Do dynamic work instructions provide an advantage over static instructions in a small scale assembly task?

Recent studies exploring the effects of instructional animations on learning compared to static graphics have yielded mixed results. Few studies have explored their effectiveness in portraying procedural-motor information. Opportunities exist within an applied (manufacturing) context for instructional animations to be used to facilitate build performance on an assembly line. The present study compares build time performance across successive builds when using animation, static diagrams or text instructions to convey an assembly sequence for a handheld device. Although an immediate facilitating effect of animation was found, yielding a significantly faster build time for Build 1, this advantage had disappeared by Build 3. (C) 2009 Elsevier Ltd. All rights reserved.

Behavior-Based branch prediction by dynamically clustering branch instructions

Conditional branches frequently exhibit similar behavior (bias, time-varying behavior,...), a property that can be used to improve branch prediction accuracy. Branch clustering constructs groups or clusters of branches with similar behavior and applies different branch prediction techniques to each branch cluster. We revisit the topic of branch clustering with the aim of generalizing branch clustering. We investigate several methods to measure cluster information, with the most effective the storage of information in the branch target buffer. Also, we investigate alternative methods of using the branch cluster identification in the branch predictor. By these improvements we arrive at a branch clustering technique that obtains higher accuracy than previous approaches presented in the literature for the gshare predictor. Furthermore, we evaluate our branch clustering technique in a wide range of predictors to show the general applicability of the method. Branch clustering improves the accuracy of the local history (PAg) predictor, the path-based perceptron and the PPM-like predictor, one of the 2004 CBP finalists.

Learning curves, taking instructions, and patient safety:using a theoretical domains framework in an interview study to investigate prescribing errors among trainee doctors

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