Vol. 3 No. 2 (2026)

View Vol. 3 No. 2 (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: 3

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: 2026-03-05

Articles

  • Evaluating Recruitment Channel Effectiveness with Causal Inference and Predictive Analytics
    Authors: Yuerong Yan
    Resource Type: Article
    Disciplines: Statistics & Data Science | Subjects: Statistical Inference
    Publication ID: v3n2a01
    Abstract: In the context of fierce market competition and increasing talent demand, effective recruitment channels are crucial for enterprises to attract high-quality talents, reduce recruitment costs, and enhance core competitiveness. However, traditional...
    1-10
    DOI Icon Abstract views: 13 | DOI Icon PDF downloads: 4 | DOI Icon SUAS Digital Library downloads: 0 | DOI Icon references: 18
    DOI Icon DOI: 10.70393/6a6374616d.343035
    DOI Icon ARK: ark:/40704/JCTAM.v3n2a01
  • AI-Assisted Structured Interview Analysis Using Natural Language Processing and Speech Feature Extraction
    Authors: Yuerong Yan
    Resource Type: Article
    Disciplines: Artificial Intelligence | Subjects: Machine Learning
    Publication ID: v3n2a02
    Abstract: Structured interviews are widely used in recruitment, psychological assessment, and social research due to their standardized procedures, fixed question sets, and unified evaluation criteria, which ensure a certain degree of fairness and...
    11-20
    DOI Icon Abstract views: 10 | DOI Icon PDF downloads: 5 | DOI Icon SUAS Digital Library downloads: 0 | DOI Icon references: 15
    DOI Icon DOI: 10.70393/6a6374616d.343036
    DOI Icon ARK: ark:/40704/JCTAM.v3n2a02
  • Research on Machine Learning–Based Prediction of Heterogeneous Metal Joining Performance and Its Application in Production and Operations Management
    Authors: Zhuoxuan Li
    Resource Type: Article
    Disciplines: Artificial Intelligence | Subjects: Machine Learning
    Publication ID: v3n2a03
    Abstract: Dissimilar metal joining technology is a key process for achieving lightweight structures, and accurate prediction of welding quality is crucial for ensuring structural safety. This study constructs a machine learning framework for predicting void...
    21-29
    DOI Icon Abstract views: 12 | DOI Icon PDF downloads: 5 | DOI Icon SUAS Digital Library downloads: 0 | DOI Icon references: 25
    DOI Icon DOI: 10.70393/6a6374616d.343037
    DOI Icon ARK: ark:/40704/JCTAM.v3n2a03