Corrosion is the process of damaging materials, and corrosion of metallic materials frequently results in serious consequences. The addition of corrosion inhibitors is the most effective means of preventing metal corrosion. Until now, researchers have made unremitting efforts in the research of high-efficiency green corrosion inhibitors, and research on biomass corrosion inhibitors in a class of environmentally friendly corrosion inhibitors is currently quite promising. This work presents the classification of green biomass corrosion inhibitors in detail, including plant-based corrosion inhibitors, amino acid corrosion inhibitors, and biosurfactant corrosion inhibitors, based on the advantages of easy preparation, environmental friendliness, high corrosion inhibition efficiency, and a wide application range of biomass corrosion inhibitors. This work also introduces the preparation methods of biomass corrosion inhibitors, including hydrolysis, enzymatic digestion, the heating reflux method, and microwave extraction. In addition, the corrosion inhibition mechanisms of green biomass corrosion inhibitors, including physical adsorption, chemisorption, and film-forming adsorption, and evaluation methods of biomass corrosion inhibitors are also explicitly described. This study provides valuable insights into the development of green corrosion inhibitors.

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Network decrease caused by the removal of nodes is an important evolution process that is paralleled with network growth. However, many complex network models usually lacked a sound decrease mechanism. Thus, they failed to capture how to cope with decreases in real life. The paper proposed decrease mechanisms for three typical types of networks, including the ER networks, the WS small-world networks and the BA scale-free networks. The proposed mechanisms maintained their key features in continuous and independent decrease processes, such as the random connections of ER networks, the long-range connections based on nearest-coupled network of WS networks and the tendency connections and the scale-free feature of BA networks. Experimental results showed that these mechanisms also maintained other topology characteristics including the degree distribution, clustering coefficient, average length of shortest-paths and diameter during decreases. Our studies also showed that it was quite difficult to find an efficient decrease mechanism for BA networks to withstand the continuous attacks at the high-degree nodes, because of the unequal status of nodes.

Along with the IoT technology, the importance of indoor positioning is increasing, but the accuracy of the traditional fingerprint positioning algorithm is negatively affected by the complex indoor environment. This issue of low indoor spatial geolocation localization accuracy when the signal is collected away from the present stage occurs due to the signal instability of the iBeacon in the traditional fingerprint localization algorithm, which generates a variety of factors such as object blocking and reflection, multipath effect, etc., as well as the scarcity of reference fingerprint data points. In response, this study proposes an inverse distance-weighted optimization WKNN algorithm for indoor localization based on the GM(1,1) model. By implementing GM(1,1) model pre-process leveling, the original fingerprint library was reconstructed into a large-capacity fingerprint database using the inverse distance-weighted interpolation method. The local inverse distance-weighted interpolation was used for interpolation, combined with the WKNN algorithm to complete the coordinate solution in real time. This effectively solved the issue of low localization accuracy caused by the large fluctuation of the received signal strength (RSS) sampling measurement data and the existence of few reference fingerprint datapoints in the fingerprint database. The results show that this algorithm reduced the average positioning error by 5.9% compared with ordinary kriging (OK) interpolation leveling and reduced the average positioning error by 18.2% compared with the indoor spatial location accuracy of the original fingerprint database, which can effectively improve the positioning accuracy and provide technical support for indoor location and navigation services.