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DOE OSTI2016

SU-F-T-50: Evaluation of Monte Carlo Simulations Performance for Pediatric Brachytherapy Dosimetry

Purpose: Pediatric tumors are generally treated with multi-modal procedures. Brachytherapy can be used with pediatric tumors, especially given that in this patient population low toxicity on normal tissues is critical as is the suppression of the probability for late malignancies. Our goal is to validate the GATE toolkit on realistic brachytherapy applications, and evaluate brachytherapy plans on pediatrics for accurate dosimetry on sensitive and critical organs of interest. Methods: The GATE Monte Carlo (MC) toolkit was used. Two High Dose Rate (HDR) 192Ir brachytherapy sources were simulated (Nucletron mHDR-v1 and Varian VS2000), and fully validated using the AAPM and ESTRO protocols. A realistic brachytherapy plan was also simulated using the XCAT anthropomorphic computational model .The simulated data were compared to the clinical dose points. Finally, a 14 years old girl with vaginal rhabdomyosarcoma was modelled based on clinical procedures for the calculation of the absorbed dose per organ. Results: The MC simulations resulted in accurate dosimetry in terms of dose rate constant (Λ), radial dose gL(r) and anisotropy function F(r,θ) for both sources.The simulations were executed using ∼1010 number of primaries resulting in statistical uncertainties lower than 2%.The differences between the theoretical values and the simulated ones ranged from 0.01% up to 3.3%, with the largest discrepancy (6%) being observed in the dose rate constant calculation.The simulated DVH using an adult female XCAT model was also compared to a clinical one resulting in differences smaller than 5%. Finally, a realistic pediatric brachytherapy simulation was performed to evaluate the absorbed dose per organ and to calculate DVH with respect to heterogeneities of the human anatomy. Conclusion: GATE is a reliable tool for brachytherapy simulations both for source modeling and for dosimetry in anthropomorphic voxelized models. Our project aims to evaluate a variety of pediatric brachytherapy schemes using a population of pediatric phantoms for several pathological cases. This study is part of a project that has received funding from the European Union Horizon2020 research and innovation programme under the MarieSklodowska-Curiegrantagreement.No691203.The results published in this study reflect only the authors view and the Research Executive Agency (REA) and the European Commission is not responsible for any use that may be madeof the information it contains.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Research & Experimental Medicine↗

DOE OSTI2012

Foundation Heat Exchanger Final Report: Demonstration, Measured Performance, and Validated Model and Design Tool

