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Human factors data from Apollo, Skylab, and Space Shuttle flights are reviewed. The sources of data and collection methods are described. A classification scheme for human factors data is proposed. The implications of the results for the design of the Space Station program are considered.
Previous experience in manned space flight programs can be used to compile a data base of human factors lessons learned for the purpose of developing aids in the future design of inhabited spacecraft. The objectives are to gather information available from relevant sources, to develop a taxonomy of human factors data, and to produce a data base that can be used in the future for those people involved in the design of manned spacecraft operations. A study is currently underway at the Johnson Space Center with the objective of compiling, classifying, and summarizing relevant human factors data bearing on the lessons learned from previous manned space flights. The research reported defines sources of data, methods for collection, and proposes a classification for human factors data that may be a model for other human factors disciplines.
Structurally identifying the enzymatic intermediates of redox proteins has been elusive due to difficulty in resolving the H atoms involved in catalysis and the susceptibility of ligand complexes to photoreduction from X-rays. Cryotrapping ligands for neutron protein crystallography combines two powerful tools that offer the advantage of directly identifying hydrogen positions in redox-enzyme intermediates without radiolytic perturbation of metal-containing active sites. However, translating cryogenic techniques from X-ray to neutron crystallography is not straightforward due to the large crystal volumes and long data-collection times. Here, methods have been developed to visualize the evasive peroxo complex of manganese superoxide dismutase (MnSOD) so that all atoms, including H atoms, could be visualized. The subsequent cryocooling and ligand-trapping methods resulted in neutron data collection to 2.30 Å resolution. The P 6 1 22 crystal form of MnSOD is challenging because it has some of the largest unit-cell dimensions ( a = b = 77.8, c = 236.8 Å) ever studied using high-resolution cryo-neutron crystallography. The resulting neutron diffraction data permitted the visualization of a dioxygen species bound to the MnSOD active-site metal that was indicative of successful cryotrapping.
Situation Management is a rapidly developing science combining new techniques for data collection with advanced methods of data fusion to facilitate the process leading to correct decisions prescribing action. Current research focuses on reducing increasing amounts of diverse data to knowledge used by decision makers and on reducing time between observations, decisions and actions. No new technology is more promising for increasing the diversity and fidelity of observations than sensor networks. However, current research on sensor networks concentrates on a centralized network architecture. We believe this trend will not realize the full potential of situation management. We propose a new architecture modeled after biological ecosystems where motes are autonomous and intelligent, yet cooperate with local neighborhoods. Providing a layered approach, they sense and act independently when possible, and cooperate with neighborhoods when necessary. The combination of their local actions results in global effects. While situation management research is currently dominated by military applications, advances envisioned for industrial and business applications have similar requirements. NASA has requirements for intelligent and autonomous systems in future missions that can benefit from advances in situation management. We describe requirements for the Integrated Vehicle Health Management program where our biology-inspired architecture provides a layered approach and decisions can be made at the proper level to improve safety, reduce costs, and improve efficiency in making diagnostic and prognostic assessments of the structural integrity, aerodynamic characteristics, and operation of aircraft.
Human Reliability Analysis predicts accidents caused by human errors and is an important factor in Probabilistic Safety Assessment that comprehensively evaluates the safety of nuclear power plants. This study compares and analyzes the human performance of nuclear power plant operators according to the simulator complexity as part of the HRA data collection support method development project conducted by Idaho National Laboratory. This experiment was conducted by setting two types of simulators and scenarios as independent variables. The data collected by conducting the experiments in two different simulators were analyzed using an analysis of variance test and a correlation analysis, and four human performance charts were derived.
As more and more nonlinear estimation techniques become available, our interest is in finding out what performance improvement, if any, they can provide for practical nonlinear problems that have been traditionally solved using linear methods. In this paper we examine the problem of estimating spacecraft position using conical scan (conscan) for NASA's Deep Space Network antennas. We show that for additive disturbances on antenna power measurement, the problem can be transformed into a linear one, and we present a general solution to this problem, with the least square solution reported in literature as a special case. We also show that for additive disturbances on antenna position, the problem is a truly nonlinear one, and we present two approximate solutions based on linearization and Unscented Transformation respectively, and one 'exact' solution based on Markov Chain Monte Carlo (MCMC) method. Simulations show that, with the amount of data collected in practice, linear methods perform almost the same as MCMC methods. It is only when we artificially reduce the amount of collected data and increase the level of noise that nonlinear methods show significantly better accuracy than that achieved by linear methods, at the expense of more computation.
