predict the complex adaptation of the joint to biomechanical factors, thus providing a biomechanical basis for improved prevention and treatment of developmental disorders. This project pioneers the development of solutions that improve intervention and outcomes of conditions such as scoliosis, spina bifida, clubfoot, and developmental dysplasia of the hip. The model provides a non-invasive three-dimensional approach to study the dynamics of human movements using biomechanical parameters that are difficult or impossible to examine using physical experiments alone. Our proposed research will advance pediatric movement science and will uncover the underlying mechanisms involved in the maturation of the hip joint during early development. Results from the proposed research will: (1) Provide experimental data and computational models that can serve as the basis for developing innovative solutions for infant developmental disorders; (2) Develop innovative tools to aid clinicians, pediatricians, and physical therapists when managing joint disorders; (3) Identify factors that drive and regulate growth early in life that may have long-term benefits for prevention of early arthritis. Each of these contributions is significant given that joint disorders such as developmental dysplasia of the hip underlie around 29% of all primary hip replacements in adults.
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
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The Covariant Stone-von Neumann Theorem for Actions of Abelian Groups on C*-algebras of Compact Operators
PI Lara Ismert
This is a collaborative work with Leonard Huang, Ph.D. at the University of Nevada, Reno.
In this paper, we formulate and prove a version of the Stone-von Neumann Theorem for every C*-dynamical system of the form (G,K(H),α), where G is a locally compact Hausdorff abelian group and H is a Hilbert space. The novelty of our work stems from our representation of the Weyl Commutation Relation on Hilbert K(H)-modules, instead of just Hilbert spaces, and our introduction of two additional commutation relations, which are necessary to obtain a uniqueness theorem. Along the way, we apply one of our basic results on Hilbert C*-modules to significantly shorten the length of Iain Raeburn's well-known proof of Takai-Takesaki Duality.Categories: Faculty-Staff
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CONVECT (CONvection and water Vapor Exchange in Complex Terrain)
PI Curtis James
CO-I Ronny Schroeder
CONVECT is a major meteorological field research project being proposed for July - August 2027 in north-central Arizona. The project is aimed at improving our understanding and ability to predict the convective development, propagation, and intensification of thunderstorms during the North American Monsoon (NAM)
CONVECT is focused in north-central Arizona between the cities of Prescott and Flagstaff. This targeted region, encompassing the Bradshaw Mountains, Black Hills, Verde and Prescott Valleys, and Mogollon Rim, provides an ideal laboratory for investigating processes connecting complex terrain to boundary-layer and convective processes. During the summer monsoon season, this region experiences frequent deep, precipitating convection. These storms typically initiate over the most prominent terrain features and sometimes propagate into the populated lower lying areas or send out density currents or buoyancy bores that subsequently initiate new convection. The thunderstorms are generally spatially localized, forming over a deep convective boundary layer, but are often associated with pulse-severe conditions (damaging wind gusts or large hail). Some cells may become terrain-locked or exhibit back-building behavior, leading to intense rainfall and flash flooding.
CONVECT will also examine the water vapor sources and land-atmosphere interactions favoring convective initiation. Our previous work has shown that soil moisture is an important predictor of monsoon convection. We will therefore examine the effects of soil moisture variability on surface and PBL energy and moisture exchange over heterogeneous, sloping surfaces within a thermally driven planetary boundary layer (PBL). The proposed deployment includes a dense network of surface flux and energy balance probes, lower-tropospheric thermodynamic and kinematic profiling systems, mobile radars, and crewed and uncrewed aircraft with in-situ and remote sensors. The campaign will be carefully guided by multi-scale modeling and our machine learning model, and in turn, experimental observations will be assimilated to evaluate their impact on multi-scale predictability and the validity of surface layer and PBL parameterizations in complex terrain. The CONVECT science team of instrument scientists and numerical modelers contains the necessary, complementary expertise in the surface layer, the boundary layer, and deep convection to substantially advance understanding of water vapor exchanges between the surface and free troposphere, as well as extreme precipitation. The broader impacts will be improved forecast accuracy during the North American Monsoon by identifying improvements in operational instrumentation networks and forecast model parameterizations.Categories: Faculty-Staff
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3D Printing of Continuous Carbon Fiber Composites with Programmable Thermal Behaviors: A Proactive Safety Design for Advanced Thermal Management
PI Yizhou Jiang
CO-I Leitao Chen
CO-I Yanbing Chen
This study aimed to fabricate composite materials, i.e. continuous carbon fibers reinforced thermoset composites (CCFRTC), in a way that makes heat transfer predictable, enabling effective control measures. The ability to control thermal transfer through 3D-printing can lead to significant improvements in preventing thermal-related accidents.
Findings: Final report submitted 9/24. This study demonstrated the adaptability and precision of the team’s 3D printing method but also underscored its potential in advancing the field of thermosetting composite material manufacturing, paving the way for innovative applications, including fire suppression systems.
Categories: Faculty-Staff
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Cyber hygiene and cyber insurance current practice research
PI Wendi Kappers
CO-I Aaron Glassman
CO-I Michael Wills
Identify the market uptake and applicability of cyber hygiene models, particularly within small to medium enterprises, and relate this to current market practices in the use of cyber insurance policies and mechanisms as part of risk mitigation and management.
