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Today, in the face of the urgent need to decarbonize global transportation, the development of electric vehicles (EVs) has become one of the core strategies to address energy crises and environmental challenges [...]

Background. Primary V1 trigeminal neuralgia is a common refractory neuralgia in clinical practice, lacking effective treatments. Radiofrequency therapy has certain treatment efficacy, but its long-term efficacy remained poor and the disease might relapse. Objective. To compare the effects of different types of supraorbital foramen variations on the treatment efficacy of radiofrequency therapy for V1 trigeminal neuralgia. Methods. Data of 54 patients with V1 trigeminal neuralgia who underwent treatment in the First Hospital of Jiaxing, Zhejiang, were retrospectively analyzed. All these patients received CT-guided radiofrequency thermocoagulation of supraorbital nerve. According to the CT images, the supraorbital foramen of the patients was categorized as holes (hole group) or notches (notch group). The patient characteristics, including Numerical Rating Scale (NRS) score and effective treatment rates before and 1 d, 0.5 y, 1 y, and 2 y after operation, and numbness degree at day 1 and 2 y after the operation were compared. The short- and long-term complications during postoperative follow-up period were also recorded. Results. Among the 54 patients, 25 patients were grouped into the hole group and 29 into the notch group. The NRS scores before and at 1 d, 0.5 y, 1 y, and 2 y after operation showed no significant differences between the two groups. However, the NRS scores at the remaining time points after operation were significantly decreased when compared with scores before operation (P<0.05). The numbness and numbness degree after operation showed no significant differences between the two groups. The numbness degree at 2 y after operation was significantly lower than 1 d after operation (P<0.05). The effective rate at 1 d, 0.5 y, and 1 y after operation showed no significant differences between the hole and notch groups. However, the effective rate at 2 y after operation was significantly lower in the notch group than hole group (P<0.05). No severe short- or long-term complications were found in either group. Conclusion. The short- and long-term effective rates of radiofrequency therapy during V1 trigeminal neuralgia treatment are relatively high in patients with different types of supraorbital foramen variations. However, the effective rate is even higher in patients with hole-type supraorbital foramen. No other severe complications, except numbness, were found, and the acceptability rate remained high in patients.

In the actual knowledge extraction system, different applications have different entity classes and relationship schema, so the generalization and migration ability of knowledge extraction are very important. By training a knowledge extraction model in the source domain and applying the model to an arbitrary target domain directly, open domain knowledge extraction technology becomes crucial to mitigate the generalization and migration ability issues. Traditional knowledge extraction models cannot be directly transferred to new domains and also cannot extract undefined relation types. In order to deal with the above issues, in this paper, we proposed an end-to-end Chinese open-domain knowledge extraction model, TPORE (Extract Open-domain Relations through Token Pair linking), which combined BERT with a handshaking tagging scheme. TPORE can alleviate the nested entities and nested relations issues. Additionally, a new loss function that conducts a pairwise comparison of target category score and non-target category score to automatically balance the weight was adopted, and the experiment results indicate that the loss function can bring speed and performance improvements. The extensive experiments demonstrate that the proposed method can significantly surpass strong baselines. Specifically, our approach can achieve new state-of-the-art Chinese open Relation Extraction (ORE) benchmarks (COER and SAOKE). In the COER dataset, F1 increased from 66.36% to 79.63%, and in the SpanSAOKE dataset, F1 increased from 46.0% to 54.91%. In the medical domain, our method can obtain close performance compared with the SOTA method in the CMeIE and CMeEE datasets.

Knowledge graphs are a popular research field in artificial intelligence, and store large amounts of real-world data. Since data are enriched over time, the knowledge graph is often incomplete. Therefore, knowledge graph completion is particularly important as it predicts missing links based on existing facts. Currently, the family of translation models delivers a better performance in knowledge graph completion. However, most of these models randomly generate negative triplets during the training process, resulting in the low quality of negative triplets. In addition, such models ignore the important characteristics of triplet-mapping properties during model learning. Therefore, we propose an optimization framework based on the translation models (Op-Trans). It enhances the knowledge-graph completion effect from both negative sampling and triplet-mapping properties. First, we propose a clustering cache to generate negative triplets, which generate negative triplets based on entity similarity. This sampling method can directly use the cache to track the negative triplets with large scores. In addition, we focus on the different contributions of the triplets to the optimization goal. We calculate the distinct weight for each triplet according to its mapping properties. In this way, the scoring function deals with each triplet depending on its own weight. The experimental results show that Op-Trans can help the state-of-the-art baselines to obtain a better performance in a link prediction task.

With the continuous development of new media technology, the spiritual needs of the masses have been greatly satisfied and the aesthetic ability has also been significantly improved compared with the past. From the current point of view, “literary works,” as the spiritual food of contemporary people, are promoting social spirit. The use of natural language processing and knowledge graph technology can improve cultural cognition to promote the dissemination and development of classic English literature, which has become a necessary means of dissemination of classic English literature. Most of the existing classic English literary works are appreciated based on modern literature datasets. Nowadays, with the continuous development of new media technology, there are fewer studies on the dissemination and cultural cognition of classic English literary works. This makes it impossible for readers to obtain cultural cognition from classic English literary works, making it difficult for the dissemination and development of classic English literary works. In view of the above problems, using natural language processing and knowledge graph technology, taking Shakespeare's play “Hamlet” represented by classic English literary works as an example, the research on the construction method of knowledge graph is carried out and the cultural characteristics in literary works are extracted and analyzed. In parsing, a bidirectional gated recurrent unit network model based on hybrid character embedding is proposed. Based on n-gram embedding, by combining pretraining embedding and radical embedding, it can fully consider the rich semantic information in English literature works to extract. Feature: in terms of named entity recognition, based on the existing iterative atrous convolutional network model, an iterative atrous convolutional network model is proposed. To get the best sequence label and get the last labeled entity information, in terms of knowledge graph construction and visual query, a workflow method for building knowledge graph from unstructured text is proposed and a flask-based knowledge graph visual query system is designed, which applies the best model of the above two tasks. We decode the complete “Hamlet” text, extract entities and their semantic links as nodes and relationships in the knowledge graph, store knowledge through the graph database, and finally form a visual query system that combines the front and back end.