Results for “Condition Monitoring” — Page 68

NASA NTRS2015

Spatial and Temporal Variation in Primary Productivity (NDVI) of Coastal Alaskan Tundra: Decreased Vegetation Growth Following Earlier Snowmelt

In the Arctic, earlier snowmelt and longer growing seasons due to warming have been hypothesized to increase vegetation productivity. Using the Normalized Difference Vegetation Index (NDVI) from both field and satellite measurements as an indicator of vegetation phenology and productivity, we monitored spatial and temporal patterns of vegetation growth for a coastal wet sedge tundra site near Barrow, Alaska over three growing seasons (2000-2002). Contrary to expectation, earlier snowmelt did not lead to increased productivity. Instead, productivity was associated primarily with precipitation and soil moisture, and secondarily with growing degree days, which, during this period, led to reduced growth in years with earlier snowmelt. Additional moisture effects on productivity and species distribution, operating over a longer time scale, were evident in spatial NDVI patterns associated with microtopography. Lower, wetter regions dominated by graminoids were more productive than higher, drier locations having a higher percentage of lichens and mosses, despite the earlier snowmelt at the more elevated sites. These results call into question the oft-stated hypothesis that earlier arctic growing seasons will lead to greater vegetation productivity. Rather, they agree with an emerging body of evidence from recent field studies indicating that early-season, local environmental conditions, notably moisture and temperature, are primary factors determining arctic vegetation productivity. For this coastal arctic site, early growing season conditions are strongly influenced by microtopography, hydrology, and regional sea ice dynamics, and may not be easily predicted from snowmelt date or seasonal average air temperatures alone. Our comparison of field to satellite NDVI also highlights the value of in-situ monitoring of actual vegetation responses using field optical sampling to obtain detailed information on surface conditions not possible from satellite observations alone.

Growing↗

NASA NTRS1994

Airplane takeoff and landing performance monitoring system

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (VR) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information.

Middleton, David B.↗

NASA NTRS2020

Evaluation of NASA’s Remote-Sensing Capabilities in Coastal Environments

This work represents the second part of a two-part study set up by the National Aeronautics and Space Administration (NASA) and Bureau of Ocean Energy Management (BOEM) in 2017. The study summarizes a three-year investigation on the feasibility of using satellite remote sensing to monitor emissions of oil and gas operations over the outer continental shelf (OCS) to determine if they have negative impacts on coastal air quality (AQ). The target pollutant is nitrogen dioxide (NO2), for which BOEM conducts regular emissions surveys. The major data source for this report is a May 2019 oceanographic cruise (“SCOAPE” = Satellite Coastal and Oceanic Atmospheric Pollution Experiment) conducted in the Gulf of Mexico (GoM) off Louisiana with the Research Vessel Point Sur, loaded with a suite of trace-gas analyzers and a direct-sun remote sensor Pandora spectrometer that measures column NO2. The SCOAPE cruise, augmented by in situ NO2 and Pandora measurements at Cocodrie (Louisiana; 29.25° N, 90.66° W) showed that (1) under cloud-free conditions, satellites can detect elevated column NO2 amounts near isolated large platforms and from clusters of smaller operations; (2) satellite (the European Space Agency’s TROPOspheric Monitoring Instrument [TROPOMI]) total column (TC) NO2 agrees well with TC NO2 from ground-based Pandora spectrometers (to 11–18%), with the satellite biased low when pollution levels are higher; (3) in general, NO2 measured in situ and in column amounts by Pandora or TROPOMI is greater over coastal Louisiana than over the OCS; (4) the extent to which Pandora or satellite measurements correlate with surface NO2 is highly variable.

NO2↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRS1991

Airplane takeoff and landing performance monitoring system

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane and engine performance deficiencies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a predicted nominal performance based upon given conditions, performance deficiencies are detected by the system.

Middleton, David B.↗

NASA NTRSDate not supplied

Applications and Performance of a Lightning Risk Assessment using Geostationary Lightning Mapper (GLM) Data

Lightning is a hazard globally, particularly in lesser-developed countries. Cloud-to-ground lightning strikes are a threat to human safety, motivating a desire to monitor location-based lightning risk to mitigate harm. A lightning risk assessment for human safety was created that uses a combination of probabilistic risk calculation and spatial lightning mapping data to produce a risk magnitude. This risk magnitude evolves with time and changing conditions and is compared to tolerability thresholds in order to evaluate safety. The risk assessment using lightning mapping array (LMA) flash extent density (FED) data was found to perform comparatively (with respect to issuing lightning warnings) to a more standard method of monitoring lightning safety where National Lightning Detection Network (NLDN) flashes were monitored within a 5 nautical mile radius of a location of interest. This research investigates the replacement of LMA FED with FED from the Geostationary Lightning Mapper (GLM) within the risk assessment framework. Using GLM FED would allow for risk to be calculated outside of LMA domains and anywhere within the GLM field of view, including areas outside of the United States (US). A few applications of the risk method with GLM FED are shown and discussed for locations both in and outside of the US. Additionally, the performance of the risk method is compared based on the type of lightning input source (LMA vs GLM). The end goal of this work is to provide forecasters and end users with a tool to help monitor lightning risk in decision support scenarios.

