Theoretical Research and Technological Applications of AI

Deadline for manuscript submissions: 31 December 2026

Special Issue Editors

Licheng Jiao Website E-Mail: lchjiao@mail.xidian.edu.cn
Xidian University, China
Interests: Deep Learning and Brain-Inspired Computing

Xiaohong Zhang Website E-Mail: xiaohongzhang@sust.edu.cn
Shaanxi University of Science and Technology, China
Interests: fuzzy logic and non-classical logic algebra, rough set theory, data analysis and mining, artificial intelligence foundation, intelligent control and decision-making

Tiejun Cui Website E-Mail: ctj.159@163.com (Guest Editor)
Shenyang Ligong University, China
Interests: Fundamentals of Artificial Intelligence, Safety of Intelligent Systems

Special Issue Information

Artificial intelligence (AI) has evolved into a transformative force across academic research and industrial innovation, with theoretical breakthroughs and technological advancements driving revolutionary changes in diverse fields. Traditional AI frameworks are increasingly challenged by complex real-world scenarios, including high-dimensional data processing, explainability requirements, and cross-domain integration demands. Consequently, this special issue focuses on the latest progress in theoretical research and technological applications of AI, highlighting innovative theoretical paradigms, advanced algorithmic methods, and practical application achievements. To promote the synergy between foundational research and industrial practice, original research and case studies with rigorous validation are highly encouraged. Therefore, we establish this special issue to provide a high-quality platform for scholars and engineers to exchange cutting-edge insights and technical accomplishments.

The primary topics are as follows (not limited to those listed):

Foundational theories of large language models and multimodal intelligence
Explainable AI (XAI) and trustworthy algorithm design
AI-driven intelligent optimization and decision-making systems
Edge AI and low-power computing for embedded applications
AI-enabled industrial automation and smart manufacturing solutions

Collection Editor(s): Licheng Jiao, Xiaohong Zhang, Tiejun Cui

Keywords: artificial intelligence; theoretical research; technological applications; explainable AI; smart systems

Open Access

Articles

Article ID: 4235

A data-driven approach to coal mine safety performance using swarm intelligence and ensemble learning
by Yejiao Liu, Jinliang Li, Ting Teng, Wenjie Yan, Huixin Wang, Dongqiang Cao, Fu Gao, Fengyi Jiang

Advances in Differential Equations and Control Processes, Vol.33, No.2, 2026;

At present, there are some shortcomings in the dynamic adaptability and subjectivity of coal mine safety performance evaluation, and it is difficult to realize the short-term safety performance evaluation with full staff participation. In this study, based on the Analytic Network Process-Technique for Order Preference by Similarity to an Ideal Solution (ANP-TOPSIS), a coal mine safety performance evaluation index system was constructed, and the evaluation index was optimized by a particle swarm optimization algorithm to improve the accuracy of dynamic index weight allocation. Emotional processing analysis technology is introduced, and the survey evaluation form is designed to quantify the subjective emotional tendency. Statistical methods such as the intra-group correlation coefficient, consistency test and regression model are used to improve the reliability of expert scoring data and quantitatively analyze individual subjective differences. Using the random forest classification method, combined with the term frequency-inverse document frequency (TF-IDF) to vectorize the text data, a bottom-up dynamic evaluation method of employee safety performance based on machine learning is established. The random forest model achieved an average F1-score of 0.929, with all six safety dimensions scoring above 0.8. The example shows that the short-process long-period safety performance evaluation based on ANP-TOPSIS-PSO, and a random forest model can accurately describe the coal mine safety appearance and provide scientific decision support for improving the coal mine safety performance level.

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(This article belongs to the Special Issue Theoretical Research and Technological Applications of AI)
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Early fault signals of the rolling bearing in the rotor are weak and present the characteristics of non-periodic and non-stationary; it is more difficult to carry out fault diagnosis on it. In this regard, this paper proposes a weak rolling bearing fault diagnosis algorithm based on whale optimization algorithm, simplistic geometry mode decomposition, and maximum correlated kurtosis deconvolution (WOA-SGMD-MCKD). Firstly, the vibration signal of the rotor platform is obtained, and the Symmetric Geometric Mode Decomposition (SGMD) is used to reconstruct the vibration signal. To obtain the best decomposition effect of the SGMD and overcome modal aliasing, the Whale Optimization Algorithm (WOA) is used to optimize the embedding dimension. Secondly, for the reconstructed vibration signal, the Maximum Correlated Kurtosis Deconvolution (MCKD) is used to extract its impulse component, and the WOA is used to optimize the filter length and deconvolution period of the MCKD so that the frequency envelope spectrum of the vibration signal can be obtained, which can provide the basis for the fault diagnosis of rolling bearings. Finally, the effectiveness and feasibility of the algorithm proposed are verified by a non-periodic and non-stationary simulation platform and rotor maneuvering platform in this paper.

Numerical solution of a 3D mathematical model for the progression of tumor angiogenic factor in a tissue

In this work, the movement of tumor angiogenic factor in a three-dimensional tissue is obtained by the Method of Lines. This method transforms a partial differential equation into a system of ordinary differential equations together with the initial and boundary conditions. The more the number of lines is increased, the more the accuracy of the method increases. This method results in very accurate numerical solutions for linear and non-linear problems in contrast with other existing methods. We present Matlab-generated figures, which are the movement of tumor angiogenic factor in porous medium and explain the biological importance of this progression. The computer codes are also provided.

Mathematical modelling and controllability analysis of fractional order coal mill pulverizer model

This paper investigates the controllability of nonlinear dynamical systems and their applications, with a focus on fractional-order systems and coal mill models. A novel theorem is proposed, providing sufficient conditions for controllability, including constraints on the steering operator and nonlinear perturbation bounds. The theorem establishes the existence of a contraction mapping for the nonlinear operator, enabling effective control strategies for fractional systems. The methodology is demonstrated through rigorous proof and supported by an iterative algorithm for controller design. Additionally, the controllability of a coal mill system represented as a nonlinear differential system, is analyzed. The findings present new insights into the interplay of fractional dynamics and nonlinear systems, offering practical solutions for real-world control problems.