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Predictive Modeling to Assess and Address Challenges and Limitations Associated with Clinical Care and Decision Support in Deep Space
Probabilistic risk assessment (PRA) is a method for assessing and integrating the risk of failure in a multivariate system. While it is often applied by engineers designing complex machines, it could also be applied to humans to assess the probability of a health “failure” treating diseases as the multiple “variables” and the human as the “complex machine.” The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) was developed to apply PRA to assess medical risk for exploration spaceflight and inform the design of medical systems for space flight. The NASA engineering community utilizes event-driven and fault tree probabilistic techniques to classify risk in the space flight environment by leveraging the inherent knowledge of complex space flight system design and testing to quantify risk. However, in harmonizing the risk of human space flight, answering the question of “How do we balance astronaut health, performance and resource risks with other engineering risks on exploration space missions?” remains a profoundly challenging and largely qualitative practice. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is one aspect of the Human Research Program’s efforts to represent space fight human health and performance risks quantitatively.
Computational Model Prediction and Biological Validation Using Simplified Mixed Field Exposures for the Development of a GCR Reference Field
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Predictive Modeling to Assess and Address Challenges and Limitations Associated with Clinical Care and Decision Support in Deep Space
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NAIRAS Model Predictions of the Ionizing Radiation Environment from the Surface to Low-Earth Orbit
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Evaluation of the spaceflight cardiovascular risk using the clinical prediction model Astro-CHARM and the NASA Radiation Risk Model
- Exposure to space radiation may pose a risk for cardiovascular diseases (CVD) during and after spaceflight. - The CVD risk should be quantified to plan future long-duration space missions such as the manned flight to Mars. - Assessing CVD risk is complicated by its multifactorial nature, where an individual’s risk is strongly influenced by risk factors such as family history, blood pressure, and lipid profiles. - In the NASA Space Radiation Risk Model, the metrics of radiation exposure-induced cases (REIC) and radiation exposure-induced death (REID) are used to quantify the risk attributable to radiation over a lifetime after exposure.
Evaluation of the Spaceflight Cardiovascular Disease Risk Using the Clinical Prediction Model Astro-CHARM and the NASA Radiation Risk Model
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Comparison of IMPACT Model Predictions of Medical Events to Observed Medical Events from ISS and STS Missions
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Toward Predictive Models of Launch Ascent Depressurization and Induced Particle Redistribution
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Forecasting Tomorrow(T): Exploring Cutting-Edge Weather Prediction Models and AI Architectures
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Predictive Models for Radiation Heat Transfer Through Fibrous Insulations
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Test Particle Model Predictions of SEP Electron Transport and Precipitation at Mars
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Improved Aura/OMI Solar Spectral Irradiances: Comparisons With Independent Data Sets and Model Predictions
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The Encounter of the Parker Solar Probe and a Comet-like Object Near the Sun: Model Predictions and Measurements
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AAA gunnermodel based on observer theory
The Luenberger observer theory is used to develop a predictive model of a gunner's tracking response in antiaircraft artillery systems. This model is composed of an observer, a feedback controller and a remnant element. An important feature of the model is that the structure is simple, hence a computer simulation requires only a short execution time. A parameter identification program based on the least squares curve fitting method and the Gauss Newton gradient algorithm is developed to determine the parameter values of the gunner model. Thus, a systematic procedure exists for identifying model parameters for a given antiaircraft tracking task. Model predictions of tracking errors are compared with human tracking data obtained from manned simulation experiments. Model predictions are in excellent agreement with the empirical data for several flyby and maneuvering target trajectories.
The Future of Planetary Climate Modeling and Weather Prediction
Modeling of planetary climate and weather has followed the development of tools for studying Earth, with lags of a few years. Early Earth climate studies were performed with 1-dimensionalradiative-convective models, which were soon fol-lowed by similar models for the climates of Mars and Venus and eventually by similar models for exoplan-ets. 3-dimensional general circulation models (GCMs) became common in Earth science soon after and within several years were applied to the meteorology of Mars, but it was several decades before a GCM was used to simulate extrasolar planets. Recent trends in Earth weather and and climate modeling serve as a useful guide to how modeling of Solar System and exoplanet weather and climate will evolve in the coming decade.
Ability of a regional-scale model to predict the genesis of intense mesoscale convective systems
The mesoscale part of a two-part evaluation of 30 forecasts by a mesoscale numerical weather prediction model (MASS 2.0) is presented. Unfiltered fields are combined into convective predictor fields, the loci of which are then related at two-hour intervals to the loci of strong mesoscale convective systems identifiable in national radar summary plots and GOES satellite imagery. Examples of model 'forecasts' of intense convective storm clusters, a severe squall line triggered along a dryline, orographically induced hailstorms, and sea breeze thunderstorms are provided.