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The apparent selective nature of fluvial erosion on Mars is discussed in light of observations of landscapes of glacial selective linear erosion observed on Devon Island, Arctic Canada.
The analysis required to estimate auxiliary-propulsion system requirements for a mission which includes tipoff rate reduction, acquisitions, disturbance torques, orbital disturbances, and spacecraft commanded maneuvers is presented. The comparison of several candidate auxiliary-propulsion systems and system combinations for an advanced applications technology satellite (ATS-H) is described. A generalized auxiliary-propulsion system tradeoff, based on mission cost effectiveness criteria, is examined. The specific mission assumptions for the ATS-H spacecraft are included, along with a discussion of the sensitivity of the final selection to these assumptions.
The emission line and continuum properties of new spectra of 92 QSOs are investigated. A detailed deconvolution of the semiforbidden C III 1909 A blend reveals that the secondary feature is almost certainly Al III 1859 A. A study of the Mg II 2800 A emission-line profiles suggests that the broad 'shoulders' often observed are probably intrinsic to the Mg II emission line. It is shown that the QSOs discovered by slitless spectroscopic methods do not differ significantly from those discovered by UV excess surveys. Significant correlations with luminosity are found for W(Mg II), W(semiforbidden C III), I(C III)/I(Mg II), and the continuum ratio fv(1909 A)/fv(2800 A). All of the significant correlations are substantially stronger for a radio-selected subsample compared to a radio-quiet subsample. The C IV and semiforbidden C III emission lines each have a mean blueshift relative to Mg II of 450 km/s while semiforbidden C IV has a mean blueshift relative to semiforbidden C III of 200 km/s.
Psychological factors critical to the success of short and long-duration missions have been identified in previous research; however, evaluation for such critical factors in astronaut applicants leaves much room for human interpretation. Thus, an evaluator training session was designed to standardize the interpretation of critical factors, as well as the structure of the select-in interview across evaluators. The purpose of this evaluative study was to determine the effectiveness of the evaluator training sessions and their potential impact on evaluator ratings.
A non-invasive method for analyzing the blood-brain barrier includes obtaining a Raman spectrum of a selected portion of the eye and monitoring the Raman spectrum to ascertain a change to the dynamics of the blood brain barrier. Also, non-invasive methods for determining the brain or blood level of an analyte of interest, such as glucose, drugs, alcohol, poisons, and the like, comprises: generating an excitation laser beam (e.g., at a wavelength of 600 to 900 nanometers); focusing the excitation laser beam into the anterior chamber of an eye of the subject so that aqueous humor, vitreous humor, or one or more conjunctiva vessels in the eye is illuminated; detecting (preferably confocally detecting) a Raman spectrum from the illuminated portion of the eye; and then determining the blood level or brain level (intracranial or cerebral spinal fluid level) of an analyte of interest for the subject from the Raman spectrum. In certain embodiments, the detecting step may be followed by the step of subtracting a confounding fluorescence spectrum from the Raman spectrum to produce a difference spectrum; and determining the blood level and/or brain level of the analyte of interest for the subject from that difference spectrum, preferably using linear or nonlinear multivariate analysis such as partial least squares analysis. Apparatus for carrying out the foregoing methods are also disclosed.
Technology handbook for machinists, mechanics, and technicians - shock absorbers and protective devices, machining, metal forming, welding and brazing, and precision tools
Ground rules and assumptions; operations; orbit considerations/dynamics; tether system design and dynamics; functional requirements; hardware concepts; and safety factors are examined for five scenarios: tethered effected separation of an Earth bound shuttle from the space station; tether effected orbit boost of a spacecraft (AXAF) into its operational orbit from the shuttle; an operational science/technology platform tether deployed from space station; a tether mediated rendezvous involving an OMV tether deployed from space station to rendezvous with an aerobraked OTV returning to geosynchronous orbit from a payload delivery mission; and an electrodynamic tether used in a dual motor/generator mode to serve as the primary energy storage facility for space station.
