Results for “information loading” — Page 12

NASA NTRS1986

A comparative analysis of static and dynamic load balancing strategies

The problem of uniformly distributing the load of a parallel program over a multiprocessor system was considered. A program was analyzed whose structure permits the computation of the optimal static solution. Then four strategies for load balancing were described and their performance compared. The strategies are: (1) the optimal static assignment algorithm which is guaranteed to yield the best static solution, (2) the static binary dissection method which is very fast but suboptimal, (3) the greedy algorithm, a static fully polynomial time approximation scheme, which estimates the optimal solution to arbitrary accuracy, and (4) the predictive dynamic load balancing heuristic which uses information on the precedence relationships within the program and outperforms any of the static methods. It is also shown that the overhead incurred by the dynamic heuristic is reduced considerably if it is started off with a static assignment provided by either of the three strategies.

Iqbal, M. Ashraf↗

NASA NTRS1989

Constructing R-Curves From Residual-Strength Data

Old data exploited in new concept. Method devised for estimating crack-extension resistance curve (R-curve) from residual-strength data on precracked fracture specimens. Enables inference of additional information from simple test results, and information used to estimate failure loads of more complicated structures of same material and thickness.

Orange, Thomas W.↗

NASA NTRS1989

Late quaternary geomorphology of the Great Salt Lake region, Utah, and other hydrographically closed basins in the western United States: A summary of observations

Attributes of Quaternary lakes and lake basins which are often important in the environmental prehistory of semideserts are discussed. Basin-floor and basin-closure morphometry have set limits on paleolake sizes; lake morphometry and basin drainage patterns have influenced lacustrine processes; and water and sediment loads have influenced basin neotectonics. Information regarding inundated, runoff-producing, and extra-basin spatial domains is acquired directly from the paleolake record, including the littoral morphostratigraphic record, and indirectly by reconstruction. Increasingly detailed hypotheses regarding Lake Bonneville, the largest late Pleistocene paleolake in the Great Basin, are subjects for further testing and refinement. Oscillating transgression of Lake Bonneville began about 28,000 yr B.P.; the highest stage occurred about 15,000 yr B.P., and termination occurred abruptly about 13,000 yr B.P. A final resurgence of perennial lakes probably occurred in many subbasins of the Great Basin between 11,000 and 10,000 yr B.P., when the highest stage of Great Salt Lake (successor to Lake Bonneville) developed the Gilbert shoreline. The highest post-Gilbert stage of Great Salt Lake, which has been one of the few permanent lakes in the Great Basin during Holocene time, probably occurred between 3,000 and 2,000 yr B.P.

Currey, Donald R.↗

NASA NTRS1990

Intelligent Data Reduction (IDARE)

A description of the Intelligent Data Reduction (IDARE) expert system and an IDARE user's manual are given. IDARE is a data reduction system with the addition of a user profile infrastructure. The system was tested on a nickel-cadmium battery testbed. Information is given on installing, loading, maintaining the IDARE system.

Brady, D. Michael↗

NASA NTRS1989

Smart tunnel: Docking mechanism

A docking mechanism is presented for the docking of a space vehicle to a space station comprising a flexible tunnel frame structure which is deployable from the space station. The tunnel structure comprises a plurality of series connected frame sections, one end section of which is attached to the space station and the other end attached to a docking module of a configuration adapted for docking in the payload bay of the space vehicle. The docking module is provided with trunnions, adapted for latching engagement with latches installed in the vehicle payload bay and with hatch means connectable to a hatch of the crew cabin of the space vehicle. Each frame section comprises a pair of spaced ring members, interconnected by actuator-attenuator devices which are individually controllable by an automatic control means to impart relative movement of one ring member to the other in six degrees of freedom of motion. The control means includes computer logic responsive to sensor signals of range and attitude information, capture latch condition, structural loads, and actuator stroke for generating commands to the onboard flight control system and the individual actuator-attenuators to deploy the tunnel to effect a coupling with the space vehicle and space station after coupling. A tubular fluid-impervious liner, preferably fabric, is disposed through the frame sections of a size sufficient to accommodate the passage of personnel and cargo.

