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NASA NTRS2002

Fluid Mechanics of Capillary-Elastic Instabilities in Microgravity Environment

The aim of this project is to investigate the closure and reopening of lung airways due to surface tension forces, coupled with airway elasticity. Airways are liquid-lined, flexible tubes and closure of airways can occur by a Rayleigh instability of the liquid lining, or an instability of the elastic support for the airway as the surface tension of the air-liquid interface pulls the tube shut, or both. Regardless of the mechanism, the airway is closed because the liquid lining has created a plug that prevents axial gas exchange. In the microgravity environment, surface tension forces dominate lung mechanics and would lead to more prevalent, and more uniformly distributed air-way closure, thereby creating a potential for respiratory problems for astronauts. Once closed the primary option for reopening an airway is by deep inspiration. This maneuver will pull the flexible airways open and force the liquid plug to flow distally by the incoming air stream. Airway reopening depends to a large extent on this plug flow and how it may lead to plug rupture to regain the continuity of gas between the environment and the alveoli. In addition to mathematical modeling of plug flows in liquid-lined, flexible tubes, this work has involved benchtop studies of propagating liquid plugs down tube networks that mimic the human airway tree. We have extended the work to involve animal models of liquid plug propagation in rat lungs. The liquid is radio-opaque and x-ray video imaging is used to ascertain the movement and distribution of the liquid plugs so that comparisons to theory may be made. This research has other uses, such as the delivery of liquids or drugs into the lung that may be used for surfactant replacement therapy or for liquid ventilation.

Grotberg, James B.↗

NASA NTRS2004

Medusa Sea Floor Monitoring System

The objective of the research described in this poster presentation is to develop technologies to enable fundamental research into understanding the potential for and limits to chemolithoautotrophic life. The Medusa Isosampler (isobaric sampler), for sampling fluids eminating from deep sea hydrothermal vents and cold seep sites analogous to extraterrestrial environments, is described by the presentation. The following instruments are integrated with the isosampler, and also described: in situ flow-through chemical sensor, intrinsic fluorescent-based microbial detector, isotope ratio spectral detector.

Flynn, Michael↗

NASA NTRS2016

Sleep Environment Recommendations for Future Spaceflight Vehicles

Current evidence demonstrates that astronauts experience sleep loss and circadian desynchronization during spaceflight. Ground-based evidence demonstrates that these conditions lead to reduced performance, increased risk of injuries and accidents, and short and long-term health consequences. Many of the factors contributing to these conditions relate to the habitability of the sleep environment. Noise, inadequate temperature and airflow, and inappropriate lighting and light pollution have each been associated with sleep loss and circadian misalignment during spaceflight operations and on Earth. As NASA prepares to send astronauts on long-duration, deep space missions, it is critical that the habitability of the sleep environment provide adequate mitigations for potential sleep disruptors. We conducted a comprehensive literature review summarizing optimal sleep hygiene parameters for lighting, temperature, airflow, humidity, comfort, intermittent and erratic sounds, and privacy and security in the sleep environment. We reviewed the design and use of sleep environments in a wide range of cohorts including among aquanauts, expeditioners, pilots, military personnel and ship operators. We also reviewed the specifications and sleep quality data arising from every NASA spaceflight mission, beginning with Gemini. Finally, we conducted structured interviews with individuals experienced sleeping in non-traditional spaces including oil rig workers, Navy personnel, astronauts, and expeditioners. We also interviewed the engineers responsible for the design of the sleeping quarters presently deployed on the International Space Station. We found that the optimal sleep environment is cool, dark, quiet, and is perceived as safe and private. There are wide individual differences in the preferred sleep environment; therefore modifiable sleeping compartments are necessary to ensure all crewmembers are able to select personalized configurations for optimal sleep. A sub-optimal sleep environment is tolerable for only a limited time, therefore individual sleeping quarters should be designed for long-duration missions. In a confined space, the sleep environment serves a dual purpose as a place to sleep, but also as a place for storing personal items and as a place for privacy during non-sleep times. This need for privacy during sleep and wake appears to be critically important to the psychological well-being of crewmembers on long-duration missions.