Kelley Murphy↗

NASA NTRS2022

Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint

Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwelling radiance from the ocean. The limitations in the atmospheric correction under certain conditions lead to many gaps in daily spatial coverage of ocean color retrievals. To address these limitations, we introduce a new approach that uses machine learning to estimate ocean color from top of atmosphere radiances or reflectance measurements. In this approach, a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere and the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the ozone monitoring instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI), using measurements from 320–500 nm to show that it can be used to reproduce ocean color properties in less-than-ideal conditions. This machine learning approach complements the current atmospheric correction ocean color retrievals by filling in the gaps resulting from cloud, aerosol, and sunglint contamination. This method can be applied to the future hyperspectral Ocean Color Instrument (OCI), which will be onboard NASA’s Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite set to launch in 2024.

Ocean color↗

NASA NTRS1996

Airplane takeoff and landing performance monitoring system

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information.

Middleton, David B.↗

NASA NTRS1989

Airplane takeoff and landing performance monitoring system

The invention is a real-time takeoff and landing performance monitoring system which provides the pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V sub R) within the safe zone of the runway or stopping the aircraft on the runway after landing or take off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. An important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in headwind occurring as the takeoff roll progresses. The system displays the position of the airplane on the runway, indicating runway used and runway available, summarizes the critical information into a situation advisory flag, flags engine failures and off-nominal acceleration performance, and indicates where on the runway particular events such as decision speed (V sub 1), rotation speed (V sub R) and expected stop points will occur based on actual or predicted performance. The display also indicates airspeed, wind vector, engine pressure ratios, second segment climb speed, and balanced field length (BFL). The system detects performance deficiencies by comparing the airplane's present performance with a predicted nominal performance based upon the given conditions.

Middleton, David B.↗

NASA NTRS2004

Ultraviolet-Absorption Spectroscopic Biofilm Monitor

An ultraviolet-absorption spectrometer system has been developed as a prototype instrument to be used in continuous, real-time monitoring to detect the growth of biofilms. Such monitoring is desirable because biofilms are often harmful. For example, biofilms in potable-water and hydroponic systems act as both sources of pathogenic bacteria that resist biocides and as a mechanism for deterioration (including corrosion) of pipes. Biofilms formed from several types of hazardous bacteria can thrive in both plant-growth solutions and low-nutrient media like distilled water. Biofilms can also form in condensate tanks in air-conditioning systems and in industrial heat exchangers. At present, bacteria in potable-water and plant-growth systems aboard the space shuttle (and previously on the Mir space station) are monitored by culture-plate counting, which entails an incubation period of 24 to 48 hours for each sample. At present, there are no commercially available instruments for continuous monitoring of biofilms in terrestrial or spaceborne settings.

Micheels, Ronald H.↗

NASA NTRS2018

Microbial Ecology of NASA Curation Clean Rooms

Clean room standards like ISO 14644 used for facilities that construct spacecraft and store returned samples do not explicitly account for microbial contamination. While there are associated ISO standards for monitoring and controlling bio-contamination in clean rooms it is not always standard practice to do so. The NASA Astromaterials Acquisition and Curation Office maintains seven separate clean labs for storing extraterrestrial samples from the Moon, meteorites, cosmic dust, asteroids, comets, solar wind particles, and microparticle impact samples. These labs are routinely monitored for particulate and trace metal contamination. However, the sample collections are either non-sterile at the time of collection (e.g., meteorites) or are no longer being used to address scientific questions that could be affected by non-sterile conditions (e.g., Lunar samples). Outside of isolated studies there has not been a systematic, longitudinal characterization of the microbial ecology of NASA curation clean rooms. In accordance with the advanced curation initiative, and to prepare for future sample return missions, we have initiated a routine microbiological monitoring program in the Antarctic Meteorite Lab. This monitoring program will be used to determine what microbes are capable of surviving in these oligotrophic environments and whether or not they are capable of altering the sample collections in any significant manner. Repeat sampling will allow us to understand how routine use of these labs affects the microbial ecology over time.

Regberg, A. B.↗

NASA NTRS1974

Monitoring vegetation systems in the Great Plains with ERTS

The Great Plains Corridor rangeland project utilizes natural vegetation systems as phenological indicators of seasonal development and climatic effects upon regional growth conditions. A method has been developed for quantitative measurement of vegetation conditions over broad regions using ERTS-1 MSS data. Radiance values recorded in ERTS-1 spectral bands 5 and 7, corrected for sun angle, are used to compute a band ratio parameter which is shown to be correlated with aboveground green biomass on rangelands.

