JIEAS OPEN ACCESS

Journal of Industrial Engineering and Applied Science

ISSN:3005-608X (print) | ISSN:3005-6071 (online) | Publication Frequency: Bimonthly

Current Issue

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: 7
Total number of pages in this issue: 61

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.

Year

2026

Volume

4

Number

1

Status

Archived

Published

2026 February 4

Articles

OPEN ACCESS|Research Article||4 February 2026

Construction of an Intelligent Quality and Operations Control System for Industrial Electrical Enterprises Driven by Dual Standards ISO 14001 and ISO 9001

10.70393/6a69656173.333636
ark:/40704/JIEAS.v4n1a01
Authors: Wusheng Gao.
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 (Environ...
Disciplines: Automation TechnologySubjects: Industrial AutomationReferences: 28
OPEN ACCESS|Research Article||4 February 2026

Hierarchical Needs in U.S. Automotive Customer Feedback and the Sentiment–Function Nexus

10.70393/6a69656173.333637
ark:/40704/JIEAS.v4n1a04
Authors: Ziren Zhou.
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 ...
Disciplines: Information ScienceSubjects: Information RetrievalReferences: 31
OPEN ACCESS|Research Article||4 February 2026

AI Chips and the Economics of Computer

10.70393/6a69656173.333733
ark:/40704/JIEAS.v4n1a03
Authors: Wenqiang Lu.
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 front...
Disciplines: Artificial Intelligence TechnologySubjects: Machine LearningReferences: 24
OPEN ACCESS|Research Article||4 February 2026

Optimizing Cloud-Native Lakehouse Architectures for Real-Time Semiconductor Analytics: Balancing Performance, Cost, and Energy Efficiency

10.70393/6a69656173.333833
ark:/40704/JIEAS.v4n1a07
Authors: Min Yin.
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-concurrenc...
Disciplines: Computer ScienceSubjects: Semiconductor AnalyticsReferences: 27
OPEN ACCESS|Review Article||4 February 2026

A Review of the Development of Automated Metal Parts Modeling and Management Technologies Based on BIM and Artificial Intelligence

10.70393/6a69656173.333839
ark:/40704/JIEAS.v4n1a02
Authors: Zhuoxuan Li.
Abstract: Metal components, due to their high prefabrication rate and discrete manufacturing characteristics, have become an ideal carrier for verifying intelligent construction technologies. However, tradition...
Disciplines: Artificial Intelligence TechnologySubjects: Machine LearningReferences: 42
OPEN ACCESS|Research Article||4 February 2026

KV Cache and Inference Scheduling: Energy Modeling for High-QPS Services

10.70393/6a69656173.333930
ark:/40704/JIEAS.v4n1a05
Authors: Wenwen Liu.
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 consum...
Disciplines: Computer ScienceSubjects: Artificial IntelligenceReferences: 11
OPEN ACCESS|Research Article||4 February 2026

Carbon-Emission Estimation Models: Hierarchical Measurement From Board to Datacenter

10.70393/6a69656173.333931
ark:/40704/JIEAS.v4n1a06
Authors: Wenwen Liu.
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 ac...
Disciplines: Computer ScienceSubjects: Artificial IntelligenceReferences: 14
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Journal of Industrial Engineering and Applied Science (JIEAS)
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