1    Theme                                                                                            

Cognitive abilities and professional expertise of humans are indispensable in domains such as mining, construction, industrial manufacturing, and epidemic treatment. Potential hazards, resulting from high temperatures, collapses, collision or viral infections caused by widespread epidemic, are inherent in these scenarios. This makes the virtualization of human-in-the-loop operations through the application of modeling and simulation techniques necessary.

In this way, research about modeling and simulation of human-in-the-loop systems, especially based on data mining, situation analysis, and pattern recognition technologies should be incorporated to capture human interactive behavior and better promote safe operation faced with potential hazards. In modeling, it is essential to focus on the reliability, robustness, and resilience of human-in-the-loop systems, such as renewable construction, industrial manufacturing, hospitals, etc. Whereby, further simulation of interactive operations of various systems are conducted.

2    Scope of Topics

This special issue aims to solicit recent advances and state-of-the-art works on modeling and simulation technologies oriented for human-in-the-loop systems. How to synchronously model, learn, inference the pattern of decision, cooperation, evolution of systems ranging from unit to whole choir, is an important and emerging pending issues. The manuscripts submitted to this special issue will be reviewed solely based on new and sufficient contributions excluding any contributions in the publishing and reviewing stage. You are invited to submit papers that are unpublished original work for this special issue at Complex System Modeling and Simulation.

Potential topics include, but are not limited to

• Reliability, robustness, and resilience modeling and simulation in human-in-the-loop systems.
• Methods, algorithms, frameworks, and strategies for modeling and simulation in human-in-the-loop systems.
• Data mining and pattern recognition technologies using advanced machine learning for modeling and simulation in human-in-the-loop systems.
• Data mining and pattern recognition technologies for monitoring and predicting the reliability, robustness, and resilience of human-in-the-loop systems.
• Distributed modeling and simulation of human-in-the-loop systems.
• Data mining and pattern recognition technologies for neural dialogue generation and modeling on human-in-the-loop systems.
• Data management, analysis, and exchange for modeling and simulation in human-in-the-loop systems.
• Threat and risk management for modeling and simulation in human-in-the-loop systems.

3    Guest Editors

• Prof. Ni Li

E-mail: lini@buaa.edu.cn

School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

• Prof. Cosmas Ogbuka

E-mail: cosmas.ogbuka@unn.edu.ng

Department of Electrical Engineering, University of Nigeria, Nsukka, Nigeria

• Prof.Yuanjun Laili

E-mail: lailiyuanjun@buaa.edu.cn

School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

• Prof.Wenzheng Liu

E-mail: liuwzh10@163.com

School of Computer Science, Beijing Information Science and Technology University, Beijing, China

4    Important Dates

Manuscript submission due: Dec 31, 2024

First review completed: Jan 25, 2025

Revised manuscript due: Feb 28, 2025

Second review completed: Mar 31, 2025

Possible publication: Apr 30, 2025

5    Submission Guidelines

Manuscripts  should be  submitted  through the publisher’s  online  system  at  https://mc03.manuscriptcentral.com/csms-tup. Please follow the instructions described in the submission website. Please make sure you select “Special Issue on Theory and Application of Modeling and Simulation for Human-in-the-Loop Systems” as Article Type. In preparing the manuscript, the authors are asked to closely follow the “Instructions to Authors”. Submissions will be reviewed according to CSMS rigorous standards and procedures through a peer review by at least two qualified reviewers.