Drawing on the dual system theory, a configuration model for cyberchondria was proposed to explore the complex interplay between the individual characteristics, socioemotional system, and cognitive control system in causing cyberchondria. A questionnaire was distributed to users who have searched for health information on social media platforms. A total of 320 valid responses were collected. The fuzzy-set qualitative comparative analysis (fsQCA) method was employed to analyse the data. Four equifinal configurations leading to cyberchondria were identified, with fear of missing out, perceived information overload, and perceived communication overload as core conditions. Additionally, one configuration leading to the absence of cyberchondria was identified. We further explored the configurations for the four sub-dimensions of cyberchondria. The results revealed four configurations for excessiveness, two for distress, one for compulsion, and three for reassurance. Four theoretical propositions leading to cyberchondria and its sub-dimensions are proposed. This provides a dual system perspective into how fear of missing out can evolve into cyberchondria and explains the complexities of cyberchondria between different individuals.

Generative Adversarial Networks (GAN) have been widely used in traffic data imputation to improve the accuracy of data imputation. However, existing GAN-based models often suffer from mode collapse and cannot fully reflect the complex characteristics of real-world traffic, which affects the quality of data imputation. To address these challenges, we incorporate the Diffusion Model (DM) into the GAN framework, integrating the traffic dynamics modeling process within the Diffusion-GAN network. Based on this, we propose a Diffusion-TGAN speed data imputation model to generate individual vehicle speeds. Combined with the generated vehicle speed, the group trajectory reconstruction result is further given. The model uses the forward process of DM to generate condition vectors to guide the training of GAN generator. Subsequently, the discriminator of GAN takes the traffic dynamics constraints into account during adversarial training. Traffic dynamics modeling aims to make the generated speed data consistent with the real traffic characteristics. Experiments on multiple data sets show that the proposed model effectively imputes in the spatio-temporal speed data, and reduces the RMSE of the speed considering the position by 23.4% compared with the common GAN model, and reduces the RMSE by 39.7% in the trajectory reconstruction respectively. The code and our model are available at GitHub.

This paper presents an improved Intelligent Driving Model (Sigmoid-IDM) to address the issues of excessive acceleration in traffic oscillation and following failure in free flow. The Sigmoid-IDM utilizes a Sigmoid function to enhance the starting-following characteristics, improve the output strategy of the spacing term, and stabilize the steady-state velocity in free flow. Furthermore, the model’s asymmetry is enhanced by introducing cautious following distance, caution driving factor, and segmentation function. The anti-interference ability of the Sigmoid-IDM is demonstrated through local stability and string stability analyses. The model parameters were calibrated using the Hefei dataset and High D data across various traffic scenarios: start-up, stop-go, and free-flow. The Sigmoid-IDM outperforms the IDM by significantly reducing errors and enhancing performance metrics. Specifically, in start-up and stop-go scenarios, the Sigmoid-IDM achieves a 28.57% and 19.04% reduction in Root Mean Square Error (RMSE) for acceleration, respectively. Comfort error during start-up is also lowered by 18.1%. In the free-flow scenario, the RMSE for spacing and velocity decreases by 15.64% and 16.36%, respectively. Furthermore, the Sigmoid-IDM demonstrates a more pronounced asymmetric behavior than the IDM, offering a more accurate representation of human drivers’ following patterns. The model’s efficacy was further validated through circular road simulation and Simulink-Carsim co-simulation, confirming its ability to accurately simulate the transition from synchronized flow to wide moving jams under variable parameters, as well as the traceability of its trajectory planning.

In this study, a novel traffic risk assessment framework of mega-events that integrate risk field and deep learning is proposed. Considering the inherent difference of different traffic risks, the risk quantification and standardization is conducted first. Then several risk field models are constructed to quantify the impacts of multi-source traffic risk superposition on mega-events. Then a time-series generative adversarial networks (TimeGAN) is used to predict the evolution of superposition risk. We select 2022 Beijing Winter Olympics as a case to explore the superposition effects of different traffic risks on the convoy entrance of this mega-event. The results illustrate the superposition risks are significantly associated with the strength of each traffic risk, and the distance from the traffic risk location to the convoy entrance. Furthermore, the temporal evolutions for different traffic risks and their superposition are forecasted using TimeGAN. The results show the unexpected traffic congestion risk presents the highest predictive performance (i.e., the average error for RMSE, MAE, and MSE is 0.135%) and the superposition traffic risks present the lowest predictive performance (the average error is 0.536%). Comparison between different methods demonstrates TimeGAN outperforms other methods in predicting both single traffic risks and superposition risks. The research findings could be potentially referenced in multi-source traffic risk management for mega-events.

For multiple-attribute decision making problems in Pythagorean fuzzy environment, few existing aggregation operators consider interrelationships among the attributes. To deal with this issue, this article extends the Bonferroni means to Pythagorean fuzzy sets (PFSs) to provide Pythagorean Fuzzy Bonferroni means. We first extend t-norm and its dual t-conorm to propose the generalized operational laws for PFSs, which can be considered as the extensions of the known ones. Based on these new laws, Pythagorean fuzzy weighted Bonferroni mean operator and Pythagorean fuzzy weighted geometric Bonferroni mean operator are developed, both of them can capture the correlations among Pythagorean fuzzy input arguments and their desired properties and special cases are also investigated in detail. At last, a novel approach is proposed based on the developed operators with its effectiveness being proved by an investment selection problem.

In this note, we point out an error to the proof of Theorem 3.4 in Zhang and Xu (Int J Intell Syst 2014;29(12):1061–1078) by a counterexample. We find that the inequality (i.e., ) with respect to the degrees of indeterminacy of any three Pythagorean fuzzy numbers in the proof of Theorem 3.4 in Zhang and Xu's paper is not valid. A new proof is provided in this note.