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The traditional centralized planning and scheduling of complex fast moving projects are value-added activites. However, centralized scheduling has some severe deficiencies that have plagued managers since the Polaris project when PERT analysis was invented.
The charts, that are the totality of this document, presents tasks, duration of the tasks, the start and finish of the tasks, and subtasks. Also presented are PERT charts that display the beginning, external milestones, and end points for the tasks, and sub tasks.
process for automatically generating computational models of human/computer interactions as well as graphical and textual representations of the models has been built on the conceptual foundation of a method known in the art as CPM-GOMS. This method is so named because it combines (1) the task decomposition of analysis according to an underlying method known in the art as the goals, operators, methods, and selection (GOMS) method with (2) a model of human resource usage at the level of cognitive, perceptual, and motor (CPM) operations. CPM-GOMS models have made accurate predictions about behaviors of skilled computer users in routine tasks, but heretofore, such models have been generated in a tedious, error-prone manual process. In the present process, CPM-GOMS models are generated automatically from a hierarchical task decomposition expressed by use of a computer program, known as Apex, designed previously to be used to model human behavior in complex, dynamic tasks. An inherent capability of Apex for scheduling of resources automates the difficult task of interleaving the cognitive, perceptual, and motor resources that underlie common task operators (e.g., move and click mouse). The user interface of Apex automatically generates Program Evaluation Review Technique (PERT) charts, which enable modelers to visualize the complex parallel behavior represented by a model. Because interleaving and the generation of displays to aid visualization are automated, it is now feasible to construct arbitrarily long sequences of behaviors. The process was tested by using Apex to create a CPM-GOMS model of a relatively simple human/computer-interaction task and comparing the time predictions of the model and measurements of the times taken by human users in performing the various steps of the task. The task was to withdraw $80 in cash from an automated teller machine (ATM). For the test, a Visual Basic mockup of an ATM was created, with a provision for input from (and measurement of the performance of) the user via a mouse. The times predicted by the automatically generated model turned out to approximate the measured times fairly well (see figure). While these results are promising, there is need for further development of the process. Moreover, it will also be necessary to test other, more complex models: The actions required of the user in the ATM task are too sequential to involve substantial parallelism and interleaving and, hence, do not serve as an adequate test of the unique strength of CPM-GOMS models to accommodate parallelism and interleaving.
CPM X is a computer program that predicts sequences of, and amounts of time taken by, routine actions performed by a skilled person performing a task. Unlike programs that simulate the interaction of the person with the task environment, CPM X predicts the time course of events as consequences of encoded constraints on human behavior. The constraints determine which cognitive and environmental processes can occur simultaneously and which have sequential dependencies. The input to CPM X comprises (1) a description of a task and strategy in a hierarchical description language and (2) a description of architectural constraints in the form of rules governing interactions of fundamental cognitive, perceptual, and motor operations. The output of CPM X is a Program Evaluation Review Technique (PERT) chart that presents a schedule of predicted cognitive, motor, and perceptual operators interacting with a task environment. The CPM X program allows direct, a priori prediction of skilled user performance on complex human-machine systems, providing a way to assess critical interfaces before they are deployed in mission contexts.
The NASA Risk Informed Decision Making process is used to assess a trade space of three dimensionally woven thermal protection systems for application to the Mars Sample Return Earth Entry Vehicle. Candidate architectures are assessed based on mission assurance, technical development, cost, and schedule risk. Assessment methodology differed between the architectures, utilizing a four-point quantitative scale for mission assurance and technical development and highly tailored PERT techniques for cost and schedule. Risk results are presented, in addition to a review of RIDM effectiveness for this application.
The NASA Risk Informed Decision Making process is used to assess a trade space of three dimensionally woven thermal protection systems for application to the Mars Sample Return Earth Entry Vehicle. Candidate architectures are assessed based on mission assurance, technical development, cost, and schedule risk. Assessment methodology differed between the architectures, utilizing a four-point quantitative scale for mission assurance and technical development and highly tailored PERT techniques for cost and schedule. Risk results are presented, in addition to a review of RIDM effectiveness for this application.
