A Smart Grid Overvoltage Identification System Associated with Partial Discharge Signal and Dielectric Loss Detection.

Capacitive equipment refers to its insulation design using the principle of capacitance of electrical equipment, mainly by a variety of different capacitive components in series. Most of the equipment in the substation is capacitive equipment. Once an insulation failure occurs, it will lead to extremely serious consequences. Monitoring grid overvoltage and insulation degradation of capacitive equipment is an effective means to ensure the stable operation of the power system. Therefore, in order to enhance the health management of capacitive equipment, including transformers, bushings, and current transformers, and to mitigate the risk of severe failures, it is imperative to conduct broad-spectrum frequency-domain online monitoring of overvoltages, dielectric losses, and partial discharge. However, the current monitoring work requires the utilization of multiple detection apparatuses. Aiming at the disadvantage that the existing inspection is not well integrated and requires a combination of multiple devices. This paper proposes a smart grid overvoltage identification system that utilizes partial discharge (PD) signals in correlation with dielectric loss detection. The system achieves synchronous detection of dielectric loss and high-frequency partial discharge by synchronously and in real-time acquiring four current signals from the power grid, enhancing the integration level of the hardware system.

Comparing diversity patterns and processes of microbial community assembly in water column and sediment in Lake Wuchang, China.

The study compare the diversity patterns and processes of microbial community assembly in the water and sediment of Lake Wuchang (China) using high-throughput sequencing of 16S rRNA gene amplicons. A higher microbial α-diversity in the sediment was revealed (P < 0.01), and the most common bacterial phyla in water column were Proteobacteria, Cyanobacteria and Actinobacteria, while Proteobacteria, Acidobacteria, Chloroflexi and Nitrospirae were dominant in sediment. Functions related to phototrophy and nitrogen metabolism primarily occurred in the water column and sediment, respectively. The microbial communities in water column from different seasons were divided into three groups, while no such dispersion in sediment based on PCoA and ANOSIM. According to Pearson correlation analysis, water temperature, dissolved oxygen, water depth, total nitrogen, ammonium, and nitrite were key factors in determining microbial community structure in water column, while TN in sediment, conductivity, and organic matter were key factors in sediment. However, the stochastic processes (|ßNTI| < 2) dominated community assembly in both the water column and sediment of Lake Wuchang. These data will provide a foundation for microbial development and utilization in lake water column and sediment under the circumstances of increasing tendency of lake ecological fishery in China.

A Wide-Deep-Sequence Model-Based Quality Prediction Method in Industrial Process Analysis.

Product quality prediction, as an important issue of industrial intelligence, is a typical task of industrial process analysis, in which product quality will be evaluated and improved as feedback for industrial process adjustment. Data-driven methods, with predictive model to analyze various industrial data, have been received considerable attention in recent years. However, to get an accurate prediction, it is an essential issue to extract quality features from industrial data, including several variables generated from supply chain and time-variant machining process. In this article, a data-driven method based on wide-deep-sequence (WDS) model is proposed to provide a reliable quality prediction for industrial process with different types of industrial data. To process industrial data of high redundancy, in this article, data reduction is first conducted on different variables by different techniques. Also, an improved wide-deep (WD) model is proposed to extract quality features from key time-invariant variables. Meanwhile, an long short-term memory (LSTM)-based sequence model is presented for exploring quality information from time-domain features. Under the joint training strategy, these models will be combined and optimized by a designed penalty mechanism for unreliable predictions, especially on reduction of defective products. Finally, experiments on a real-world manufacturing process data set are carried out to present the effectiveness of the proposed method in product quality prediction.

Differentially expressed immune-related genes in hemocytes of the pearl oyster Pinctada fucata against allograft identified by transcriptome analysis.

The pearl oyster Pinctada fucata is commonly cultured for marine pearls in China. To culture pearls, a mantle piece from a donor pearl oyster is grafted with a nucleus into a receptor. This transplanted mantle piece may be rejected by the immune system of the recipient oyster, thus reducing the success of transplantation. However, there have been limited studies about the oyster's immune defense against allograft. In this study, hemocyte transcriptome analysis was performed to detect the immune responses to allograft in P. fucata at 0 h and 48 h after a transplant. The sequencing reaction produced 92.5 million reads that were mapped against the reference genome sequences of P. fucata. The Gene Ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify all immune-related differentially expressed genes (DEGs). Compared with patterns at 0 h, a total of 798 DEGs were identified, including 410 up-regulated and 388 down-regulated genes at 48 h. The expression levels of interleukin receptor and toll-like receptor in hemocytes were increased significantly 48 h post-transplant, indicating that the oyster immune response was induced. Finally, altered levels of 18 randomly selected immune-related DEGs were confirmed by quantitative real-time PCR (qRT-PCR). Our results provide the basis for further analysis of the immune rejection of allotransplantation.

High carotenoids content can enhance resistance of selected Pinctada fucata families to high temperature stress.

Carotenoids are a class of natural antioxidants widely found in aquatic, and they have significant effects on the growth, survival, and immunity of these organisms. To investigate the mechanisms of carotenoids in high temperature resistance, we observed the immune response of selected pearl oyster Pinctada fucata (Akoya pearl oyster) families with different carotenoids contents to high temperature stress. The results indicated that the survival rate (SR) of P. fucata decreased significantly with increase in temperature from 26 °C to 34 °C and with the decrease of total carotenoids content (TCC); when the TCC was higher, the SR tended to be higher. TCC and total antioxidant capacity (TAC) decreased significantly at 30 °C with increasing stress time. Correlation analysis indicated that TAC was positively and linearly correlated with TCC, and SR was S-type correlated with TCC and TAC. Immune analysis indicated that levels of superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA) in selected families (with higher TCC) under temperature stress (at 30 °C) were generally significantly lower than in the control group (with lowest TCC) and from 0 to 96 h, the levels of each of these substances varied significantly. Levels of SOD, CAT, and MDA within each family first rose from 0 to 3 h, then decreased to their lowest point after 24 h, and then rose again to their highest levels at 96 h. When TCC was higher, the levels of SOD, CAT, and MDA tended to be lower. These findings indicated that carotenoids play an important role in improving survival rates of P. fucata under high temperature stress by enhancing animals' antioxidant system, and could serve as an index for breeding stress-resistant lines in selective breeding practices.