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At least 181 records · Page 10

Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning

Prediction of peptide secondary structure is challenging because of complex molecular interactions, sequence-specific behavior, and environmental factors. Traditional design strategies, based on hydrophobicity and structural propensity, can be biased and could indeed prevent discovery of interesting, diverse, and unconventional peptides with desired nanostructure assembly. Using β sheet formation in pentapeptides as a case study, we used an integrated high-throughput experimental workflow and an artificial intelligence–driven active learning framework to improve prediction accuracy of self-assembly. By focusing on sequences where machine learning (ML) predictions deviate from conventional design strategies, we synthesized and tested 268 pentapeptides, successfully finding 96 forming β sheet assemblies, including unconventional sequences (e.g., ILFSM, LMISI, MITIY, MISIW, and WKIYI) not predicted by traditional methods. Our ML models outperformed conventional β sheet propensity tables, revealing useful chemical design rules. A web interface is provided to facilitate community access to these models. This work highlights the value of ML-driven approaches in overcoming the limitations of current peptide design strategies.

Talluri, Y. Nissi [Indian Inst. of Technology (IIT

The 1980 Aircraft Safety and Operating Problems, part 1

It is difficult to categorize aircraft operating problems, human factors and safety. Much of NASA's research involves all three and considers the important inter-relationships between man, the machine and the environment, whether the environment be man-made or natural. Topics covered in 20 papers include terminal-area operations; avionics and human factors; and the atmospheric environment.

Stickle, J. W.

Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity

The enzymatic depolymerization of poly(ethylene terephthalate) (PET) is emerging as a leading chemical recycling technology for waste polyester. As part of this endeavor, new candidate enzymes identified from natural diversity can serve as useful starting points for enzyme evolution and engineering. In this study, we improved upon HMM searches by applying an iterative machine learning strategy to identify 400 putative PET-degrading enzymes (PET hydrolases) from naturally occurring homologs. Using high-throughput (HTP) experimental techniques, we successfully expressed and purified >200 enzyme candidates and assayed them for PET hydrolysis activity as a function of pH, temperature, and substrate crystallinity. From this library, we discovered 91 previously unknown PET hydrolases, 35 of which retain activity at pH 4.5 on crystalline material, which are conditions relevant to developing more efficient commercial processes. Notably, four enzymes showed equal to or higher activity than LCC-ICCG, a benchmark PET hydrolase, at this challenging condition in our screening assay, and 11 of which have pH optima <7. Using these data, we identified regions of PETases statistically correlated to activity at lower pH. We additionally investigated the effect of condition-specific activity data on trained machine learning predictors and found a precision (putative hit rate) improvement of up to 30% compared to a Hidden Markov Model alone. Our findings show that by pointing enzyme discovery toward conditions of interest with multiple rounds of experimental and machine learning, we can discover large sets of active enzymes and explore factors associated with activity at those conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Human Factors Engineering: Current and Emerging Dual-Use Applications

Human Factors Engineering is a multidisciplinary endeavor in which information pertaining to human characteristics is used in the development of systems and machines. Six representatives considered to be experts from the public and private sectors were surveyed in an effort to identify the potential dual-use of human factors technology. Each individual was asked to provide a rating as to the dual-use of 85 identified NASA technologies. Results of the survey were as follows: nearly 75 percent of the technologies were identified at least once as high dual-use by one of the six survey respondents, and nearly 25 percent of the identified NASA technologies were identified as high dual-use technologies by a majority of the respondents. The perceived level of dual-use appeared to be independent of the technology category. Successful identification of dual-use technology requires expanded input from industry. As an adjunct, cost-benefit analysis should be conducted to identify the feasibility of the dual-use technology. Concurrent with this effort should be an examination of precedents established by other technologies in other industrial settings. Advances in human factors and systems engineering are critical to reduce risk in any workplace and to enhance industrial competitiveness.

Chandlee, G. O.

Notched strength of beryllium powder and ingot sheets.

The effects of notches in thin beryllium sheets were studied as functions of material variables and notch severity. Double edge notched samples having stress concentration factors of 1.0 to 15.4 were prepared by milling to size, etching, and electrical discharge machining the notches. Strength was not reduced greatly by sharp notches, and duller notches were more deleterious than sharp notches. The trend was for reduced strength for dull notches, increased strength for sharper notches, and reduced strength for very sharp notches. Differences in material purity or source of the sheet had little affect on notch sensitivity. The most important factors appear to be oxide content and directionality of the sheet microstructure; high oxide content and highly directional microstructure tend to give more notch sensitivity than low oxide content, and more bidirectional microstructure. Postulated causes of the change in notched/unnotched strength are given.

Moss, R. G.

