About the Journal

Academic Journal of Natural Science (AJNS) is a scholarly journal published by SUAS. It employs editor-invited peer review and editorial assessment through a standardized scoring framework evaluating submissions on originality, transparency, ethical integrity, and domain relevance.

Quarterly publication with Online First.

It covers domains including Biological Sciences, Chemistry, Physics, Earth Sciences, Environmental Sciences, Materials Sciences, Astronomy, Computer Science, Engineering, and Medical Sciences Domains; assigns DOIs and ARKs; and is indexed in WorldCat, OpenAIRE, Scilit, and BASE.

Announcements

Current Issue

Vol. 3 No. 1 (2026)
View Vol. 3 No. 1 (2026)

This issue contains articles accepted following editor-invited peer review and editorial assessment. All manuscripts underwent standardized similarity screening as part of SUAS Press’s quality assurance protocol.

Articles: 2

For scientific inquiries about a specific article, contact the corresponding author directly. To report concerns regarding editorial integrity, publication ethics, or content quality, please contact the SUAS Press Quality Supervision Committee at qsc@suaspress.org.

Published: 2026-02-20

Articles

  • Dynamic Optimization and Multi-Regional Performance Validation of Automotive Sales Strategies in the United States
    Authors: Zhou Ziren
    Resource Type: Article
    Disciplines: Computer Science | Subjects: Data Science
    Publication ID: v3n1a01
    Abstract: In the context of global automotive market restructuring, this study proposes a comprehensive framework for dynamic sales optimization and performance governance to enhance adaptability and resilience in multi-regional automotive markets. Drawing...
    1-7
    DOI Icon Abstract views: 10 | DOI Icon PDF downloads: 5 | DOI Icon SUAS Digital Library downloads: 0 | DOI Icon references: 17
    DOI Icon DOI: 10.70393/616a6e73.333934
    DOI Icon ARK: ark:/40704/AJNS.v3n1a01
  • AI-Enabled Data Visualization Marketing for Automated Production Lines: Building Customer Trust and Improving Lead-to-Order Conversion
    Authors: Li Wensi
    Resource Type: Article
    Disciplines: Computer Science | Subjects: Data Science
    Publication ID: v3n1a02
    Abstract: In many B2B acquisition and transaction workflows, companies still rely on static, fragmented marketing materials that are difficult to update consistently across channels, weakening narrative coherence and customer trust. To address this gap,...
    8-13
    DOI Icon Abstract views: 10 | DOI Icon PDF downloads: 7 | DOI Icon SUAS Digital Library downloads: 0 | DOI Icon references: 9
    DOI Icon DOI: 10.70393/616a6e73.333938
    DOI Icon ARK: ark:/40704/AJNS.v3n1a02
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