Introduction: The recent selection of two missions to Venus has renewed the importance of deter-mining weathering reactions between minerals and the Venusian atmosphere, and the spectral signatures of minerals before, during, and after these reactions. The rate at which weathering reactions progress also constrain show long unstable minerals will be present on the surface (e.g., [1]), and enables the use of mineralogy as a constraint on surface age(e.g., [2-3]).In order to gain an understanding of how mineral compositions and spectra change with weathering, we have begun conducting experiments in a 1 atm experimental setup at Wesleyan University. This setup exposes minerals to the temperature and most abundant gases of the Venus atmosphere (CO2, SO2, N2).We conducted initial experiments using biotite, calcite, and montmorillonite in order to test our methodology with minerals that may be relevant to recording the history of water on Venus. Methods: Experiments were conducted in Thermo Fisher Scientific Lindberg/Blue M Mini-Mite horizontal tube furnaces at Wesleyan University. Experiments used natural mineral chips and powders, and were conducted at 1 atmosphere and 460 °C under pre-mixed gases provided by Air Gas(Table 1). The furnaces are set up in a flow through configuration so that solid samples are exposed to a fixed gas composition, and quartz glass process tubes were used in all experiments (1/4” diameter for experiment V4, ½” diameter for all others). These conditions were maintained for the du-rations listed in Table 1, at which point the furnace was turned off with gas flowing until the sample was cool enough to extract under N2and be placed in a desiccator for storage. Run products were carbon coated and examined using a Hitachi SU5000 Field Emission Gun Scanning Electron Microscope (SEM) equipped with an EDAX Octane Pro EDS detector located at Wesley-an University. Visible-Near Infrared Spectroscopy (VNIR) analysis was performed on powdered samples under a nitrogen atmosphere using an ASD Fieldspec Proover the 350-2500 nm range. Powdered mineral samples were milled to a particle size of < 45μm and were spiked with an internal standard (Al2O3, corundum) to obtain quantitative mineralogy. Samples were analyzed using a Panalytical X’Pert pro X-ray Diffractometer (XRD), with an X’Celerator high speed detector and Co Kα radiation, with data collected at a step size of 0.02 ̊/minute step counting rate from 2 to 80 degrees 2θ at 45 mA/40kV in the X-ray Diffraction Laboratory located at NASA Johnson Space Center. Materials Data Inc (MDI) software suite, JadeTMv9 was used for Rietveld refinement to determine phase abundances and mineral identification by comparing XRD patterns to International Center for Diffraction Data (ICDD) database patterns. Table 1: Experimental matrix. Experiment Name Duration (Days)Gas Com-position (trace gas)MineralsV487SO2/N2(1.4%)Montmorillonite, biotiteV519SO2/N2(1.4%)CalciteV619CO2/SO2/N2(1.4% SO2, 2.1% N2)Montmorillonite, biotiteV828SO2/N2(1.4%)Biotite, calcite Results: Calcite. In both experiments, calcite was exposed to the SO2/N2gas mixture, and in both experimental run products, XRD analysis detected anhydrite, which is consistent with EDS measurements conducted in the SEM. The XRD analyses show greater amounts of anhydrite present after 28 days than 19, suggesting the calcite reaction progressed further given longer duration. Grain surface morphology as seen in the SEM also shows secondary mineral growth (Fig. 1). VNIR spectra show no change, as expected since anhydrite lacks spectral features in this wavelength range. Montmorillonite. Montmorillonite was exposed to two different gas mixtures, the SO2/N2mix and CO2/SO2/N2mix over different durations (87 and 19 days, respectively). VNIR analyses of both run products show a reduction in the 1441and1910 nm water features as well as a shift of the 1411 and 2011 nm features to shorter wavelengths that may indicate re-structuring in the crystal lattice and the production of amorphous phases [4]. XRD results are consistent with this, showing a shift of the 001 peak from 15Åto 10Åin both run products. The amount of X-ray amorphous material in the montmorillonite run products was greater than that present in the unreacted clay, being the greatest in the 87 day V4 experiment. No other secondary phases were detected in the run products, however sulfur was present in EDS analyses of powder samples.
After years of improving module efficiency while targeting mean degradation rates of 0.5 to 0.6 %/y for crystalline silicon technology, there is much evidence that the degradation rates are now increasing significantly. Contributing factors include:Radiation damage (UV-Light Induced Degradation): Trina solar: -4.5%/y degradation rate in Singapore, DOE National Laboratory Regional Test Centers showed degradation of -1% < r < -2%/y in crystalline silicon modules, Jinko Solar: -4% < r < -7% efficiency loss from 540 MJ/m-2 of UV-A light, ISFH: 15% relative power loss during of 1.8 GJ/m-2 UV exposure, attributed to H+. Electrical bias from positive system voltage (e.g., +1000 V, +1500 V) can drive ions and metallization through the encapsulation, NREL: Ion transport can affect the cell passivation, resulting in power loss of 5% to 40% in p-PERC+ (bifacial), NREL: Damage at cell rear with up to 17% power at cell fronts in n-PERT modules. Delamination can also occur due to cell surface reactions driven by light and bias.
High efficiency silicon solar cells are characterized using current-voltage curves, electroluminescence imaging, impedance spectroscopy, capacitance transients, microwave photoconductive decay, and time-resolved photoluminescence imaging. The sample set is composed of cells from different manufacturers and includes an n-type silicon heterojunction (SHJ), an n-type passivated emitter rear totally diffused (PERT), and five different p-type passivated emitter rear contact (PERC) cells. Carrier lifetimes, both photoconductivity and photoluminescence, are measured co-located with the light excitation pulse and within the cell but away from the light spot. Luminescence intensity and excess carrier lifetimes correlate to cell voltage. The capacitance transient time constants correlate to the capacitance values extracted from impedance spectroscopy.
