Abstract.As an important indicator of employees' psychological contract and organizational trust, psychological safety is a kind of safety awareness based on the psychological climate of certain events in organization, current scholars generally divided it into three levels: individual, group, organizational psychological safety. Its influencing factors can be divided into individual factors, interpersonal factors, leadership features and organizational context four aspects;its main outcome variables conclude the knowledge sharing, voice, innovation, job involvement and job performance. Finally, the study points out the shortcomings of existing research and future research directions.

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Flight mission planning is one of the key technologies of aircraft system. In addition, it is the basis of precise guidance and testified in practices. Research and implement of aircraft mission planning are very important work of building common designing and testing platform. Mission planning technologies and algorithms including general technology of mission planning, processing of some restricted conditions, pretreatment of digital map, flight planning algorithms, flight re-planning methods, system integrating, system testing, system assessing and so on. All these works are essential to build software tools and farther studying.

In pervasive computing, the computer is no longer a single computing device, but an information device with the embedded processor, memory, communication module and various sensors together. This paper puts forward an algorithm to improve precision of navigation for space aircraft based on time compensation. The algorithm involves two-body movement time to correct the oblateness perturbation flying time, and then, improve the precision of navigation. On this basis, taking the advantage of pervasive computing in space aircraft application field, we involve pervasive computing technology into oblateness perturbation correction calculation. It provides the guarantee to precision of navigation for space aircraft, and enhances the auto-adaptability of space aircraft in complex space environment.

Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.