The framework within which the Applications Systems Verification Tests (ASVTs) are performed and the economic consequences of improved meteorological information demonstrated is described. This framework considers the impact of improved information on decision processes, the data needs to demonstrate the economic impact of the improved information, the data availability, the methodology for determining and analyzing the collected data and demonstrating the economic impact of the improved information, and the possible methods of data collection. Three ASVTs are considered and program outlines and plans are developed for performing experiments to demonstrate the economic consequences of improved meteorological information. The ASVTs are concerned with the citrus crop in Florida, the cotton crop in Mississippi and a group of diverse crops in Oregon. The program outlines and plans include schedules, manpower estimates and funding requirements.
Over time, advances in unmanned aircraft systems (UAS) have enabled a shift in the operational paradigm from one operator managing one aircraft to that of multiple operators working together to manage multiple aircraft. This shift has highlighted the need for effective human-autonomy teaming methods to maintain manageable workload levels for operators as well as high standards of system performance and safety. This paper presents a study aimed at evaluating whether automation can help operators manage workload during small UAS (sUAS) package delivery scenarios featuring contingency situations. These contingency situations, resulting from unplanned UAS Volume Reservations (UVRs), required flight path reroutes for multiple aircraft simultaneously. The study manipulated the number of aircraft affected by the UVRs and the level of automation support. The presence of terrain conflicts was also controlled within each scenario. Due to the COVID-19 pandemic, subjects were not able to gain direct access to the Ground Control System (GCS). Therefore, the study was conducted using a subject-surrogate paradigm that required subjects to relay commands through a verbal protocol from remote locations outside of the lab to a researcher surrogate who had direct control of the GCS interfaces at the lab location. Results show that the automated support condition was associated with faster reroute response times, more efficient reroute maneuvers, and significantly lower levels of perceived workload than the manual reroute condition. However, the automation support level did not significantly impact pilots’ ability to avoid the UVR successfully; pilots were overwhelmingly capable of avoiding the UVR in all conditions. The presence of terrain conflicts primarily impacted pilot performance by leading to multiple uploads per vehicle, which was not typically required when pilots only needed to maneuver laterally. Although subjects did not have direct control over the GCS, subjective ratings indicate that the displays under test provided them with sufficient information to manage their aircraft and promptly respond to the unplanned UVRs. Overall, the objective and subjective data strongly suggest that the verbal protocol and subject-surrogate paradigm were effective methods for collecting data remotely amid the COVID-19 pandemic.
With the increasing presence of unmanned aerial vehicles (UAVs) in everyday environments, the user base of these powerful and potentially intelligent machines is expanding beyond exclusively highly trained vehicle operators to include non-expert system users. Scientists seeking to augment costly and often inflexible methods of data collection historically used are turning towards lower cost and reconfigurable UAVs. These new users require more intuitive and natural methods for UAV mission planning. This paper explores two natural language interfaces - gesture and speech - for UAV flight path generation through individual user studies. Subjects who participated in the user studies also used a mouse-based interface for a baseline comparison. Each interface allowed the user to build flight paths from a library of twelve individual trajectory segments. Individual user studies evaluated performance, efficacy, and ease-of-use of each interface using background surveys, subjective questionnaires, and observations on time and correctness. Analysis indicates that natural language interfaces are promising alternatives to traditional interfaces. The user study data collected on the efficacy and potential of each interface will be used to inform future intuitive UAV interface design for non-expert users.
