At present, span-based entity recognition methods are mainly used to accurately identify the span (entity) boundary for entity recognition, in which the relative position information of the span boundary and the information of words in the span region are routinely ignored. This information can be used to improve entity recognition performance. Therefore, a nested entity recognition model, which integrates the relative position information of the span and the region information within the span, is proposed. The span representation is first obtained with a triaffine attention. Then, the relative position of the span boundary and the word information in the span region, as well as the previous span representation, are fused to obtain a new label-level span representation with another triaffine attention. Finally, the span (entity) recognition task is carried out by a cooperative biaffine mechanism. Experiments were conducted on some public datasets, including ACE2004, ACE2005 and GENIA. The results show that the F1-scores achieved using the proposed method were 87.66%, 86.86% and 80.90% on ACE2004, ACE2005 and GENIA, respectively. These experiments show that the method achieved state-of-the-art (SOTA) results. Moreover, the proposed model has fewer parameters and needs fewer resources with a lower time complexity than the existing triaffine mechanism model.

SN 2023ehl, a normal Type Ia supernova with a typical decline rate, was discovered in the galaxy UGC 11555 and offers valuable insights into the explosion mechanisms of white dwarfs. We present a detailed analysis of SN 2023ehl, including spectroscopic and photometric observations. The supernova exhibits high-velocity features in its ejecta, which are crucial for understanding the physical processes during the explosion. We compared the light curves of SN 2023ehl with other well-observed Type Ia supernovae, finding similarities in their evolution. The line strength ratio R(Si ii) was calculated to be 0.17 ± 0.04, indicating a higher photospheric temperature compared to other supernovae. The maximum quasi-bolometric luminosity was determined to be 1.52 × 10⁴³ erg s⁻¹, and the synthesized ⁵⁶Ni mass was estimated at 0.77 ± 0.05 M _⊙. The photospheric velocity at B-band maximum light was measured as 10,150 ± 240 km s⁻¹, classifying SN 2023ehl as a normal velocity Type Ia supernova. Our analysis suggests that SN 2023ehl aligns more with both the gravitationally confined detonation, providing a comprehensive view of the diversity and complexity of Type Ia supernovae.

BACKGROUND Breast cancer is one of the most common malignant tumors in women, severely threatening the health of women worldwide. Designing an effective data management and processing system to help collect, manage, and use variables for breast cancer diagnosis and treatment has become an urgent need. As an important part of artificial intelligence, a knowledge graph provides an ideal means to solve this problem. OBJECTIVE Our study intends to utilize the natural language processing (NLP) technique on Chinese breast cancer mammography reports to effectively identify and extract the features related to breast cancer and construct a knowledge graph for breast cancer diagnosis. METHODS This paper focuses on the knowledge graph frame structure and feature extraction that were the main challenges for constructing a Chinese breast cancer diagnosis knowledge graph. Based on mammography examination guidelines and specifications, as well as clinical experiences and recommendations of experts in the hospital, we define entities, entity attributes, and entity relationships for constructing the concept layer of a knowledge graph. From mammography examination reports, we extract mammographic features using deep learning models, with which we build a knowledge graph for breast cancer diagnosis. RESULTS When annotating mammography examination reports in NLP tasks, we have identified 15 important types of mammographic features. To improve the versatility of the constructed knowledge graph, we have added additional 7 types of mammographic features. Mammographic features are extracted from a total of 1171 mammography examination reports. For the overall results of the model, the recognition accuracy rate is 98.97%, the accuracy rate is 97.16%, the recall rate is 98.06%, and F1 is 97.61. Based on the structure of the concept layer of the knowledge graph, we import the demographic risk factors and mammographic features extracted from the text reports into the Neo4j graph database to complete the construction of the knowledge graph. CONCLUSIONS We constructed a Breast Cancer Diagnosis Knowledge Graph Based on Chinese Electronic Medical Records. Through the evaluation of the design of the concept layer, the construction of the data layer, and the functions of the application layer, the rationality, effectiveness, and practicability of the knowledge graph are demonstrated. This study provides a reference for the rapid design and construction of knowledge graph for other disease diagnosis and treatment.