Geothermal heat pumps, sometimes called ground-source heat pumps (GSHPs), have been proven capable of significantly reducing energy use and peak demand in buildings. Conventional equipment for controlling the temperature and humidity of a building, or supplying hot water and fresh outdoor air, must exchange energy (or heat) with the building's outdoor environment. Equipment using the ground as a heat source and heat sink consumes less non-renewable energy (electricity and fossil fuels) because the earth is cooler than outdoor air in summer and warmer in winter. The most important barrier to rapid growth of the GSHP industry is high first cost of GSHP systems to consumers. The most common GSHP system utilizes a closed-loop ground heat exchanger. This type of GSHP system can be used almost anywhere. There is reason to believe that reducing the cost of closed-loop systems is the strategy that would achieve the greatest energy savings with GSHP technology. The cost premium of closed-loop GSHP systems over conventional space conditioning and water heating systems is primarily associated with drilling boreholes or excavating trenches, installing vertical or horizontal ground heat exchangers, and backfilling the excavations. This project investigates reducing the cost of horizontal closed-loop ground heat exchangers by installing them in the construction excavations, augmented when necessary with additional trenches. This approach applies only to new construction of residential and light commercial buildings or additions to such buildings. In the business-as-usual scenario, construction excavations are not used for the horizontal ground heat exchanger (HGHX); instead the HGHX is installed entirely in trenches dug specifically for that purpose. The potential cost savings comes from using the construction excavations for the installation of ground heat exchangers, thereby minimizing the need and expense of digging additional trenches. The term foundation heat exchanger (FHX) has been coined to refer exclusively to ground heat exchangers installed in the overcut around the basement walls. The primary technical challenge undertaken by this project was the development and validation of energy performance models and design tools for FHX. In terms of performance modeling and design, ground heat exchangers in other construction excavations (e.g., utility trenches) are no different from conventional HGHX, and models and design tools for HGHX already exist. This project successfully developed and validated energy performance models and design tools so that FHX or hybrid FHX/HGHX systems can be engineered with confidence, enabling this technology to be applied in residential and light commercial buildings. The validated energy performance model also addresses and solves another problem, the longstanding inadequacy in the way ground-building thermal interaction is represented in building energy models, whether or not there is a ground heat exchanger nearby. Two side-by-side, three-level, unoccupied research houses with walkout basements, identical 3,700 ft{sup 2} floor plans, and hybrid FHX/HGHX systems were constructed to provide validation data sets for the energy performance model and design tool. The envelopes of both houses are very energy efficient and airtight, and the HERS ratings of the homes are 44 and 45 respectively. Both houses are mechanically ventilated with energy recovery ventilators, with space conditioning provided by water-to-air heat pumps with 2 ton nominal capacities. Separate water-to-water heat pumps with 1.5 ton nominal capacities were used for water heating. In these unoccupied research houses, human impact on energy use (hot water draw, etc.) is simulated to match the national average. At House 1 the hybrid FHX/HGHX system was installed in 300 linear feet of excavation, and 60% of that was construction excavation (needed to construct the home). At House 2 the hybrid FHX/HGHX system was installed in 360 feet of excavation, 50% of which was construction excavation. There are six pipes in all excavations (three parallel circuits - out and back), and the multiple instances of FHX and/or HGHX are all connected in series. The working fluid is 20% by weight propylene glycol in water. Model and design tool development was undertaken in parallel with constructing the houses, installing instrumentation, and monitoring performance for a year. Several detailed numerical models for FHX were developed as part of the project. Essentially the project team was searching for an energy performance model accurate enough to achieve project objectives while also having sufficient computational efficiency for practical use in EnergyPlus. A 3-dimensional, dual-coordinate-system, finite-volume model satisfied these criteria and was included in the October 2011 EnergyPlus Version 7 public release after being validated against measured data.

15 GEOTHERMAL ENERGY↗

NASA NTRS2013

Development of Large-Scale Spacecraft Fire Safety Experiments

The status is presented of a spacecraft fire safety research project that is under development to reduce the uncertainty and risk in the design of spacecraft fire safety systems by testing at nearly full scale in low-gravity. Future crewed missions are expected to be more complex and longer in duration than previous exploration missions outside of low-earth orbit. This will increase the challenge of ensuring a fire-safe environment for the crew throughout the mission. Based on our fundamental uncertainty of the behavior of fires in low-gravity, the need for realistic scale testing at reduced gravity has been demonstrated. To address this gap in knowledge, a project has been established under the NASA Advanced Exploration Systems Program under the Human Exploration and Operations Mission directorate with the goal of substantially advancing our understanding of the spacecraft fire safety risk. Associated with the project is an international topical team of fire experts from other space agencies who conduct research that is integrated into the overall experiment design. The experiments are under development to be conducted in an Orbital Science Corporation Cygnus vehicle after it has undocked from the ISS. Although the experiment will need to meet rigorous safety requirements to ensure the carrier vehicle does not sustain damage, the absence of a crew removes the need for strict containment of combustion products. The tests will be fully automated with the data downlinked at the conclusion of the test before the Cygnus vehicle reenters the atmosphere. A computer modeling effort will complement the experimental effort. The international topical team is collaborating with the NASA team in the definition of the experiment requirements and performing supporting analysis, experimentation and technology development. The status of the overall experiment and the associated international technology development efforts are summarized.

