SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This record provides the data reported in Schwartz et al. "Incorporating Variable Sediment Microbial Activity into Methylmercury Production Kinetics Modeling".
The ERTS Data Collection System makes it feasible for the first time to monitor the level of activity at widely separated volcanoes and to relay these data rapidly to one central office for analysis. While prediction of specific eruptions is still an evasive goal, early warning of a reawakening of quiescent volcanoes is now a distinct possibility. A prototypical global volcano surveillance system was established under the ERTS program. Instruments were installed in cooperation with local scientists on 15 volcanoes in Alaska, Hawaii, Washington, California, Iceland, Guatemala, El Salvador and Nicaragua. The sensors include 19 seismic event counters that count four different sizes of earthquakes and six biaxial borehole tiltmeters that measure ground tilt with a resolution of 1 microradian. Only seismic and tilt data are collected because these have been shown in the past to indicate most reliably the level of volcano activity at many different volcanoes. Furthermore, these parameters can be measured relatively easily with new instrumentation.
The interactions between sulfate-reducing microorganisms and iron oxides influence a number of important redox-sensitive biogeochemical processes including the formation of iron sulfides. Enzymes, such as hydrogenase which catalyze the reversible oxidation of molecular hydrogen, are known to mediate electron transfer to metals and may contribute to the formation and speciation of ferrous sulfides formed at the cell–mineral interface. In the present study, we compared the whole cell hydrogenase activity of Desulfovibrio desulfuricans strain Essex 6 growing as biofilms on hematite (hematite-associated) or as suspended populations using different metabolic pathways. Hematite-associated cells exhibited significantly greater hydrogenase activity than suspended populations during sulfate respiration but not during pyruvate fermentation. The enhanced activity of the hematite-associated, sulfate-grown cells appears to be dependent on iron availability rather than a general response to surface attachment since the activity of glass-associated cells did not differ from that of suspended populations. Hydrogenase activity of pyruvate-fermenting cells was stimulated by addition of iron as soluble Fe(II)Cl2 and, in the absence of added iron, both sulfate-reducing and pyruvate-fermenting cells displayed similar rates of hydrogenase activity. These data suggest that iron exerts a stronger influence on whole cell hydrogenase activity than either metabolic pathway or mode of growth. The location of hydrogenase to the cell envelope and the enhanced activity at the hematite surface in sulfate-reducing cells may influence the redox conditions that control the species of iron sulfides on the mineral surface.
Promyelocytic leukaemia (PML) is a tumor suppressor protein covalently conjugated with SUMO family proteins, leading to the formation of PML nuclear bodies (NBs). PML-NBs provide a platform for efficient posttranslational modification of targets and protein-protein interaction, contributing to the adjustment of gene expression and chromatin integrity. Although PML SUMOylation is thought to play important roles in diverse cellular functions, the control mechanisms of adequate modification levels have remained unsolved. Here, we report that Cullin-related protein CACUL1/CAC1 (CACUL1) inhibits PML posttranslational modification. CACUL1 interacts with PML and suppresses PML SUMOylation, leading to the regulation of PML-NB size in the nucleus. We also found that Ubc9, a SUMO-conjugating enzyme, binds to CACUL1 and antagonizes the interaction between CACUL1 and PML. Furthermore, CACUL1 attenuates p53 transcriptional activity. These data suggest that CACUL1 is a novel regulator that negatively controls p53 activity through the regulation of PML SUMOylation. - Highlights: • CACUL1 suppresses PML SUMOylation. • CACUL1 regulates PML-NBs size. • CACUL1 attenuates p53 transcriptional activity.
The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, ‘non-routine events’ (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model’s independent variables. In the M&V process NREs must be accounted for as ‘adjustments’ to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation. Here in this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics.
In this study, we reported that the optical and electrical analysis of amorphous Se{sub 90-x}S{sub x}Zn{sub 5} (x=0.2, 2, 5, 10) thin films. Bulk samples of the investigated material were prepared by melt quenching technique. Thin films of ~ 300nm thickness were deposited on cleaned glass substrates by thermal evaporation technique. The morphological study of the investigated material in powder form carried out by scanning electron microscopy (SEM) confirms the disorder of the material increases at lower sulfur doping (up to 5%) whereas at higher (S) doping (10%) the defects of the material decreases. The optical parameters were estimated from optical absorption spectra data measured from UV-Vis-spectrophotometer in the wavelength range 200-900 nm. It was found that the value of optical band gap (E{sub g}) of the investigated thin films decreases up to lower S doping and increases at higher (S) doping. The other optical parameters such as absorption coefficient (α) and extinction coefficient (K) increases up to lower S doping and decreases at higher S doping. This remarkable change in the values of optical parameters was interpreted on the basis of model proposed by Davis and Mott. Electrical parameters of the investigated thin films were carried out in the temperature range 309-370 K. Analysis of data shows activation energy decreases with the increase of concentration of (S) increases up to 5% and at 10% it increases again.