Flynn-Evans, Erin E.↗

NASA NTRS2020

Linkage among Ice Crystal Microphysics, Mesoscale Dynamics and Cloud and Precipitation Structures Revealed By Collocated Microwave Radiometer and Multi-Frequency Radar Observations

Ice clouds and falling snow are ubiquitous globally and play important roles in the Earth’s radiation budget and precipitation processes. Ice particle microphysical properties (e.g., size, habit and orientation) are not only influenced by ambient environment’s dynamic and thermodynamic conditions, but also intimately connect to the cloud radiative effects and particle fall speeds, which therefore impact up to the future climate projection and down to the details of the surface precipitation (e.g., onset-time, location, type and strength). Our previous work revealed that high-frequency Polarimetric radiance Difference (PD) from passive microwave sensors is a good indicator of the bulk aspect ratio of horizontally oriented ice particles that are often occur inside anvil clouds and/or stratiform precipitations. In this current work, we further investigate the dynamic/thermodynamic mechanisms and cloud/precipitation structures associated with ice-phase microphysics corresponding to different PD signals. In order to do so, collocated CloudSat radar (W-band) and Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM-DPR, Ku/Ka bands) observations as well as European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere background profiles are grouped according to the magnitude of PD for only stratiform precipitation and/or anvil cloud scenes. We found that horizontally-oriented snow aggregates or large snow particles are likely the major contributor to the high-PD signals at 166 GHz, while low-PD magnitudes can be attributed to small cloud ice, randomly oriented snow aggregates, riming snow or super-cooled water. Further, high (low) PD scenes are found to be associated with stronger (weaker) wind shear and higher (lower) ambient humidity, both of which help promote (prohibit) the growth of frozen particles and the organization of convective systems. An ensemble of squall line cases is studied at the end to demonstrate that the PD asymmetry in the leading and trailing edges of the deep convection line is closely tied to the anvil cloud and stratiform precipitation layers respectively, suggesting the potential usefulness of PD as a proxy of stratiform/convective precipitation flag, as well as a proxy of convection life stage.

Ice crystal microphysics↗

NASA NTRSDate not supplied

Evaluation of Markerless Motion Capture for Monitoring Sensorimotor Performance

BACKGROUND Astronauts returning from long-duration exposure to microgravity frequently exhibit alterations in sensorimotor function leading to postural imbalance, impaired locomotion, and operational challenges to manual control. Mission duration and individual responses often influence both the severity of performance decrements and the variability in adaptation timelines. Postflight disruptions during functional tasks are often detected through body-worn inertial measurement unit (IMU) devices. While IMU sensors are relatively compact, the long-term wear may lead to discomfort, displacement of the sensors on the body, and restrictions in movement or crew behavior. Although IMU data offers valuable insights from a research standpoint, interpreting changes in pre- and post-flight measures can be difficult for crew support personnel beyond the research domain, which can hinder the application for medical assessments and rehabilitation. Finally, the availability of inertial sensors in-flight is limited. There is a need for unobtrusive monitoring tools to improve our ability to monitor adaptation following gravitational transitions in various postflight evaluations and rehabilitation settings. Markerless motion capture (MMC) is an evolving unobtrusive technology that builds upon decades of research with marker-based motion capture systems to provide 3D human pose estimation from multiple synchronized 2D camera views using deep learning algorithms. Markerless technology can revolutionize how data is captured pre- and post-flight and potentially in-flight during intravehicular activity by enabling pose estimation of multiple crew members from onboard camera hardware. METHODS The following presents the initial evaluation of a state-of-the-art commercial-off-the-shelf MMC system, Theia Markerless, compared to IMU devices during various ground-based functional tasks and environmental conditions. The featured functional tasks include assessments from Human Research Program (HRP) funded studies such as Sensorimotor Standard Measures and Sensorimotor Assessments. Synchronous data collected using both motion capture and IMUs are analyzed for six male and female subjects of varying anthropometry. The analysis includes limited assessments of clothing, capture volume configurations, and the tool's sensitivity to detecting performance changes after a spaceflight analog centrifuge exposure. The development of visualization tools to enhance the application of the pose estimation output is also presented. RESULTS Initial results demonstrate comparable root mean square error (RMSE) to existing literature evaluating markerless and marker-based motion capture systems. Considering the relative functional range of motion of the cervical spine, normalized error values for the markerless system’s accuracy of the head was 0.032 in pitch, 0.025 in roll, and 0.018 in yaw plane of motion across a subset of functional tasks. The raw RMSE values were 3.49, 2.25, and 2.81 degrees respectively. For the torso, results suggest normalized errors of 0.469 in pitch, 0.191 in roll, and 0.307 in yaw planes of motion and raw RMSE values of 3.52, 1.53, and 3.07 degrees respectively. The data suggests the functional demands of a particular task influences the estimation accuracy of the MMC system where more dynamic motion and cases where subjects are not upright may introduce diminished tracking accuracy. DISCUSSION The following work lays the foundation for future implementations leveraging markerless motion capture to assess the time course of recovery and provide insight for rehabilitation protocols to enhance crew readiness for the resumption of daily activities. These tools offer effective methods for anonymizing sensitive crew data, facilitating numerous applications across research, medical, and rehabilitation groups. Collaborations with the Anthropometry and Biomechanics Facility will provide further comparisons of the Markerless system to a marker-based system. ACKNOWLEDGEMENT</ This work is supported by NASA’s Exploration Systems Development Mission Directorate Mars Campaign Office Crew Health Countermeasures.

