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The high consequence areas of long-distance oil and gas pipelines are usually provided with technical defense by video surveillance to identify the third-party damage events such as manual excavation, mechanical excavation and heavy vehicles rolling around the pipelines in high consequence areas. However, video surveillance often relies on the screen monitoring of the personnel on duty, which has insufficient effectiveness. For this reason, an intelligent identification method of third-party damage in high consequence areas of the pipeline based on depth learning was proposed. Specifically, the collected images and videos along the line were analyzed, the feature targets were extracted, and an image intelligent identification model based on YOLO v5 was established. Generally, the model improves the optimization speed and target detection accuracy. After 226 iterations, the model training has the process loss function values of training and validation close to 0 and 0.01, respectively, reaching the optimal state. The research method was tested in the video surveillance of a pipeline section in a high consequence area in Tianjin, with a detection accuracy of up to 99.33%, which verified the effectiveness of the method and provided engineering application reference for the subsequent intelligent identification and real-time early warning of the video surveillance system in the high consequence areas.

Graphitic carbon nitride (g-C3N4) has attracted extensive research interest in pollutants remediation. However, the photocatalytic activity of g-C3N4 was significantly limited by its small specific surface area. In this work, a green, high-energy ball milling method was used to fabricate g-C3N4 nanosheets. The structure, morphology, and optical properties of the prepared g-C3N4 nanosheets were characterized. The effect of ball milling parameters on the photocatalytic performance evaluated by Rhodamine B (RhB) and tetracycline (TC) was investigated systemically. Among the tested samples, the g-C3N4 sample milled with a 4 mL isopropanol solution at a rotation speed of 420 rpm, ball-to-powder weight ratio of 10:1, and milling time of 24 h exhibited the highest RhB degradation efficiency of 91.4% and TC degradation efficiency of 70.2%. The enhanced photocatalytic activity after ball milling was ascribed to the increase in specific surface area and efficient separation of electron–hole pairs. The trapping experiment indicated that holes and superoxide radicals were the main active species in the degradation reaction. Moreover, the photocatalytic degradation mechanism of organic pollutants on g-C3N4 nanosheets was also discussed in detail.

Background Cardiovascular disease (CVD) is a common and frequently occurring disease, and the prevalence and mortality of which are increasing rapidly. Atherosclerosis (AS) is the pathological basis of ischemic CVD. Studies have shown that epicardial adipose tissue (EAT) promotes the progression of AS by secreting exosomes and bioactive substances, but the mechanism of action still needs to be further studied. Objective To perform a bioinformatics analysis of role of EAT in coronary artery disease (CAD) at cellular and molecular levels by identifying the differentially expressed genes (DEGs) in EAT to explore the status of immune cell infiltration, and to assess and verify whether EAT is derived from DEGs in exosomes in CAD patients. Methods We downloaded GSE64554 and GSE120774 datasets about EAT from the GEO database and performed a bioinformatics analysis using R language and related packages. We first used R language to screen the DEGs in EAT and CAD patients, then used GO/KEGG enrichment analysis to establish a protein interaction network to explore biological functions of the screened genes and transcription factors potentially involved in their regulation process. After that, we conducted a weighted gene co-expression network analysis (WGCNA) of EAT in GSE64554 dataset to obtain a gene module related to CAD phenotype, then crossed the hub genes in this module and DEGs in EAT to obtain the key common genes. We used Cibersort to characterize the immune cell infiltration in EAT. Then we obtained DEGs from blood exosomes of CAD patients and healthy controls included in the exoRbase database, crossed DEGs in EAT and blood exosomes to identify the common genes to be used as diagnostic and therapeutic markers for CAD, and their values were tested by qRT-PCR measurement of clinical samples. The selected genes were analyzed by GO/KEGG and Metascape enrichment analyses. Results A total of 1 511 DEGs in EAT of CAD patients were identified, including 956 with up-regulated expression and 555 with down-regulated expression. By crossing the DEGs in EAT and hub genes in modules associated with CAD closely, we identified DDX47, FEM1C, NOL11, SRP54, ABI1, PATL1, BNIP2, C1orf159, and CHCHD4 as key genes in the development of CAD. Immune cell infiltration analysis showed that the abundance of immature CD4+ T cells increased while expression abundance of resting dendritic cells decreased in EAT of CAD patients (P0.05), and decreased F2RmRNA expression level (P