Rouse, J. W., Jr.↗

NASA NTRS1984

Ultrasonic and optical evaluation of surgical implant materials and devices. A durability study of pericardial bioprostheses

Laser Doppler Anemometry (LDA) and accelerated fatigue testing were used in an attempt to assess the durability of two cardiac value bioprostheses. The LDA system was used to monitor the function of the cardiac valves over time. This was done through flow characterization in an aortic flow chamber, designed to closely simulate in vivo conditions, both in the near vicinity (sinuses of valsalva region) and also somewhat downstream (aortic region) from the values. The accelerated fatigue tester was operated by opening and closing the valves at a rate of 1300 R.P.M., about 18 x the normal rate. The results from the two test valves indicate a definite change in the flow characteristics downstream from the valve after certain accelerated test intervals. The high velocity cross-sectional flow area seems to increase over time in use, causing a decrease in the peak velocity. The tissue became more flaccid in certain areas, and tears were apparent at about 9.4 million cycles for the Ionescu-Shiley valve and at 24 million cycles for the Carpentier-Edwards valve. The use of Doppler ultrasound as a technique for monitoring the function of bioprostheses over time in vivo is also discussed.

Schuster, P. R.↗

NASA NTRS2013

Near Real Time Tools for ISS Plasma Science and Engineering Applications

The International Space Station (ISS) program utilizes a plasma environment forecast for estimating electrical charging hazards for crews during extravehicular activity (EVA). The process uses ionospheric electron density and temperature measurements from the ISS Floating Potential Measurement Unit (FPMU) instrument suite with the assumption that the plasma conditions will remain constant for one to fourteen days with a low probability for a space weather event which would significantly change the environment before an EVA. FPMU data is typically not available during EVA's, therefore, the most recent FPMU data available for characterizing the state of the ionosphere during EVA is typically a day or two before the start of an EVA or after the EVA has been completed. In addition to EVA support, information on ionospheric plasma densities is often needed for support of ISS science payloads and anomaly investigations during periods when the FPMU is not operating. This presentation describes the application of space weather tools developed by MSFC using data from near real time satellite radio occultation and ground based ionosonde measurements of ionospheric electron density and a first principle ionosphere model providing electron density and temperature run in a real time mode by GSFC. These applications are used to characterize the space environment during EVA periods when FPMU data is not available, monitor for large charges in ionosphere density that could render the ionosphere forecast and plasma hazard assessment invalid, and validate the assumption of "persistence of conditions" used in deriving the hazard forecast. In addition, the tools are used to provide space environment input to science payloads on ISS and anomaly investigations during periods the FPMU is not operating.

Minow, Joseph I.↗

NASA NTRS1991

Gene activity during germination of spores of the fern, Onoclea sensibilis: RNA and protein synthesis and the role of stored mRNA

Pattern of 3H-uridine incorporation into RNA of spores of Onoclea sensibilis imbibed in complete darkness (non-germinating conditions) and induced to germinate in red light was followed by oligo-dT cellulose chromatography, gel electrophoresis coupled with fluorography and autoradiography. In dark-imbibed spores, RNA synthesis was initiated about 24 h after sowing, with most of the label accumulating in the high mol. wt. poly(A) -RNA fraction. There was no incorporation of the label into poly(A) +RNA until 48 h after sowing. In contrast, photo-induced spores began to synthesize all fractions of RNA within 12 h after sowing and by 24 h, incorporation of 3H-uridine into RNA of irradiated spores was nearly 70-fold higher than that into dark-imbibed spores. Protein synthesis, as monitored by 3H-arginine incorporation into the acid-insoluble fraction and by autoradiography, was initiated in spores within 1-2 h after sowing under both conditions. Autoradiographic experiments also showed that onset of protein synthesis in the cytoplasm of the germinating spore is independent of the transport of newly synthesized nuclear RNA. One-dimensional sodium dodecyl sulphate-polyacrylamide gel electrophoresis of 35S-methionine-labelled proteins revealed a good correspondence between proteins synthesized in a cell-free translation system directed by poly(A) +RNA of dormant spores and those synthesized in vivo by dark-imbibed and photo-induced spores. These results indicate that stored mRNAs of O. sensibilis spores are functionally competent and provide templates for the synthesis of proteins during dark-imbibition and germination.

NASA Discipline Number 29-20↗

NASA NTRS1971

Study program of NASA/GSFC weld quality monitor system

The Weld Quality Monitor has provided online control through the application of linear transducers. The electronic systems must be upgraded to increase confidence level required in production. The Weld Quality Monitor has been used with a recent model solid state power supply to evaluate ability to differentiate between sound and defective weld joints. The Weld Quality Monitor has been evaluated on Dumet and Kovar to interconnect materials Nickel and Alloy 180 in weld joints used on microelectronic module fabrication. Distinct separation was achieved between low strength welds and sound welds by the Quality Monitor. Evaluation performed indicates a drift level at less than 2 percent on a series of 50 consecutive welds. Operation under shop conditions identified need for controlled electrode impact. This was achieved with pneumatic actuation.

Hutchinson, W. R.↗