Hannah M. Weiss↗

DOE OSTI2020

Real-time Intrusion Detection for High-bandwidth Research Networks using Unsupervised Deep Learning

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks across distributed collaboration sites for processing, storage, and analysis. Like general purpose networks, research networks experience attacks. However, monitoring traffic in such high-bandwidth network environments is challenging with conventional deep packet inspection (DPI)- based network intrusion detection systems (NIDS). Recently, machine learning (ML) algorithms have shown potential in detecting anomalous patterns with better generalizability and faster speed. Compared to the general purpose networks, traffic movement in research network is less volatile and more likely to benefit from the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.

Gong, Qian↗

DOE OSTI2022

Microbial colonization and persistence in deep fractured shales is guided by metabolic exchanges and viral predation

Background: Microbial colonization of subsurface shales following hydraulic fracturing offers the opportunity to study coupled biotic and abiotic factors that impact microbial persistence in engineered deep subsurface ecosystems. Shale formations underly much of the continental USA and display geographically distinct gradients in temperature and salinity. Complementing studies performed in eastern USA shales that contain brine-like fluids, here we coupled metagenomic and metabolomic approaches to develop the first genome-level insights into ecosystem colonization and microbial community interactions in a lower-salinity, but high-temperature western USA shale formation. Results: We collected materials used during the hydraulic fracturing process (i.e., chemicals, drill muds) paired with temporal sampling of water produced from three different hydraulically fractured wells in the STACK (Sooner Trend Anadarko Basin, Canadian and Kingfisher) shale play in OK, USA. Relative to other shale formations, our metagenomic and metabolomic analyses revealed an expanded taxonomic and metabolic diversity of microorganisms that colonize and persist in fractured shales. Importantly, temporal sampling across all three hydraulic fracturing wells traced the degradation of complex polymers from the hydraulic fracturing process to the production and consumption of organic acids that support sulfate- and thiosulfate-reducing bacteria. Furthermore, we identified 5587 viral genomes and linked many of these to the dominant, colonizing microorganisms, demonstrating the key role that viral predation plays in community dynamics within this closed, engineered system. Lastly, top-side audit sampling of different source materials enabled genome-resolved source tracking, revealing the likely sources of many key colonizing and persisting taxa in these ecosystems. Conclusions: These findings highlight the importance of resource utilization and resistance to viral predation as key traits that enable specific microbial taxa to persist across fractured shale ecosystems. We also demonstrate the importance of materials used in the hydraulic fracturing process as both a source of persisting shale microorganisms and organic substrates that likely aid in sustaining the microbial community. Moreover, we showed that different physicochemical conditions (i.e., salinity, temperature) can influence the composition and functional potential of persisting microbial communities in shale ecosystems. Together, these results expand our knowledge of microbial life in deep subsurface shales and have important ramifications for management and treatment of microbial biomass in hydraulically fractured wells.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Methods for Finding Legacy Wells in Large Areas

