On large models of systems
Operations research study of large mathematical models for large social systems
SOCIOLOGY↗
SEARCH · Engineering Papers
Results for “human systems modeling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Leveraging Prior Concept Learning Improves Generalization From Few Examples in Computational Models of Human Object Recognition
Humans quickly and accurately learn new visual concepts from sparse data, sometimes just a single example. The impressive performance of artificial neural networks which hierarchically pool afferents across scales and positions suggests that the hierarchical organization of the human visual system is critical to its accuracy. These approaches, however, require magnitudes of order more examples than human learners. We used a benchmark deep learning model to show that the hierarchy can also be leveraged to vastly improve the speed of learning. We specifically show how previously learned but broadly tuned conceptual representations can be used to learn visual concepts from as few as two positive examples; reusing visual representations from earlier in the visual hierarchy, as in prior approaches, requires significantly more examples to perform comparably. These results suggest techniques for learning even more efficiently and provide a biologically plausible way to learn new visual concepts from few examples.
Rule, Joshua S.↗
Modeling Acute Health Effects of Astronauts from Exposure to Large Solar Particle Events
In space exploration outside the Earth s geomagnetic field, radiation exposure from solar particle events (SPE) presents a health concern for astronauts, that could impair their performance and result in possible failure of the mission. Acute risks are of special concern during extra-vehicular activities because of the rapid onset of SPE. However, most SPEs will not lead to acute risks but can lead to mission disruption if accurate projection methods are not available. Acute Radiation Sickness (ARS) is a group of clinical syndromes developing acutely (within several seconds to 3 days) after high dose whole-body or significant partial-body ionizing radiation exposures. The manifestation of these syndromes reflects the disturbance of physiological processes of various cellular groups damaged by radiation. Hematopoietic cells, skin, epithelium, intestine, and vascular endothelium are among the most sensitive tissues of human body to ionizing radiation. Most ARS symptoms are directly related to these tissues and other systems (nervous, endocrine, and cardiovascular, etc.) with coupled regulations. Here we report the progress in bio-mathematical models to describe the dose and time-dependent early human responses to ionizing radiation. The responses include lymphocyte depression, granulocyte modulation, fatigue and weakness syndrome, and upper gastrointestinal distress. The modest dose and dose-rates of SPEs are predicted to lead to large sparing of ARS, however detailed experimental data on a range of proton dose-rates for organ doses from 0.5 to 2 Gy is needed to validate the models. We also report on the ARRBOD code that integrates the BRYNTRN and SUMDOSE codes, which are used to estimate the SPE organ doses for astronauts under various space travel scenarios, with our models of ARS. The more recent effort is to provide easy web access to space radiation risk assessment using the ARRBOD code.
Hu, Shaowen↗
Predicting Pilot Behavior in Medium Scale Scenarios Using Game Theory and Reinforcement Learning
Effective automation is critical in achieving the capacity and safety goals of the Next Generation Air Traffic System. Unfortunately creating integration and validation tools for such automation is difficult as the interactions between automation and their human counterparts is complex and unpredictable. This validation becomes even more difficult as we integrate wide-reaching technologies that affect the behavior of different decision makers in the system such as pilots, controllers and airlines. While overt short-term behavior changes can be explicitly modeled with traditional agent modeling systems, subtle behavior changes caused by the integration of new technologies may snowball into larger problems and be very hard to detect. To overcome these obstacles, we show how integration of new technologies can be validated by learning behavior models based on goals. In this framework, human participants are not modeled explicitly. Instead, their goals are modeled and through reinforcement learning their actions are predicted. The main advantage to this approach is that modeling is done within the context of the entire system allowing for accurate modeling of all participants as they interact as a whole. In addition such an approach allows for efficient trade studies and feasibility testing on a wide range of automation scenarios. The goal of this paper is to test that such an approach is feasible. To do this we implement this approach using a simple discrete-state learning system on a scenario where 50 aircraft need to self-navigate using Automatic Dependent Surveillance-Broadcast (ADS-B) information. In this scenario, we show how the approach can be used to predict the ability of pilots to adequately balance aircraft separation and fly efficient paths. We present results with several levels of complexity and airspace congestion.