Life Support↗

NASA NTRSDate not supplied

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Duffy↗

NASA NTRSDate not supplied

Assessments of Physiology and Cognition in Hybrid-Reality Environments (APACHE)

NASA is planning to return to the Moon in the mid-2020s as a stepping stone to Mars missions in the 2030s. Spacewalks, or extravehicular activities (EVAs), performed on the Moon and Mars will differ in a variety of ways from those that have been performed in decades past. NASA has identified multiple risks to human health and performance associated with a crewed mission to Mars, especially those associated with exploration EVAs which are expected to be a primary mission activity. Crew may be expected to conduct up to 24 hours of EVA per person per week, where the likelihood of injury and/or mental mistakes are increased compared to ground-based training or current microgravity EVAs and the consequences of which can be catastrophic. Current test environments for exploration EVA research and technology development are large, costly facilities that are limited in their availability or capabilities. Spacesuit testing in a reduced gravity environment such as NASA’s Neutral Buoyancy Laboratory, while a good representation of the crew’s physical workload during exploration EVAs, typically has small datasets and is difficult to integrate physiological sensors or other types of crew performance measures. Meanwhile, scientific field-based testing such as NASA’s Desert Research and Technology Studies offers an operationally relevant environment for exploration EVAs, particularly for cognitive workload, but is also limited by small datasets, lack of a pressurized spacesuit, and obtrusive measures. The limitations of current analogs for exploration EVAs identify a need for a new test environment that can approximate both the physical and cognitive demands associated with exploration EVAs to enable rapid, controlled, and repeatable evaluations of human health and performance risks of exploration missions. In response, the Human Physiology, Performance, Protection, and Operations Laboratory (H-3PO) at NASA Johnson Space Center has developed a hybrid reality exploration EVA analog named the Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) to address these limitations using a combination of virtual, physical, and hybrid reality techniques. The APACHE facility resides at NASA Johnson Space Center and serves as a large “sandbox” for EVA research and simulation. At its center is a roughly 15x20ft space surrounded by a 14” tall sandbox partially filled with lunar regolith simulant to emulate the physical feeling of walking on a planetary surface and to allow for simulated geology operations. Nearby, a curved passive treadmill (Skillmill Connect, Technogym, Fairfield, NJ) and an omnidirectional treadmill (Infinadeck, Infinadeck, Rocklin, CA) are included to enable exploration of these large virtual environments while also imposing the physical demands, representative timelines, and cognitive burdens required to navigate and traverse these distances during exploration EVA. A 6DOF motion platform is used to simulate rover operations and supports various human performance evaluations and associated risks. Lastly, APACHE can support two extravehicular (EV) crewmembers working in tandem. A computer workstation is located nearby and also supports an intravehicular (IV) crewmember as part of a full mission simulation. The IV crewmember has direct video and audio communication with the EV crew in VR to provide operational and procedural support. The software used in APACHE was created by the JSC Engineering Directorate, in partnership with Buendea, powered by a custom Unreal Engine 5 (UE5.3, Epic Games) project. APACHE currently utilizes the HTC Vive Pro Eye in a wireless configuration for VR simulations. There are two virtual environments that subjects can explore within APACHE, a Lunar and Martian surface. The virtual Lunar surface was created from LIDAR data of the Lunar South Pole to create roughly 16 sq km of explorable terrain. The virtual Martian surface contains roughly 400 sq km of explorable terrain derived from Mars Reconnaissance Orbiter LIDAR data of the Jezero Crater. The immersion and related cognitive burdens of conducting a planetary EVA is simulated through a series of EVA-relevant tasks performed in the VR environment, using these high-fidelity visual representations. Additionally, APACHE includes biosensor driven informatics, such as real-time heart rate monitoring and/or derived values from model simulations, for active monitoring by the EV crew and added cognitive demand. A “Wizard of Oz” control panel enables test operators to activate contingency events such as simulated spacesuit malfunctions, loss of communications, and/or limited visibility. Embedded performance measures such as accuracy, completeness, and execution time have been developed for various exploration tasks to objectively quantify crew performance during an EVA and compare impacts to performance when different environmental stressors, both physical and cognitive, are added to or removed from the simulation. Additionally, validated cognitive and operational performance measures such as the Digit Symbol Substitution Task have been recreated and embedded in VR for direct and relatively unobtrusive measurement of motor perception. The APACHE environment currently supports multiple research studies at NASA. Examples include the CHAPEA project, a series of simulated year-long missions on Mars by a 4-person crew; and the CO2 Contingency Walk Back Study, an investigation of elevated CO2 exposure on crew performance during a contingency EVA scenario. APACHE also provides a test environment to support the development of the Crew State and Risk Model, which is a collection of individualized, mathematical models of crew physical and cognitive state; and the Personalized EVA Informatics and Decision Support system, an operational tool for flight controllers, and eventually a self-reliant Martian crew, to make biomedically-informed decisions in real-time to optimize the EVA planning and execution with respect to crew health and performance. Some technical challenges associated with developing the APACHE environment, as well as current limitations, include VR limitless natural walking with a hybrid spacesuit simulator, optimizing performance for wireless PC VR streaming while maintaining a high degree of visual fidelity, and the integration of various physiological (metabolic masks) and psychometric (eye tracking) sensors with the VR headset.

