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Contact transfer of anions from human hands can result in contamination of materials, increasing their rate of corrosion. Two types of hand lotion were applied to the hands: one was specially formulated for cleanroom use and the other was a popular commercial lotion. The effect on contact transfer of anions was measured versus anion transfer from washed hands without lotions.
A set of notes was developed to aid the beginner in hand-held radiometry. The electromagnetic spectrum is reviewed, and pertinent terms are defined. View areas of multiband radiometers are developed to show the areas of coincidence of adjacent bands. The amounts of plant cover seen by radiometers having different fields of view are described. Vegetation indices are derived and discussed. Response functions of several radiometers are shown and applied to spectrometer data taken over 12 wheat plots, to provide a comparison of instruments and bands within and among instruments. The calculation of solar time is reviewed and applied to the calculation of the local time of LANDSAT satellite overpasses for any particular location in the Northern Hemisphere. The use and misuse of hand-held infrared thermometers are discussed, and a procedure for photographic determination of plant cover is described. Some suggestions are offered concerning procedures to be followed when collecting hand-held spectral and thermal data. A list of references pertinent to hand-held radiometry is included.
Astronauts have the task of retrieving and deploying satellites and handling massive objects in a around the payload bay. Concerns were raised that manual handling of such massive objects might induce loads to the shuttle suits exceeding the design-certified loads. The Crew and Thermal Division of NASA JSC simulated the satellite handling tasks (Satellite Manload Tests 1 and 3) and determined the maximum possible load that a suited member could impart onto the suit. In addition, the tests revealed that the load to the suit by an astronaut could be calculated from the astronaut's maximum hand grasp breakaway strength. Thus, this study was conducted to document that hand grasp breakaway strengths of the astronauts who were scheduled to perform EVA during the upcoming missions. In addition, this study verified whether the SML 3 test results were sufficient for documenting the maximum possible load. An attempt was made to predict grasp strength from grip strength and hand anthropometry. Based on the results from this study, the SML 3 test results were deemed sufficient to document the maximum possible load on the suit. Finally, prediction of grasp strength from grip strength was not as accurate as expected. Hence, it was recommended that grasp strength be collected from the astronauts in order to obtain accurate load estimation.
In the previous three reports in this series we demonstrated that the EF-hand family of proteins evolved by a complex pattern of gene duplication, transposition, and splicing. The dendrograms based on exon sequences are nearly identical to those based on protein sequences for troponin C, the essential light chain myosin, the regulatory light chain, and calpain. This validates both the computational methods and the dendrograms for these subfamilies. The proposal of congruence for calmodulin, troponin C, essential light chain, and regulatory light chain was confirmed. There are, however, significant differences in the calmodulin dendrograms computed from DNA and from protein sequences. In this study we find that introns are distributed throughout the EF-hand domain and the interdomain regions. Further, dendrograms based on intron type and distribution bear little resemblance to those based on protein or on DNA sequences. We conclude that introns are inserted, and probably deleted, with relatively high frequency. Further, in the EF-hand family exons do not correspond to structural domains and exon shuffling played little if any role in the evolution of this widely distributed homolog family. Calmodulin has had a turbulent evolution. Its dendrograms based on protein sequence, exon sequence, 3'-tail sequence, intron sequences, and intron positions all show significant differences.
Longitudinal aerodynamic data on large-scale semispan V/STOL tilt-wing configuration having single propeller with left and right hand rotation
While parallel processing promises to speed up applications by several orders of magnitude, the performance achieved still depends upon several factors, including the multiprocessor architecture, system software, data distribution and alignment, as well as the methods used for partitioning the application and mapping its components onto the architecture. The existence of the Gorden Bell Prize given out at Supercomputing every year suggests that while good performance can be attained for real applications on general purpose multiprocessors, the large investment in man-power and time still has to be repeated for each application-machine combination. As applications and machine architectures become more complex, the cost and time-delays for obtaining performance by hand will become prohibitive. Computer users today can turn to three possible avenues for help: parallel libraries, parallel languages and compilers, interactive parallelization tools. The success of these methodologies, in turn, depends on proper application of data dependency analysis, program structure recognition and transformation, performance prediction as well as exploitation of user supplied knowledge. NASA has been developing multidisciplinary applications on highly parallel architectures under the High Performance Computing and Communications Program. Over the past six years, the transition of underlying hardware and system software have forced the scientists to spend a large effort to migrate and recede their applications. Various attempts to exploit software tools to automate the parallelization process have not produced favorable results. In this paper, we report our most recent experience with CAPTOOL, a package developed at Greenwich University. We have chosen CAPTOOL for three reasons: 1. CAPTOOL accepts a FORTRAN 77 program as input. This suggests its potential applicability to a large collection of legacy codes currently in use. 2. CAPTOOL employs domain decomposition to obtain parallelism. Although the fact that not all kinds of parallelism are handled may seem unappealing, many NASA applications in computational aerosciences as well as earth and space sciences are amenable to domain decomposition. 3. CAPTOOL generates code for a large variety of environments employed across NASA centers: MPI/PVM on network of workstations to the IBS/SP2 and CRAY/T3D.
