JOURNAL ARTICLE
Digital covering spaces, Pseudo covering spaces, and related works.
Published In: Discrete Mathematics, Algorithms & Applications, 2026, v. 18, n. 2. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Han, Sang-Eon 3 of 3
Abstract
The paper aims to find a condition which supports an equivalence among a local (k 0 , k 1) -isomorphism, a local (k 0 , k 1) -isomorphic surjection, and a (k 0 , k 1) -covering map. Indeed this work still remains open. To make a success of addressing this issue, the paper first compares among a digital-topological (D T -, for brevity) k -embedding, a local (L -, for short) k -isomorphism, a weakly local (W L -, for simplicity) k -isomorphic surjection, a (k 0 , k 1) -covering map, and a pseudo- k -covering map and further, deals with some related works. Second, assume a function f : (X , k 0) → (Y , k 1). Then we point out that the k 1 -connectedness of (Y , k 1) plays a crucial role in characterizing some relationships between an L - (k 0 , k 1) -isomorphism and a (k 0 , k 1) -covering map. Besides, a (k 0 , k 1) -covering map is proved to be stronger than a new version of the pseudo- (k 0 , k 1) -covering map in [S.-E. Han, Remarks on pseudocovering spaces in a digital topological setting, Filomat 38(2) (2024) 1–9]. Third, we propose a condition supporting the product property of an L - (k 0 , k 1) -isomorphism and a pseudo- (k 0 , k 1) -covering map. Finally, the map p : S C k n , l ′ → S C k n , l above is an L - k -isomorphism if and only if l ′ = t l , t ∈ ℕ. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Discrete Mathematics, Algorithms & Applications. 2026/03, Vol. 18, Issue 2, p1
- Document Type:Article
- Subject Area:Mathematics
- Publication Date:2026
- ISSN:1793-8309
- DOI:10.1142/S179383092550034X
- Accession Number:192050516
- Copyright Statement:Copyright of Discrete Mathematics, Algorithms & Applications is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.