Human Performance↗

DOE OSTI2017

Determination of a Generic Human Error Probability Distribution, Part 1: A Dynamic Formulation of SPAR-H

In almost all major nuclear power plants (NPP) events include a significant human error component. As an example, in Fukushima Daiichi (2011) nuclear meltdown, the human actions related to the high-pressure injection cooling strongly affected accident evolution. Furthermore, in the Three Mile Island accident (1979) (which was initiated by a stuck open valve and thus was triggered a loss of coolant accident) the unit operators made decisions based upon false assumptions that contributed negatively to the accident. Given these premises, the ability to accurately model human interactions with plant evolution and, in particular, dynamically calculate human error probability (HEP) plays a major role in a probabilistic risk analysis (PRA). Human reliability analysis (HRA) identifies and models the causes of human errors, and quantifies HEP by applying different methodologies. HRA methods identify errors and reflect on the understanding of error-prone people and systems. There are a number of HRA methods available that assist in identifying the vulnerabilities in the system and provide guidance in determining the HEP. HEP is the probability of a human failure event (HFE), and quantification of HEPs under various situations is utilized in PRA of large complex systems. One of the more prominent HRA methods is the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method that was developed to estimate HEPs for the use in the Standardized Plant Analysis Risk (SPAR) PRA models employed in U.S. NPPs. SPAR-H is implemented in U.S. NPPs by regulators at the U.S. Nuclear Regulatory Commission (NRC) to determine the HEP based on expert estimation through the use of calculation worksheets. In SPAR-H, the nominal HEPs (NHEPs) are multiplied by performance shaping factors (PSF) multiplier values to obtain the overall error probability. PSFs encompass the human components, individual characteristics, their environment, and aspects of the task. With PSFs, the goal is to capture all factors that can affect human performance. In SPAR-H there are eight PSFs used, each having respective levels corresponding to multipliers: Available Time, Stress/Stressors, Complexity, Experience/Training, Procedures, Ergonomics/Human Machine Interface, Fitness for Duty, and Work Processes. The PSF multipliers indicate enhanced, nominal, or degraded human performance. The SPAR-H method estimates the HEP through multiplying a NHEP with PSFs. Hence, SPAR-H is a traditional HRA method that assesses human performance at a fixed, or static, point in time. In a dynamic HRA framework, human performance is assessed on a continuous time basis. SPAR-H can be used within a dynamic PRA framework through the use of simulation and stochastic models. The goal of dynamic HRA is to build a more realistic model of the human-system interaction to enhance calculation of reliability, risk and safety analysis. The progression of events in dynamic SPAR-H considers that some PSFs may experience gradual change, while other change can be sudden due to individualistic differences or rapid plant status changes. The PSF changes are caused by contextual changes in the scenario. For example, as more time passes, the realization that a task should be completed sooner may cause the operator more stress. In another example, a sudden relay of information about sickness of a family member may alter the operator's general stress level and fitness for duty. Each PSF has a different dynamic nature; PSFs like stress and complexity change more quickly over the course of a task than experience and training. While the experience of an operator changes over the course of attaining the objective, the amount that this added experience changes the HEP is very minimal. In contrast, the stress, complexity, and procedures PSFs may change quite a bit over the course of task completion.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Deep learning multiphysics network for imaging CO 2 saturation and estimating uncertainty in geological carbon storage

Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation. Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding trade-off parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real-time monitoring tool for geological carbon storage. In this paper, we present a deep learning (DL) multiphysics network for imaging CO 2 saturation in real time. The multiphysics network consists of three encoders for analysing seismic, electromagnetic and gravity data and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO 2 saturation. The network is trained on pairs of CO 2 label models and multiphysics data so that it can directly image CO 2 saturation. Here we use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO 2 saturation images. Using realistic CO 2 label models and multiphysics data derived from the Kimberlina CO 2 storage model, we evaluate the performance of the deep learning multiphysics network and compare its imaging results to those from the deep learning single-physics networks. Our modelling experiments show that the deep learning multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO 2 saturation images. Our results also suggest that the deep learning multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be used as an effective low-cost monitoring tool in between regular seismic monitoring.

58 GEOSCIENCES↗

NASA NTRS1974

The manual control of vehicles undergoing slow transitions in dynamic characteristics

The manual control was studied of a vehicle with slowly time-varying dynamics to develop analytic and computer techniques necessary for the study of time-varying systems. The human operator is considered as he controls a time-varying plant in which the changes are neither abrupt nor so slow that the time variations are unimportant. An experiment in which pilots controlled the longitudinal mode of a simulated time-varying aircraft is described. The vehicle changed from a pure double integrator to a damped second order system, either instantaneously or smoothly over time intervals of 30, 75, or 120 seconds. The regulator task consisted of trying to null the error term resulting from injected random disturbances with bandwidths of 0.8, 1.4, and 2.0 radians per second. Each of the twelve experimental conditons was replicated ten times. It is shown that the pilot's performance in the time-varying task is essentially equivalent to his performance in stationary tasks which correspond to various points in the transition. A rudimentary model for the pilot-vehicle-regulator is presented.

Moriarty, T. E.↗

NASA NTRS2000

Analysis and Test of a Proton Exchange Membrane Fuel Cell Power System for Space Power Applications

An effort is underway to develop a prototype Proton Exchange Membrane (PEM) Fuel Cell breadboard system for fuhlre space applications. This prototype will be used to develop a comprehensive design basis for a space-rated PEM fuel cell powerplant. The prototype system includes reactant pressure regulators, ejector-based reactant pumps, a 4-kW fuel cell stack and cooling system, and a passive, membranebased oxygen / water separator. A computer model is being developed concurrently to analytically predict fluid flow in the oxidant reactant system. Fuel cells have historically played an important role in human-rated spacecraft. The Gemini and Apollo spacecraft used fuel cells for vehicle electrical power. The Space Shuttle currently uses three Alkaline Fuel Cell Powerplants (AFCP) to generate all of the vehicle's 15-20kW electrical power. Engineers at the Johnson Space Center have leveraged off the development effort ongoing in the commercial arena to develop PEM fuel cel ls for terrestrial uses. The prototype design originated from efforts to develop a PEM fuel cell replacement for the current Space Shuttle AFCP' s. In order to improve on the life and an already excellent hi storical record of reliability and safety, three subsystems were focused on. These were the fuel cell stack itself, the reactant circulation devices, and reactant / product water separator. PEM fuel cell stack performance is already demonstrating the potential for greater than four times the useful life of the current Shuttle's AFCP. Reactant pumping for product water removal has historically been accomplished with mechanical pumps. Ejectors offer an effective means of reactant pumping as well as the potential for weight reduction, control simplification, and long life. Centrifugal water separation is used on the current AFCP. A passive, membrane-based water separator offers compatibility with the micro-gravity environment of space, and the potential for control simplification, elimination of moving parts in an oxygen environment, and long life. The prototype system has been assembled from components that have previously been tested and evaluated at the component level. Preliminary data obtained from tests performed with the prototype system, as well as other published data, has been used to validate the analytical component models. These components have been incorporated into an integrated oxidant fluid system model. Results obtained from both the performance tests and the analytical model are presented.