Electrochemistry is already and will increasingly be amongst the major drivers of our energetic mix decarbonization, so to fight anthropogenic global warming. The hydrogen economy vector (ideally produced in water electrolysers using renewable electricity and transformed back into electricity in fuel cells), in parallel with the conversion of carbon dioxide and nitrogen gas into value-added products are seen as pillars of the so-called green and sustainable Energy Revolution. The hydrogen technologies presently attract increasing interest, their momentum growing irremediably in Europe, Asia and Americas. Here, this Virtual Special Issue (VSI) gathers the latest scientific results of major research laboratories worldwide, all addressing various aspects of electrochemical energy and material conversion processes.
Utilities have been experimenting with integrated demand side management (IDSM) programs since the 2000s. The potential benefits of improved program cost-effectiveness and customer engagement from combining energy efficiency (EE), demand flexibility (DF), and other distributed energy resources into an integrated customer offering have been recognized although there are several known regulatory and program administrative challenges. In addition, as buildings adopt EE measures, the baseline load profile change generally reduces the potential load that can be shed or shifted. This has been a significant technical barrier for customers and program implementers. However, it is a myth that EE always reduces DF. Load change from EE can be time-varying. Therefore, whether EE improves or reduces DF should be evaluated on an individual measure basis accounting for weather dependencies and interactions. For IDSM program design purpose, it is useful to understand how common EE features influence DF and the underlying building physics. In this paper, we use parametric simulations of a prototype medium office building to evaluate how various EE features influence DF, measured by a “demand decrease intensity” (W/ft2) metric. These EE features cover envelope characteristics, internal loads, and airside HVAC system. The parametric analysis shows that some efficient HVAC control measures will increase DF but not the traditional building envelope, lighting, and ventilation-related efficiency measures. These findings contribute to the technical basis for achieving enhanced energy benefits by packaging appropriate HVAC control measures in IDSM program design. Program developers should further validate these results in targeted pilot projects.
Aerodynamic characteristics of large semispan tilting wing model having double-slotted flap, rotating propeller, and fuselage
Longitudinal aerodynamic characteristics of large semispan V/STOL tilt-wing configuration with single propeller rotating in both left and right directions
Optical properties and chemical analysis of contaminants on Gemini spacecraft windows
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Introduction: During the 2022 suited injury summit, it was hypothesized that there will be concerns for hand and glove injuries for future exploration space missions, especially given the fact that the “total number of Extravehicular activity (EVA) hours and frequency” for lunar surface missions is expected to vastly increase [1]. It has been reported that the hands experienced the greatest “absolute numbers” of reported injuries and far exceeds other injuries during EVA [1, 2]. It was reported that the most fatiguing part of the surface EVA was the repetitive gripping tasks [3]. It was recommended that a “glove sub-team” be created to look at possible injury mechanism and mitigation strategies. Some of the recommendations that were suggested [1] are as follows: examine hand fatigue, utilize motion capture, examine the duration and frequency of hand movements, and identify frequent hand motions. We started assessing hardware and instrumentation to measure hand grasp activity in the pressurized glove environment. The purpose of this test was to perform a hardware evaluation for motion capture (MoCap) gloves obtained from StretchSense (Auckland, New Zealand). The specific gloves used were the Pro Fidelity and SuperSplay to determine the repeatability, reliability, feasibility, and useability inside of a pressurized gloved environment. Methods: The MoCap gloves were customized (e.g., battery/Bluetooth pack relocated to upper arm) to better suit the pressurized testing environment and protect the subject from unintentional injury (Fig. 1). Fourteen total subjects from different demographics (i.e., gender and pressurized glove experience level) participated in this test series. Testing included one session each of a baseline data collection (NASA Johnson Space Center (JSC) building 21) and a spacesuit glove box (Fig. 2 at JSC building 7 room 2027) data collection (under vacuum down to 4.3 psid), where each session lasted 3-5 hours. Controlled and reproducible tasks to systematically evaluate the repeatability and reliability of the hardware were performed during baseline data collection. Additionally, subjects performed simulated EVA-like tasks in a pressurized gloved environment. For all sessions, MoCap gloves were placed on each of the subjects’ hands and the signal from it, or the raw capacitance (Fig. 3), was analysed. The raw capacitance was used to estimate the open and closed hand states between the testing conditions and allow us to provide an offset caused by the pressurized environment. Results & Discussion: Initial observation with the bare hands (baseline) condition showed that the MoCap gloves appeared to track grasping and releasing of the fingers (opening and closing fist) with both high- and low-speed conditions, while