Entity recognition of railway signal equipment fault information based on RoBERTa-wwm and deep learning integration.

The operation and maintenance of railway signal systems create a significant and complex quantity of text data about faults. Aiming at the problems of fuzzy entity boundaries and low accuracy of entity recognition in the field of railway signal equipment faults, this paper provides a method for entity recognition of railway signal equipment fault information based on RoBERTa-wwm and deep learning integration. First, the model utilizes the RoBERTa-wwm pretrained language model to get the word vector of text sequences. Second, a parallel network consisting of a BiLSTM and a CNN is constructed to obtain the context feature information and the local attention information, respectively. Third, the feature vectors output from BiLSTM and CNN are combined and fed into MHA, focusing on extracting key feature information and mining the connection between different features. Finally, the label sequences with constraint relationships are outputted in CRF to complete the entity recognition task. The experimental analysis is carried out with fault text of railway signal equipment in the past ten years, and the experimental results show that the model has a higher evaluation index compared with the traditional model on this dataset, in which the precision, recall and F1 value are 93.25%, 92.45%, and 92.85%, respectively.

Improved active disturbance rejection speed control for autonomous driving of high-speed train based on feedforward compensation.

Due to the complex and changeable train operation environment and the unstable and time-varying parameters, accurate modeling is limited. Therefore, a modified active disturbance rejection control algorithm based on feedforward compensation (FC-MADRC) is proposed targeting the speed control problem of trains under the circumstances of external disturbances, which reduces the dependence on the train model. Firstly, the state space equation is established based on the single-particle mathematical model of the train, and all the running resistances are regarded as disturbances. Secondly, the FC-MADRC algorithm is designed. Based on the terminal attractor function and the novel Sigmoid function, an improved tracking differentiator (ITD) is designed. An improved fal (nsfal) function with better smoothness is constructed by using the properties of the Dirac δ function, and an ameliorative nonlinear state error feedback (ANLSEF) and a modified extended state observer (IESO) are designed based on the nsfal function. Furthermore, based on the thought of PID, the integral term of error is introduced into ANLSEF for the nonlinear operation to reduce the steady-state error of train speed tracking. In order to promote the robustness and control accuracy of the system, the feedforward compensation term and disturbance compensation term are combined to perform dynamic compensation for disturbances in real time. Finally, the simulation is carried out with CRH380A train data. The results indicate that compared with conventional ADRC and 2DOF-PID, FC-MADRC has the more vital anti-disturbance ability and higher tracking accuracy. FC-MADRC has the advantages of solid anti-disturbance, fast response, and high tracking accuracy. Under the premise of external disturbance, it can still achieve accurate speed tracking under different road conditions.

Research on Named Entity Recognition Method of Metro On-Board Equipment Based on Multiheaded Self-Attention Mechanism and CNN-BiLSTM-CRF.

Massive and complex unstructured fault text data will be generated during the operation of subway trains. A named entity recognition model of subway on-board equipment based on Multiheaded Self-attention mechanism and CNN-BiLSTM-CRF is proposed to address the issue of low recognition accuracy and incomplete recognition features of unstructured fault data named entity recognition task of subway on-board equipment: BiLSTM-CNN parallel network extracts context feature information and local attention information, respectively; In the MHA layer, the features learned from different dimensions are fused through the Multiheaded Self-attention mechanism, and the dependencies of various ranges in the sequence are captured to yield the internal structure information of the features. The conditional random field CRF is used to learn the internal relationship between tags to ensure their sequence. This model is tested with other named entity recognition models on the marked subway on-board fault data. The experimental results demonstrate that this model is able to recognize 10 kinds of labels in the dataset. Moreover, the recognition effect of each label has a good performance in the three evaluation indexes of P, R, and F1 score. Moreover, the weighted average evaluation indexes Avg - P, Avg - R, and Avg - F 1 of 10 labels in this model reach the highest 95.39%, 95.48%, and 95.37%, which has high evaluation indexes and can be applied to the named entity recognition of Metro on-board equipment.

Effects of Compound Active Peptides on Protecting Liver and Intestinal Epithelial Cells from Damages and Preventing Hyperglycemia.

Active peptides have good effectiveness in controlling or preventing many diseases. Compound active peptides (CAP) obtained from animal, plant, and sea food proteins were used in this study to explore their effects on antioxidation, anti-inflammation, and antihyperglycemia in vitro and in vivo. The results demonstrated that 10 µg/mL CAP could increase cell viability (P < 0.05) and decrease reactive oxygen species (ROS) levels and cell apoptosis (P < 0.05) when WRL68 cells were induced by H2O2 for 6 h. Moreover, incubation with 20 µg/mL CAP for 6 h significantly increased cell viability and Bcl-2 expression level (P < 0.05) and decreased expression levels of IL-6, IL-8, TNF-α, Bax, and Caspase 3 and the ratio of Bax/Bcl-2 (P < 0.05) when swine jejunal epithelial cells (IPEC-J2) were induced by deoxynivalenol (DON). In addition, adding CAP individually or combined with Liuweidihuang pills (LDP, Chinese medicine) and low-dose glibenclamide could lower blood glucose levels in alloxan-induced hyperglycemic model mice. These results suggested that CAP was probably a beneficial ingredient for alleviating H2O2-induced oxidative stress and DON-induced cell inflammation and apoptosis and preventing hyperglycemia.

Expression of amyloid precursor-like protein 2 (APLP2) in glioblastoma is associated with patient prognosis.

The purpose of this study was to investigate the expression status of amyloid precursor-like protein 2 (APLP2) and its clinical relevance in patients with glioblastoma. The publically available database Project Betastasis involving Repository for Molecular Brain Neoplasia Data (REMBRANDT) and The Cancer Genome Atlas (TCGA) was first utilized to analyze the expression and prognostic potential of APLP2 in glioblastoma. Compared with normal controls, the glioblastoma group from each dataset showed no significant difference of APLP2 expression (p > 0.05). However, when connected to glioblastoma patient's prognosis, a high APLP2 expression was found to be associated with short overall survival in REMBRANDT cases (p = 0.0323) but not the TCGA group (p = 0.0578). Consistently, APLP2 expression detected by immunohistochemistry in our cohort revealed an undifferentiated expression pattern between glioblastoma (n = 114) and normal brain (n = 16) (p = 0.265) and among all grade gliomas. Furthermore, univariate and multivariate analyses identified a high APLP2 expression as an independent risk factor for overall survival (hazard ratio = 1.537, p = 0.041) and progression-free survival (hazard ratio = 1.783, p = 0.037) of glioblastoma patients. In conclusion, the expression of APLP2 might correlate with tumor development and be a prognostic factor for patients with glioblastoma.