Temporal and spatial variation of sea ice type in the Arctic is an indicator of regional and global change. Arctic sea ice can be classified into two major categories: multiyear ice (MYI) and first-year ice. In this paper, classification method based on machine learning is established and applied to produce daily sea ice classification data set during the winter (November–April) from 2002 to 2017 using active microwave data from QuikSCAT and Advanced Scatterometer as well as passive microwave data from Advanced Microwave Scanning Radiometer for EOS, Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2 radiometer. First, the open water area is flagged out using brightness temperature (Tb) from the passive microwave sensor. Then, K-means algorithm is applied to identify the clusters of the two ice types in the Tb/backscatter parameter space and finally assign pixels to each class. Two optimization methods based on the movement of MYI and marginal ice zone are used to correct the misclassification of MYI. The results have shown a decrease of MYI in winter from 2002 to 2017, especially in 2008 and 2013 with a remarkable recovery in 2014. The classifications are consistent with results by visual interpretation from synthetic aperture radar images in the Canadian Arctic Archipelago with overall classification accuracy over 93%. Comparison with classifications from previous studies and products shows that our method could reflect more differences in MYI declining trend interannually and less anomalous fluctuations in certain years.
Data include 222Rn activities and complimentary geochemical data for multiple field experiments as part of an EGS project
The report documents melter and off-gas performance results obtained on the DM1200 during processing of LAW Sub-Envelope Cl feed. The principal objectives of the DM1200 melter testing were to demonstrate suitable processing and production rates for LAW Cl feed; characterize the glass product for elemental composition, the presence of organic compounds, and TCLP response for metals; and measure destruction and removal efficiencies (DREs) for organic compounds across the melter and off-gas treatment components. The feed was spiked with organic compounds for part of the testing duration. Data were collected for both process engineering needs and to support regulatory activities. The data needed for regulatory support were collected using EPA methods and independent data analyses and data checking were performed.
The Software Engineering Laboratory was monitoring software development at NASA Goddard Space Flight Center since 1976. The data collection activities of the Laboratory and some of the difficulties of obtaining reliable data are described. In addition, the application of this data collection process to a current prototyping experiment is reviewed.
We examine 3 years of interplanetary data and geomagnetic activity indices (1973-1975) to determine the causes of geomagnetic storms and substorms during the descending phase of the solar cycle. In this paper, we specifically studied the year 1974 where two long lasting coronating streams existed.
The isothermal oxidation kinetics of as-cast Cu-17%Cr and Cu-17%Cr-5%Al in air were studied between 773 and 1173 K under atmospheric pressure. These observations reveal that Cu-17%Cr-5%Al oxidizes at significantly slower rates than Cu-17%Cr. The rate constants for the alloys were determined from generalized analyses of the data without an a priori assumption of the nature of the oxidation kinetics. Detailed analyses of the isothermal thermogravimetric weight change data revealed that Cu-17%Cr exhibited parabolic oxidation kinetics with an activation energy of 165.9+/-9.5 kJ/mol. In contrast, the oxidation kinetics for the Cu-17%Cr-5%Al alloy exhibited a parabolic oxidation kinetics during the initial stages followed by a quartic relationship in the later stages of oxidation. Alternatively, the oxidation behavior of Cu-17%CR-5%Al could be better represented by a logarithmic relationship. The parabolic rate constants and activation energy data for the two alloys are compared with literature data to gain insights on the nature of the oxidation mechanisms dominant in these alloys.
The isothermal oxidation kinetics of as-cast Cu-17%Cr and Cu-17%Cr-5%Al in air were studied between 773 and 1173 K under atmospheric pressure. These observations reveal that Cu- 17%Cr-5%Al oxidizes at significantly slower rates than Cu-17%Cr. The rate constants for the alloys were determined from generalized analyses of the data without an a priori assumption of the nature of the oxidation kinetics. Detailed analyses of the isothermal thermogravimetric weight change data revealed that Cu-17%Cr exhibited parabolic oxidation kinetics with an activation energy of 165.9 +/- 9.5 kJ/mol. In contrast, the oxidation kinetics for the Cu-17%Cr- 5%Al alloy exhibited a parabolic oxidation kinetics during the initial stages followed by a quartic relationship in the later stages of oxidation. Alternatively, the oxidation behavior of Cu-17%CR- 5%Al could be better represented by a logarithmic relationship. The parabolic rate constants and activation energy data for the two alloys are compared with literature data to gain insights on the nature of the oxidation mechanisms dominant in these alloys.
