Performance prediction of urban tunnel purification system based on the verified random number screening algorithm

Xia, Jie; Zhao, Hao-Yi; Ren, Hui; Chen, L.‐W. Antony; Li, Fang; Shi, Wei-Rong; Xie, Yi-Song; Tan, Mei-Lan; Li, Fei; Zhou, Bin

doi:10.1016/j.scs.2024.106047

Sustainable Cities and Society

2025

DOI: 10.1016/j.scs.2024.106047

|Get access via publisher |Cite

Performance prediction of urban tunnel purification system based on the verified random number screening algorithm

Jie Xia¹,

Hao-Yi Zhao²,

Hui Ren³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Relationship

Self Cite0

Independent0

Authors

Journals

Cited by 0 publications

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

No citations

Set email alert for when this publication receives citations?

Scite is an AI-powered research tool that helps researchers better discover and evaluate scientific literature through Smart Citations—a revolutionary system that shows whether articles support, contrast, or simply mention a given claim. Founded in 2018, and now part of Research Solutions, Scite has indexed over 1.3 billion citations and partnered with more than 30 major publishers to provide researchers with unparalleled access to scientific literature. With its Scite Assistant, Smart Citation Index, and advanced search capabilities, the platform addresses critical challenges such as information overload and research reproducibility. Trusted by two million active users worldwide, Scite is reshaping how researchers interact with scholarly content—building ethical, transparent AI tools that support rigorous, copyright-compliant research.