Schliesing, John A.↗

NASA NTRS1993

Delaminations in composite plates under transverse impact loads - Experimental results

Tests were performed measuring the locations and geometries of delaminations in Fiberite T300/976 graphite/epoxy, Fiberite IM7/977-2 graphite-toughened epoxy, and ICI APC-2 graphite/PEEK plates subjected to transverse impact loads. The data provide specific information on the effects of impactor velocity, impactor mass, material, thickness of back ply group, difference in fiber orientation between adjacent ply groups, plate thickness, and impactor nose radius. The data were compared to the results of the Finn-Springer model. The model was found to describe the data with reasonable accuracy.

Finn, Scott R.↗

NASA NTRSDate not supplied

Clinical Decision Support - Concepts of Operation

We are entering a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses the gap Medical-701 within the Inflight Medical Conditions risk: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant. The aim of the CDS project is to develop and provide recommended requirements for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. The CDSS is envisioned as a software-based tool that will augment a crewmembers’ knowledge, skills and abilities to assist in decision-making and crew health and performance (CHP) management thus increasing CHP systems capabilities. The human interface will be context aware and lessen the cognitive load to assimilate and use information as well as combine large disparate data sets in such a manner that provides the crew with actionable insight to decisions related to crew medical, health and performance management. Crew autonomy will be provided through a CDS that presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. The CDS project addresses the need for crew members to operate independently during long duration space exploration missions that require medical Levels of Care (LoC) V, the highest level specified by NASA-STD-3001 and described in more detail by the ExMC interpretation of LoC document (NASA/TM-2017-219290), where significant changes to in-flight and habitat medical care necessitate increasing crew autonomy in decision making and task performance. The CDS project will develop and test a series of iterative and increasingly more complex system prototypes. These annual demonstrations of the data system integration with the crew health and performance domain will inform exploration medical system requirements for an on-board Clinical Decision Support System (CDSS) through a series of use cases that guide CDS prototype functionality. CDS concepts are based on ExMC Concept of Operations documents (presented separately) and will highlight architecture extensibility to other more complex analyses and tests using core crew health and performance integrated data management, processing and visualization capabilities. This approach also establishes how externally developed analyses and approaches could be added to expand a clinical decision support system and thus highlight how a comprehensive system can be commercially and/or globally developed. The CDS project will build upon the concept of an integrated data management approach based on the Medical Data Architecture (MDA) project to more fully address challenges associated with in-flight and habitat medical, health and performance care due to constraints on mass, volume, power, crew time and medical evacuation capabilities required for medical LoC V. These requirements will be derived through systems engineering approaches and software prototype developments over the course of the multi-year CDS project to address crew health and performance decision-making and task performance, often autonomously executed by the crew, in a manner that is consistent with the appropriate medical level of care for the mission. This presentation will provide an overview of the vision for the CDS project and highlight the initial accomplishments in project planning, implementation and requirements identification in fiscal year 2020.

clinical decision support↗

NASA NTRSDate not supplied

Clinical Decision Support - Overview and Update

We are entering a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses the gap Medical-701 within the Inflight Medical Conditions risk: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant. The aim of the CDS project is to develop and provide recommended requirements for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. The CDSS is envisioned as a software-based tool that will augment a crewmembers’ knowledge, skills and abilities to assist in decision-making and crew health and performance (CHP) management thus increasing CHP systems capabilities. The human interface will be context aware and lessen the cognitive load to assimilate and use information as well as combine large disparate data sets in such a manner that provides the crew with actionable insight to decisions related to crew medical, health and performance management. Crew autonomy will be provided through a CDS that presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. The CDS project addresses the need for crew members to operate independently during long duration space exploration missions that require medical Levels of Care (LoC) V, the highest level specified by NASA-STD-3001 and described in more detail by the ExMC interpretation of LoC document (NASA/TM-2017-219290), where significant changes to in-flight and habitat medical care necessitate increasing crew autonomy in decision making and task performance. The CDS project will develop and test a series of iterative and increasingly more complex system prototypes. These annual demonstrations of the data system integration with the crew health and performance domain will inform exploration medical system requirements for an on-board Clinical Decision Support System (CDSS) through a series of use cases that guide CDS prototype functionality. CDS concepts are based on ExMC Concept of Operations documents (presented separately) and will highlight architecture extensibility to other more complex analyses and tests using core crew health and performance integrated data management, processing and visualization capabilities. This approach also establishes how externally developed analyses and approaches could be added to expand a clinical decision support system and thus highlight how a comprehensive system can be commercially and/or globally developed. The CDS project will build upon the concept of an integrated data management approach based on the Medical Data Architecture (MDA) project to more fully address challenges associated with in-flight and habitat medical, health and performance care due to constraints on mass, volume, power, crew time and medical evacuation capabilities required for medical LoC V. These requirements will be derived through systems engineering approaches and software prototype developments over the course of the multi-year CDS project to address crew health and performance decision-making and task performance, often autonomously executed by the crew, in a manner that is consistent with the appropriate medical level of care for the mission. This presentation will provide an overview of the vision for the CDS project and highlight the initial accomplishments in project planning, implementation and requirements identification in fiscal year 2020.