The isothermal oxidation kinetics of as-cast Cu-17%Cr and Cu-17%Cr-5%Al in air were studied between 773 and 1173 K under atmospheric pressure. These observations reveal that Cu- 17%Cr-5%Al oxidizes at significantly slower rates than Cu-17%Cr. The rate constants for the alloys were determined from generalized analyses of the data without an a priori assumption of the nature of the oxidation kinetics. Detailed analyses of the isothermal thermogravimetric weight change data revealed that Cu-17%Cr exhibited parabolic oxidation kinetics with an activation energy of 165.9 9.5 kJ mol-1. In contrast, the oxidation kinetics for the Cu-17%Cr- 5%Al alloy exhibited a parabolic oxidation kinetics during the initial stages followed by a quartic relationship in the later stages of oxidation. Alternatively, the oxidation behavior of Cu-17%CR- 5%Al could be better represented by a logarithmic relationship. The parabolic rate constants and activation energy data for the two alloys are compared with literature data to gain insights on the nature of the oxidation mechanisms dominant in these alloys.
The International Space Station Program at NASA is constantly seeking to improve the processes and systems that support safe space operations. To that end, the ISS Program decided to upgrade their Safety and Hazard data systems with 3 goals: make safety and hazard data more accessible; better support the interconnection of different types of safety data; and increase the efficiency (and compliance) of safety-related processes. These goals are accomplished by moving data into a web-based structured data system that includes strong process support and supports integration with other information systems. Along with the data systems, ISS is evolving its submission requirements and safety process requirements to support the improved model. In contrast to existing operations (where paper processes and electronic file repositories are used for safety data management) the web-based solution provides the program with dramatically faster access to records, the ability to search for and reference specific data within records, reduced workload for hazard updates and approval, and process support including digital signatures and controlled record workflow. In addition, integration with other key data systems provides assistance with assessments of flight readiness, more efficient review and approval of operational controls and better tracking of international safety certifications. This approach will also provide new opportunities to streamline the sharing of data with ISS international partners while maintaining compliance with applicable laws and respecting restrictions on proprietary data. One goal of this paper is to outline the approach taken by the ISS Progrm to determine requirements for the new system and to devise a practical and efficient implementation strategy. From conception through implementation, ISS and NASA partners utilized a user-centered software development approach focused on user research and iterative design methods. The user-centered approach used on the new ISS hazard system utilized focused user research and iterative design methods employed by the Human Computer Interaction Group at NASA Ames Research Center. Particularly, the approach emphasized the reduction of workload associated with document and data management activities so more resources can be allocated to the operational use of data in problem solving, safety analysis, and recurrence control. The methods and techniques used to understand existing processes and systems, to recognize opportunities for improvement, and to design and review improvements are described with the intent that similar techniques can be employed elsewhere in safety operations. A second goal of this paper is to provide and overview of the web-based data system implemented by ISS. The software selected for the ISS hazard systemMission Assurance System (MAS)is a NASA-customized vairant of the open source software project Bugzilla. The origin and history of MAS as a NASA software project and the rationale for (and advantages of) using open-source software are documented elsewhere (Green, et al., 2009).
Lunar ExoCam is a remotely deployed lunar sensor suite payload that can land within an operable ranged distance of approx. 5 – 20 m to lander. The sensor suite will enable a fully immersive 360° environmental characterization of regolith dispersion captured during the final descent of the landing craft. Ejecting and reaching the lunar surface prior to the descending vehicle, the ExoCam sensor suite is then poised to gather in-situ measurements throughout the full cycle of plume-surface interaction (PSI), utilizing various onboard instrumentation to capture and relay synchronous data over time. Following completion of the landing cycle and brief surface activity, these data are relayed via Wi-Fi back to the lander for subsequent transmission to Earth
In this paper, we consider incorporating data associated with the sun’s north and south polar field strengths to improve solar flare prediction performance using machine learning models. When used to supplement local data from active regions on the photospheric magnetic field of the sun, the polar field data provides global information to the predictor. While such global features have been previously proposed for predicting the next solar cycle’s intensity, in this paper we propose using them to help classify individual solar flares. We conduct experiments using HMI data employing four different machine learning algorithms that can exploit polar field information. Additionally, we propose a novel probabilistic mixture of experts model that can simply and effectively incorporate polar field data and provide on-par prediction performance with state-of-the-art solar flare prediction algorithms such as the Recurrent Neural Network (RNN). Our experimental results indicate the usefulness of the polar field data for solar flare prediction, which can improve Heidke Skill Score (HSS2) by as much as 10.1%.
The cybersecurity research community has focused primarily on the analysis and automation of intrusion detection systems by examining network traffic behaviors. Expanding on this expertise, advanced cyber defense analysis is turning to host-based data to use in research and development to produce the next generation network defense tools. The ability to perform deep packet inspection of network traffic is increasingly harder with most boundary network traffic moving to HTTPS. Additionally, network data alone does not provide a full picture of end-to-end activity. These are some of the reasons that necessitate looking at other data sources such as host data. We outline our investigation into the processing, formatting, and storing of the data along with the preliminary results from our exploratory data analysis. In writing this report, it is our goal to aid in guiding future research by providing foundational understanding for an area of cybersecurity that is rich with a variety of complex, categorical, and sparse data, with a strong human influence component. Including suggestions for guiding potential directions for future research.