Game Theory↗
Neuroimmune responses to space radiation
One of the main health risks in human deep space exploration is central nervous system (CNS) damage by ionizing radiation due to exposure to galactic cosmic rays (GCRs). In animal models, irradiation with simulated GCRs or their components has been shown to cause neurodegeneration and neuroinflammation associated with cognitive and behavioral dysfunction. The extent of CNS damage is partially mediated by the blood-brain barrier (BBB), which regulates the interaction between CNS and systemic responses to stressors in the rest of the body. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuronal death, neuroinflammation and oxidative stress. However, studies on BBB and astrocyte functions in regulating CNS responses to ionizing radiation have been limited, especially in human tissue/organ analogs. Therefore, we developed a high-throughput 3D organ-on-a-chip system to study human CNS and BBB impairments caused by deep space radiation. We investigated both immediate and delayed CNS responses to major GCR components: 0.3-0.8Gy 600MeV/n 56Fe ions; as well as to 0.5-1Gy X-rays. We observed ionizing radiation-mediated increases in BBB permeability that was exacerbated by astrocyte presence and accompanied by damage to endothelial cells and tight junctions, altered cytokine expression including TNFalpha upregulation, and increased oxidative stress. In particular, 600MeV/n 56Fe particle irradiation selectively induced astrocyte damage and increased blood-brain barrier permeability only in models that contained astrocytes in addition to endothelial cells, indicating astrocytes as a particularly radiosensitive component of the CNS that could therefore be a suitable a target for neuroprotection. Future studies will compare human and mouse CNS model responses to simulated GCRs and evaluate the induction of an anti-inflammatory phenotype in astrocytes as a potential countermeasure. Furthermore, in our lab we have been exploring the individual variability, genomic associations and secreted biomarkers of responses to space radiation, which could eventually be combined to address personalized CNS health risk and develop individual countermeasures. Ultimately, we aim to expand upon these results to uncover novel cellular and mechanistic targets for countermeasure development to mitigate human CNS damage in deep space exploration.
space radiation↗
Supporting Arctic Research, Engagement, and Policy With GMAO’s Next Generation Reanalysis & Prediction Systems
Modeling efforts at NASA aim to advance scientific understanding of the Earth system and its response to natural and human-induced changes and to improve our ability to predict climate, weather, and natural hazards. These efforts are particularly relevant for communities, scientists and stakeholders working and living in the Arctic. Here, we highlight current and planned Earth system prediction and data assimilation products from the Global Modeling and Assimilation Office (GMAO). We invite feedback and discussion on GMAO’s model and reanalysis development in support of Arctic research, engagement, and policy.
Lauren C Andrews↗
CATMoS: Collaborative Acute Toxicity Modeling Suite
Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different endpoints: LD50 value, U.S. Environmental Protection Agency hazard categories, Globally Harmonized System for Classification and Labelling hazard categories, very toxic chemicals (LD50 =50 mg/kg), and non-toxic chemicals (LD50 >2000 mg/kg). Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared to in vivo results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for over 800,000 chemicals have been made available via the NTP’s Integrated Chemical Environment. The models are also implemented in a free, standalone open-source tool, OPERA, which allows predictions of new and untested chemicals to be made.