Vasquez, Arturo↗

DOE OSTI2020

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Layer, Lukas↗

DOE OSTI2015

Tailoring automatic exposure control toward constant detectability in digital mammography

Purpose: The automatic exposure control (AEC) modes of most full field digital mammography (FFDM) systems are set up to hold pixel value (PV) constant as breast thickness changes. This paper proposes an alternative AEC mode, set up to maintain some minimum detectability level, with the ultimate goal of improving object detectability at larger breast thicknesses. Methods: The default “OPDOSE” AEC mode of a Siemens MAMMOMAT Inspiration FFDM system was assessed using poly(methyl methacrylate) (PMMA) of thickness 20, 30, 40, 50, 60, and 70 mm to find the tube voltage and anode/filter combination programmed for each thickness; these beam quality settings were used for the modified AEC mode. Detectability index (d′), in terms of a non-prewhitened model observer with eye filter, was then calculated as a function of tube current-time product (mAs) for each thickness. A modified AEC could then be designed in which detectability never fell below some minimum setting for any thickness in the operating range. In this study, the value was chosen such that the system met the achievable threshold gold thickness (T{sub t}) in the European guidelines for the 0.1 mm diameter disc (i.e., T{sub t} ≤ 1.10 μm gold). The default and modified AEC modes were compared in terms of contrast-detail performance (T{sub t}), calculated detectability (d′), signal-difference-to-noise ratio (SDNR), and mean glandular dose (MGD). The influence of a structured background on object detectability for both AEC modes was examined using a CIRS BR3D phantom. Computer-based CDMAM reading was used for the homogeneous case, while the images with the BR3D background were scored by human observers. Results: The default OPDOSE AEC mode maintained PV constant as PMMA thickness increased, leading to a reduction in SDNR for the homogeneous background 39% and d′ 37% in going from 20 to 70 mm; introduction of the structured BR3D plate changed these figures to 22% (SDNR) and 6% (d′), respectively. Threshold gold thickness (0.1 mm diameter disc) for the default AEC mode in the homogeneous background increased by 62% in going from 20 to 70 mm PMMA thickness; in the structured background, the increase was 39%. Implementation of the modified mode entailed an increase in mAs at PMMA thicknesses >40 mm; the modified AEC held threshold gold thickness constant above 40 mm PMMA with a maximum deviation of 5% in the homogeneous background and 3% in structured background. SDNR was also held constant with a maximum deviation of 4% and 2% for the homogeneous and the structured background, respectively. These results were obtained with an increase of MGD between 15% and 73% going from 40 to 70 mm PMMA thickness. Conclusions: This work has proposed and implemented a modified AEC mode, tailored toward constant detectability at larger breast thickness, i.e., above 40 mm PMMA equivalent. The desired improvement in object detectability could be obtained while maintaining MGD within the European guidelines achievable dose limit. (A study designed to verify the performance of the modified mode using more clinically realistic data is currently underway.)

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

Digital Lunar Exploration Sites (DLES)

