Nerve Signal Transferring Mechanism and Mathematical Modeling of Artificial Biological System Design
Our investigation demonstrates the necessity of mathematical modeling and design methodologies for nerve signals in the creation of artificial arms. Nerve impulses vary widely in speed; for example, unmyelinated nerves transmit impulses at around one mile per hour, while myelinated nerves conduct im...
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MDPI AG
2024-11-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/8/11/648 |
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| author | Jeongseop Park Sehwan Yoo Taikyeong Jeong |
| author_facet | Jeongseop Park Sehwan Yoo Taikyeong Jeong |
| author_sort | Jeongseop Park |
| collection | DOAJ |
| description | Our investigation demonstrates the necessity of mathematical modeling and design methodologies for nerve signals in the creation of artificial arms. Nerve impulses vary widely in speed; for example, unmyelinated nerves transmit impulses at around one mile per hour, while myelinated nerves conduct impulses at around 200 miles per hour. The electrical signals originating from the brain, such as those measured by electroencephalography, are translated into chemical reactions in each organ to produce energy. In this paper, we describe the mechanism by which nerve signals are transferred to various organs, not just the brain or spinal cord, as these signals account for the measured amounts of physical force—i.e., energy—as nerve signals. Since these frequency signals follow no fixed pattern, we consider wavelength and amplitude over a particular time frame. Our simulation results begin with the mechanical distinction that occurs throughout the entire process of nerve signal transmission in the artificial arm as an artificial biological system, and show numerical approaches and algebraic equations as a matrix in mathematical modeling. As a result, the mathematical modeling of nerve signals accurately reflects actual human nerve signals. These chemical changes, involving <i>K</i> (potassium), <i>Na</i> (sodium), and <i>Cl</i> (chloride), are linked to muscle states as they are converted into electrical signals. Investigating and identifying the neurotransmitter signal transmission system through theoretical approaches, mechanical analysis, and mathematical modeling reveals a strong relationship between mathematical simulation and algebraic matrix analysis. |
| format | Article |
| id | doaj-art-cfa95b1f92e345d492a5649fa9f868fd |
| institution | OA Journals |
| issn | 2504-3110 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-cfa95b1f92e345d492a5649fa9f868fd2025-08-20T01:53:41ZengMDPI AGFractal and Fractional2504-31102024-11-0181164810.3390/fractalfract8110648Nerve Signal Transferring Mechanism and Mathematical Modeling of Artificial Biological System DesignJeongseop Park0Sehwan Yoo1Taikyeong Jeong2Department of Math and Computer Science, University of Advancing Technology, Tempe, AZ 85283, USAFaculty of Sciences and Humanities, State University of New York, Incheon 21985, Republic of KoreaSchool of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Republic of KoreaOur investigation demonstrates the necessity of mathematical modeling and design methodologies for nerve signals in the creation of artificial arms. Nerve impulses vary widely in speed; for example, unmyelinated nerves transmit impulses at around one mile per hour, while myelinated nerves conduct impulses at around 200 miles per hour. The electrical signals originating from the brain, such as those measured by electroencephalography, are translated into chemical reactions in each organ to produce energy. In this paper, we describe the mechanism by which nerve signals are transferred to various organs, not just the brain or spinal cord, as these signals account for the measured amounts of physical force—i.e., energy—as nerve signals. Since these frequency signals follow no fixed pattern, we consider wavelength and amplitude over a particular time frame. Our simulation results begin with the mechanical distinction that occurs throughout the entire process of nerve signal transmission in the artificial arm as an artificial biological system, and show numerical approaches and algebraic equations as a matrix in mathematical modeling. As a result, the mathematical modeling of nerve signals accurately reflects actual human nerve signals. These chemical changes, involving <i>K</i> (potassium), <i>Na</i> (sodium), and <i>Cl</i> (chloride), are linked to muscle states as they are converted into electrical signals. Investigating and identifying the neurotransmitter signal transmission system through theoretical approaches, mechanical analysis, and mathematical modeling reveals a strong relationship between mathematical simulation and algebraic matrix analysis.https://www.mdpi.com/2504-3110/8/11/648mathematical modelingelectric signal transmittingneurotransmitterneuroscience |
| spellingShingle | Jeongseop Park Sehwan Yoo Taikyeong Jeong Nerve Signal Transferring Mechanism and Mathematical Modeling of Artificial Biological System Design Fractal and Fractional mathematical modeling electric signal transmitting neurotransmitter neuroscience |
| title | Nerve Signal Transferring Mechanism and Mathematical Modeling of Artificial Biological System Design |
| title_full | Nerve Signal Transferring Mechanism and Mathematical Modeling of Artificial Biological System Design |
| title_fullStr | Nerve Signal Transferring Mechanism and Mathematical Modeling of Artificial Biological System Design |
| title_full_unstemmed | Nerve Signal Transferring Mechanism and Mathematical Modeling of Artificial Biological System Design |
| title_short | Nerve Signal Transferring Mechanism and Mathematical Modeling of Artificial Biological System Design |
| title_sort | nerve signal transferring mechanism and mathematical modeling of artificial biological system design |
| topic | mathematical modeling electric signal transmitting neurotransmitter neuroscience |
| url | https://www.mdpi.com/2504-3110/8/11/648 |
| work_keys_str_mv | AT jeongseoppark nervesignaltransferringmechanismandmathematicalmodelingofartificialbiologicalsystemdesign AT sehwanyoo nervesignaltransferringmechanismandmathematicalmodelingofartificialbiologicalsystemdesign AT taikyeongjeong nervesignaltransferringmechanismandmathematicalmodelingofartificialbiologicalsystemdesign |