Spacesuit mobility has historically been defined and characterized by a combination of range of motion and joint torque of the individual anatomical joints when performing isolated motions meant to drive that joint only in a given orthogonal plane. While this has been the standard approach for several decades, there are numerous shortcomings that suit designers and engineers would like to see rectified. First, the lack of a standardized method for collecting both range of motion and joint torque translates to many different test setups, procedures and methods of data analysis. Second, all of these previously used methods for data collection lack some degree of repeatability, even within the same test setup and the same conductor; in addition, attempts at higher fidelity data collection techniques require high overhead and cost with minimal improvement. Lastly, isolated motions in standard anatomical planes are not representative of real‐world tasks that a crewmember would be performing during an EVA, be it microgravity or surface exploration based. To address these shortcomings, options are being explored within the Space Suit and Crew Survival Systems Branch to ascertain the feasibility of an alternative approach to defining mobility - one that is more repeatable, lower overhead, and more tied to functional EVA tasks. This paper serves to document the first attempt at such an alternative option - one that looks at the metabolic energy‐cost of a spacesuit. In other words, can we objectively compare the mobility of a spacesuit by evaluating the metabolic cost of that suit to the wearer while performing a battery of functional EVA tasks?
Travel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-use data collection procedure in Chicago and Sydney using the Fourstep app. Social media platforms were utilised as a solution to recruit participants in Chicago, where an international market research company failed to accomplish the task. This paper also discusses the challenges we faced and suggests ways to overcome them, offering valuable guidance to researchers in recruiting participants for smartphone application-based data collection. It also offers an analysis of travel, time-use, and travel-based multitasking behaviours based on the data collected from the Chicago and Sydney samples.
Electricity production is one of the largest sources of environmental emissions - especially greenhouse gases (GHGs) - in the USA. Emission factors (EFs) vary from region to region, which requires the use of spatially relevant EF data for electricity production while performing life cycle assessments (LCAs). Uncertainty information, which is sought by LCA practitioners, is rarely supplied with available life cycle inventories (LCIs). To address these challenges, we present a method for collecting data from different sources for electricity generation and environmental emissions; discuss the challenges involved in agglomerating such data; provide relevant suggestions and solutions to merge the information; and calculate EFs for electricity generation processes from various fuel sources for different spatial regions and spatial resolutions. The EFs from the US 2016 Electricity Life Cycle Inventory (eLCI) are analyzed and explored in this study. We also explore the method of uncertainty information derivation for the EFs. We explore the EFs from different technologies across Emissions & Generation Resource Integrated Database (eGRID) regions in the USA. We find that for certain eGRID regions, the same electricity production technology may have worse emissions. This may be a result of the age of the plants in the region, the quality of fuel used, or other underlying factors. Region-wise life cycle impact assessment (LCIA) ISO 14040 impacts for total generation mix activities provide an overview of the total sustainability profile of electricity production in a particular region, rather than only global warming potential (GWP). We also find that, for different LCIA impacts, several eGRID regions are consistently worse than the US average LCIA impact for every unit of electricity generated. Here this work describes the development of an electricity production LCI at different spatial resolutions by combining and harmonizing information from several databases. The inventory consists of emissions, fuel inputs, and electricity and steam outputs from different electricity production technologies located across various regions of the USA. This LCI for electricity production in the USA will prove to be an enormous resource for all LCA researchers - considering the detailed sources of the information and the breadth of emissions covered by it.
The New Jersey Heritage program surveyed available mapping information on landcover and vegetative communities and found that most commonly used sources, such as local land use maps and aerial photographs, were useful for individual sites, but either varied in their classifications or could not be used over extensive areas. A demonstration project using LANDSAT satellite information for the Great Egg Harbor/Tuckahoe watersheds was initiated with the goals of application (providing an inventory of vegetative communities and land use) flexibility (providing a method of collecting data which can be updated or modified in the future); and efficiency (allowing for an acceptable cost level by having staff undertake the project and avoid the more costly methods from air photo interpretation or on-site surveying. The classification procedure used is described and the spatial distributions of the 10 landcover classes determined are listed.