More than 10 million wells have been drilled during 150 years of oil and gas production in the United States. When abandoned, many wells were not adequately sealed and now provide a potential conduit for the vertical movement of liquids and gases. Today, groundwater aquifers can be contaminated by surface pollutants flowing down wells or by deep, saline water diffusing upwards. Likewise, natural gas, carbon dioxide (CO 2 ), or radon can travel upwards via these wells to endanger structures or human health on the surface. Recently, the need to find and plug wells has become critical with the advent of carbon dioxide injection into geologic formations for enhanced oil recovery (EOR) or carbon storage. The potential for natural gas or brine leakage through existing wells has also been raised as a concern in regions where shale resources are hydraulically fractured for hydrocarbon recovery. In this study, the National Energy Technology Laboratory (NETL) updated existing, effective well finding techniques to be able to survey large areas quickly using helicopter or ground-vehicle-mounted magnetometers, combined with mobile methane detection. For this study, magnetic data were collected using airborne and ground vehicles equipped with two boom-mounted magnetometers, or on foot using a hand-held magnetometer with a single sensor. Data processing techniques were employed to accentuate well-casing-type magnetic signatures. To locate wells with no magnetic signature (wells where the steel well casing had been removed), the team monitored for anomalous concentrations of methane, which could indicate migration of volatile compounds from deeper sedimentary strata along a well or fracture pathway. Methane measurements were obtained using the ALPIS DIfferential Absorption Lidar (DIAL) sensor for helicopter surveys and the Apogee leak detection system (LDS) for ground surveys. These methods were evaluated at a 100-year-old oilfield in Wyoming, where a helicopter magnetic survey accurately located 93% of visible wells. In addition, 20% of the wells found by the survey were previously unknown or inaccurately located. This study found helicopter magnetic surveys to be an accurate, cost- and time-effective means to locate steel-cased wells in large areas, and is a first step in evaluating whether well detection techniques can be applied effectively for well location screening across broad geographic areas.

03 NATURAL GAS↗

NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗

NASA NTRSDate not supplied

Oxidative Stress and the Neuroconsequences of Spaceflight Environment - Immune Dysregulation and Antioxidant Dietary Countermeasure Efficacy

In this project, we will test the hypothesis that Ionizing Radiation (IR), microgravity and social isolation combine synergistically to trigger an oxidative stress response that alters immune homeostasis, brain structure and function, and neurobehavioral and cognitive performance. Specific Aims for this project are: (1) Determine dose-response curves for acute ‘Five-Ion GCR Simulation’ exposure for immune, brain and performance responses in crew age-matched adult male and female mice; (2) Determine effects of acute ‘Five-Ion GCR Simulation’ exposure singly and in combination with simulated microgravity and social isolation, on immune, brain and performance responses in crew age-matched male and female mice mimicking deep space missions; and (3) Determine efficacy of the dietary antioxidant, Nicotinamide Mononucleotide (NMN), a key intermediate in nicotinamide adenine dinucleotide (NAD+) biosynthesis. The project relies on established and highly translatable ground-based mouse models and assays with IR exposures to be performed at the NASA Space Radiation Laboratory (NSRL). The experimental approach will provide definitive data on the timing and mechanisms involved in the oxidative stress response, immune and brain changes, and ensuing functional (behavioral/cognitive) impairments expected during human transit to Mars. This project will identify potential biomarkers for, and mechanisms underlying, structural and functional changes in the immune and nervous systems leading to behavioral/cognitive performance deficits, and its potential application to develop effective countermeasures to mitigate negative health effects of long duration space habitation. The project will address NASA’s efforts to rapidly advance the characterization of risks and identify appropriate countermeasures in anticipation of future deep space missions. Ensuring crew health and performance during extended transits necessitates that sensorimotor and cognitive abilities remain strong to avoid potentially catastrophic health and safety outcomes. Further, despite historically low numbers of female astronauts, the two most recent NASA Astronaut Corps class selections, comprised of ~50% women as compared to men, signal the need to understand how sex differences affect physiological adaptation and health in the space environment. Here, we will determine how key features of the deep space environment may interact to increase risk to crewmembers by negatively impacting health and performance, and identify and develop strategies to characterize and mitigate the potential risks via countermeasures. Supported by the NASA Human Research Program (HRP) Human Factors Behavioral Performance Element Grant 18-18FLAG_2-0028.

radiation↗

DOE OSTI2022

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn–Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. Further, this scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What’s more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely “ST-VQC”, to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm-cairo with 27 qubits and ibmq-lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the STVQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.

Li, Jinyang↗

NASA NTRS1994

An Algorithm to Generate Deep-Layer Temperatures from Microwave Satellite Observations for the Purpose of Monitoring Climate Change

An algorithm for generating deep-layer mean temperatures from satellite-observed microwave observations is presented. Unlike traditional temperature retrieval methods, this algorithm does not require a first guess temperature of the ambient atmosphere. By eliminating the first guess a potentially systematic source of error has been removed. The algorithm is expected to yield long-term records that are suitable for detecting small changes in climate. The atmospheric contribution to the deep-layer mean temperature is given by the averaging kernel. The algorithm computes the coefficients that will best approximate a desired averaging kernel from a linear combination of the satellite radiometer's weighting functions. The coefficients are then applied to the measurements to yield the deep-layer mean temperature. Three constraints were used in deriving the algorithm: (1) the sum of the coefficients must be one, (2) the noise of the product is minimized, and (3) the shape of the approximated averaging kernel is well-behaved. Note that a trade-off between constraints 2 and 3 is unavoidable. The algorithm can also be used to combine measurements from a future sensor (i.e., the 20-channel Advanced Microwave Sounding Unit (AMSU)) to yield the same averaging kernel as that based on an earlier sensor (i.e., the 4-channel Microwave Sounding Unit (MSU)). This will allow a time series of deep-layer mean temperatures based on MSU measurements to be continued with AMSU measurements. The AMSU is expected to replace the MSU in 1996.

Goldberg, Mitchell D.↗

NASA NTRS2008

Experiments Advance Gardening at Home and in Space

Aeroponics, the process of growing plants suspended in air without soil or media, provides clean, efficient, and rapid food production. Crops can be planted and harvested year-round without interruption, and without contamination from soil, pesticides, and residue. Aeroponic systems also reduce water usage by 98 percent, fertilizer usage by 60 percent, and eliminate pesticide usage altogether. Plants grown in aeroponic systems have been shown to absorb more minerals and vitamins, making the plants healthier and potentially more nutritious. The suspended system also has other advantages. Since the growing environment can be kept clean and sterile, the chances of spreading plant diseases and infections commonly found in soil and other growing media are greatly reduced. Also, seedlings do not stretch or wilt while their roots are forming, and once the roots are developed, the plants can be easily moved into any type of growing media without the risk of transplant shock. Lastly, plants tend to grow faster in a regulated aeroponic environment, and the subsequent ease of transplant to a natural medium means a higher annual crop yield. For example, tomatoes are traditionally started in pots and transplanted to the ground at least 28 days later; growers using an aeroponic system can transplant them just 10 days after starting the plants in the growing chamber. This accelerated cycle produces six tomato crops per year, rather than the traditional one to two crop cycles. These benefits, along with the great reduction in weight by eliminating soil and much of the water required for plant growth, illustrate why this technique has found such enthusiastic support from NASA. Successful long-term missions into deep space will require crews to grow some of their own food during flight. Aeroponic crops are also a potential source of fresh oxygen and clean drinking water, and every ounce of food produced and water conserved aboard a spacecraft reduces payload weight, decreasing launch costs and freeing room for other cargo.

