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Surface sampler activities on Mars during the Viking extended mission are considered, including excavation of deep trenches, construction of conical piles of materials, backhoe touchdown experiments, and acquisition of contiguous pictures of the surface beneath number 2 terminal descent engines using mirrors. Results of the Physical Properties Investigation that are relevant to aeolian processes are also discussed. Both pictures and surface sampler data indicate that the surface materials in the sample fields of the Viking landers may be grouped, in order of increasing strength, into drift material, crusty to cloddy material, blocky material and rocks.
As an essential posttranscriptional regulator of gene expression, microRNA (miRNA) levels must be strictly maintained. The biogenesis of many miRNAs is mediated by trans-acting protein partners through a variety of mechanisms, including remodeling of the RNA structure. miR-31 functions as an oncogene in numerous cancers, and interestingly, its biogenesis is not known to be regulated by protein-binding partners. Therefore, the intrinsic structural properties of the precursor element of miR-31 (pre-miR-31) can provide a mechanism by which its biogenesis is regulated. We determined the solution structure of pre-miR-31 to investigate the role of distinct structural elements in regulating processing by the Dicer–TRBP complex. We found that the presence or absence of mismatches within the helical stem does not strongly influence Dicer–TRBP processing of the pre-miRNAs. However, both the apical loop size and structure at the Dicing site are key elements for discrimination by the Dicer–TRBP complex. Interestingly, our NMR-derived structure reveals the presence of a triplet of base pairs that link the Dicer cleavage site and the apical loop. Mutational analysis in this region suggests that the stability of the junction region strongly influences processing by the Dicer–TRBP complex. Our results enrich our understanding of the active role that RNA structure plays in regulating miRNA biogenesis, which has direct implications for the control of gene expression.
Investigations of working memory tend to focus on the retention of verbal information. The present experiments were designed to characterize the active maintenance rehearsal process used in the retention of visuospatial information. Rhesus monkeys (Macaca mulatta; N = 6) were tested as well as humans (total N = 90) because these nonhuman primates have excellent visual working memory but, unlike humans, cannot verbally recode the stimuli to employ verbal rehearsal mechanisms. A series of experiments was conducted using a distractor-task paradigm, a directed forgetting procedure, and a dual-task paradigm. No evidence was found for an active maintenance process for either species. Rather, it appears that information is maintained in the visuospatial sketchpad without active rehearsal.
EXPORTS -EXport Processes in the Ocean from RemoTe Sensing is NASA’s large field campaign focusing on development of a predictive understanding of the export, fate and carbon cycle impacts of the global net primary production. This co-funded program (NSF, private funding1, and international participation) was conceived in 2013, with EXPORTS Science plan published in 2016 (Siegel et al 2016, EXPORTS Writing Team 2015) and its implementation plan finalized in 2016 (EXPORTS Science Definition Team 2016). More details are presented in Siegel et al (2021). EXPORTS campaign is structured as a multiyear effort (Figure 1). It started with “Pre-EXPORTS” modeling and data-mining activity followed by a first phase with two major field programs and a second synthesis and modeling phase. The “Pre-EXPORTS” projects, total of 6 of them (Table 1), funded under A.3 Ocean Biology and Biogeochemistry 2015 call, helped to plan the field campaign (Resplandy et al 2019, Rousseaux & Gregg 2017), and supporting further global synthesis with datasets mined from the literature (e.g. Bisson et al (2020), Bisson et al (2018), Kramer and Siegel (2019)), directly responding to objectives outlined in Science Plan (see section 6 in Team (2015)) and Implementation team (see Figure 1 in Team (2016)). This document presents a compilation of the final reports of the Pre-EXPORTS funded projects, in hope of synthesizing the outcomes, and insuring the legacy of this program. Each of these reports contains a list of published papers, and reader should refer to them to see results in details.
The core activities performed during this time period have been concerned with tracking the TEMPEST experiments on the shuttle with drops of Zr, Ni, and Nb alloys. In particular a lot of Zr drops are being made to better define the recalescence characteristics of that system so that accurate comparisons of the drop tube results with Tempest can be made. A new liner, with minimal reflectivity characteristics, has been inserted into the drop tube in order to improve the recalescence measurements of the falling drops. The first installation to make the geometric measurements to ensure a proper fit has been made. The stovepipe sections are currently in the shop at MSFC being painted with low reflectivity black paint. Work has also continued on setting up the MEL apparatus obtained from Oak Ridge in the down stairs laboratory at the Drop Tube Facilities. Some ground-based experiments on the same metals as are being processed on TEMPEST are planned for the MEL. The flight schedules for the KC-135 experiments are still to be determined in the near future.
Activities of the project for studies in atmospheric processes from space platforms are described. Topics include feasibility studies for a Low Altitude Research Satellite system, a satellite borne Doppler lidar wind monitoring system, the relationship between solar variability and weather, atmospheric modeling, cloud physics, and aerosol studies.
There is an ever-increasing emphasis on the part of government agencies, academia, and industry on enhancing our understanding of atmospheric processes and assessing the impact of human activities on these processes. While issues such as the ozone hole and rising levels of greenhouse gases have received major attention. relatively little is known about the types, concentrations, sources, and sinks of hydrocarbons in the troposphere and stratosphere. Such information would be of tremendous utility in assessing the roles of various anthropogenic and biogenic processes on global carbon cycles. An ion trap mass spectrometer has been developed for monitoring trace levels of hydrocarbons in the atmosphere on NASA's DC-8 "flying laboratory". This aircraft is used to provide measurements in support of a number of "Mission to Planet Earth" activities and tropospheric chemistry experiments. In past missions, specific compounds have been monitored via highly specialized instrumentation, fast GO, or collection of whole air samples for subsequent ground-based analysis. The ion trap has several features. including small size. excellent sensitivity, and broad applicability, which make it highly atttrat:ive for atmospheric monitoring. The design of this instrument, its air sampling interface. and the various complications associated with aircraft-deployment will be described. Data showing the sensitivity of the instrument for detecting hydrocarbons at mixing ratios below one part-per-billion, and the use of MS/MS for direct, on-line, real-time monitoring will be presented.
Statistical evaluations of the significance of the relationship of abrupt changes in solar activity and discontinuities in the multi-year pattern of an epidemic process are reported. They reliably (with probability of more than 99.9%) show the real nature of this relationship and its great specific weight (about half) in the formation of discontinuities in the multi-year pattern of the processes in question.
Here, this paper describes an active learning approach for part-to-part iterative machining process optimization using a hybrid surrogate tool life model. A probabilistic interpolating tool life model is developed by combining the empirical Taylor-type tool life equation and the model fit error. The probabilistic tool life model is then used to calculate the machining cost per part distribution. The optimal machining parameters are selected using an expected improvement in machining cost per part criterion. The method is validated numerically using experimental results; the results show a median convergence error of 2.2% after three tests over 400 simulations. The method is validated experimentally on two industrial applications for Ti-6Al-4V roughing resulting in a cost per part reduction greater than 23% after two tests. The described method is a robust solution for rapid convergence to optimal machining parameters in an industrial production environment.
My research activities for the summer of 2003 consisted of two projects: One project was concerned with determining a method for predicting the static and dynamic assembly properties of nano-structures using laser tweezers. The other project was to investigate the generation of Laguerre-Gaussian modes using a spatial light modulator incorporated into an optical tweezers system. Concerning the first project, I initially pursued the approach suggested by my NASA colleague Dr. Art Decker. This approach involved mimicking the model of the structure of atomic nucleus for the assembly of 1 to 100 atoms using allowed quadruple transitions induced by orbital angular momentums of a Laguerre- Gaussian (Doughnut) laser mode. After realizing the inaptness of the nuclear model with the nanostructure model as far as the binding forces and transitions were concerned, I focused on using quantum dot modei. This model was not attuned also for the host lattice influences the electronic structure of the quantum dot. Thus one other option that I decided to pursue was the approach of molecular quantum mechanics. In this approach the nanostructure is treated as a large (10-100 nm) molecule constructed from single element or multi-elements. Subsequent to consultation with Dr. Fred Morales, a chemical engineer at NASA GRC, and Dr. David Ball, a computational chemist at Cleveland State University, I acquired a molecular-quantum computation software, Hyperchem 7.0. This software allows simulation of different molecular structures as far as their static and dynamic behaviors are concerned. The time that I spent on this project was about eight weeks. Once this suitable approach was identified, I realized the need to collaborate with a computational quantum chemist to pursue searching for stable nanostructures in the range of 10-100 nm that we can be assembled using laser tweezers. The second project was about generating laser tweezers that possess orbital angular momentum. As shown, we were able to generate laser tweezers modes of different orbital angular momentum using a spatial light modulator incorporated into a laser tweezers system. The motivation for investigating these types of modes stems from being able to spin particles at high speeds and also to orient two particles in separate traps and then join them together. Also, there has been recent intense interest on fundamental physics research on orbital angular momentum of light. The fact that circularly polarized light may have associated with it angular momentum that relates to the spin of individual photons (spin 0 for the plane polarized light, spin +1 for the right-circularly polarized light and spin -1 for the left-circularly polarized light) was first demonstrated by Beth in 1936. Orbital angular momentum is, however, distinct from spin in that the spin angular momentum of light is intrinsically linked to the behavior of the electric field in the light whereas orbital angular momentum is a consequence of inclined wavefronts. In 1992 L. Allen, et al showed that the Laguerre-Gaussian (LG) modes could possess well-defined orbital angular momentum that can exceed 1 planck's constant, i.e. l plancks constant per photon, where l is the azimuthal index of the mode.