This paper presents early results from aircraft measurements made by a Low-LET Radiation Spectrometer (LoLRS), as part of a long-range effort to study the complex dynamics of the atmospheric radiation field. For this purpose, a comprehensive data base is being generated to enable a multivariable global mapping (and eventually modeling) of doses and Linear-Energy-Transfer (LET) spectra at aviation altitudes. To accomplish this, a methodical collection of data from the LoLRS (and other instruments), is planned over extended periods of time, in a manner that complements some previous isolated and sporadic measurements by other workers, with the objective to generate a detailed long-range description of the cosmic-ray induced particle environment and to study its variability and dependence on atmospheric thickness, magnetic latitude, L-shell or rigidity, space weather, solar particle events, solar cycle effects, magnetic field variation, diurnal and seasonal effects, and atmospheric weather. Analysis of initial data indicates that the dose is rising with increasing altitude and increasing magnetic latitude. Comparison of total doses with predictions is in good agreement.
The TADPLOT Program, Version 2.0 is described. The TADPLOT program is a software package coordinated by a single, easy-to-use interface, enabling the researcher to access several standard file formats, selectively collect specific subsets of data, and create full-featured publication and viewgraph quality plots. The user-interface was designed to be independent from any file format, yet provide capabilities to accommodate highly specialized data queries. Integrated with an applications software network, data can be assessed, collected, and viewed quickly and easily. Since the commands are data independent, subsequent modifications to the file format will be transparent, while additional file formats can be integrated with minimal impact on the user-interface. The graphical capabilities are independent of the method of data collection; thus, the data specification and subsequent plotting can be modified and upgraded as separate functional components. The graphics kernel selected adheres to the full functional specifications of the CORE standard. Both interface and postprocessing capabilities are fully integrated into TADPLOT.
The propagation of downwelling irradiance at wavelength lambda from surface to a depth (z) in the ocean is governed by the diffuse attenuation coefficient, K(sup -)(sub d)(lambda). There are two standard methods for the derivation of K(sup -)(sub d)(lambda) in remote sensing, which both are based on empirical relationships involving the blue-to-green ratio of ocean color. Recently, a semianalytical method to derive K(sup -)(sub d)(lambda) from reflectance has also been developed. In this study, using K(sup -)(sub d)(490) and K(sup -)(sub d)(443) as examples, we compare the K(sup -)(sub d)(lambda) values derived from the three methods using data collected in three different regions that cover oceanic and coastal waters, with K(sup -)(sub d)(490) ranging from approximately 0.04 to 4.0 per meter. The derived values are compared with the data calculated from in situ measurements of the vertical profiles of downwelling irradiance. The comparisons show that the two standard methods produced satisfactory estimates of K(sup -)(sub d)(lambda) in oceanic waters where attenuation is relatively low but resulted in significant errors in coastal waters. The newly developed semianalytical method appears to have no such limitation as it performed well for both oceanic and coastal waters. For all data in this study the average of absolute percentage difference between the in situ measured and the semianalytically derived K(sup -)(sub d) is approximately 14% for lambda = 490 nm and approximately 11% for lambda = 443 nm.
A simple ranking system for use with data and property values compiled in the Molten Salt Thermal Database-Thermophysical Properties (MSTDB-TP) is presented. The approach includes assessments of five features of data collection: the method used to measure the property value, calibration of devices used to make the measurements, the quantified uncertainty of the results, control of the environmental conditions, and determination of the salt composition. The verifiability of how the property value was determined is also assessed based on information provided or cited within the data set. The quality of each facet is ranked as High, Moderate, or Incomplete based on objective metrics. The overall quality of the dataset is ranked based on rankings of the five data features and the reported property value, with rankings of A and B indicating the data and property value are deemed suitable for quantitative use and U indicating uncertainties that restrict the recommended use to qualitative purposes. The system is suitable for assessing data already in the MSTDB-TP and screening new entries.
Manned tests in Chamber B at NASA JSC were conducted in May and June of 1990 to better quantify the Space Shuttle Extravehicular Mobility Unit's (EMU) thermal performance in the cold environmental extremes of space. Use of an infrared imaging camera with real-time video monitoring of the output significantly added to the scope, quality and interpretation of the test conduct and data acquisition. Results of this test program have been effective in the thermal certification of a new insulation configuration and the '5000 Series' glove. In addition, the acceptable thermal performance of flight garments with visually deteriorated insulation was successfully demonstrated, thereby saving significant inspection and garment replacement cost. This test program also established a new method for collecting data vital to improving crew thermal comfort in a cold environment.