Michael Freed | Search NASA

NASA NTRS2019

Heating Features of Interesting Supra-Arcade Downflows

Supra-arcade downflows (SADs) have been observed above flare loops during the decay phase of flare. They appear as tadpole-like dark plasma voids traveling towards the Sun. In areas surrounding where they appear, temperatures are often high. We aim to investigate temperature and heating mechanism of SADs. We apply our analysis to the M1.7 flare that occurred on 2012 July 12 and was observed by the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory. There are many obvious SADs above the arcade during this event in the AIA 131 Å channel. We calculate the differential emission measure and emission measure weighted temperature with AIA data in the region where SADs are concentrated. We find that the temperature in SADs region tends to be lower than the surrounding plasma. We also calculate velocities of SADs using the Fourier Local Correlation Tracking (FLCT, Fisher & Welsch, 2008) method to derive velocities in the supra-arcade region. Using corks to track the calculated velocities, we find our velocity results are consistent with the SAD motions in the AIA 131 Å intensity movie. We use the velocities to derive the adiabatic heating caused by the compression of plasma. Preliminary results indicate that there is adiabatic heating in front of the SADs.

Li, Zhuofei↗

NASA NTRS2002

Automating CPM-GOMS

CPM-GOMS is a modeling method that combines the task decomposition of a GOMS analysis with a model of human resource usage at the level of cognitive, perceptual, and motor operations. CPM-GOMS models have made accurate predictions about skilled user behavior in routine tasks, but developing such models is tedious and error-prone. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a cognitive modeling tool called Apex. Resource scheduling in Apex automates the difficult task of interleaving the cognitive, perceptual, and motor resources underlying common task operators (e.g. mouse move-and-click). Apex's UI automatically generates PERT charts, which allow modelers to visualize a model's complex parallel behavior. Because interleaving and visualization is now automated, it is feasible to construct arbitrarily long sequences of behavior. To demonstrate the process, we present a model of automated teller interactions in Apex and discuss implications for user modeling. available to model human users, the Goals, Operators, Methods, and Selection (GOMS) method [6, 21] has been the most widely used, providing accurate, often zero-parameter, predictions of the routine performance of skilled users in a wide range of procedural tasks [6, 13, 15, 27, 28]. GOMS is meant to model routine behavior. The user is assumed to have methods that apply sequences of operators and to achieve a goal. Selection rules are applied when there is more than one method to achieve a goal. Many routine tasks lend themselves well to such decomposition. Decomposition produces a representation of the task as a set of nested goal states that include an initial state and a final state. The iterative decomposition into goals and nested subgoals can terminate in primitives of any desired granularity, the choice of level of detail dependent on the predictions required. Although GOMS has proven useful in HCI, tools to support the construction of GOMS models have not yet come into general use.

GOMS↗