Some human factors issues in the development and evaluation of cockpit alerting and warning systems

A set of general guidelines for evaluating a newly developed cockpit alerting and warning system in terms of human factors issues are provided. Although the discussion centers around a general methodology, it is made specifically to the issues involved in alerting systems. An overall statement of the current operational problem is presented. Human factors problems with reference to existing alerting and warning systems are described. The methodology for proceeding through system development to system test is discussed. The differences between traditional human factors laboratory evaluations and those required for evaluation of complex man-machine systems under development are emphasized. Performance evaluation in the alerting and warning subsystem using a hypothetical sample system is explained.

Randle, R. J., Jr.

Energy potential and early operational experience for large wind turbines

Projections for the total potential output of large wind turbines in the U.S. are reviewed. NASA has developed nine large windpowered generators, of 100 kW, 200 kW, 2 MW, and 2.5 MW capacities, with rotors 100-300 ft in diameter, and all with horizontal axes. Approximately 214,000 sq miles of the U.S. have been determined as having substantial wind regimes and terrain suitable for large wind turbine siting. This translates into 340,000 Mod 2 (2.5 MW) wind turbines producing 4.9 quads of electricity annually, equivalent to saving 2.5 billion barrels of oil/yr. The cost of electricity is seen as the critical factor in utility acceptance of large wind turbines, and the Mod 2 machines are noted to achieve the 2-4 cents/kWh (1977 dollars) COE which is necessary. Problems such as pollution, including visual, auditory, EM, and land use difficulties are considered, and solutions are indicated.

Robbins, W. H.

The Real Time Interactive Display Environment (RTIDE), a display building tool developed by Space Shuttle flight controllers

NASA's Mission Control Center, located at Johnson Space Center, is incrementally moving from a centralized architecture to a distributed architecture. Starting with STS-29, some host-driven console screens will be replaced with graphics terminals driven by workstations. These workstations will be supplied realtime data first by the Real Time Data System (RTDS), a system developed inhouse, and then months later (in parallel with RTDS) by interim and subsequently operational versions of the Mission Control Center Upgrade (MCCU) software package. The Real Time Interactive Display Environment (RTIDE) was built by Space Shuttle flight controllers to support the rapid development of multiple new displays to support Shuttle flights. RTIDE is a display building tool that allows non-programmers to define object-oriented, event-driven, mouseable displays. Particular emphasis was placed on upward compatibility between RTIDE versions, ability to acquire data from different data sources, realtime performance, ability to modularly upgrade RTIDE, machine portability, and a clean, powerful user interface. The operational and organizational factors that drove RTIDE to its present form, the actual design itself, simulation and flight performance, and lessons learned in the process are discussed.

Kalvelage, Thomas A.

Direct glass bonded high specific power silicon solar cells for space applications

A lightweight, radiation hard, high performance, ultra-thin silicon solar cell is described that incorporates light trapping and a cover glass as an integral part of the device. The manufacturing feasibility of high specific power, radiation insensitive, thin silicon solar cells was demonstrated experimentally and with a model. Ultra-thin, light trapping structures were fabricated and the light trapping demonstrated experimentally. The design uses a micro-machined, grooved back surface to increase the optical path length by a factor of 20. This silicon solar cell will be highly tolerant to radiation because the base width is less than 25 microns making it insensitive to reduction in minority carrier lifetime. Since the silicon is bonded without silicone adhesives, this solar cell will also be insensitive to UV degradation. These solar cells are designed as a form, fit, and function replacement for existing state of the art silicon solar cells with the effect of simultaneously increasing specific power, power/area, and power supply life. Using a 3-mil thick cover glass and a 0.3 g/sq cm supporting Al honeycomb, a specific power for the solar cell plus cover glass and honeycomb of 80.2 W/Kg is projected. The development of this technology can result in a revolutionary improvement in high survivability silicon solar cell products for space with the potential to displace all existing solar cell technologies for single junction space applications.

Dinetta, L. C.

The human power amplifier technology at the University of California, Berkeley

A human's ability to perform physical tasks is limited by physical strength, not by intelligence. We define "extenders" as a class of robot manipulators worn by humans to augment human mechanical strength, while the wearer's intellect remains the central control system for manipulating the extender. Our research objective is to determine the ground rules for the design and control of robotic systems worn by humans through the design, construction, and control of several prototype experimental direct-drive/non-direct-drive multi-degree-of-freedom hydraulic/electric extenders. The design of extenders is different from the design of conventional robots because the extender interfaces with the human on a physical level. Two sets of force sensors measure the forces imposed on the extender by the human and by the environment (i.e., the load). The extender's compliances in response to such contact forces were designed by selecting appropriate force compensators. This paper gives a summary of some of the selected research efforts related to Extender Technology, carried out during 1980s. The references, at the end of this article, give detailed description of the research efforts.