Source record↗

DOE OSTI2015

Structural stability and defect energetics of ZnO from diffusion quantum Monte Carlo

We have applied the many-body ab initio diffusion quantum Monte Carlo (DMC) method to study Zn and ZnO crystals under pressure and the energetics of the oxygen vacancy, zinc interstitial, and hydrogen impurities in ZnO. We show that DMC is an accurate and practical method that can be used to characterize multiple properties of materials that are challenging for density functional theory (DFT) approximations. DMC agrees with experimental measurements to within 0.3 eV, including the band-gap of ZnO, the ionization potential of O and Zn, and the atomization energy of O{sub 2}, ZnO dimer, and wurtzite ZnO. DMC predicts the oxygen vacancy as a deep donor with a formation energy of 5.0(2) eV under O-rich conditions and thermodynamic transition levels located between 1.8 and 2.5 eV from the valence band maximum. Our DMC results indicate that the concentration of zinc interstitial and hydrogen impurities in ZnO should be low under n-type and Zn- and H-rich conditions because these defects have formation energies above 1.4 eV under these conditions. Comparison of DMC and hybrid functionals shows that these DFT approximations can be parameterized to yield a general correct qualitative description of ZnO. However, the formation energy of defects in ZnO evaluated with DMC and hybrid functionals can differ by more than 0.5 eV.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection

A peripherally inserted central catheter (PICC) is a thin catheter that is inserted via arm veins and threaded near the heart, providing intravenous access. The final catheter tip position is always confirmed on a chest radiograph (CXR) immediately after insertion since malpositioned PICCs can cause potentially life-threatening complications. Although radiologists interpret PICC tip location with high accuracy, delays in interpretation can be significant. In this study, we proposed a fully-automated, deep-learning system with a cascading segmentation AI system containing two fully convolutional neural networks for detecting a PICC line and its tip location. A preprocessing module performed image quality and dimension normalization, and a post-processing module found the PICC tip accurately by pruning false positives. Our best model, trained on 400 training cases and selectively tuned on 50 validation cases, obtained absolute distances from ground truth with a mean of 3.10 mm, a standard deviation of 2.03 mm, and a root mean squares error (RMSE) of 3.71 mm on 150 held-out test cases. This system could help speed confirmation of PICC position and further be generalized to include other types of vascular access and therapeutic support devices.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2012

OBSERVATIONS OF DARK AND LUMINOUS MATTER: THE RADIAL DISTRIBUTION OF SATELLITE GALAXIES AROUND MASSIVE RED GALAXIES

We study the projected radial distribution of satellite galaxies around more than 28,000 luminous red galaxies (LRGs) at 0.28 < z < 0.40 and trace the gravitational potential of LRG groups in the range 15 < r/kpc < 700. We show that at large radii the satellite number-density profile is well fitted by a projected Navarro-Frenk-White (NFW) profile with r{sub s} {approx} 270 kpc and that at small radii this model underestimates the number of satellite galaxies. Utilizing the previously measured stellar light distribution of LRGs from deep imaging stacks, we demonstrate that this small-scale excess is consistent with a non-negligible baryonic mass contribution to the gravitational potential of massive groups and clusters. The combined NFW+scaled stellar profile provides an excellent fit to the satellite number-density profile all the way from 15 kpc to 700 kpc. Dark matter dominates the total mass profile of LRG halos at r > 25 kpc whereas baryons account for more than 50% of the mass at smaller radii. We calculate the total dark-to-baryonic mass ratio and show that it is consistent with measurements from weak lensing for environments dominated by massive early-type galaxies. Finally, we divide the satellite galaxies in our sample into three luminosity bins and show that the satellite light profiles of all brightness levels are consistent with each other outside of roughly 25 kpc. At smaller radii we find evidence for a mild mass segregation with an increasing fraction of bright satellites close to the central LRG.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Absorption Spectra of Acetylene, Vinylidene, and Their Deuterated Isotopologues on Ab Initio Potential Energy and Dipole Moment Surfaces

Here, the absorption spectra of acetylene (HCCH) and vinylidene (H 2 CC) as well as their deuterated isotopologues are investigated theoretically on a near spectroscopically accurate full-dimensional potential energy surface reported in an earlier publication, using dipole moment surfaces reported in this work, which are constructed with a neural network method from a large number of ab initio data points. These global surfaces cover not only the deep acetylene well, but also the vinylidene well, as well as the transition region between the two isomers. The agreement with available experimental data for acetylene is excellent, validating both the potential energy surface and the dipole moment surfaces. The infrared spectra of vinylidene and its deuterated isotopologues are predicted. The potential and dipole moment surfaces lay the foundation for future spectroscopic studies of the acetylene-vinylidene isomerization involving large amplitude motions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