63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
A new large-scale suspended sediment model and its application over the United States
Abstract. Suspended sediment plays a vital role in the regional and global cycling of carbon and nutrients by carrying carbon and nutrients from headwaters into lowland rivers and the oceans. Sediment transport through river systems is often fundamentally modified by human activities such as reservoir management. However, a physically based representation of sediment transport is still missing in most existing Earth system models (ESMs), which are essential tools for modeling and predicting Earth system changes. Here, we introduce a multi-process river sediment module for ESMs, which includes (1) hillslope soil erosion and sediment discharge into streams, (2) sediment transport processes through river networks, (3) reservoir operation based on the inflows from upstream areas and water demand from downstream areas, and (4) sediment trapping by reservoirs. All model parameters are estimated a priori without calibration. We apply this new sediment modeling framework to the contiguous United States and validate it against historical observations of monthly streamflow and sediment discharges at 35 river gauges. The model reasonably well captures the long-term balance and seasonal variations of suspended sediment in large river systems. Furthermore, our model results show that suspended sediment discharge in managed rivers is affected more by reservoirs' direct trapping of sediment particles than by their flow regulation. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
54 ENVIRONMENTAL SCIENCES↗
Equivalent-noise model for contrast detection and discrimination
Models for the detection and the discrimination of low-contrast signals by human observers typically assume that the observer is limited by the filtering action of the visual system and by the noisy character of its processing. For some models both the filtering and the noise can be represented by a noise in the stimulus domain, the input equivalent noise of the model. The present study derives some formulas for computing this noise, describes the calculation of a sample, and discusses some implications of this approach.
Ahumada, A. J., Jr.↗
NASA Galactic Cosmic Radiation Environment Model: Badhwar - O'Neill (2014)
The Badhwar-O'Neill (BON) Galactic Cosmic Ray (GCR) flux model has been used by NASA to certify microelectronic systems and in the analysis of radiation health risks for human space flight missions. Of special interest to NASA is the kinetic energy region below 4.0 GeV/n due to the fact that exposure from GCR behind shielding (e.g., inside a space vehicle) is heavily influenced by the GCR particles from this energy domain. The BON model numerically solves the Fokker-Planck differential equation to account for particle transport in the heliosphere due to diffusion, convection, and adiabatic deceleration under the assumption of a spherically symmetric heliosphere. The model utilizes a comprehensive database of GCR measurements from various particle detectors to determine boundary conditions. By using an updated GCR database and improved model fit parameters, the new BON model (BON14) is significantly improved over the previous BON models for describing the GCR radiation environment of interest to human space flight.
Golge, S.↗
Integrating Large Scale Data Sets to Develop Predictive Hypotheses of Low-Dose Radiation-Induced Health Effects
Over one hundred years of radiation biology research has revealed much about the DNA damages induced by the deposition of energy from exposure to ionizing radiation and the subsequent cellular responses. However, there are still significant gaps in our understanding of how these might lead to detrimental health effects, particularly at low doses (100 mGy (milligray)). Recent advances in high throughput omics technologies enable interrogation of induced radiation effects at the genomic, proteomic and metabolomic levels. These include changes in gene expression, protein modifications, e.g., phosphorylation, acetylation, and methylation, and metabolic changes. We will discuss the integration of data obtained from multiple omics platforms to understand radiation dose, and dose rate effects in a complex human tissue model as a function of time. We will use as an example our results on the low dose responses in a 3D human skin model.
ionizing radiation↗
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.
54 ENVIRONMENTAL SCIENCES↗
Cabin Noise Studies for the Orion Spacecraft Crew Module
Controlling cabin acoustic noise levels in the Crew Module (CM) of the Orion spacecraft is critical for adequate speech intelligibility, to avoid fatigue and to prevent any possibility of temporary and permanent hearing loss. A vibroacoustic model of the Orion CM cabin has been developed using Statistical Energy Analysis (SEA) to assess compliance with acoustic Constellation Human Systems Integration Requirements (HSIR) for the on-orbit mission phase. Cabin noise in the Orion CM needs to be analyzed at the vehicle-level to assess the cumulative acoustic effect of various Orion systems at the crewmember's ear. The SEA model includes all major structural and acoustic subsystems inside the CM including the Environmental Control and Life Support System (ECLSS), which is the primary noise contributor in the cabin during the on-orbit phase. The ECLSS noise sources used to excite the vehicle acoustic model were derived using a combination of established empirical predictions and fan development acoustic testing. Baseline noise predictions were compared against acoustic HSIR requirements. Key noise offenders and paths were identified and ranked using noise transfer path analysis. Parametric studies were conducted with various acoustic treatment packages in the cabin to reduce the noise levels and define vehicle-level mass impacts. An acoustic test mockup of the CM cabin has also been developed and noise treatment optimization tests were conducted to validate the results of the analyses.
Dandaroy, Indranil↗
Prospects of a mathematical theory of human behavior in complex man-machine systems tasks
A hierarchy of human activities is derived by analyzing automobile driving in general terms. A structural description leads to a block diagram and a time-sharing computer analogy. The range of applicability of existing mathematical models is considered with respect to the hierarchy of human activities in actual complex tasks. Other mathematical tools so far not often applied to man machine systems are also discussed. The mathematical descriptions at least briefly considered here include utility, estimation, control, queueing, and fuzzy set theory as well as artificial intelligence techniques. Some thoughts are given as to how these methods might be integrated and how further work might be pursued.
Johannsen, G.↗
Modeling NASA’s Procedural Requirement Processes – Implications for a Digital Future
The National Aeronautics and Space Administration (NASA) has an ongoing Digital Transformation effort and to leverage and showcase the power of Digital Transformation, an effort is underway to develop an integrated, datacentric, model representing NASA’s key process requirements. The task was divided into three phases: As Is modeling, Analysis, and To Be Planning. As part of this effort, a team has completed the first Phase I of the modeling task and is nearing completion of the second phase. This effort will capture the key elements as requirements, responsibilities, allocations, roles, products, and associated lifecycle elements. The scope of modeling included NASA’s NPR 7120.5 (Project and Program Management), NPR 7123.1 (Systems Engineering) and NPRs 8705.2 (Risk classification for Robotic Missions) and 8705.4 (Human-Rating Requirements for Space Missions).
NPR↗
The Discharging of Roving Objects in the Lunar Polar Regions
In 2007, the National Academy of Sciences identified the lunar polar regions as special environments: very cold locations where resources can be trapped and accumulated. These accumulated resources not only provide a natural reservoir for human explorers, but their very presence may provide a history of lunar impact events and possibly an indication of ongoing surface reactive chemistry. The recent LCROSS impacts confirm that polar crater floors are rich in material including approx 5%wt of water. An integral part of the special lunar polar environment is the solar wind plasma. Solar wind protons and electrons propagate outward from the Sun, and at the Moon's position have a nominal density of 5 el/cubic cm, flow speed of 400 km/sec, and temperature of 10 eV (approx. equal 116000K). At the sub-solar point, the flow of this plasma is effectively vertically incident at the surface. However, at the poles and along the lunar terminator region, the flow is effectively horizontal over the surface. As recently described, in these regions, local topography has a significant effect on the solar wind flow. Specifically, as the solar wind passes over topographic features like polar mountains and craters, the plasma flow is obstructed and creates a distinct plasma void in the downstream region behind the obstacle. An ion sonic wake structure forms behind the obstacle, not unlike that which forms behind a space shuttle. In the downstream region where flow is obstructed, the faster moving solar wind electrons move into the void region ahead of the more massive ions, thereby creating an ambipolar electric field pointing into the void region. This electric field then deflects ion trajectories into the void region by acting as a vertical inward force that draws ions to the surface. This solar wind 'orographic' effect is somewhat analogous to that occurring with terrestrial mountains. However, in the solar wind, the ambipolar E-field operating in the collision less plasma replaces the gradient in pressure that would act in a collisional neutral gas. Human systems (roving astronauts or robotic systems created by humans) may be required to gain access to the crater floor to collect resources such as water and other cold-trapped material. However, these human systems are also exposed to the above-described harsh thermal and electrical environments in the region. Thus, the objective of this work is to determine the nature of charging and discharging for a roving object in the cold, plasma-starved lunar polar regions. To accomplish this objective, we first define the electrical charging environment within polar craters. We then describe the subsequent charging of a moving object near and within such craters. We apply a model of an astronaut moving in periodic steps/cadence over a surface regolith. In fact the astronaut can be considered an analog for any kind of moving human system. An astronaut stepping over the surface accumulates charge via contact electrification (tribocharging) v.lith the lunar regolith. We present a model of this tribo-charge build-up. Given the environmental plasma in the region, we determine herein the dissipation time for the astronaut to bleed off its excess charge into the surrounding plasma.
Jackson, T. L.↗
Exploration Systems Development (ESD) Approach to Enterprise Risk Management
The National Aeronautics and Space Administration (NASA) Exploration Systems Development (ESD) Division has implemented an innovative approach to Enterprise Risk Management under a unique governance structure and streamlined integration model. ESD's mission is to design and build the capability to extend human existence to deep space. The Enterprise consists of three Programs: Space Launch System (SLS), Orion, and Ground Systems Development and Operations (GSDO). The SLS is a rocket and launch system that will be capable of powering humans, habitats, and support systems to deep space. Orion will be the first spacecraft in history capable of taking humans to multiple destinations within deep space. GSDO is modernizing Kennedy's spaceport to launch spacecraft built and designed by both NASA and private industry. ESD's approach to Enterprise Risk Management is commensurate with affordability and a streamlined management philosophy. ESD Enterprise Risk Management leverages off of the primary mechanisms for integration within the Enterprise. The Enterprise integration approach emphasizes delegation of authority to manage and execute the majority of cross-program activities and products to the individual Programs, while maintaining the overall responsibility for all cross-program activities at the Division. The intent of the ESD Enterprise Risk Management approach is to improve risk communication, to avoid replication and/or contradictory strategies, and to minimize overhead process burden. This is accomplished by the facilitation and integration of risk information within ESD. The ESD Division risks, Orion risks, SLS risks, and GSDO risks are owned and managed by the applicable Program. When the Programs have shared risks with multiple consequences, they are jointly owned and managed. When a risk is associated with the integrated system that involves more than one Program in condition, consequence, or mitigation plan, it is considered an Exploration Systems Integration (ESI) Risk. An ESI risk may require visibility and risk handling by multiple organizations. The Integrated Risk Working Group (IRWG) is a small team of Risk experts that are responsible for collaborating and communicating best practices. In addition, the forum facilitates proper integration of risks across the Enterprise. The IRWG uses a Continuous Risk Management approach for facilitating the identification, analysis, planning, tracking, and controlling of ESI Risks. The ESD Division, Programs, and Integrated Task Teams identify ESI Risks. The IRWG maintains a set of metrics for understanding Enterprise Risk process and the overall Risk Posture. The team is also actively involved in the modeling of risk for Enterprise Performance Management. With the Enterprise being constrained in Schedule and Budget, and with significant technical complexity, the appropriate use of Risk Management techniques is crucial to the success of the Enterprise. The IRWG achieves this through the modified approach, providing a forum for collaboration on risks that cross boundaries between the separate entities.
Bauder, Stephen P.↗
Pilot-aircraft system reponse to wind shear
The nonlinear aircraft motion and automatic control model is expanded to incorporate the human pilot into simulations of aircraft response to wind to wind shear. The human pilot is described by a constant gains lag filter. Two runs are carried out using pilot transfer functions. Fixed-stick, autopilot, and manned computer simulations are made with an aircraft having characteristics of a small commuter type aircraft flown through longitudinal winds measured by a Doppler radar beamed along the glide slope. Simulations are also made flying an aircraft through sinusoidal head wind and tail wind shears at the phugoid frequency to evaluate the response of manned aircraft in thunderstorm wind environments.
Turkel, B. S.↗