Microbial community analysis in biocathode microbial fuel cells packed with different materials

Biocathode MFCs using microorganisms as catalysts have important advantages in lowering cost and improving sustainability. Electrode materials and microbial synergy determines biocathode MFCs performance. In this study, four materials, granular activated carbon (GAC), granular semicoke (GS), granular graphite (GG) and carbon felt cube (CFC) were used as packed cathodic materials. The microbial composition on each material and its correlation with the electricity generation performance of MFCs were investigated. Results showed that different biocathode materials had an important effect on the type of microbial species in biocathode MFCs. The microbes belonging to Bacteroidetes and Proteobacteria were the dominant phyla in the four materials packed biocathode MFCs. Comamonas of Betaproteobacteria might play significant roles in electron transfer process of GAC, GS and CFC packed biocathode MFCs, while in GG packed MFC Acidovorax may be correlated with power generation. The biocathode materials also had influence on the microbial diversity and evenness, but the differences in them were not positively related to the power production

Evaluation and Improvement of Pumping Well Operating Conditions in an Oil Field Block Based on Grey Correlation Analysis

The "Oil and Gas Water Well Production Data Management System Database" provides great assistance for oilfield production, monitoring, and management. However, due to the harsh conditions of oil field wells and the lack of some test data, traditional management methods are no longer suitable for present condition. At the same time, optimization analysis for a single oil well has a high cost and low efficiency, and it is difficult to achieve the modern management goal of large-scale pumping well groups. In this paper, the grey correlation method is used to analyze the direct correlation between the influencing factors and the system efficiency, surface equipment driving efficiency, and wellbore lifting efficiency, and the improvement method against factors with strong correlation is prioritized. A multi-node evaluation index system for pumping well systems and corresponding improvement methods were constructed, and evaluation software was compiled. This technology considers the running condition of the pumping unit in one oil field block, and selects the oil wells to be improved according to the evaluation index, and puts forward the targeted improvement methods according to the common problems of the oil well. This paper provides a set of reliable technical methods for the efficient management of the oil well in the oil field block

Robust Mid-Pass Filtering Graph Convolutional Networks

Graph convolutional networks (GCNs) are currently the most promising paradigm for dealing with graph-structure data, while recent studies have also shown that GCNs are vulnerable to adversarial attacks. Thus developing GCN models that are robust to such attacks become a hot research topic. However, the structural purification learning-based or robustness constraints-based defense GCN methods are usually designed for specific data or attacks, and introduce additional objective that is not for classification. Extra training overhead is also required in their design. To address these challenges, we conduct in-depth explorations on mid-frequency signals on graphs and propose a simple yet effective Mid-pass filter GCN (Mid-GCN). Theoretical analyses guarantee the robustness of signals through the mid-pass filter, and we also shed light on the properties of different frequency signals under adversarial attacks. Extensive experiments on six benchmark graph data further verify the effectiveness of our designed Mid-GCN in node classification accuracy compared to state-of-the-art GCNs under various adversarial attack strategies.Comment: Accepted by WWW'2