Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Network Pharmacology of Xian-Lian-Jie-Du Decoction in Ameliorating Colorectal Cancer

Objective:In this study,we screened for therapeutic targets of the Xian-Lian-Jie-Du decoction(XLJDD)for colorectal cancer(CRC)and explored the underlying mechanisms using network pharmacology techniques.Methods:Genes associated with CRC were collected from the Gene Cards database.The names of the active compounds of XLJDD were used as keywords in the“chemical name”in the Traditional Chinese Medicine Systems Pharmacology(TCMSP)database to search the targets.The protein-protein interaction(PPI)network was constructed using Cytoscape 3.8.1.Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses were performed to identify key target proteins.Results:A total of 234 XLJDD-related targets and 250 cross-targets between XLJDD and CRC were collected based on the TCMSP and HIT 2.0 databases.A PPI network constructed based on the STRING database revealed interactions for all 250 proteins.The network results revealed TP53,MYC,CCND1,AKT1,CASP3,and STAT3 as core potential targets.KEGG pathway analysis of the 250 potential XLJDD targets for CRC in the Metascape database was performed using RStudio software.The top 12 gene ratio aggregated analysis results were visualized in bubble charts.The interleukin(IL)-17 signaling pathway had the highest correlation with the tumor necrosis factor(TNF)signaling pathway.Conclusions:XLJDD may be effective in ameliorating CRC by controlling inflammatory factors related to the IL-17 and TNF pathways and targeting proto-oncogenes and tumor suppressor genes,including MYC,CCND1,CTNNB1,and TP53.