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The purpose of this paper is to focus on flight exceedances in pilots' operations. With some bad conditions, such as a bad weather, flight exceedances might lead to serious consequences. They are significant hidden dangers of aviation. Risk analysis is carried out to identify pilots' high‐risk or low‐risk operations.

A multi‐objective optimization model is proposed for risk analysis of flight operations. An evolutionary algorithm is designed to divide flight operation state‐space into some high‐risk and low‐risk sub‐spaces.

Through the empirical study of a certain flight exceedance with the analysis model, the authors discover some high‐risk flight operations, which indicate coordination problems in coordinate control of airplane's speed, rate of descent, heading, roll and pitch, etc.

This paper employs a quantitative model to carry out risk analysis of flight operations. The results are useful to pilots' training and may improve flight safety fundamentally. The risk analysis of flight exceedance is one specific case in airlines safety risk management. Some other problems, such as cause analysis of flight delay, aircraft faults diagnosis, can be addressed in the same way and dealt with by specific model adjustments and algorithm designs.

Prefabricated construction is often hindered by scheduling delays. This paper aims to propose a schedule delay prediction model system, which can provide the key information for controlling the delay effects of risk-related factors on scheduling in prefabricated construction.

This paper combines SD (System Dynamics) and BP (Back Propagation) neural network to predict risk related delays. The SD-based prediction model focuses on dynamically presenting the interrelated impacts of risk events and activities along with workflow. While BP neural network model is proposed to evaluate the delay effect for a single risk event disrupting a single job, which is the necessary input parameter of SD-based model.

The established model system is validated through a structural test, an extreme condition test, a sensitivity test, and an error test, and shows an excellent performance on aspect of reliability and accuracy. Furthermore, 5 scenarios of case application during 3 different projects located in separate cities prove the prediction model system can be applied in a wide range.

This paper contributes to academic research on combination of SD and BP neural network at the operational level prediction, and a practical prediction tool supporting managers to take decision-making in a timely manner against delays.

Based on the construal level theory, the purpose of this study is to prove the effect of interaction between construal level and visual crowding on consumers' buying intention. The study tries to explain the reasons behind the different buying intention toward visual crowding among consumers with different construal level.

This study was conducted through two situational simulation experiments. The main data analysis methods are ANOVA and bootstrap analysis.

(1) the matching of construal level and visual crowding has a significant effect on consumers’ buying intention. (2) Perceptual fluency mediates the interaction between the construal level and visual crowding on buying intention.

This study measures consumers' buying intention through situational experiments but does not measure consumers' buying behavior through real scenarios.

According to the study conclusions, consumers prefer visually crowded packaging that matches their construal level. Enterprises should consider the impact of the construal level on the effect of packaging stimulation.

This study enriches the theory related to construal level and highlights the mediating role of perceptual fluency. The addition of perceptual fluency explains the mechanism by which visual crowding affects consumers' buying intention. This extends the research on the antecedents and effects of perceptual fluency.

This study innovatively introduces visual crowding into packaging and matches visual crowding to construal level, explaining why different consumers buy different visually crowded packaging.