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      Retracted: Effect of Basicity on the Microstructure of Sinter and Its Application Based on Deep Learning

      retraction
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation The presence of these indicators undermines our confidence in the integrity of the article's content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process. Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

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          Effect of Basicity on the Microstructure of Sinter and Its Application Based on Deep Learning

          The influence of the evolution rule of basicity (0.6∼2.4) on the mineral composition and microstructure of sinter is studied by using a polarizing microscope, and the comprehensive application analysis of the drum index, vertical sintering speed, and yield of sinter shows that, over the course of an increase in basicity (0.6∼1.0), the mineral structure changed from the original porphyritic-granular structure to a porphyritic structure. At the same time, there was no calcium ferrite phase in the bonding phase at a basicity of less than 1.0; therefore, the downward trend of the three indicators is obvious. When the basicity was further increased to approximately 1.6, the main structure of the mineral phase changed from a corrosion structure to an interweaving corrosion structure. Because of the existence of a porphyritic-granular structure, the structure of the mineral phase was extremely inhomogeneous and most complex near the basicity of 1.6; although a small amount of calcium ferrite displayed an acicular structure, the drum index appeared to show a very low value. With an increase in basicity to 2.0, the mineral phase structure was dominated by an interweaving corrosion structure with a uniform framework, and the content of calcium ferrite reached the highest value. Moreover, a clear acicular structure developed, and the drum index also increased to the highest value. At a basicity of more than 2.0, a mineral structure began to appear and the corrosion, porphyritic-granular structure, and the drum index also showed a slightly declining trend. Therefore, in the actual production process, basicity should be avoided as far as possible at around 1.0 and 1.6 and it should be controlled at around 2.0. At the same time, based on the mineral facies data set of this paper, the convolutional neural network is used to carry out a simple prediction model experiment on the basicity corresponding to the mineral facies photos, and the effect is quite good, which provides a new idea and method for the follow-up study of mineral facies.
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            Author and article information

            Contributors
            Journal
            Comput Intell Neurosci
            Comput Intell Neurosci
            cin
            Computational Intelligence and Neuroscience
            Hindawi
            1687-5265
            1687-5273
            2023
            28 June 2023
            28 June 2023
            : 2023
            : 9792016
            Affiliations
            Article
            10.1155/2023/9792016
            10322252
            8fb0ea75-2135-4287-a046-261d09d3661a
            Copyright © 2023 Computational Intelligence and Neuroscience.

            This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            History
            : 27 June 2023
            : 27 June 2023
            Categories
            Retraction

            Neurosciences
            Neurosciences

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