Six key metabolic enzymes plus glutaminase and glutamate decarboxylase, as well as glutamate, aspartate and GABA, were measured in 11 regions of the hippocampal formation of synchronous, flight and tail suspension rats. Major differences were observed in the normal distribution patterns of each enzyme and amino acid, but no substantive effects of either microgravity or tail suspension on these patterns were clearly demonstrated.
Aerosol Optical Depth (AOD) and Angstrom exponent (AE) values derived with the MODIS retrieval algorithm over land (Collection 5) are compared with ground based sun photometer measurements at eleven sites spanning the globe. Although, in general, total AOD compares well at these sites (R2 values generally over 0.8), there are cases (from 2 to 67% of the measurements depending on the site) where MODIS clearly retrieves the wrong spectral dependence, and hence, an unrealistic AE value. Some of these poor AE retrievals are due to the aerosol signal being too small (total AOD<0.3) but in other cases the AOD should have been high enough to derive accurate AE. However, in these cases, MODIS indicates AE values close to 0.6 and zero fine model weighting (FMW), i.e. dust model provides the best fitting to the MODIS observed reflectance. Yet, according to evidence from the collocated sun photometer measurements and back-trajectory analyses, there should be no dust present. This indicates that the assumptions about aerosol model and surface properties made by the MODIS algorithm may have been incorrect. Here we focus on problems related to parameterization of the land-surface optical properties in the algorithm, in particular the relationship between the surface reflectance at 660 and 2130 nm.
In the literature to date on thermophotovoltaic (TPV) generators, two types of infrared emitter's have been emphasized : gray body emitters and rare earth oxide selective emitters. The gray body emitter is defined as an emitter with a spectral emissivity independent of wavelength whereas the rare earth oxide selective emitter is idealized as a delta function emitter with a high emissivity at a select wavelength and a near zero emissivity at all other wavelengths. Silicon carbide is an example of a gray body emitter and ER-YAG is an example of a selective emitter. The Welsbach mantle in a common lantern is another example of an oxide selective emitter. Herein, we describe an alternative type of selective emitter, a selective metallic emitter. These metallic emitters are characterized by a spectral emissivity curve wherein the emissivity monotonically increases with shorter infrared wavelengths as is shown. The metal of curve "A", tungsten, typifies this class of selective metallic emitter's. In a thermophotovoltaic generator, a photovoltaic cell typically converts infrared radiation to electricity out to some cut-off wavelength. For example, Gallium Antimonide (GaSb) TPV cells respond out to 1.7 microns. The problem with gray body emitters is that they emit at all wavelengths. Therefore, a large fraction of the energy emitted will be outside of the response band of the TPV cell. The argument for the selective emitter is that, ideally, all the emitted energy can be in the cells response band. Unfortunately, rare earth oxide emitters are not ideal. In order to suppress the emissivity toward zero away from the select wavelength, the use of thin fiber's is necessary. This leads to a fragile emitter typical of a lantern mantle. Even given a thin ER-YAG emitter, the measured emissivity at the select wavelength of 1.5 microns has been reported to be 0.6 while the off wavelength background emissivity falls to only 0.2 at 5 microns. This gives a selectivity ratio of only 3. Another problem with a delta function selective emitter is its low power density at practical temperatures because of its narrow emission bandwidth. The concept of selectivity can be generalized by noting that we simply wish to maximize the ratio of in-cell-band power to out-of-cell-band power. Using this generalized selectivity concept and assuming a GaSb cell covered by a simple dielectric filter, we note that the emissivity selectivity ratio for tungsten is 0.3 (at 1.5 microns) / 0.07 (at 5 microns) = 4.3. In the folloy4ng sections, we note that the selective metallic emitters can be valuable in both radioisotope TPV generators in space and in hydrocarbon fired TPV generators here on earth.