clinical decision support system↗

DOE OSTI2020

Behind the Meter Storage for Electric Vehicle Charging, Electrochemical and Thermal Energy Storage, and Solar Photovoltaic

In response to the potentially large and irregular demand from EVs, along with changing load profiles from buildings with on-site generation, utilities are evaluating multiple options for managing dynamic loads, including time-of-use pricing, demand charges, battery storage, and curtailment of variable generation. Buildings, as well as commercial, public, and workplace EV charging operations, can use a combination of electrochemical battery storage and thermal energy storage coupled with on-site generation to manage energy costs as well as provide resiliency and reliability for EV charging and building energy loads. We are completing a behind the meter storage analysis that focuses on determining the optimal system designs and energy flows for thermal and electrochemical behind the meter storage with on-site solar photovoltaic (PV) generation enabling electric vehicle charging in various climates, building types, and utility rate structures. In completing this analysis, we have developed a tool that combines existing battery models via the System Advisor Model (SAM) and building modeling software via EnergyPlus into a single interface. This tool allows us to simulate a building with a detailed battery model to properly size the battery, thermal energy storage, and solar PV systems to maximize profit for the system owner. This also allows us to assess how the battery degrades under various supervisory control dispatch algorithms to control charging/discharging; we can also see how thermal energy storage is created and used to complement the battery to reduce thermal loads in the building. With this project, we can analyze new batteries that are designed specifically for energy storage, rather than designed to be extremely energy dense for electric vehicle applications, using battery lifetime models from other national labs and the existing SAM battery model, which has detailed lifetime and degradation parameters. We can also assess novel thermal storage technologies by integrating them into the whole building energy simulation program EnergyPlus. Because the model calls both SAM and EnergyPlus, required inputs need to be compatible for both models. These inputs include, on a high-level, the following: weather files, building and electric vehicle load profiles, electricity rate tariff information, and system cost information for the stationary battery, solar PV, and thermal storage system. The various buildings we are studying for this analysis are retail big-box grocery store, commercial office building, fleet vehicle depot and operations facility, multi-family residential, and electric vehicle charging station. For these different applications, the battery and thermal storage will be dispatched differently, and the various technologies are sized differently to optimize cost.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Pump-and-Treat Impacts on Aquifer Conditions and the Development of System-Wide Performance Strategies - 20377

