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The purpose of this paper is to develop and assess an organizational intervention consisting of psychological first aid (PFA) and Open Space Technology (OST), and its impact on individual resilience and perceived organization support.

The study used a non-experimental, pre-test and post-test design. Measures of employee post-trauma, resilience and organizational support were measured before and after the PFA intervention.

Paired sample t-tests revealed significant pre/post-increases in individual resilience and perceived organization support. Correlational analysis revealed that resilience was associated with perceived organization support. Evaluations revealed that participants found the small group sharing, information about coping and the open space problem-solving activities particularly worthwhile.

A limitation of the study was the lack of a randomized control group in the design. Future research may utilize more robust designs such as experimental and longitudinal studies to evaluate impact.

This study indicates how the use of an organization-based intervention can be adopted for employees who undergo an emergency in their workplace. The combination of PFA and OST was found to be valuable in improving individual resilience and perceived organization support. In addition, OST can better facilitate problem-solving performance in intact groups, as it enhances collective interaction and community efficacy among survivors.

The study contributes to the dearth of knowledge on the use of PFA when used in an intact organization as part of its crisis intervention.

This paper aims to automate the picking task needed in robotic assembly. Parts supplied to an assembly process are usually randomly staked in a box. If randomized bin-picking is introduced to a production process, we do not need any part-feeding machines or human workers to once arrange the objects to be picked by a robot. The authors introduce a learning-based method for randomized bin-picking.

The authors combine the learning-based approach on randomized bin-picking (Harada et al., 2014b) with iterative visual recognition (Harada et al., 2016a) and show additional experimental results. For learning, we use random forest explicitly considering the contact between a finger and a neighboring object. The iterative visual recognition method iteratively captures point cloud to obtain more complete point cloud of piled object by using 3D depth sensor attached at the wrist.

Compared with the authors’ previous research (Harada et al., 2014b) (Harada et al., 2016a), their new finding is as follows: by using random forest, the number of training data becomes extremely small. By adding penalty to occluded area, the learning-based method predicts the success after point cloud with less occluded area. We analyze the calculation time of the iterative visual recognition. We furthermore make clear the cases where a finger contacts neighboring objects.

The originality exists in the part where the authors combined the learning-based approach with the iterative visual recognition and supplied additional experimental results. After obtaining the complete point cloud of the piled object, prediction becomes effective.