It has been almost 50 years since humans last set foot on the Moon. With NASA’s Artemis program, the United States and its international and commercial partners are embarking on a new endeavor to explore the lunar surface. Before we return, we will have simulated every aspect of these future missions. Many of these models and simulations (M&S) will rely on well-known and commonly-used technologies, some of which trace their origins back to the Apollo program. However, M&S has advanced significantly, as have the underlying computational capabilities. As a result, we are able to model many more aspects of the Artemis vehicles and support systems with significantly improved detail and confidence. Accurately and efficiently modeling the lunar environment will be critical to simulating the Artemis elements and mission activities. This includes characterizing and modeling lunar topography, smaller craters, exposed surface rocks, lunar regolith, surface lighting, and ambient thermal environment. These are all necessary for understanding fundamental behaviors and performance of vehicles and support systems in the lunar environment and are often determining factors in the selection of exploration sites and defining mission profiles. The Astromaterials Research and Exploration Sciences (ARES) team in the Exploration Integration and Science Directorate at NASA’s Johnson Space Center (JSC) and the NASA Exploration Systems Simulations (NExSyS) team in the Simulation and Graphics Branch at JSC are developing and maintaining the Digital Lunar Exploration Sites (DLES) data, documentation, and software packages. This data is being fed directly into a diverse collection of graphics and simulation environments, where it is used to construct the closest known truth for numerous potential Lunar South Pole landing sites. DLES is available to projects across NASA and particularly the Artemis program to support coordinated digital representations of the lunar environment. This paper describes the fundamental need for DLES, the science data sets that are going into DLES, some of the processes used to integrate this data into DLES products, the basic products that constitute DLES, and some examples of DLES in use.

Lunar↗

DOE OSTI2016

High-performance web services for querying gene and variant annotation

Efficient tools for data management and integration are essential for many aspects of high-throughput biology. In particular, annotations of genes and human genetic variants are commonly used but highly fragmented across many resources. Here, we describe MyGene.info and MyVariant.info, high-performance web services for querying gene and variant annotation information. These web services are currently accessed more than three million times permonth. They also demonstrate a generalizable cloud-based model for organizing and querying biological annotation information. MyGene.info and MyVariant.info are provided as high-performance web services, accessible at http://mygene.info and http://myvariant.info. Both are offered free of charge to the research community.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2011

Should Pruning be a Pre-Processor of any Linear System?

There are many real-world problems whose mathematical models turn out to be linear systems Ax = b , where A is an m by x n matrix. Each equation of the linear system is an information. An information, in a physical problem, such as 4 mangoes, 6 bananas, and 5 oranges cost $10, is mathematically modeled as 4x(sub 1) + 6x(sub 2) + 5x (sub 3) = 10, where x(sub 1), x(sub 2), x(sub 3) are each cost of one mango, that of one banana, and that of one orange, respectively. All the information put together in a specified context, constitutes the physical problem and need not be all distinct. Some of these could be redundant, which cannot be readily identified by inspection. The resulting mathematical model will thus have equations corresponding to this redundant information and hence are linearly dependent and thus superfluous. Consequently, these equations once identified should be better pruned in the process of solving the system. The benefits are (i) less computation and hence less error and consequently a better quality of solution and (ii) reduced storage requirements. In literature, the pruning concept is not in vogue so far although it is most desirable. In a numerical linear system, the system could be slightly inconsistent or inconsistent of varying degree. If the system is too inconsistent, then we should fall back on to the physical problem (PP), check the correctness of the PP derived from the material universe, modify it, if necessary, and then check the corresponding mathematical model (MM) and correct it. In nature/material universe, inconsistency is completely nonexistent. If the MM becomes inconsistent, it could be due to error introduced by the concerned measuring device and/or due to assumptions made on the PP to obtain an MM which is relatively easily solvable or simply due to human error. No measuring device can usually measure a quantity with an accuracy greater that 0.005% or, equivalently with a relative error less than 0.005%. Hence measurement error is unavoidable in a numerical linear system when the quantities are continuous (or even discrete with extremely large number). Assumptions, though not desirable, are usually made when we find the problem sufficiently difficult to be solved within the available means/tools/resources and hence distort the PP and the corresponding MM. The error thus introduced in the system could (not always necessarily though) make the system somewhat inconsistent. If the inconsistency (contradiction) is too much then one should definitely not proceed to solve the system in terms of getting a least-squares solution or a minimum norm solution or the minimum-norm least-squares solution. All these solutions will be invariably of no real-world use. If, on the other hand, inconsistency is reasonably low, i.e. the system is near-consistent or, equivalently, has near-linearly-dependent rows, then the foregoing solutions are useful. Pruning in such a near-consistent system should be performed based on the desired accuracy and on the definition of near-linear dependence. In this article, we discuss pruning over various kinds of linear systems and strongly suggest its use as a pre-processor or as a part of an algorithm. Ideally pruning should (i) be a part of the solution process (algorithm) of the system, (ii) reduce both computational error and complexity of the process, and (iii) take into account the numerical zero defined in the context. These are precisely what we achieve through our proposed O(mn2) algorithm presented in Matlab, that uses a subprogram of solving a single linear equation and that has embedded in it the pruning.

