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. 5 (2025)
View Vol. 3 No. 5 (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: 4
Total number of pages in this issue: 40

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: 2025-10-02

Articles

  • Supply Chain Coordination with Dynamic Pricing Advertising and Consumer Welfare An Economic Application
    Authors: Huijie Tang, Zhoufan Yu, Huanyu Liu
    Resource Type: Article
    Disciplines: Applied Mathematics | Subjects: Mathematical Modeling
    Publication ID: v3n5a01
    Abstract: In this paper, we construct a supply chain coordination model that considers the complex relationships among dynamic pricing, advertising investment, and consumer welfare. The model focuses on a supply chain consisting of a manufacturer and a...
    1-6
    DOI Icon Abstract views: 29 | DOI Icon PDF downloads: 9 | DOI Icon references: 13
    DOI Icon DOI: 10.70393/6a69656173.333230
    DOI Icon ARK: ark:/40704/JIEAS.v3n5a01
  • AI-Powered Customer Engagement Sequence Analysis and Conversion Funnel Optimization in Multi-Channel E-Commerce Marketing
    Authors: Me Sun, Haozhe Wang
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v3n5a02
    Abstract: Multi-channel e-commerce environments generate complex customer engagement sequences that traditional funnel analysis cannot effectively model, limiting marketing optimization capabilities. This paper presents an AI-powered framework that...
    7-20
    DOI Icon Abstract views: 32 | DOI Icon PDF downloads: 7 | DOI Icon references: 54
    DOI Icon DOI: 10.70393/6a69656173.333138
    DOI Icon ARK: ark:/40704/JIEAS.v3n5a02
  • AI-Powered Financial Insights: Using Large Language Models to Improve Government Decision-Making and Policy Execution
    Authors: Luqing Ren
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Natural Language Processing
    Publication ID: v3n5a03
    Abstract: Given the complexity of fiscal data types and the lengthy policy execution chain, this study constructs an application framework for language models supporting government decision-making. It systematically investigates task modules including...
    21-26
    DOI Icon Abstract views: 23 | DOI Icon PDF downloads: 7 | DOI Icon references: 5
    DOI Icon DOI: 10.70393/6a69656173.333139
    DOI Icon ARK: ark:/40704/JIEAS.v3n5a03
  • Machine Learning-Based Building Energy Consumption Prediction and Carbon Reduction Potential Assessment in U.S. Metropolitan Areas
    Authors: Daiyang Zhang, Qichang Zheng
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v3n5a04
    Abstract: Building energy consumption accounts for 40% of U.S. energy usage, presenting critical challenges for urban sustainability. This paper presents a machine learning framework integrating energy consumption prediction with carbon reduction assessment...
    27-40
    DOI Icon Abstract views: 31 | DOI Icon PDF downloads: 8 | DOI Icon references: 70
    DOI Icon DOI: 10.70393/6a69656173.333137
    DOI Icon ARK: ark:/40704/JIEAS.v3n5a04
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