adduction and abduction of the fingers were not relatively tracked. A hardware evaluation was done outside of the glove box to assess the reliability and repeatability of the MoCap glove. In one task, a point force was applied to various locations on the back of the hand. When the point force was applied to the space between the 1st digit and the pointer finger, there was a noticeable distortion to the MoCap data. Another task examining an increasing force from a 10 lb. sandbag applied to the back of the hand while lying flat on a table, showed a constant flat line with only a distortion when the weight was increased or added to the back of the hand. Fig. 3 shows an object relocation task where you can see when each individual finger “opened” and “closed” (changed position) when picking up and setting down the dumbbell. When the fingers were stationary, the signal remained relatively flat compared to the peaks and valleys that can be observed in Fig. 3. This study showed promising results and imperative input into an attempt to discriminate between hand states across various functional tasks and should be evaluated with context to the repeatability and reliability outcomes. Depending on the task done inside of the pressurized glove box environment and outside, the results appear to be affected by many different factors (i.e., drift, pressure, hand size, etc.). Significance: If this hardware proves to be reliable and repeatable in determining the open and closed hand states then this may provide critical insight into assisting in the characterization of the pressurized gloved environment and the effect on crew member exertion level. Ultimately, this tool will provide useful data for quantifying the repetitive nature of EVA training and tasks. Acknowledgments: The authors would like to acknowledge the NASA Mars Campaign Office for providing funding for this research. Lastly, thanks to all the engineers and technicians at NASA JSC who helped with this data collection. References: [1] Reiber, et al. (2022), NASA/TM-20220007605; [2] Scheuring, et al. (2009), Av., Sp., and Envir. Med. 80(2). [3] Scheuring, et al. (2007), NASA/TM–2007–214755.
Basic hand grips and sensor interactions that a dextrous robot hand will need as part of the operation of an EVA Retriever are analyzed. What is to be done with a dextrous robot hand is examined along with how such a complex machine might be controlled. It was assumed throughout that an anthropomorphic robot hand should perform tasks just as a human would; i.e., the most efficient approach to developing control strategies for the hand would be to model actual hand actions and do the same tasks in the same ways. Therefore, basic hand grips that human hands perform, as well as hand grip action were analyzed. It was also important to examine what is termed sensor fusion. This is the integration of various disparate sensor feedback paths. These feedback paths can be spatially and temporally separated, as well as, of different sensor types. Neural networks are seen as a means of integrating these varied sensor inputs and types. Basic heuristics of hand actions and grips were developed. These heuristics offer promise of control dextrous robot hands in a more natural and efficient way.
An algorithm has been developed to solve the problem of mapping from (1) a glove instrumented with joint-angle sensors to (2) an anthropomorphic robot hand. Such a mapping is needed to generate control signals to make the robot hand mimic the configuration of the hand of a human attempting to control the robot. The mapping problem is complicated by uncertainties in sensor locations caused by variations in sizes and shapes of hands and variations in the fit of the glove. The present mapping algorithm is robust in the face of these uncertainties, largely because it includes a calibration sub-algorithm that inherently adapts the mapping to the specific hand and glove, without need for measuring the hand and without regard for goodness of fit. The algorithm utilizes a forward-kinematics model of the glove derived from documentation provided by the manufacturer of the glove. In this case, forward-kinematics model signifies a mathematical model of the glove fingertip positions as functions of the sensor readings. More specifically, given the sensor readings, the forward-kinematics model calculates the glove fingertip positions in a Cartesian reference frame nominally attached to the palm. The algorithm also utilizes an inverse-kinematics model of the robot hand. In this case, inverse-kinematics model signifies a mathematical model of the robot finger-joint angles as functions of the robot fingertip positions. Again, more specifically, the inverse-kinematics model calculates the finger-joint commands needed to place the fingertips at specified positions in a Cartesian reference frame that is attached to the palm of the robot hand and that nominally corresponds to the Cartesian reference frame attached to the palm of the glove. Initially, because of the aforementioned uncertainties, the glove fingertip positions calculated by the forwardkinematics model in the glove Cartesian reference frame cannot be expected to match the robot fingertip positions in the robot-hand Cartesian reference frame. A calibration must be performed to make the glove and robot-hand fingertip positions correspond more precisely. The calibration procedure involves a few simple hand poses designed to provide well-defined fingertip positions. One of the poses is a fist. In each of the other poses, a finger touches the thumb. The calibration subalgorithm uses the sensor readings from these poses to modify the kinematical models to make the two sets of fingertip positions agree more closely.
