Artificial Intelligence Generated Content (AIGC) tools are developed and guided by human beings, utilizing algorithms that have undergone extensive training. These tools can assist in thesis writing; however, users must independently assess the authenticity and reliability of the results to avoid potential issues related to research integrity.
The journal requires authors to maintain openness and transparency regarding their use of generative AI tools, including clarifications on copyright, data sources, and data processing methods. While this journal permits the use of AI-generated content (AIGC) for language enhancement, literature integration, formatting generation, and other non-intellectual aspects of the manuscript, it strictly prohibits employing AIGC for formulating research hypotheses, analyzing causes, interpreting results, or discussing findings—tasks that necessitate human intellectual engagement. Authors are required to specify in the Acknowledgements or Materials and Methods section where AI assistance was utilized in their work; they should also include the version number of AIGC used and justify its application. Failure to adequately disclose such usage or incorporating text from AIGC into the manuscript without proper acknowledgment may be considered academic misconduct.
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AIGC can enhance the readability of articles by refining the text, but it is essential to note that using AIGC to write entire articles is prohibited. Authors must be vigilant in assessing the copyright and authenticity of AIGC-generated content and ensure proper citation of sources.
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.
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.
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.