41698316
Cloud tracing in convection-diffusion systems
The paper describes a highly interactive method for computer visualization of simultaneous three-dimensional vector and scalar flow fields in convection-diffusion systems. This method allows a computational fluid dynamics user to visualize the basic physical process of dispersion and mixing rather than just the vector and scalar values computed by the simulation. It is based on transforming the vector field from a traditionally Eulerian reference frame into a Lagrangian reference frame. Fluid elements are traced through the vector field for the mean path as well as the statistical dispersion of the fluid elements about the mean position by using added scalar information about the root mean square value of the vector field and its Lagrangian time scale. In this way, clouds of fluid elements are traced not just mean paths. We have used this method to visualize the simulation of an industrial incinerator to help identify mechanisms for poor mixing.<<ETX>>
1517725
Virtual Smoke: an interactive 3D flow visualization technique
A technique is given for computer visualization of simultaneous three-dimensional vector and scalar fields such as velocity and temperature in reacting fluid flow fields. The technique, which is called Virtual Smoke, simulates the use of colored smoke for experimental gaseous fluid flow visualization. However, it is noninvasive and can animate, in particular, the dynamic behaviors of steady-state or instantaneous flow fields obtained from numerical simulations. Virtual Smoke is based on volume seeds and volume seedlings, which are direct volume visualization methods previously developed for highly interactive scalar volume data exploration. Data from combustion simulations are used to demonstrate the effectiveness of Virtual Smoke.<<ETX>>
117003092
VISUALIZING TURBULENT MIXING OF GASES AND PARTICLES
A physical model and interactive computer graphics techniques have been developed for the visualization of the basic physical process of stochastic dispersion and mixing from steady-state CFD calculations. The mixing of massless particles and inertial particles is visualized by transforming the vector field from a traditionally Eulerian reference frame into a Lagrangian reference frame. Groups of particles are traced through the vector field for the mean path as well as their statistical dispersion about the mean position by using added scalar information about the root mean square value of the vector field and its Lagrangian time scale. In this way, clouds of particles in a turbulent environment are traced, not just mean paths. In combustion simulations of many industrial processes, good mixing is required to achieve a sufficient degree of combustion efficiency. The ability to visualize this multiphase mixing can not only help identify poor mixing but also explain the mechanism for poor mixing. The information gained from the visualization can be used to improve the overall combustion efficiency in utility boilers or propulsion devices. We have used this technique to visualize steady-state simulations of the combustion performance in several furnace designs.
61905558
Adaptive teaching strategies to improve student learning in problem-based learning environments
The purpose of this study is to investigate the use of adaptive teaching strategies and resource allocation to enhance training in cooperative learning settings. The Biology Sleuth is a multi-media learning environment developed as a testbed to vary the distribution of critical resources and the use of teaching strategies, and to study their effects on group dynamics and individual learning. Students work in a cooperative environment, aided by various resources including a knowledge-base, each other, the software, and the teacher. This study examines the effects of two teaching and resource allocation strategies in this environment and suggests these strategies can have a large impact on both group dynamics and individual learning.
33249562
The Transfusion Medicine Tutor: Using Expert Systems Technology to Teach Domain-Specific Problem-Solving Skills
This study provides data regarding the effectiveness of the expert system-based Transfusion Medicine Tutor (TMT) when used by medical technology students to learn an important problem-solving task, the identification of alloantibodies in a patient's blood for the purpose of finding compatible blood for transfusion. The results show that the students who were taught by an instructor using TMT to provide the instructional environment went from 0% correct on a pre-test case to 87%–93% correct on post-tests (N=15). This compares with an improvement rate of 20% by a control group (N=15) who used a passive version of the system with the tutoring functions turned off. The results also demonstrate the importance of relying on objective performance data rather than questionnaire data to evaluate systems, as there was no difference in the subjective responses of the students to these two different versions of the system.
74708114
The transfusion medicine tutor: the use of expert-systems technology to teach students and provide support to practitioners in antibody identification
The studies presented here provide data regarding the effectiveness of the expert-system-based Transfusion Medicine Tutor (TMT) when used by medical technology students and medical technologists in performing an important problem-solving task, the identification alloantibodies in a patient's blood for the purpose of finding compatible blood for transfusion. TMT monitors for slips and mistakes during the evaluation of patient cases, and provides suggestions to the student or technologist when a potential concern is detected. Use of that system was found to reduce errors in final answers on various multiple antibody cases by 30%-62% by practicing medical technologists. Similarly, use of a modified version of the system for teaching was found to enhance learning by 87--93%.
38655618
EP-X: A Knowledge-Based System to Aid in Searches of the Environmental Pollution Literature
38064374
A Catalog of Errors
Abstract This paper reviews various errors that have been described by comparing human behavior to the norms of probability, causal connection and logical deduction. For each error we review evidence on whether the error has been demonstrated to occur. For many errors, the occurrence of a bias has not been demonstrated; for other, a bias does occur, but arguments can be made that the bias is not always an error. Based on the conclusions of this review, we caution researchers and practitioners in referring to well known biases and errors.