About the Journal

Journal of Industrial Engineering and Applied Science is peer-reviewed journal of Southern United Academy of Sciences published in the field of Engineering Technology. The journal registers independent DOI and ARK for each article. The DOI prefix is ​​10.70393 and the ARK prefix is ​​40704.

ISSN 3005-6071 (Print), ISSN 3005-608X (Online), International CODEN (CAS): JIEAAE

The journal focuses on the following fields:

  • Applied Mathematics - Computational Mathematics, Statistical Analysis, Mathematical Modeling
  • Applied Physics - Condensed Matter Physics, Applied Optics, Plasma Physics
  • Applied Chemistry - Chemical Engineering, Polymer Chemistry, Environmental Chemistry
  • Information Science - Data Management, Information Retrieval, Library Science
  • Computer Science - Software Engineering, Computer Networks, Cybersecurity
  • Mechanical Science - Thermodynamics, Fluid Mechanics, Robotics
  • Materials Science - Nanomaterials, Biomaterials, Composite Materials
  • Automation Technology - Control Systems, Industrial Automation, Process Automation
  • Artificial Intelligence Technology - Machine Learning, Natural Language Processing, Computer Vision
  • Aerospace Technology - Aerodynamics, Avionics, Spacecraft Design

All articles published by JCTAM are indexed by OpenAIRE, BASE, WorldCat and ICI.

(OpenAIRE - DataSources | BASE - Bielefeld Academic Search Engine | WorldCat - OCLC)

All articles published are rigorously reviewed meeting the Journal Quality standards.

Announcements

Current Issue

Vol. 3 No. 1 (2025)
View Vol. 3 No. 1 (2025)

All articles published in this issue have undergone a thorough peer review process, and stringent checks for repetition rates have been implemented to ensure the integrity of the content.

Total number of articles in this issue: 2
Total number of pages in this issue: 19

For inquiries regarding the content of specific articles, please feel free to contact the respective authors via their provided email addresses. For questions related to the journal itself, please reach out directly to SUAS Press.

Published: 2025-02-11

Articles

  • Generative AI Models Theoretical Foundations and Algorithmic Practices
    Authors: Yongnian Cao, Xuechun Yang, Rui Sun
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Natural Language Processing
    Publication ID: v3n1a01
    Abstract: Generative models in AI are an entirely new paradigm for machine learning, allowing computers to create realistic data in all kinds of categories, like text (NLP), images, and even physics simulations. In this paper this formalism is used to guide...
    1-9
    DOI Icon Abstract views: 59 | DOI Icon PDF downloads: 20 | DOI Icon references: 30
    DOI Icon DOI: 10.70393/6a69656173.323633
    DOI Icon ARK: ark:/40704/JIEAS.v3n1a01
  • Multi-Agent Large Language Models for Traditional Finance and Decentralized Finance
    Authors: Antio De La Cruz
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v3n1a02
    Abstract: This paper investigates the application of multi-agent large language models (LLMs) in traditional finance (TradFi) and decentralized finance (DeFi), with a focus on addressing challenges such as inefficiencies, security vulnerabilities, and...
    10-19
    DOI Icon Abstract views: 129 | DOI Icon PDF downloads: 35 | DOI Icon references: 49
    DOI Icon DOI: 10.70393/6a69656173.323634
    DOI Icon ARK: ark:/40704/JIEAS.v3n1a02
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