NASA Discipline Space Human Factors

2005 AG20/20 Annual Review

Topics covered include: Implementation and Validation of Sensor-Based Site-Specific Crop Management; Enhanced Management of Agricultural Perennial Systems (EMAPS) Using GIS and Remote Sensing; Validation and Application of Geospatial Information for Early Identification of Stress in Wheat; Adapting and Validating Precision Technologies for Cotton Production in the Mid-Southern United States - 2004 Progress Report; Development of a System to Automatically Geo-Rectify Images; Economics of Precision Agriculture Technologies in Cotton Production-AG 2020 Prescription Farming Automation Algorithms; Field Testing a Sensor-Based Applicator for Nitrogen and Phosphorus Application; Early Detection of Citrus Diseases Using Machine Vision and DGPS; Remote Sensing of Citrus Tree Stress Levels and Factors; Spectral-based Nitrogen Sensing for Citrus; Characterization of Tree Canopies; In-field Sensing of Shallow Water Tables and Hydromorphic Soils with an Electromagnetic Induction Profiler; Maintaining the Competitiveness of Tree Fruit Production Through Precision Agriculture; Modeling and Visualizing Terrain and Remote Sensing Data for Research and Education in Precision Agriculture; Thematic Soil Mapping and Crop-Based Strategies for Site-Specific Management; and Crop-Based Strategies for Site-Specific Management.

Ross, Kenton W.

Manufacturing Challenges Associated with the Use of Metal Matrix Composites in Aerospace Structures

Metal Matrix Composites (MMCs) consist of a metal alloy reinforced with ceramic particles or fibers. These materials possess a very high strength to weight ratio, good resistance to impact and wear, and a number of other properties which make them attractive for use in aerospace and defense applications. MMCs have found use in the space shuttle orbiter's structural tubing, the Hubble Space Telescope's antenna mast, control surfaces and propulsion systems for aircraft, and tank armors. The size of MMC components is severely limited by difficulties encountered in joining these materials using fusion welding. Melting of the material results in formation of an undesirable phase (formed when molten Aluminum reacts with the reinforcement) which leaves a strength depleted region along the joint line. Friction Stir Welding (FSW) is a relatively nascent solid state joining technique developed at The Welding Institute (TWI) in 1991. The process was first used at NASA to weld the super lightweight external tank for the Space Shuttle. Today FSW is used to join structural components of the Delta IV, Atlas V, and Falcon IX rockets as well as NASA's Orion Crew Exploration Vehicle and Space Launch System. A current focus of FSW research is to extend the process to new materials, such as MMCs, which are difficult to weld using conventional fusion techniques. Since Friction Stir Welding occurs below the melting point of the workpiece material, this deleterious phase is absent in FSW-ed MMC joints. FSW of MMCs is, however, plagued by rapid wear of the welding tool, a consequence of the large discrepancy in hardness between the steel tool and the reinforcement material. This chapter summarizes the challenges encountered when joining MMCs to themselves or to other materials in structures. Specific attention is paid to the influence of process variables in Friction Stir Welding on the wear process characterizes the effect of process parameters (spindle speed, traverse rate, and length of joint) on the wear process. A phenomenological model of the wear process was constructed based on the rotating plug model of Friction Stir Welding. The effectiveness of harder tool materials (such as Tungsten Carbide, high speed steel, and tools with diamond coatings) to combat abrasive wear is also explored. In-process force, torque, and vibration signals are analyzed to assess the feasibility of in situ monitoring of tool shape changes as a result of wear (an advancement which would eliminate the need for off-line evaluation of tool condition during joining). Monitoring, controlling, and reducing tool wear in FSW of MMCs is essential to implementation of these materials in structures (such as launch vehicles) where they would be of maximum benefit. The work presented here is extendable to machining of MMCs, where wear of the tool is also a limiting factor.

Prater, Tracie

Identification of Flux Rope Orientation via Neural Networks

Geomagnetic disturbance forecasting is based on the identification of solar wind structures and accurate determination of their magnetic field orientation. For nowcasting activities, this is currently a tedious and manual process. Focusing on the main driver of geomagnetic disturbances, the twisted internal magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a convolutional neural network’s (CNN) ability to predict the embedded magnetic flux rope’s orientation once it has been identified from in situ solar wind observations. Our work uses CNNs trained with magnetic field vectors from analytical flux rope data. The simulated flux ropes span many possible spacecraft trajectories and flux rope orientations. We train CNNs first with full duration flux ropes and then again with partial duration flux ropes. The former provides us with a baseline of how well CNNs can predict flux rope orientation while the latter provides insights into real-time forecasting by exploring how accuracy is affected by percentage of flux rope observed. The process of casting the physics problem as a machine learning problem is discussed as well as the impacts of different factors on prediction accuracy such as flux rope fluctuations and different neural network topologies. Finally, results from evaluating the trained network against observed ICMEs from Wind during 1995–2015 are presented.