Nuclear criticality safety is an extremely important part of the work at Los Alamos National Laboratory (LANL). As part of the work LANL performs to continue to keep criticality safety a top priority, LANL has developed and regularly teaches nuclear criticality safety training classes for both the United States Department of Energy Nuclear Criticality Safety Program as well as internal trainings for LANL employees. A portion of the training classes is comprised of hands-on demonstrations, where students get the opportunity to handle special nuclear material at the National Criticality Experiments Research Center (NCERC). One hands-on demonstration uses the “Class foils,” thin HEU foils which are stacked with lucite moderator plates. A hand-stack is performed until the multiplication reaches the “three-quarters rule,” where the demonstration is continued remotely on a vertical lift assembly up until the system is critical. This hands-on demonstration eventually achieves a critical configuration and follows the ANS-1 guidelines on an approach to critical. Another hands-on demonstration involves handling clad plutonium and neptunium spheres, and follows procedures using criticality safety evaluations to ensure that the hands-on demonstrations remain subcritical.This hands-on demonstration also involves the use of polyethylene shells around the plutonium sphere to demonstrate how additional reflector increases the criticality of a system. This paper is focused on developing a new hands-on demonstration using Zero Power Physics Reactor (ZPPR) plates. This new hands-on demonstration will follow the ANS-8 standards as it is not desired to achieve criticality with the ZPPR plates during the hands-on demonstration. A hands-on demonstration using multiple plutonium parts will likely be more applicable to personnel who handle plutonium on a daily basis, such as LANL glovebox operators.
The continental flood inundation mapping (CFIM) framework is a high-performance computing (HPC)-based computational framework for the Height Above Nearest Drainage (HAND)-based inundation mapping methodology. Using the 10m Digital Elevation Model (DEM) data produced by U.S. Geological Survey (USGS) 3DEP (the 3-D Elevation Program) and the NHDPlus hydrography dataset produced by USGS and the U.S. Environmental Protection Agency (EPA), a hydrological terrain raster called HAND is computed for HUC6 units in the conterminous U.S. (CONUS). The value of each raster cell in HAND is an approximation of the relative elevation between the cell and its nearest water stream. Derived from HAND, a hydraulic property table is established to calculate river geometry properties for each of the 2.7 million river reaches covered by NHDPlus (5.5 million kilometers in total length). This table is a lookup table for water depth given an input stream flow value. Such lookup is available between water depth 0m and 25m at 1-foot interval. The flood inundation map is then computed by using HAND and this lookup table based on the near real-time water forecast from the National Water Model (NWM) at the National Oceanic and Atmospheric Administration (NOAA). HAND and the Hydraulic Property Table version v0.21 is an upgrade to v0.2. Version 0.21 applies NHD HR etching to DEMs for all HUC6 units before running the HAND workflow. This new version also fixed the issue of void areas along HUC boundary. This dataset comprises 331 HUC6 units for CONUS (excluding the five great lakes units), each is a downloadable zip file. Version 0.1 was computed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urban-Champaign in 2016 and is currently hosted at the Texas Advanced Computing Center (TACC). Please cite this data DOI and the following publications and data DOIs when you use HAND and the hydraulic property table: Liu, Yan Y. Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS - Version 0.2. (20200301). Oak Ridge Leadership Computing Facility. (2020). DOI: 10.13139/ORNLNCCS/1608331 Liu, Yan Y., David R. Maidment, David G. Tarboton, Xing Zheng, and Shaowen Wang. 'A CyberGIS integration and computation framework for high-resolution continental-scale flood inundation mapping.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 770-784. DOI: 10.1111/1752-1688.12660 Zheng, Xing, David G. Tarboton, David R. Maidment, Yan Y. Liu, and Paola Passalacqua. 'River channel geometry and rating curve estimation using height above the nearest drainage.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 785-806. DOI: 10.1111/1752-1688.12661 Liu, Y. (2018). Height Above Nearest Drainage (HAND) for CONUS, HydroShare, https://doi.org/10.4211/hs.69f7d237675c4c73938481904358c789 LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.
The author describes a newly developed mechanical hand system. The robot hand is in human-like configuration with a thumb and three fingers, a palm, a wrist, and the forearm in which the hand and wrist actuators are located. Each finger and the wrist has its own active electromechanical compliance system, allowing the joint drive trains to be stiffened or loosened. This mechanism imitates the human muscle dual function of positioner and stiffness controller. This is essential for soft grappling operations. The hand-wrist assembly has 16 finger joints, three wrist joints, and five compliance mechanisms for a total of 24 degrees of freedom. The strength of the hand is roughly half that of the human hand and its size is comparable to a male hand. The hand is controlled through an exoskeleton glove controller that the operator wears. The glove provides the man-machine interface in telemanipulation control mode: it senses the operator's inputs to guide the mechanical hand in hybrid position and force control. The hand system is intended for dexterous manipulations in structured environments. Typical applications will include work in hostile environment such as space operations and nuclear power plants.