Fruit color is one of the quality indicators to judge the freshness of a plum. The coloring process of plum skin is valuable for research due to the high nutritional quality of anthocyanins found in plums. ‘Cuihongli’ (CHL) and its precocious mutant variety ‘Cuihongli Red’ (CHR) were used to analyze the changes of fruit quality and anthocyanin biosynthesis during plum development. The results showed that, during the development of the two plums, the total soluble solid and soluble sugar contents were highest at the mature stage, as the titratable acid trended gradually downward as the fruits of the two cultivars matured, and the CHR fruit showed higher sugar content and lower acid content. In addition, the skin of CHR turned red in color earlier than CHL. Compared with CHL, the skin of CHR had higher anthocyanin concentrations, higher activities of phenylalanine ammonia-lyase (PAL), chalcone isomerase (CHI), dihydroflavonol-4-reductase (DFR), and UDPglucose: flavonoid-3-O-glucosyltransferase (UFGT), and higher transcript levels of genes associated with anthocyanin production. In the flesh of the two cultivars, no anthocyanin content was detected. Taken together, these results suggest that the mutation exerted a major effect on anthocyanin accumulation via modification of the level of transcription; thus, CHR advances the ripening period of ‘Cuihongli’ plum and improves the fruit quality.
This study describes the variation in residue behavior of fluopyram in soil, carrot root, and carrot leaf samples after the application of fluopyram (41.7% suspension, Bayer) by foliar spray or root irrigation at the standard of 250.00 g active ingredient per hectare (a.i./ha) and double-dose treatment (500.00 g a.i./ha). Fluopyram and its metabolite fluopyram-benzamide were extracted and cleaned up using the QuEChERS method and subsequently quantified with LC-QQQ-MS/MS. The LOD and LOQ of the developed method were in the range of 0.05–2.65 ug/kg and 0.16–8.82 ug/kg, respectively. After root irrigation, the final residues detected in edible parts were 0.60 and 1.80 mg/kg, respectively, when 250.00 and 500.00 g a.i./ha were applied, which is much higher than the maximum residue limit in China (0.40 mg/kg). In contrast, after spray application, most of the fluopyram dissipated from the surface of carrot leaves, and the final residues in carrot roots were both only 0.05 mg/kg. Dietary risk assessments revealed a 23–40% risk quotient for the root irrigation method, which was higher than that for the foliar spray method (8–14%). This is the first report comparing the residue behavior of fluopyram applied by root irrigation and foliar spray. This study demonstrates the difference in risk associated with the two application methods and can serve as a reference for the safe application of fluopyram.
In the traditional security situation awareness methods of power network monitoring systems, there are deviations in the analysis of different sample data sets, which affect the security situation threat value perception results of power network monitoring systems. Therefore, this paper designs a security situation awareness method for a power network monitoring system based on data mining. The identification structure of security situation elements is determined, the self-encoder is used for mapping, the security situation elements of the power network monitoring system are extracted according to the obtained parameter samples, and the security situation awareness model of the power network monitoring system is established. The RBF neural network is used for data mining of the monitoring system, and the design of the security situation awareness method of the power network monitoring system based on data mining is completed. The experimental results show that the recognition accuracy of the design method for samples with different threat levels is more than 90%. Compared with the traditional methods, the security situation awareness results obtained by the design method are closer to the expected output threat value.
Existing network security detection technologies often detect various network security problems separately. There are limitations in these technologies because of the correlation between entity attack detection and attack capital information. To resolve the problem of poor detection performance of existing network attack link detection methods, this paper proposes a network attack link detection method based on the knowledge graph. Specifically, the link knowledge graph is constructed, the embedded calculation of the knowledge graph of the link attack node is established, a knowledge graph audit model of link attack security detection is constructed, and the detection rules of the link attack knowledge graph are established to identify the association between the link network attack entity and the attack asset attribute, and improve the identification and detection of the attack source by associating the knowledge graph. Experimental results show that the proposed method has better link attack association awareness than other methods reported before.
When selecting network security indicators, the conventional security situational awareness system ignores the impact of operation and maintenance index and assets on network security, resulting in a small number of attack events perceived by the system when network nodes are attacked. In order to solve this problem, this research designs a power monitoring network security situational awareness system based on knowledge map. In terms of system hardware, the overall system architecture is composed of data layer, function layer and interface layer, and the data collector is used to collect network security risk data; In terms of system software, the knowledge map graph is used to describe the network security risk data. After calculating the network security vulnerability index, assets, security event compensation and operation and maintenance index, the calculation results are used as the network security index to obtain the network security situation value. The test results show that the system can reflect the attack events in all attack stages, and the number of perceived attack events is significantly more than that of the conventional system, indicating that the system can effectively perceive the security situation of the power monitoring network.
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