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Advanced Mathematical Approaches to Engineering and Computational Problems, 2nd Edition
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Advanced Mathematical Approaches to Engineering and Computational Problems, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2296

Special Issue Editor

Prof. Dr. Junzo Watada

E-Mail Website
Guest Editor
Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan
Interests: artificial intelligence; image processing; soft computing, including meta heuristics, fuzzy systems, rough set analysis; model building; optimization; data analytics; big data mining; management engineering; financial engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, various innovative approaches are being proposed to solve engineering and computational problems, ranging from real engineering challenges to factorial issues, as well as issues related to intelligence methodologies, meta-heuristic methodologies, and deep learning. This Special Issue emphasizes the advanced mathematical, statistical, and computational aspects of such methodologies, including but not limited to the following:

  • Meta-heuristics methodologies for solving various engineering, computational, and industrial problems;
  • Artificial intelligence methodologies for solving various engineering, computational, and industrial problems;
  • Statistical approaches for solving various engineering, computational, and industrial problems;
  • Deep learning approaches for solving various engineering, computational, and industrial problems.

These problems should encompass management, marketing, and behavioral issues. We are interested in updated advanced methodologies and new problem areas.

As the title suggests, this Special Issue is a continuation of the Special Issue “Advanced Mathematical Approaches to Engineering and Computational Problems”, which was successful and included some excellent papers. We hope that this new Special Issue will attract the attention of more scholars who wish to publish their interesting insights in this journal.

Prof. Dr. Junzo Watada
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 2600 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

  • mathematics
  • statistics
  • advanced methodologies
  • meta-heuristics
  • artificial intelligence
  • deep learning
  • engineering problems
  • industrial problems
  • financial engineering problems
  • management problems
  • marketing problems
  • behavioral problems

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

26 pages, 3775 KB  
Article
Expanding the Scope: Modified Fortescue Theory for Frequency Unbalance Resolution in Control Strategies
by Karim Mansouri, Brahim Metoui, Cristian Nichita, Amr Yousef and Ezzeddine Touti
Mathematics 2025, 13(21), 3548; https://doi.org/10.3390/math13213548 - 5 Nov 2025
Viewed by 754
Abstract
To analyze unbalanced electrical systems, the mathematical technique “symmetrical components method” developed by Charles LeGeyt Fortescue in the early 20th century has been very successful in this field. By decomposing three-phase systems into three symmetrical components: positive sequence, negative sequence, and zero sequence, [...] Read more.
To analyze unbalanced electrical systems, the mathematical technique “symmetrical components method” developed by Charles LeGeyt Fortescue in the early 20th century has been very successful in this field. By decomposing three-phase systems into three symmetrical components: positive sequence, negative sequence, and zero sequence, the Fortescue theory provides an important analyzing method. It allows for the calculation of these symmetrical components, which helps in understanding and addressing issues related to unbalance in amplitude within electrical systems. This theory deals only with amplitude unbalances in electrical systems to analyze and solve those problems. Since this technique is limited only to amplitude unbalance, our objective is to propose a modified Fortescue theory, which will resolve frequency unbalance problems in electrical systems. The new balanced components, at the conclusion of this new theory, will be used as references to be assigned in the adopted control strategies in a subsequent research paper. Full article
(This article belongs to the Special Issue Advanced Mathematical Approaches to Engineering and Computational Problems, 2nd Edition)
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24 pages, 4989 KB  
Article
Interval-Valued Multi-Step-Ahead Forecasting of Green Electricity Supply Using Augmented Features and Deep-Learning Algorithms
by Tzu-Chi Liu, Chih-Te Yang, I-Fei Chen and Chi-Jie Lu
Mathematics 2025, 13(19), 3202; https://doi.org/10.3390/math13193202 - 6 Oct 2025
Viewed by 713
Abstract
Accurately forecasting the interval-valued green electricity (GE) supply is challenging due to the unpredictable and instantaneous nature of its source; yet, reliable multi-step-ahead forecasting is essential for providing the lead time required in operations, resource allocation, and system management. This study proposes an [...] Read more.
Accurately forecasting the interval-valued green electricity (GE) supply is challenging due to the unpredictable and instantaneous nature of its source; yet, reliable multi-step-ahead forecasting is essential for providing the lead time required in operations, resource allocation, and system management. This study proposes an augmented-feature multi-step interval-valued forecasting (AFMIF) scheme that aims to address the challenges in forecasting interval-valued GE supply data by extracting additional features hidden within an interval. Unlike conventional methods that rely solely on original interval bounds, AFMIF integrates augmented features that capture statistical and dynamic properties to reveal hidden patterns. These features include basic interval boundaries and statistical distributions from an interval. Three effective forecasting methods, based on gated recurrent units (GRUs), long short-term memory (LSTM), and a temporal convolutional network (TCN), are constructed under the proposed AFMIF scheme, while the mean ratio of exclusive-or (MRXOR) is used to evaluate the forecasting performance. Two different real datasets of wind-based GE supply data from Belgium and Germany are used as illustrative examples. Empirical results demonstrate that the proposed AFMIF scheme with GRUs can generate promising results, achieving a mean MRXOR of 0.7906 from the Belgium data and 0.9719 from the Germany data for one-step- to three-steps-ahead forecasting. Moreover, the TCN yields an average improvement of 13% across all time steps with the proposed scheme. The results highlight the potential of the AFMIF scheme as an effective alternative approach for accurate multi-step-ahead interval-valued GE supply forecasting that offers practical benefits supporting GE management. Full article
(This article belongs to the Special Issue Advanced Mathematical Approaches to Engineering and Computational Problems, 2nd Edition)
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