Train drivers are therefore expected to regularly perform security procedures in response to reports of suspicious items on the train.
In real-life working conditions, all the participants had received the report of a suspicious item on the train at least once, with a maximum of six reports received by two participants.
On days that a specific warning was issued about a terrorist attack, the drivers believed that there was a greater probability of a suspicious item on the train being an actual explosive device than on other days of operation.
Both during their classroom training, and while driving the simulated train, the participants rehearsed various SOPs, including the procedure corresponding to the report of a suspicious item on the train.
Therefore, it is possible that their views on management of a suspicious item on the train were different from older, more seasoned drivers with a longer experience of driving trains and responding to a large number of security incidents.
An important part of the focus group interviews was generation of a hypothetical scenario that required each participant to imagine the report of a suspicious item on the train, and then talk through it.
Clayton et al. (2003b) (see also Salwiczek et al. 2008) have argued that learning about the properties of the food items during the training in their experiment with the scrub jays could be viewed as the acquisition of semantic information that is applicable to different events in a flexible way.
The authors examined item-specificity of the trained items across each block.
Including an item in the training programme of the teachers on the "constraints and/or barriers" encountered in the course of implementation of the edutainment programme and on ways and means to overcome them will help them devise solutions as appropriate to overcome them.
With all different classification settings, we performed a leave-one-out cross-validation: each item in the training set is classified with a model built with the rest of the training set items.
The zero-shot decoding model was trained by using 58 of the target items and the training data was used to predict the semantic coordinates of the two left-out target items.