Integral benchmark experiments are vital in the adjustment and validation of the nuclear data that govern predictive simulations across the nuclear community. The nuclear data sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®) transport code are currently limited; however, expanding sensitivity capabilities will allow benchmark experiments to be designed to resolve compensating errors and adjust nuclear data where previously prohibitively difficult. This paper provides details of a preliminary verification for the use of (i.) the recently revised perturbation and (ii.) developmental fixed-source sensitivity tools within MCNP to calculate sensitivities of tallied responses (such as current integrated over a surface, F1, and flux averaged over a cell, F4) to nuclear data in fixed-source simulations. Energy-binned and energy-integrated sensitivities calculated with these tools are compared against sensitivities calculated using a central-difference approximation. Here, the verification is completed for four configurations of a benchmarked system using a 4.5-kg plutonium sphere surrounded by varying amounts of copper and/or polyethylene. The results show that sensitivities calculated with the perturbation and fixed-source sensitivity tools agree with the central-difference-based approach.
Multicomponent polymer mixtures are ubiquitous in biological self-organization but are notoriously difficult to study computationally. Plagued by both slow single molecule relaxation times and slow equilibration within dense mixtures, molecular dynamics simulations are typically infeasible at the spatial scales required to study the stability of mesophase structure. Polymer field theories offer an attractive alternative, but analytical calculations are only tractable for mean-field theories and nearby perturbations, constraints that become especially problematic for fluctuation-induced effects such as coacervation. Here, we show that a recently developed technique for obtaining numerical solutions to partial differential equations based on operator learning, neural operators, lends itself to a highly scalable training strategy by parallelizing per-species operator maps. We illustrate the efficacy of our approach on six-component mixtures with randomly selected compositions and that it significantly outperforms the state-of-the-art pseudospectral integrators for field-theoretic simulations, especially as polymer lengths become long.
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The hydrogen isotopes deuterium (D) and tritium (T) have become essential tools in chemistry, biology and medicine1. Beyond their widespread use in spectroscopy, mass spectrometry and mechanistic and pharmacokinetic studies, there has been considerable interest in incorporating deuterium into drug molecules1. Deutetrabenazine, a deuterated drug that is promising for the treatment of Huntington’s disease2, was recently approved by the United States’ Food and Drug Administration. The deuterium kinetic isotope effect, which compares the rate of a chemical reaction for a compound with that for its deuterated counterpart, can be substantial1,3,4. The strategic replacement of hydrogen with deuterium can affect both the rate of metabolism and the distribution of metabolites for a compound5, improving the efficacy and safety of a drug. The pharmacokinetics of a deuterated compound depends on the location(s) of deuterium. Although methods are available for deuterium incorporation at both early and late stages of the synthesis of a drug6,7, these processes are often unselective and the stereoisotopic purity can be difficult to measure7,8. Here we describe the preparation of stereoselectively deuterated building blocks for pharmaceutical research. As a proof of concept, we demonstrate a four-step conversion of benzene to cyclohexene with varying degrees of deuterium incorporation, via binding to a tungsten complex. Using different combinations of deuterated and proteated acid and hydride reagents, the deuterated positions on the cyclohexene ring can be controlled precisely. In total, 52 unique stereoisotopomers of cyclohexene are available, in the form of ten different isotopologues. This concept can be extended to prepare discrete stereoisotopomers of functionalized cyclohexenes. Such systematic methods for the preparation of pharmacologically active compounds as discrete stereoisotopomers could improve the pharmacological and toxicological properties of drugs and provide mechanistic information related to their distribution and metabolism in the body.
Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Here, using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.
One EUCLID project goal is to create a NEW capability in the MCNP6 code. This is being tested on reaction rate sensitivities and compared with SENSMG results. Reaction rate ratio sensitivities were added to EUCLID sensitivity library and investigated to determine value toward designing optimized experiments aimed at reducing compensating errors in nuclear data. The use of adjustment using multiple responses (and underlying/required capabilities) may benefit many applications. These include a tool for adjustment using multiple integral responses (ND adjustment and validation community); sensitivities of additional responses (ND adjustment and validation community); sensitivity capabilities (users in many application areas including safeguards/nonproliferation, criticality safety, etc.); and experiment optimization capability (applications which can benefit from integral experiments). Team is working to build and test future tools and to understand compensating errors
Program evaluation and review technique covering progress, cost estimates, contracts, personnel panels, management and operational philosophies
Applications of Program Evaluation and Review Technique /PERT/ in NASA