Special Issue "Engineering Calculation and Data Modeling"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 2190

Special Issue Editors

Prof. Dr. Aimin Yang
E-Mail Website
Guest Editor
College of Science, North China University of Science and Technology, Tangshan 063000, China
Interests: fractional calculus; mathematical modeling of metallurgical problems; big data of steel; intelligent ore matching; sintering process optimization; research on quality prediction model of sinter
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Li
E-Mail Website
Co-Guest Editor
College of Metallurgy and Energy, North China University of Science and Technology, Caofeidian, Tangshan 063200, China
Interests: mineral-phase feature identification and extraction; CO emission reduction and pollutant treatment of sintering flue gas; metallurgical energy saving and resource optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chunxiao Yu
E-Mail Website
Co-Guest Editor
School of Science, Yanshan University, Haigang Distinction, Qinhuangdao 066000, Hebei, China
Interests: theoretical research of multipole boundary element method; the numerical simulation
Dr. Jianbo Liu
E-Mail Website
Co-Guest Editor
School of mathematics and statistics, Northeastern University at Qinhuangdao, Haigang Distinction, Qinhuangdao 066000, Hebei, China
Interests: algebra, lie algebra, quantum groups, formal concept analysis, big data visualization

Special Issue Information

Dear Colleagues,

Engineering calculation, that is, the general problems in real engineering are solved by mathematical thinking. Data modeling, that is, the data generated in the project, is used to train the mathematical model. The general problems in engineering can be divided into optimal solution problems, optimization problems, prediction problems, and evaluation problems. For example, in the field of iron and steel metallurgy, how to reduce the carbon content in the ironmaking process and how to improve the hardness of the billet. We need to use the optimization model to achieve the engineering purpose by constantly adjusting parameters. Engineering problem is also a real problem, so it has attracted much attention at present. The innovation of the model is particularly important and practical.

All submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this Special Issue.

Prof. Dr. Aimin Yang
Dr. Jie Li
Prof.Dr. Chunxiao Yu
Dr. Jianbo Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image recognition
  • data analysis
  • deep learning
  • intelligent manufacturing
  • numerical simulation
  • Intelligent recommendation
  • fractal
  • machine learning
  • green metallurgy
  • medical Engineering

Published Papers (2 papers)

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Research

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Article
Information Leakage Detection and Risk Assessment of Intelligent Mobile Devices
Mathematics 2022, 10(12), 2011; https://doi.org/10.3390/math10122011 - 10 Jun 2022
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Abstract
(1) Background: Smart mobile devices provide conveniences to people’s life, work, and entertainment all the time. The basis of these conveniences is the data exchange across the entire cyberspace, and privacy data leakage has become the focus of attention. (2) Methods: First, we [...] Read more.
(1) Background: Smart mobile devices provide conveniences to people’s life, work, and entertainment all the time. The basis of these conveniences is the data exchange across the entire cyberspace, and privacy data leakage has become the focus of attention. (2) Methods: First, we used the method of directed information flow to conduct an API test for all applications in the application market, then obtained the application data transmission. Second, by using tablet computers, smart phones, and bracelets as the research objects, and taking the scores of senior users on the selected indicators as the original data, we used the fusion information entropy and Markov chain algorithm skillfully to build a data leakage risk assessment mode to obtain the steady-state probability values of different risk categories of each device, and then obtained the entropy values of three devices. (3) Results: Tablet computers have the largest entropy in the risk of data leakage, followed by bracelets and mobile phones. (4) Conclusions: This paper compares the risk situation of each risk category of each device, and puts forward simple avoidance opinions, which might lay a theoretical foundation for subsequent research on privacy protection strategies, image steganography, and device security improvements. Full article
(This article belongs to the Special Issue Engineering Calculation and Data Modeling)
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Review

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Review
Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods
Mathematics 2022, 10(11), 1921; https://doi.org/10.3390/math10111921 - 03 Jun 2022
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Abstract
With the advent of big data and the information age, the data magnitude of various complex networks is growing rapidly. Many real-life situations cannot be portrayed by ordinary networks, while hypergraphs have the ability to describe and characterize higher order relationships, which have [...] Read more.
With the advent of big data and the information age, the data magnitude of various complex networks is growing rapidly. Many real-life situations cannot be portrayed by ordinary networks, while hypergraphs have the ability to describe and characterize higher order relationships, which have attracted extensive attention from academia and industry in recent years. Firstly, this paper described the development process, the application areas, and the existing review research of hypergraphs; secondly, introduced the theory of hypergraphs briefly; then, compared the learning methods of ordinary graphs and hypergraphs from three aspects: matrix decomposition, random walk, and deep learning; next, introduced the structural optimization of hypergraphs from three perspectives: dynamic hypergraphs, hyperedge weight optimization, and multimodal hypergraph generation; after that, the applicability of three uncertain hypergraph models were analyzed based on three uncertainty theories: probability theory, fuzzy set, and rough set; finally, the future research directions of hypergraphs and uncertain hypergraphs were prospected. Full article
(This article belongs to the Special Issue Engineering Calculation and Data Modeling)
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