The current system for psychological selection of U.S. astronauts is divided into two phases: The select-out phase and the select-in phase. The select-out phase screens candidates for psychopathology; candidates who do not meet the baseline psychiatric requirements are immediately disqualified. The select-in phase assesses candidates for suitability to fly short- and long-duration missions. Suitability ratings are given for ten factors found to be critical for short and long-duration space missions. There are qualitative differences in the purpose of the two phases (select-in vs. select-out) and in the nature of the information collected in each phase. Furthermore, there are different logistic, ethical, and legal issues related to a medical or psychiatric (select-out) screening versus a suitability (select-in) psychological screening process . The purpose of this presentation is to contrast the ethical and legal environment surrounding the select-out and select-in phases of the psychological selection system. Issues such as data collection, data storage and management, the federal statutory environment, and personnel training will be discussed. Further, a summary of the new standards for psychological testing is presented, along with their implications for astronaut selection.
We present a detection of the splashback feature around galaxy clusters selected using the Sunyaev–Zel’dovich (SZ) signal. Recent measurements of the splashback feature around optically selected galaxy clusters have found that the splashback radius, rsp, is smaller than predicted by N-body simulations. A possible explanation for this discrepancy is that rsp inferred from the observed radial distribution of galaxies is affected by selection effects related to the optical cluster-finding algorithms. We test this possibility by measuring the splashback feature in clusters selected via the SZ effect in data from the South Pole Telescope SZ survey and the Atacama Cosmology Telescope Polarimeter survey. The measurement is accomplished by correlating these cluster samples with galaxies detected in the Dark Energy Survey Year 3data. The SZ observable used to select clusters in this analysis is expected to have a tighter correlation with halo mass and to be more immune to projection effects and aperture-induced biases, potentially ameliorating causes of systematic error for optically selected clusters. We find that the measured rsp for SZ-selected clusters is consistent with the expectations from simulations, although the small number of SZ-selected clusters makes a precise comparison difficult. In agreement with previous work, when using optically selected red MaPPer clusters with similar mass and redshift distributions,rspis∼2σsmaller than in the simulations. These results motivate detailed investigations of selection biases in optically selected cluster catalogues and exploration of the splashback feature around larger samples of SZ-selected clusters. Additionally, we investigate trends in the galaxy profile and splashback feature as a function of galaxy colour, finding that blue galaxies have profiles close to a power law with no discernible splashback feature, which is consistent with them being on their first in fall into the cluster.
Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.
For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.
Gene stacking, the process of introducing multiple genes into a single plant to enhance desired traits, is essential for plant genetic improvement through both conventional breeding and genetic transformation. In general, transformation-based gene stacking can be achieved through either co-transformation to simultaneously introduce multiple genes or sequential multi-round transformation. While co-transformation is generally faster and more efficient than sequential multi-round transformation, it often requires two selectable marker genes, which confer resistance to antibiotics, for selecting transgenic events. However, in most cases, there is only one best selectable marker gene for a specific plant species or genotype. Also, it is harder to optimize the concentrations of two antibiotics for co-transformation than using one antibiotic for selecting transgenic events. To overcome this challenge, we recently developed an innovative split selectable marker system for plant co-transformation, allowing the use of one selectable marker gene to select transgenic events. This method involves constructing two binary vectors, each carrying a subset of genes of interest and a partial fragment of the selectable marker gene, which is connected to a partial intein fragment. Following Agrobacterium -mediated co-transformation, plants harboring both binary vectors are selected using a single antibiotic, such as kanamycin. This split-marker system can be used to co-transform multiple genes into both herbaceous and woody plants, accelerating genetic improvement of polygenic traits or integrative improvement of multiple traits to simultaneously increase crop yield and quality.
