Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Endomorphic GE-derivations

  • Received: 01 September 2024 Revised: 15 January 2025 Accepted: 20 January 2025 Published: 24 January 2025
  • MSC : 03G25, 06F35

  • Using the binary operation "" on a GE-algebra X given by (x,y)=(yx)x and the GE-endomorphism Ω:XX, the notion of Ω(l,r)-endomorphic (resp., Ω(r,l)-endomorphic) GE-derivation is introduced, and several properties are investigated. Also, examples that illustrate these are provided. Conditions under which Ω(l,r)-endomorphic GE-derivations or Ω(l,r)-endomorphic GE-derivations to satisfy certain equalities and inequalities are studied. We explored the conditions under which f becomes order preserving when f is an Ω(l,r)-endomorphic GE-derivation or an Ω(r,l)-endomorphic GE-derivation on X. The f-kernel and Ω-kernel of f formed by the Ω(r,l)-endomorphic GE-derivation or Ω(l,r)-endomorphic GE-derivation turns out to be GE-subalgebras. It is observed that the Ω-kernel of f is a GE-filter of X. The condition under which the f-kernel of f formed by the Ω(r,l)-endomorphic GE-derivation or Ω(l,r)-endomorphic GE-derivation becomes a GE-filter is explored.

    Citation: Young Bae Jun, Ravikumar Bandaru, Amal S. Alali. Endomorphic GE-derivations[J]. AIMS Mathematics, 2025, 10(1): 1792-1813. doi: 10.3934/math.2025082

    Related Papers:

    [1] Ancheng Deng, Xiaoqiang Sun . Dynamic gene regulatory network reconstruction and analysis based on clinical transcriptomic data of colorectal cancer. Mathematical Biosciences and Engineering, 2020, 17(4): 3224-3239. doi: 10.3934/mbe.2020183
    [2] Qian Li, Minawaer Hujiaaihemaiti, Jie Wang, Md. Nazim Uddin, Ming-Yuan Li, Alidan Aierken, Yun Wu . Identifying key transcription factors and miRNAs coregulatory networks associated with immune infiltrations and drug interactions in idiopathic pulmonary arterial hypertension. Mathematical Biosciences and Engineering, 2023, 20(2): 4153-4177. doi: 10.3934/mbe.2023194
    [3] Jiaxin Luo, Lin Wu, Dinghui Liu, Zhaojun Xiong, Linli Wang, Xiaoxian Qian, Xiaoqiang Sun . Gene regulatory network analysis identifies key genes and regulatory mechanisms involved in acute myocardial infarction using bulk and single cell RNA-seq data. Mathematical Biosciences and Engineering, 2021, 18(6): 7774-7789. doi: 10.3934/mbe.2021386
    [4] Youlong Lv, Jie Zhang . A genetic regulatory network based method for multi-objective sequencing problem in mixed-model assembly lines. Mathematical Biosciences and Engineering, 2019, 16(3): 1228-1243. doi: 10.3934/mbe.2019059
    [5] Changxiang Huan, Jiaxin Gao . Insight into the potential pathogenesis of human osteoarthritis via single-cell RNA sequencing data on osteoblasts. Mathematical Biosciences and Engineering, 2022, 19(6): 6344-6361. doi: 10.3934/mbe.2022297
    [6] Ming-Xi Zhu, Tian-Yang Zhao, Yan Li . Insight into the mechanism of DNA methylation and miRNA-mRNA regulatory network in ischemic stroke. Mathematical Biosciences and Engineering, 2023, 20(6): 10264-10283. doi: 10.3934/mbe.2023450
    [7] Cicely K. Macnamara, Mark A. J. Chaplain . Spatio-temporal models of synthetic genetic oscillators. Mathematical Biosciences and Engineering, 2017, 14(1): 249-262. doi: 10.3934/mbe.2017016
    [8] Ignacio Alvarez-Castro, Jarad Niemi . Fully Bayesian analysis of allele-specific RNA-seq data. Mathematical Biosciences and Engineering, 2019, 16(6): 7751-7770. doi: 10.3934/mbe.2019389
    [9] David Iron, Adeela Syed, Heidi Theisen, Tamas Lukacsovich, Mehrangiz Naghibi, Lawrence J. Marsh, Frederic Y. M. Wan, Qing Nie . The role of feedback in the formation of morphogen territories. Mathematical Biosciences and Engineering, 2008, 5(2): 277-298. doi: 10.3934/mbe.2008.5.277
    [10] Shengjue Xiao, Yufei Zhou, Ailin Liu, Qi Wu, Yue Hu, Jie Liu, Hong Zhu, Ting Yin, Defeng Pan . Uncovering potential novel biomarkers and immune infiltration characteristics in persistent atrial fibrillation using integrated bioinformatics analysis. Mathematical Biosciences and Engineering, 2021, 18(4): 4696-4712. doi: 10.3934/mbe.2021238
  • Using the binary operation "" on a GE-algebra X given by (x,y)=(yx)x and the GE-endomorphism Ω:XX, the notion of Ω(l,r)-endomorphic (resp., Ω(r,l)-endomorphic) GE-derivation is introduced, and several properties are investigated. Also, examples that illustrate these are provided. Conditions under which Ω(l,r)-endomorphic GE-derivations or Ω(l,r)-endomorphic GE-derivations to satisfy certain equalities and inequalities are studied. We explored the conditions under which f becomes order preserving when f is an Ω(l,r)-endomorphic GE-derivation or an Ω(r,l)-endomorphic GE-derivation on X. The f-kernel and Ω-kernel of f formed by the Ω(r,l)-endomorphic GE-derivation or Ω(l,r)-endomorphic GE-derivation turns out to be GE-subalgebras. It is observed that the Ω-kernel of f is a GE-filter of X. The condition under which the f-kernel of f formed by the Ω(r,l)-endomorphic GE-derivation or Ω(l,r)-endomorphic GE-derivation becomes a GE-filter is explored.