The pump-and-treat (P and T) system on the Hanford Site's Central Plateau currently treats multiple contaminants within the 200-ZP-1 operable unit, including nitrate through its biological treatment processes. Although it was initially designed to support the 200-ZP-1 Remedial Action Objectives, the P and T system has evolved in use to address contaminants from other regions of the aquifer as well. The P and T influents are biogeochemically distinct, treating groundwaters with elevated concentrations of cyanide, uranium, technetium-99, and nitrate. Given the varying geochemical signatures from current and potential future sources, operational conditions, and amendments considered for treatment processes, there is a need to identify the impacts that the P and T effluent has on the aquifer, and how the aquifer affects P and T performance. To this end, laboratory efforts have focused on characterizing P and T effluent and its biogeochemical effects on the sediments through a series repacked column experiments. The P and T effluent is rich in particulate matter. A systematic inspection of this particulate matter in the P and T effluent showed significant levels of manganese/iron oxides, calcite crystals, relatively large organic matter molecules, and biomass. Therefore, the column experiments were designed and conducted for four different treatment systems: artificial groundwater, filtered P and T effluent, non-filtered P and T effluent, and chlorinated P and T effluent. Different levels of microbiologically- and geochemically-induced changes in the columns occurred and modified the flow systems. For example, physical filtration occurred in the sediments where the particulate material had accumulated in the top portion of the column. Reactive transport modeling of the laboratory experiments is being carried out so that the reaction network can be extrapolated to the field scale. These simulations will provide predictive evaluations of system performance based on P and T operational changes and aquifer mass loading and capacity estimates. This information can be used to address combined subsurface and operational challenges, such as the need for frequent well redevelopment to maintain injection capacity. This information also provides the technical basis for decisions related to the P and T system operations that support remedy optimization efforts and short- and long-term remedy decisions related to multiple operable units on the Hanford Site Central Plateau. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗

NASA NTRS1985

Estimating the R-curve from residual strength data

A method is presented for estimating the crack-extension resistance curve (R-curve) from residual-strength (maximum load against original crack length) data for precracked fracture specimens. The method allows additional information to be inferred from simple test results, and that information can be used to estimate the failure loads of more complicated structures of the same material and thickness. The fundamentals of the R-curve concept are reviewed first. Then the analytical basis for the estimation method is presented. The estimation method has been verified in two ways. Data from the literature (involving several materials and different types of specimens) are used to show that the estimated R-curve is in good agreement with the measured R-curve. A recent predictive blind round-robin program offers a more crucial test. When the actual failure loads are disclosed, the predictions are found to be in good agreement.

Orange, T. W.↗

NASA NTRS2003

Impact of Climatic Variability on Atmospheric Mass Distribution and GRACE-Derived Gravity Fields

During the period we calculated the atmospheric data sets related to its mass and angular momentum distribution. For mass, we determined the various harmonics from the NCEP-NCAR reanalysis, especially the low-order harmonics that are useful in studying the gravitation distribution as will be determined from the GRACE mission. Atmospheric mass is also related to the atmospheric loading on the solid Earth; we cooperated with scientists who needed the atmospheric mass information for understanding its contributions to the overall loading, necessary for vertical and horizontal coordinate estimation. We calculated atmospheric angular momentum from the NCEP-NCAR reanalyses and 4 operational meteorological centers, based on the motion (wind) terms and the mass (surface pressure) terms. These are associated with motions of the planet, including its axial component causing changes in the length of day, more related to the winds, and the equatorial component related to motions of the pole, more related to the mass. Tasks related to the ocean mass and angular momentum were added to the project as well. For these we have noted the ocean impact on motions of the pole as well as the torque mechanisms that relate the transfer of angular momentum between oceans and solid earth. The activities of the project may be summarized in the following first manuscript written in December 2002, for a symposium that Dr. Salstein attended on Geodynamics. We have continued to assess ocean angular momentum (OAM) quantities derived from bottom pressure and velocity fields estimated with our finite-difference barotropic (single layer) model. Three years of output (1993-95) from a run without any data constraints was compared to output from a corresponding run that was constrained by altimeter data using a Kalman filter and smoother scheme. Respective OAM time series were combined with corresponding atmospheric series and compared to observed polar motion. The constrained OAM series provided slightly better variance reduction than the unconstrained series. Analysis provided a check on the estimation scheme and pointed to further work to improve the determination of OAM using this method. A significant effort was also devoted to quantifying effects of uncertainties in high frequency winds on the mean and seasonal momentum exchange between atmosphere and oceans.

