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Based on the Markov model, a fault warning system for power grid operation is proposed. Its main objective is to improve the execution effect of alarm commands. Firstly, the power grid operation reliability evaluation model based on the Markov model is established. According to the set critical value to judge the change of power grid operation state. Develop abnormal alarm commands for power grid operation. Strange events are described in detail using natural language technology, and corresponding instructions are completed in the system. After the comparative test, the new alarm improved more remarkably than the traditional intelligent alarm mode.

This paper introduces a communication network health monitoring technology and integrated operation management platform based on a power switching network. Firstly, network characteristics are described from technology, structure and service function. Separate regular communication networks. This paper studies a new condition-monitoring technology based on a power-switching network. This paper studies the topology of the AC power network. The condition of the network is carried out through the neural network. According to the perception results, an integrated operation management system is constructed to provide users with relevant data. This section describes the network fault status, fault diagnosis, and features of fiber hopping services. This paper offers some suggestions for operating and maintaining a power-switched network.

Purpose: This study aims to enhance the efficiency and effectiveness of Chinese Clinical Named Entity Recognition by improving the Bert-BiLSTM-CRF model through the adoption of the RoBERTa pre-training model. Design/methodology/approach: A deep learning approach is employed, combining the RoBERTa pre-training model, Bi-directional Long Short-Term Memory (BiLSTM) network, and Conditional Random Field (CRF) model to form a Named Entity Recognition (NER) model. The model takes the pre-training model trained by the deep network model as input, mitigates the scarcity of annotated datasets, leverages the strong advantage of BiLSTM in learning the context information of words, and combines the CRF model to infer the ability of labels through global information. Findings: The RoBERTa-BiLSTM-CRF model has shown satisfactory results in the experiment. It enhances the reasoning ability between characters, allows the model to fully learn the feature information of the text, and improves the model performance to a certain extent. Originality/value: This paper proposes a RoBERTa medical named entity recognition model for the scarcity of annotated data in medical named entity recognition tasks and BERT's inability to obtain word-level information. The model is not limited to medical entity recognition tasks and shows potential for other medical natural language processing tasks, considering data enhancement, data optimization, and domain transfer on the model to improve model performance and generalization capabilities. [ABSTRACT FROM AUTHOR]

In view of poor timeliness， low accuracy and insufficient visualization of existing methods of mountain fire prevention inspection and monitoring in transmission channels， the paper proposes a method based on relaying cooperative operation of two UAVs to carry out mountain fire inspection in transmission channels in combination with the characteristics of UAVs such as high efficiency and flexibility. The cooperative inspection mode of UAV can maintain the UAV data link through relaying technology and transmit back the video data collected by the UAV in real time， which facilitates to timely carry out disaster prevention decision and deployment and solves the problems of complex terrain-blocked data link signal and real-time monitoring of fire in transmission channels. The practical results show that the high-frequency UAV relaying cooperative fire prevention inspection operation for important transmission channels during the period of a high incidence of mountain fires is effective in discovering the fire hazards at the initial stage and provides a new technical means for the differentiated fire prevention inspection of transmission channels.

This study focuses on three different regions of moso bamboo (Phyllostachys edulis): an inner layer (IB), middle layer (MB), and outer layer (OB), to comprehensively characterize the structural features, chemical composition (ash, extractives and lignin contents), and the lignin monomeric composition as determined by analytical pyrolysis. The results show that bamboo presents a gradient structure. From the IB to OB, the vascular bundle density and fiber sheath ratio increase, the porosity decreases (from 45.92% to 18.14%), and the vascular bundle diameter–chord ratio increases (from 0.85 to 1.48). In terms of chemical composition, the ash, extractives, and acid-soluble lignin content gradually decrease from IB to OB. The holocellulose content follows the trend: MB (66.3%) > OB (65.9%) > IB (62.8%), while the acid-insoluble lignin content exhibits the opposite trend: IB (22.6%) > OB (17.8%) > MB (17.7%). Pyrolysis products reveal the diversity of carbohydrates and lignin derivatives, with a lignin monomeric composition rich in syringyl and guaiacyl units and lower amounts of H-units: the IB has an H:G:S relation of 18:26:55, while 15:27:58 is the ratio for the MB and 15:40:45 for the OB; S/G ratio values were, respectively, 1.22, 1.46, and 0.99. A comprehensive analysis highlights significant gradient variations in the structure and chemistry of bamboo, providing robust support for the classification and refinement methods of bamboo residues for potential applications. [ABSTRACT FROM AUTHOR]

It is important to improve the overall quality of rice grown in northeastern China, which is famous for its high yield. Therefore, it is necessary to study the quality of japonica rice resources in the region. This study analyzed 71 rice samples from the Liaoning, Heilongjiang, and Jilin Provinces, focusing on grain shape, endosperm composition, and taste. Significant regional differences were observed in taste and edibility, with the samples from Heilongjiang and Jilin generally scoring higher taste value, which was significantly higher than the Liaoning variety. However, the Liaoning varieties were more stable in terms of the sensory evaluation of taste. Obvious differences in grain length were detected among the samples, with the varieties from Jilin and Heilongjiang Provinces predominantly having short and medium-to-long grains, respectively, and those from Liaoning Province having diverse grain shapes. However, there was no correlation between grain shape and taste. This indicated that the taste of rice is not affected solely by its external morphology but also by multiple interacting factors. There were significant differences in endosperm composition among the three provinces, but the amylose content of the tested varieties was generally less than 18%, and the protein content was about 6%. Additionally, the Jilin Province varieties had the lowest apparent amylose content and long chain of amylopectin (Fb3), the Heilongjiang Province varieties had the lowest protein content, and there were more medium chains of amylopectin. Notably, protein content was negatively correlated with taste but positively correlated with pasting properties. It can be seen that the premise of varieties in different provinces showing good taste characteristics is to ensure the content of amylose and protein is low, and adjust the proportion of amylopectin chain length. [ABSTRACT FROM AUTHOR]