In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.
Unsteady flows of granular media are ubiquitous yet remain largely unexplored. In this research, we apply unsteady flows to strongly segregating granular materials to control the segregation pattern and enhance overall mixing. Sizebidisperse granular mixtures with large size ratios flowing onto a quasi-2D bounded heap form stratified layers of large and small particles when the flow rate is modulated. These layers exhibit better average mixing than the segregated patterns generated by steady feed rates. The mechanisms of layer formation under modulated flow differ from those for spontaneous stratification and are related to changes in the composition of the flowing layer at different stages in each feed cycle. The thickness and length of the stratified layers can be controlled by changing the feed rates and feed cycle durations, which is potentially useful for reducing segregation in industrial processes.
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