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A spin-polarized vertical cavity surface emitting laser, with InAs∕GaAs self-organized quantum dots as the active gain media, has been fabricated and characterized. Electron spin injection is achieved via a MnAs∕GaAs Schottky tunnel contact. The laser is operated at 200K and, at this temperature, the degree of circular polarization in the output is 8% and the maximum threshold current reduction is 14%. These effects are not observed in identical control devices with nonmagnetic contacts.

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Transcription typing is one of the basic and common activities in human-machine interaction and 34 transcription typing phenomena have been discovered involving many aspects of human performance including interkey time, typing units and spans, typing errors, concurrent task performance, eye movements, and skill effects. Based on the queuing network theory of human performance [Liu 1996;1997] and current discoveries in cognitive and neural science, this article extends and applies the Queuing Network-Model Human Processor (QN-MHP [Liu et al. 2006]) to model 32 transcription typing phenomena. The queuing network model of transcription typing offers new insights into the mechanisms of cognition and human-computer interaction. Its value in proactive ergonomics design of user interfaces is illustrated and discussed.

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The use of electric bikes (e-bikes) in China has grown tremendously in the past decade. Traffic safety for e-bike riders is an issue of growing public concern because the number of fatalities and injuries is increasing. A study was conducted to identify risk factors affecting involvement of e-bike riders in accidents and to establish the relationships between safety attitudes, risk perception, and aberrant riding behaviors. The data used for analysis were obtained from a self-reported questionnaire survey of a sample of 603 e-bike riders in two large cities in China. The results showed that both gender and automobile driving experience were significantly associated with at-fault accident involvement. Males were more likely to have at-fault accidents than were females, and riders with an automobile driver's license were less likely to have accidents than were those without a driver's license. Two types of aberrant riding behaviors, errors and aggressive behaviors, were found to be significant factors for predicting at-fault accident involvement. Analysis with a structural equation model indicated that safety attitudes and risk perception both significantly affected aberrant riding behaviors. E-bike riders with stronger positive attitudes toward safety and more worry and concern about their traffic risk tended to be less likely to have aberrant riding behaviors. Practical implications for improving road safety of e-bike riders are discussed.