About the Journal

Journal of Industrial Engineering and Applied Science (JIEAS) 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.

Bimonthly publication.

It covers domains including Applied Mathematics, Industrial Engineering, Mechanical Engineering, Materials Science, Electrical & Systems Engineering, Chemical Engineering, Computer Science & Software, and Management Science Domains; assigns DOIs and ARKs; and is indexed in WorldCat, OpenAIRE, Scilit, and BASE.

Announcements

Current Issue

Vol. 4 No. 1 (2026)
View Vol. 4 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: 7

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: 2026-02-04

Articles

  • Construction of an Intelligent Quality and Operations Control System for Industrial Electrical Enterprises Driven by Dual Standards ISO 14001 and ISO 9001
    Authors: Wusheng Gao
    Resource Type: Article
    Disciplines: Automation Technology | Subjects: Industrial Automation
    Publication ID: v4n1a01
    Abstract: This paper focuses on the development of intelligent quality and operational control systems for industrial electrical appliance companies, examining whether these systems integrate ISO 14001 (Environmental Management System) and ISO 9001 (Quality...
    1-7
    DOI Icon Abstract views: 3 | DOI Icon PDF downloads: 2 | DOI Icon references: 28
    DOI Icon DOI: 10.70393/6a69656173.333636
    DOI Icon ARK: ark:/40704/JIEAS.v4n1a01
  • A Review of the Development of Automated Metal Parts Modeling and Management Technologies Based on BIM and Artificial Intelligence
    Authors: Zhuoxuan Li
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v4n1a02
    Abstract: Metal components, due to their high prefabrication rate and discrete manufacturing characteristics, have become an ideal carrier for verifying intelligent construction technologies. However, traditional modeling methods face bottlenecks such as...
    8-18
    DOI Icon Abstract views: 7 | DOI Icon PDF downloads: 1 | DOI Icon references: 42
    DOI Icon DOI: 10.70393/6a69656173.333839
    DOI Icon ARK: ark:/40704/JIEAS.v4n1a02
  • AI Chips and the Economics of Computer
    Authors: Wenqiang Lu
    Resource Type: Article
    Disciplines: Artificial Intelligence Technology | Subjects: Machine Learning
    Publication ID: v4n1a03
    Abstract: Because chips exhibit large differences in speed, power consumption, and cost across tasks, performance, energy use, and time often involve intricate trade-offs under heterogeneous workloads. As frontier models continue to scale, compute is...
    19-26
    DOI Icon Abstract views: 2 | DOI Icon PDF downloads: 1 | DOI Icon references: 24
    DOI Icon DOI: 10.70393/6a69656173.333733
    DOI Icon ARK: ark:/40704/JIEAS.v4n1a03
  • Hierarchical Needs in U.S. Automotive Customer Feedback and the Sentiment–Function Nexus
    Authors: Ziren Zhou
    Resource Type: Article
    Disciplines: Information Science | Subjects: Information Retrieval
    Publication ID: v4n1a04
    Abstract: This paper analyzes a non-experimental declarative framework for interpreting changes in the U.S. automotive market, including chip shortages, accelerated car adoption, and the continued dominance of SUVs and trucks. It proposes an in-depth...
    27-33
    DOI Icon Abstract views: 2 | DOI Icon PDF downloads: 1 | DOI Icon references: 31
    DOI Icon DOI: 10.70393/6a69656173.333637
    DOI Icon ARK: ark:/40704/JIEAS.v4n1a04
  • KV Cache and Inference Scheduling: Energy Modeling for High-QPS Services
    Authors: Wenwen Liu
    Resource Type: Article
    Disciplines: Computer Science | Subjects: Artificial Intelligence
    Publication ID: v4n1a05
    Abstract: High-QPS (Queries Per Second) services, such as large language model (LLM) inference and real-time recommendation systems, are increasingly pervasive in AI-driven applications, but their energy consumption has become a critical...
    34-41
    DOI Icon Abstract views: 3 | DOI Icon PDF downloads: 1 | DOI Icon references: 11
    DOI Icon DOI: 10.70393/6a69656173.333930
    DOI Icon ARK: ark:/40704/JIEAS.v4n1a05
  • Carbon-Emission Estimation Models: Hierarchical Measurement From Board to Datacenter
    Authors: Wenwen Liu
    Resource Type: Article
    Disciplines: Computer Science | Subjects: Artificial Intelligence
    Publication ID: v4n1a06
    Abstract: Data centers have become a core contributor to global digital carbon emissions, with their carbon footprint growing 19% annually alongside the expansion of AI and cloud services. Traditional carbon accounting methods are either trapped in...
    42-48
    DOI Icon Abstract views: 1 | DOI Icon PDF downloads: 1 | DOI Icon references: 14
    DOI Icon DOI: 10.70393/6a69656173.333931
    DOI Icon ARK: ark:/40704/JIEAS.v4n1a06
  • Optimizing Cloud-Native Lakehouse Architectures for Real-Time Semiconductor Analytics: Balancing Performance, Cost, and Energy Efficiency
    Authors: Min Yin
    Resource Type: Article
    Disciplines: Computer Science | Subjects: Semiconductor Analytics
    Publication ID: v4n1a07
    Abstract: Currently, semiconductor data analysis requires processing massive amounts of real-time data, and traditional data warehouses face challenges in meeting the demands for low latency and high-concurrency queries. Therefore, this paper proposes a...
    49-61
    DOI Icon Abstract views: 1 | DOI Icon PDF downloads: 0 | DOI Icon references: 27
    DOI Icon DOI: 10.70393/6a69656173.333833
    DOI Icon ARK: ark:/40704/JIEAS.v4n1a07
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