Sen, Syamal K.↗

DOE OSTI2016

Atomistic modelling of scattering data in the Collaborative Computational Project for Small Angle Scattering (CCP-SAS)

The capabilities of current computer simulations provide a unique opportunity to model small-angle scattering (SAS) data at the atomistic level, and to include other structural constraints ranging from molecular and atomistic energetics to crystallography, electron microscopy and NMR. This extends the capabilities of solution scattering and provides deeper insights into the physics and chemistry of the systems studied. Realizing this potential, however, requires integrating the experimental data with a new generation of modelling software. To achieve this, the CCP-SAS collaboration (http://www.ccpsas.org/) is developing open-source, high-throughput and user-friendly software for the atomistic and coarse-grained molecular modelling of scattering data. Robust state-of-the-art molecular simulation engines and molecular dynamics and Monte Carlo force fields provide constraints to the solution structure inferred from the small-angle scattering data, which incorporates the known physical chemistry of the system. The implementation of this software suite involves a tiered approach in which GenApp provides the deployment infrastructure for running applications on both standard and high-performance computing hardware, and SASSIE provides a workflow framework into which modules can be plugged to prepare structures, carry out simulations, calculate theoretical scattering data and compare results with experimental data. GenApp produces the accessible web-based front end termed SASSIE-web , and GenApp and SASSIE also make community SAS codes available. Applications are illustrated by case studies: (i) inter-domain flexibility in two- to six-domain proteins as exemplified by HIV-1 Gag, MASP and ubiquitin; (ii) the hinge conformation in human IgG2 and IgA1 antibodies; (iii) the complex formed between a hexameric protein Hfq and mRNA; and (iv) synthetic `bottlebrush' polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Probing the limits of metal plasticity with molecular dynamics simulations

Ordinarily, the strength and plasticity properties of a metal are defined by dislocations—line defects in the crystal lattice whose motion results in material slippage along lattice planes. Dislocation dynamics models are usually used as mesoscale proxies for true atomistic dynamics, which are computationally expensive to perform routinely. However, atomistic simulations accurately capture every possible mechanism of material response, resolving every “jiggle and wiggle” of atomic motion, whereas dislocation dynamics models do not. We present fully dynamic atomistic simulations of bulk single-crystal plasticity in the body-centred-cubic metal tantalum. Our goal is to quantify the conditions under which the limits of dislocation-mediated plasticity are reached and to understand what happens to the metal beyond any such limit. In our simulations, the metal is compressed at ultrahigh strain rates along its [001] crystal axis under conditions of constant pressure, temperature and strain rate. To address the complexity of crystal plasticity processes on the length scales (85–340 nm) and timescales (1 ns–1μs) that we examine, we use recently developed methods of in situ computational microscopy to recast the enormous amount of transient trajectory data generated in our simulations into a form that can be analysed by a human. Our simulations predict that, on reaching certain limiting conditions of strain, dislocations alone can no longer relieve mechanical loads; instead, another mechanism, known as deformation twinning (the sudden re-orientation of the crystal lattice), takes over as the dominant mode of dynamic response. Below this limit, the metal assumes a strain-path-independent steady state of plastic flow in which the flow stress and the dislocation density remain constant as long as the conditions of straining thereafter remain unchanged. In this distinct state, tantalum flows like a viscous fluid while retaining its crystal lattice and remaining a strong and stiff metal.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Artificial Intelligence

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

97 MATHEMATICS AND COMPUTING↗