FIESTA: An operational decision aid for space network fault isolation

The Fault Tolerance Expert System for Tracking and Data Relay Satellite System (TDRSS) Applications (FIESTA) is a fault detection and fault diagnosis expert system being developed as a decision aid to support operations in the Network Control Center (NCC) for NASA's Space Network. The operational objectives which influenced FIESTA development are presented and an overview of the architecture used to achieve these goals are provided. The approach to the knowledge engineering effort and the methodology employed are also presented and illustrated with examples drawn from the FIESTA domain

Assessing the impact of typeface design in a text-rich automotive user interface

Text-rich driver–vehicle interfaces are increasingly common in new vehicles, yet the effects of different typeface characteristics on task performance in this brief off-road based glance context remains sparsely examined. Subjects completed menu selection tasks while in a driving simulator. Menu text was set either in a ‘humanist’ or ‘square grotesque’ typeface. Among men, use of the humanist typeface resulted in a 10.6% reduction in total glance time as compared to the square grotesque typeface. Total response time and number of glances showed similar reductions. The impact of typeface was either more modest or not apparent for women. Error rates for both males and females were 3.1% lower for the humanist typeface. This research suggests that optimised typefaces may mitigate some interface demands. Future work will need to assess whether other typeface characteristics can be optimised to further reduce demand, improve legibility, increase usability and help meet new governmental distraction guidelines

Cause-Effect Pairs in Time Series with a Focus on Econometrics

This chapter addresses the problem of identifying the causal structure between two time-series processes. We focus on the setting typically encountered in econometrics, namely stationary or difference-stationary multiple autoregressive processes with additive white noise terms. We review different methods and algorithms, distinguishing between methods that filter the series through a vector autoregressive (VAR) model and methods that apply causal search directly to time series data. We also propose an additive noise model search algorithm tailored to the specific task of distinguishing among causal structures on time series pairs, under different assumptions, among which causal sufficiency