Salstein, David A.↗

NASA NTRS2024

Intermountain West Wildland Fires: Mapping Tree Mortality and Burn Patches using NASA Earth Observations to Determine Fire Risk and Inform Fire Management Practices

Within the intermountain west, monitoring fuel loads is a major concern for wildland fire management efforts. To address this concern, we partnered with the U.S. Forest Service to inform the agency which forested areas should be prioritized for prescribed burning and fuel reduction near human communities in the Bridger-Teton National Forest, Wyoming. We created burn maps, fuel load maps, and a tutorial document to identify forest impact trends and provide the partner with the tools to replicate project methods for expansion to other wildfire crisis strategy sites. These end products were made using two NASA Earth observations: Landsat 8 Operational Land Imager and Shuttle Radar Topography Mission. Based on our random forest analysis, our maps identified 998 acres within the Wildland Urban Interface that are predicted to have high fuel loading and high burn severity within the Bridger-Teton National Forest. Forested areas closer to heavily populated areas such as Jackson, Kelly, Moran, New Forks Lake, and Star Valley Ranch should be prioritized for fuel reduction. However, our random forest model analysis was limited to using vegetation and topographical indices with no field data for model validation. Therefore, future studies should use field data for model validation to improve model accuracy and additionally incorporate Global Ecosystem Dynamics Investigation data into models to create better predictions of forested areas with high fuel load and high burn severity.

Remote Sensing↗

NASA NTRS1986

Knowledge-based load leveling and task allocation in human-machine systems

Conventional human-machine systems use task allocation policies which are based on the premise of a flexible human operator. This individual is most often required to compensate for and augment the capabilities of the machine. The development of artificial intelligence and improved technologies have allowed for a wider range of task allocation strategies. In response to these issues a Knowledge Based Adaptive Mechanism (KBAM) is proposed for assigning tasks to human and machine in real time, using a load leveling policy. This mechanism employs an online workload assessment and compensation system which is responsive to variations in load through an intelligent interface. This interface consists of a loading strategy reasoner which has access to information about the current status of the human-machine system as well as a database of admissible human/machine loading strategies. Difficulties standing in the way of successful implementation of the load leveling strategy are examined.

Chignell, M. H.↗

NASA NTRS2015

Booster Interface Loads

The interaction between shock waves and the wake shed from the forward booster/core attach hardware results in unsteady pressure fluctuations, which can lead to large buffeting loads on the vehicle. This task investigates whether computational tools can adequately predict these flows, and whether alternative booster nose shapes can reduce these loads. Results from wind tunnel tests will be used to validate the computations and provide design information for future Space Launch System (SLS) configurations. The current work combines numerical simulations with wind tunnel testing to predict buffeting loads caused by the boosters. Variations in nosecone shape, similar to the Ariane 5 design (fig. 1), are being evaluated with regard to lowering the buffet loads. The task will provide design information for the mitigation of buffet loads for SLS, along with validated simulation tools to be used to assess future SLS designs.

Gentz, Steve↗

NASA NTRS1956

A reevaluaion of data on atmospheric turbulence and airplane gust loads for application in spectral calculations

The available information on the spectrum of atmospheric turbulence is first briefly reviewed. On the basis of these results, methods are developed for the conversion of available gust statistics normally given in terms of counts of gusts or acceleration peaks into a form appropriate for use in spectral calculations. The fundamental quantity for this purpose appears to be the probability distribution of the root-mean-square gust velocity. Estimates of this distribution are derived from data for a number of load histories of transport operations; also, estimates of the variation of this distribution with altitude and weather condition are derived from available data and the method of applying these results to the calculation of airplane gust-response histories in operations is also outlined. (author)

Press, Harry↗

NASA NTRS1993

Population-based learning of load balancing policies for a distributed computer system

Effective load-balancing policies use dynamic resource information to schedule tasks in a distributed computer system. We present a novel method for automatically learning such policies. At each site in our system, we use a comparator neural network to predict the relative speedup of an incoming task using only the resource-utilization patterns obtained prior to the task's arrival. Outputs of these comparator networks are broadcast periodically over the distributed system, and the resource schedulers at each site use these values to determine the best site for executing an incoming task. The delays incurred in propagating workload information and tasks from one site to another, as well as the dynamic and unpredictable nature of workloads in multiprogrammed multiprocessors, may cause the workload pattern at the time of execution to differ from patterns prevailing at the times of load-index computation and decision making. Our load-balancing policy accommodates this uncertainty by using certain tunable parameters. We present a population-based machine-learning algorithm that adjusts these parameters in order to achieve high average speedups with respect to local execution. Our results show that our load-balancing policy, when combined with the comparator neural network for workload characterization, is effective in exploiting idle resources in a distributed computer system.

Mehra, Pankaj↗