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This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

This study aims to control the oxidation resistance of Co-based deformed superalloys by adding trace elements Hf and Si.

The effects and mechanism of trace elements Hf and Si on the oxidation behavior of Co-Ni-Al-W-based forged superalloys were investigated by cyclic oxidation at 900°C.

The results show that the addition of trace elements Hf and Si does not affect the type of surface oxides of Co-Ni-based superalloys, and the oxidation layers of the alloys are TiO₂, spinel, Cr₂O₃, TaTiO₄, Al₂O₃ and TiN from outside to inside. However, the addition of elements can affect the activity of Cr and Ti elements; decrease the formation of TiO₂ and TaTiO₄ layers, which are harmful to the oxidation performance; and then improve the oxidation resistance of the alloy.

The relevant research results can not only optimize the microalloying element content of Co-Ni-Al-W-based superalloys, but also provide a new perspective for the composition optimization design of superalloys.