The hydrogenation of 4-nitrostyrene was investigated over Pt/TiO 2 catalysts to understand the effects of different catalyst coatings on product selectivity and reaction rates. This reaction is a much-investigated selectivity probe due to difficulty in controlling reactivity between the nitro and vinyl groups of 4-nitrostyrene, with C=C reduction generally being more favorable. 4-Nitrostyrene hydrogenation reactions were carried out in a batch reactor using uncoated, organothiol coated, phosphonic acid (PA) coated, and ion-conducting polymer (ionomer) coated catalysts. Thiols form dense coatings on Pt surfaces, whereas PAs preferentially accumulate on TiO 2 ; ionomer binding preference is unknown for this type of catalyst. The coating densities of the thiols and PAs were varied to observe the effects of loading on catalytic activity and reaction selectivity relative to the uncoated catalyst. Changes in selectivity were observed for catalysts with all coatings, with thiol coatings shifting selectivity towards the nitro reduction product, 4-vinylaniline (4-VA), and both ionomer and PA coatings shifting towards 4-ethylnitrobenzene, the olefin hydrogenation product. It was observed that selectivity toward 4-VA increased with thiol coating density, where 1,2-benzenedithiol (BDT) coated catalysts exhibited a selectivity of >99%. These changes were attributed to the identity of the adsorption sites blocked by the different coatings: blocking of metal sites via thiols decreased the rate of C=C hydrogenation, whereas blocking of TiO 2 sites by PAs decreased the rate of nitro group hydrogenation. The changes observed for ionomer and PA coatings suggested that ionomers, like PAs, bind preferentially to the metal oxide support rather than the active metal. This work provides new support to the proposal that nitro group hydrogenation preferentially occurs on TiO 2 sites after hydrogen spillover and underscores how selection of ligand functionality can be used to tune catalyst selectivity.
Orbital selectivity is pivotal in dictating the phase diagrams of multiorbital systems, with prominent examples including the orbital-selective Mott phase and superconductivity. The intercalation of anisotropic layers represents an effective method for enhancing orbital selectivity and thereby shaping the low-energy physics of multiorbital systems. Despite its potential, related experimental studies, especially those elucidating the correlation between orbital selectivity and magnetism, remain limited. In this work, we systematically examine the interplay between orbital selectivity and magnetism in the newly discovered anisotropic kagome TbTi 3 Bi 4 single crystal, and report the coexistence of orbital-selective dual-band modulations (𝑞 1 ∼ 1/3𝑎*, 𝑞 2 ∼ 0.28𝑏*) within the antiferromagnetic (AFM) state. By combining soft x-ray and vacuum ultraviolet angle-resolved photoemission spectroscopy measurements, neutron powder diffraction, scanning tunneling microscopy, and density-functional-theory calculations, we identify these dual-band reconstructions as manifestations of the AFM order driven by a (approximately 1/3, 0.28, 0) nesting instability of the intercalated Tb 5𝑑 𝑥𝑧 orbitals. These orbital-selective modulations induce unusual momentum-dependent band folding and lead to the emergence of Dirac cones only at the $\bar{M}$ 1 point, signaling a topological phase transition in the AFM state. Importantly, the discovery of orbital-selective (approximately 1/3, 0.28, 0) AFM order offers crucial insights into the mechanism underlying the fractional magnetization plateau in this kagome AFM metal. Our findings not only underscore the essential role of both conducting and localized electrons in determining the magnetic orders of LnTi 3 Bi 4 (Ln = lanthanide) kagome metals but also offer a pathway for manipulating magnetism through selective control of anisotropic electronic structures.
The purpose of this paper was to provide insight into the nature of response selection by reviewing the literature on stimulus-response compatibility (SRC) effects and the psychological refractory period (PRP) effect individually and jointly. The empirical findings and theoretical explanations of SRC effects that have been studied within a single-task context suggest that there are two response-selection routes-automatic activation and intentional translation. In contrast, all major PRP models reviewed in this paper have treated response selection as a single processing stage. In particular, the response-selection bottleneck (RSB) model assumes that the processing of Task 1 and Task 2 comprises two separate streams and that the PRP effect is due to a bottleneck located at response selection. Yet, considerable evidence from studies of SRC in the PRP paradigm shows that the processing of the two tasks is more interactive than is suggested by the RSB model and by most other models of the PRP effect. The major implication drawn from the studies of SRC effects in the PRP context is that response activation is a distinct process from final response selection. Response activation is based on both long-term and short-term task-defined S-R associations and occurs automatically and in parallel for the two tasks. The final response selection is an intentional act required even for highly compatible and practiced tasks and is restricted to processing one task at a time. Investigations of SRC effects and response-selection variables in dual-task contexts should be conducted more systematically because they provide significant insight into the nature of response-selection mechanisms.