The International Space Station (ISS) Program initially implemented safety requirements in a series of bilateral agreements between NASA and each International Partner. As the program matured and multilateral processes began to be developed, the differences between these bilaterally agreed requirement sets became more significant. Efforts to develop multilateral safety requirements were hampered for many reasons including assessment of national standards used in the bilateral agreements, requirements baselines for existing contracts, and resource limitations to address requirements changes late in the development and operations phases. To avoid similar requirements issues in the future, international safety requirements need to be developed for human spaceflight. This paper will provide the background of the ISS bilateral Safety and Mission Assurance requirements and processes, describe the activities to develop multilateral safety requirements and processes, and give examples of issues that were encountered. Further, the paper will make recommendations regarding the development of international safety requirements for human spaceflight and the safety topics to be addressed.
Heightened fire activity is straining existing firefighting resources and budgets across the nation and worldwide. For this reason, the 2021 NASA Academy at Langley Research Center has the challenge of applying NASA technology to the challenges faced by wild-land firefighting professionals. The approach is three-fold: Data Acquisition, Communications, and Equipment Development. The Communications Team focused on improving data latency for fire mapping and advancing communications between civilians and incident dispatch. The team developed two main concepts to address these areas: First, they tried to identify, map, and transmit fire perimeter data via an Uncrewed Aerial System (UAS), and second, they suggest a fully functioning web-app for early fire detection and fire monitoring using citizen science. This report outlines the materials and methods of these concepts, as well as future work necessary to bring the proposed technology into the field.
A cathode active material precursor for a lithium metal oxide is provided. The cathode active material precursor comprises a metal-containing oxyhydroxide. The metal-containing oxyhydroxide comprises nickel and an additional metal. At least 50 mol. % of the nickel of the metal-containing oxyhydroxide has an oxidation state of +3. A method of forming a cathode active material precursor is also provided. The method comprises combining a nickel-containing compound, an additional metal-containing compound, an oxidizing agent, and a solvent to form a solution. The method further comprises exposing the solution to heat at a temperature of from about 30° C. to about 90° C. to form a precipitate comprising the metal-containing oxyhydroxide.
The validation process comprises the activities required to insure the agreement of system realization with system specification. A preliminary validation methodology for fault tolerant systems documented. A general framework for a validation methodology is presented along with a set of specific tasks intended for the validation of two specimen system, SIFT and FTMP. Two major areas of research are identified. First, are those activities required to support the ongoing development of the validation process itself, and second, are those activities required to support the design, development, and understanding of fault tolerant systems.
The relative dispersion of cloud and fog droplets has significant impacts on aerosol indirect effects, radiative transfer, and microphysical processes. However, previous studies have been mostly concerned with clouds, with limited studies on fog, particularly those that examine the combined influences of all key physical processes and their roles during fog evolution. As such, this study aims to conduct a comprehensive investigation by examining the relationships between relative dispersion and other microphysical variables, as well as the underlying microphysical and dynamic processes, based on field fog campaigns in polluted and clean conditions. In polluted fog, droplet concentrations are higher, leading to smaller droplets and increased dispersion. The correlation between dispersion and droplet volume-mean radius is positive in the polluted fog, but shifts to negative in clean fog. Here, we attribute the difference to various microphysical processes like aerosol activation, condensation, collision-coalescence, and entrainment-mixing. In polluted fog, high aerosol concentrations, low supersaturations, and strong turbulence (entrainment-mixing) provide suitable conditions for the simultaneous occurrence of droplet condensation and aerosol activation, resulting in a positive correlation between dispersion and volume-mean radius, especially during the fog formation stage. In contrast, during the mature stage in clean fog, condensation is dominant with weak aerosol activation leading to a negative correlation between relative dispersion and volume-mean radius. The collision-coalescence process is more active in the mature stage, increasing radii and leading to the negative correlation between dispersion and volume-mean radius. This result sheds new light on understanding the relative dispersion and mechanisms in fog under different aerosol backgrounds.
Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.
Abstract Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active‐source seismic data, which probe asperity‐scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P‐wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to “learn” about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics‐informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.