The cybersecurity and information risk management marketplace abounds in "top ten" lists of risks, recommended strategies and tactics, and advice; yet the uptake and successful implementation of these measures across SMB / SME (less than 250-500 person) organizations is lackluster. Cyber insurance underwriting, too, is showing strains, especially in light of 2020-2021's ransomware and related siruption attacks reacinc pandemic-seeming proportions. This research forms the first part of a process to develop, calibrate, and use models of risk avoidance, management, and acceptance behaviors.Categories: Faculty-Staff
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Meta-Analyses of the Effects of Standardized Handoff Protocols on Patient, Provider, and Organizational Outcomes
PI Joseph Keebler
CO-I Elizabeth Lazzara
This meta-analysis attempts to understand the benefits of a structured communication process on patient, provider, and organizational outcomes. Studies have found that one of the most crucial points during a patient’s hospital stay is the transition of care between one or more providers, often referred to as a patient handoff. These brief interactions between providers are often especially vulnerable to communication breakdowns due to interruptions, omission of pertinent information by the sender or receiver of the information. To illustrate, upwards of 80% of severe, preventable medical errors have been attributed to miscommunication during handoffs. In other words, failures in communication during handoff are potentially responsible for the loss of hundreds of thousands of lives every year in the United States.
Standardized protocols – usually in the form of a short mnemonic (e.g. SBAR – situation, background, assessment, recommendation) or a longer multi-item checklist - have been required by the Joint Commission, but meta-analytic integration of handoff protocol research has not been conducted. Meta-analysis is a statistical technique that quantitatively assesses effects across multiple studies, providing a summary of the current state of the science. The overall purpose of this study was to understand the effects of handoff protocols using meta-analytic approaches. Handoff information passed during transitions of care, patient outcomes, provider outcomes, and organizational outcomes are the primary outcomes studied for this research.
Initially 4,556 articles were identified across a multitude of literature databases, with 4,520 removed. This process left a final set of 36 articles, all which included pre-/postintervention designs implemented in live clinical/hospital settings. Meta-analyses were conducted on 34,527 pre- and 30,072 postintervention data points.
Results indicate positive effects on all four outcomes: handoff information, patient outcomes, provider outcomes, and organizational outcomes. We found protocols to be effective, but there is significant publication bias and heterogeneity in the literature. Publication bias indicates that only studies with significant findings are being published, while heterogeneity indicates that studies are not being conducted the same way – usually lacking standardized metrics. These results demonstrate that handoff protocols tend to improve results on multiple levels, including handoff information passed and patient, provider, and organizational outcomes. Significant effects were found for protocols across provider types, regardless of expertise or area of clinical focus. It also appears that more thorough protocols lead to more information being passed, especially when those protocols consist of 12 or more items. This research has continued to this day, with a recent dissertation (Kristen Welsh-Webster) completed i in 2017 on implementation of handoffs in a live anesthesia unit. Keebler and Lazzara’s team are currently writing multiple grants in collaboration with local and national hospital systems to improve their handoffs and team processes surrounding care transitions.
Categories: Faculty-Staff
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ERAU NASA In-Time Safety Management Data Development and Analysis Year Two
PI Kristy Kiernan
CO-I Albert Boquet
CO-I Stephen Rice
CO-I Robert Waltz
CO-I Joel Samu
CO-I Lucas Epperson
CO-I Shashank Kumar
CO-I Lidiaruth Jones
CO-I Sierra Juliano
CO-I Joseph O'Brien
This project explores new data sources and analytical tools for extracting learning opportunities from all aviation operations.
This project explores new data sources and analytical tools for extracting learning opportunities from all aviation operations. To continue to learn from a system with very low mishap and incident rates, new data streams must be found that uncover strategies and practices that promote resilience. This project examines both existing mishap data, existing data collected from the NASA Human Contribution to Safety (HC2S) test bed, and data collected independently to identify realistic, actionable methods to support and encourage continuous learning, both at the operator level and the organizational level. The project will be divided into two tasks: Task 1 builds upon the work being done in the HC2S testbed by exploring the existing data and generating new data to address how resilient performance can be manifest at the level of the operator and also in the broader system; Task 2 examines NTSB accident dockets for evidence of resilient performance at the level of the operator and also in the broader system.Categories: Faculty-Staff
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Integrated Structural Health Sensors for Inflatable Space Habitats
PI Dae Won Kim
PI Sirish Namilae
Under this research project we will develop an innovative structural health monitoring system for inflatable space habitat structures by integrating nanocomposite piezoresistive sensors
Inflatable structures for space habitats are highly prone to damage caused by micrometeoroid and orbital debris impacts. Although the structures are effectively shielded against these impacts through multiple layers of impact resistant materials, there is a necessity for a health monitoring system to monitor the structural integrity and damage state within the structures. Assessment of damage is critical for the safety of personnel in the habitat, as well as predicting the repair needs and the remaining useful life of the habitat. We are developing a unique impact detection and health monitoring system based on hybrid nanocomposite sensors composed of carbon nanotube sheet and coarse graphene platelets. An array of these sensors sandwiched between soft good layers in a space habitat can act as a damage detection layer for inflatable structures. We will further develop algorithms to determine the event of impact, its severity, and location on the sensing layer for active health monitoring. Our sensor system will be tested in the hypervelocity impact testing facility at UDRI in future.Categories: Faculty-Staff
81-90 of 204 results