Thomas Narock

Improved Rice Residue Burning Emissions Estimates: Accounting for Practice-Specific Emission Factors in Air Pollution Assessments of Vietnam

In Southeast Asia and Vietnam, rice residues are routinely burned after the harvest to prepare fields for the next season. Specific to Vietnam, the two prevalent burning practices include: a). piling the residues after hand harvesting; b). burning the residues without piling, after machine harvesting. In this study, we synthesized field and laboratory studies from the literature on rice residue burning emission factors for Particulate Matter less than 2.5 microns (PM2.5). We found significant differences in the resulting burning-practice specific emission factors, with 16.9 grams per square kilogram (plus or minus 6.9) for pile burning and 8.8 grams per square kilogram (plus or minus 3.5) for non-pile burning. We calculated burning practice specific emissions based on rice area data, region-specific fuel-loading factors, combined emission factors, and estimates of burning from the literature. Our results for year 2015 estimate 180 gigagrams of PM2.5 result from the pile burning method and 130 gigagrams result from non-pile burning method, with the most-likely current emission scenario of 150 gigagrams PM2.5 emissions for Vietnam. For comparison purposes, we calculated emissions using generalized agricultural emission factors employed in global biomass burning studies. These results estimate 80 gigagrams PM2.5, which is only 44 percent of the pile burning-based estimates, suggesting underestimation in previous studies. We compare our emissions to an existing all-combustion sources inventory, results show emissions account for 14-18 percent of Vietnam's total PM2.5 depending on burning practice. Within the highly-urbanized and cloud-covered Hanoi Capital region (HCR), we use rice area from Sentinel-1A to derive spatially-explicit emissions and indirectly estimate residue burning dates. Results from HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) back-trajectory analysis stratified by season show autumn has most emission trajectories originating in the North, while spring has most originating in the South, suggesting the latter may have bigger impact on air quality. From these results, we highlight locations where emission mitigation efforts could be focused and suggest measures for pollutant mitigation. Our study demonstrates the need to account for emissions variation due to different burning practices.

Air Pollution

Data driven investigation to understand the influence of total solids on biological biogas upgrading

In situ biogas upgrading achieves CO 2 conversion to CH 4 via hydrogenotrophic methanogenesis; however, gas-liquid mass transfer constraints limit the upgrading performance. Recognizing that optimization studies often underrepresent the effects of total solids (TS) and organic loading rate (OLR), this study undertook a holistic, statistics driven assessment of operating conditions for in situ H 2 assisted biogas upgrading, centering the analysis on TS and OLR. A dataset of 31 studies was compiled and comprised 99 observations. A rigorous analytical framework was employed, combining data standardization, fixed- and random-effects (REML) weighted regressions with cluster-robust errors, stratified analyses, and machine learning. Mixed-effects meta regression indicated that TS was the main factor explaining differences of methane fraction (CH 4 %) when considering the between studies heterogeneity. Focusing on a near-stoichiometric subset (H 2 /CO 2 ≈ 4:1), TS remained significant. Stratified results showed a stronger negative relationship between TS and CH 4 % in UASB reactors than in CSTRs, with a negative effect under mesophilic conditions and no significant effect under thermophilic conditions. A Random Forest model corroborated the statistical findings, consistently ranking H 2 /CO 2 ratio, OLR, TS, and hydrogen injection rate (HIR) as the most influential predictors. These findings delineate trends across increasing TS levels, particularly between 1% and 10%, and provide preliminary insights for TS above 15% in in situ biogas upgrading. They further provide insights for the influence of TS by reactor type and temperature, thereby advancing the evidence base for implementing biological CO 2 conversion to CH 4 in practice.

In situ biogas upgrading

Aerosol Influences on Cloud Water: Insights From ARM EPCAPE Observations With Explainable Machine Learning

This study employs an explainable machine learning (ML) framework (XGBoost-SHapley Additive exPlanations analysis) to investigate controlling factors on cloud liquid water path (LWP) using EPCAPE observations near the California coast. Aerosols are found to be the dominant factor explaining LWP variability, surpassing meteorological factors (MFs). By isolating aerosol effects from meteorological influences, the ML reveals a negative linear relationship between LWP and cloud droplet number concentration (Nd) in log space, likely driven by entrainment drying via evaporation-entrainment feedback. This aligns with the negative regime of the inverted-V relationship reported in previous studies, while no positive LWP responses are found due to a limited number of precipitating cases in EPCAPE. Furthermore, the sensitivity of LWP to Nd shows a non-linear dependence on MFs like moisture contrast between surface and free troposphere and lower-tropospheric stability. This occurs due to the interplay between the MFs' direct effects on entrainment drying and indirect effects through LWP adjustments.

EPCAPE observations