    [1] R. K. Bandaru, A. Borumand Saeid, Y. B. Jun, On GE-algebras, Bull. Sect. Log., 50 (2021), 81–96. https://doi.org/10.18778/0138-0680.2020.20 doi: 10.18778/0138-0680.2020.20
    [2] R. K. Bandaru, A. Borumand Saeid, Y. B. Jun, Belligerent GE-filter in GE-algebras, J. Indones. Math. Soc., 28 (2022), 31–43.
    [3] S. Celani, A note on homomorphisms of Hilbert algebras, Int. J. Math. Math. Sci., 29 (2002), 55–61. https://doi.org/10.1155/S0161171202011134 doi: 10.1155/S0161171202011134
    [4] S. Celani, Hilbert algebras with supremum, Algebra Univers., 67 (2012), 237–255 https://doi.org/10.1007/s00012-012-0178-z doi: 10.1007/s00012-012-0178-z
    [5] A. Diego, Sur les algebres de Hilbert, 1966. https://doi.org/10.1017/S0008439500028885
    [6] W. A. Dudek, On ideals in Hilbert algebras, Acta Univ. Palacki. Olomuc, Fac. Rerum Nat. Math., 38 (1999), 31–34.
    [7] C. Jana, T. Senapati, M. Pal, Derivation, f-derivation and generalized derivation of KUS-algebras, Cogent Math., 2 (2015), 1064602. https://doi.org/10.1080/23311835.2015.1064602 doi: 10.1080/23311835.2015.1064602
    [8] Y. B. Jun, R. K. Bandaru, GE-derivations, Algebraic Struct. Appl., 9 (2022), 11–35.
    [9] Y. B. Jun, R. K. Bandaru, GE-filter expansions in GE-algebras, Jordan J. Math. Stat., 15 (2022), 1153–1171.
    [10] K. H. Kim, S. M. Lee, On derivations of BE-algebras, Honam Math. J., 36 (2014), 167–178.
    [11] A. Rezaei, R. K. Bandaru, A. Borumand Saeid, Y. B. Jun, Prominent GE-filters and GE-morphisms in GE-algebras, Afr. Mat., 32 (2021), 1121–1136. https://doi.org/10.1007/s13370-021-00886-6 doi: 10.1007/s13370-021-00886-6
  • This article has been cited by:

    1. William Chad Young, Ka Yee Yeung, Adrian E Raftery, Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks, 2019, 19, 1471-082X, 444, 10.1177/1471082X18776577
    2. Feng Liu, Qicheng Mei, Fenglan Sun, Xinmei Wang, Hua O Wang, 2018, Stability and Neimark-Sacker Bifurcation Analysis for Single Gene Discrete System with Delay, 978-988-15639-5-8, 961, 10.23919/ChiCC.2018.8483728
    3. Xiao Liang, William Chad Young, Ling-Hong Hung, Adrian E. Raftery, Ka Yee Yeung, Integration of Multiple Data Sources for Gene Network Inference Using Genetic Perturbation Data, 2019, 26, 1557-8666, 1113, 10.1089/cmb.2019.0036
    4. Xiao Liang, William Chad Young, Ling-Hong Hung, Adrian E. Raftery, Ka Yee Yeung, 2018, Integration of Multiple Data Sources for Gene Network Inference using Genetic Perturbation Data, 9781450357944, 601, 10.1145/3233547.3233692
    5. Sanrong Liu, Haifeng Wang, 2019, Chapter 15, 978-981-15-0120-3, 186, 10.1007/978-981-15-0121-0_15
    6. Bei Yang, Yaohui Xu, Andrew Maxwell, Wonryull Koh, Ping Gong, Chaoyang Zhang, MICRAT: a novel algorithm for inferring gene regulatory networks using time series gene expression data, 2018, 12, 1752-0509, 10.1186/s12918-018-0635-1
    7. William Chad Young, Adrian E. Raftery, Ka Yee Yeung, Model-based clustering with data correction for removing artifacts in gene expression data, 2017, 11, 1932-6157, 10.1214/17-AOAS1051
    8. Nimrita Koul, Sunilkumar S Manvi, 2020, A Perturbation based Algorithm for Inference of Gene Regulatory Networks for Multiple Myeloma, 978-1-7281-4108-4, 862, 10.1109/ICESC48915.2020.9155886
    9. Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez, Machine learning applications in drug development, 2020, 18, 20010370, 241, 10.1016/j.csbj.2019.12.006
    10. Chengye Zou, Xiaopeng Wei, Qiang Zhang, Changjun Zhou, Passivity of Reaction–Diffusion Genetic Regulatory Networks with Time-Varying Delays, 2018, 47, 1370-4621, 1115, 10.1007/s11063-017-9682-7
    11. Wenxia Zhou, Xuejun Li, Lu Han, Shengjun Fan, 2021, Chapter 2, 978-981-16-0752-3, 35, 10.1007/978-981-16-0753-0_2
  • Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(623) PDF downloads(59) Cited by(0)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog