This study investigated the removal of sulfur and iron from shungite rocks through different methods after fine grinding: flotation, magnetic separation, microwave treatment, and chemical leaching. In this work, first, a mineralogical study of shungite was conducted. The carbon, silica, iron, and sulfur compositions in the as-received shungite were 45.4%, 38.3%, 4.6%, and 2.4%, respectively. In flotation, a sulfur grade of 1.4% was obtained. In the wet high-gradient magnetic separation at a magnetic flux density of 1 tesla, the iron and sulfur grades in the nonmagnetic fraction were 2.8% and 1.9%, respectively. Furthermore, the sulfur reduced to 0.2% by the 9 min microwave irradiation. In addition, chemical leaching using chelating reagents and inorganic acids was utilized to remove iron and sulfur. Nitrilotriacetic acid (NTA) could reduce the iron and sulfur grades to 2.0% and 0.9%, respectively. For leaching using reverse aqua regia, the iron and sulfur grades were reduced to 0.9% and 0.23%, respectively. For leaching using a 6N HCl with H2O2 aqueous solution, the iron and sulfur grades were reduced to 0.8% and 0.34%, respectively. Overall, chemical leaching using HCl with H2O2 was the most effective for iron and sulfur removal from shungite.

A seif-learning function, in which the statistical methodsand production rules were eftectively combined, was applied to an expert system for blast furnace heat control to improve controllability of temperature and chemical composition of hot metal and to improve mentenance of the system. This self-learning function consists of a short term self-learning function and a long term self-learning function. The former has been in operation since the expert system for blast furnace heat control, which was namedBAISYS, was started and the latter has been utilized since March, 1988. In the short term self-learning function, the reference values of sensor data, hot metal temperature difterences among tapholes and rising patterns of hot metal temperature in the casting are periodically judged and automatically processed.In the long term self-learning function, the guidance to modify the three-dimensional membershipfunctions and the weight coefticients of all sensors is performed. Through the test application of the self-iearning function, the followings were confirmed.(1) Guidance by the self-leaming function is very efiective for the evaluation of weight coefficients of various sensors.(2) The three-dimensionai membershipfunctions for all sensor data, which are madeby self-learning function, are applicable.(3) The standard deviations of hot metal temperature and silicon content in hot metal have been decreased and the application ratio of the expert system has been kept at high level by the introduction of the self-learning function.

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Content regarding various illegal activities, such as weapon and drug trafficking, is shared on the dark web. Most of the illegal content is distributed on anonymous networks that cannot be directly accessed from the World Wide Web. A number of studies have been conducted on the network structure of the World Wide Web since its advent. Similar to the World Wide Web, the dark web is connected by hypertext transfer protocol (http); this makes it possible to use the methods developed for the web in the dark web. Many studies have investigated the dark web and its network structure. However, few studies have focused on the visualization of the dark web network structure, and there have been no studies investigating the temporal changes in the network structure.In this study, to understand the hypertext markup language (html) network structure of the dark web, we created and visualized a graph of the html hyperlink relations of the Tor network, which is popular on the dark web. We then compared the insights gained from graph centrality metrics with those gained from visualizations. The analyzed dataset comprised 25,270,157 pages of html text files crawled from the Tor network by breadth-first search from June 1, 2018, to January 30, 2021. Subsequently, we acquired half-yearly snapshots from the collected data and investigated the change in the dark web network over time using a time-series graph. Then, we derived the centrality metrics from the created graph data and confirmed the differences between the centrality metrics and visualizations. The results obtained in this study provided new insights into the dark web. First, we found that the dark web fluctuated significantly; the structure of the dark web network was more strongly interconnected. Second, most of the nodes that had increased in the past two years may have disappeared rapidly after May 2020. Third, analysis of each snapshot revealed that the proportion of highly volatile domains increased from 40% to 75% during the observation period. Fourth, after calculating the network centrality metrics from each snapshot and comparing the transition of hub nodes in chronological order, we observed that the importance of link-collection sites as the main information retrieval method used in the dark web decreased. Finally, we estimated the size of the dark web based on our observed dark web measurements using the mark-recapture method. To the best of our 354 International Journal of Networking and Computing knowledge, this is the first study to use the mark-recapture method to estimate the size of the dark web network.