Intelligent detection of locomotive signal lights and pedestrians on railway tracks is of great significance to the safe operation of locomotives, especially under bad illumination conditions. Due to the highly complicated operation environment of railway locomotives, it is relatively difficult to apply deep neural network-based object detection methods in the recognition of locomotive signal lights and railway pedestrians. This work, for the first time, proposes a real-time detection method based on improved YOLOv4 to recognize locomotive signal lights and railway pedestrians. In our method, the Region-of-Interest is introduced into the original network of YOLOv4 to improve the detection precision of pedestrians on railway tracks. Importantly, we establish a dataset called detection of locomotive signal lights and railway pedestrians (DLSLRP), which is dedicated to the training, testing, and validation of related convolutional neural networks. We evaluated the proposed detector on DLSLRP dataset, the experimental results suggest that our method can detect locomotive signal lights and railway pedestrians with high speed and accuracy under different illumination conditions. The mAP reaches 93.52%, and the detection speed achieves 25 FPS.

Despite much progress in exploring the coupled epidemic-awareness dynamics in multiplex networks, little attention has been paid to the joint impacts of behavioral observability and social proof on epidemic spreading. Since both the protective actions taken by direct neighbors and the observability of these actions have essential influence on individuals’ decisions. Thus, we propose a UAPU-SIR model by integrating the effects of these two factors into the decision-making process of taking preventive measures. Specifically, a new state called taken protective actions is introduced into the original unaware-aware-unaware (UAU) model to characterize the action-taken state of individuals after getting epidemic-related information. Using the Microscopic Markov Chain Approach (MMCA), the methods and model are described, and the epidemic threshold is analytically derived. We find that both observability of protecting behaviors and social proof can reduce the epidemic prevalence and raise the epidemic threshold. Moreover, only if observability of protection actions reaches a certain threshold can accelerating information diffusion is able to inhibit disease spreading and result in higher epidemic threshold. We also discover that, reducing the forgetting rate of information is able to decrease epidemic size.

In order to investigate the impact of two immunization strategies—vaccination targeting susceptible individuals to reduce their infection rate and clinical medical interventions targeting infected individuals to enhance their recovery rate—on the spread of infectious diseases in complex networks, this study proposes a bilinear SIR infectious disease model that considers bidirectional immunization. By analyzing the conditions for the existence of endemic equilibrium points, we derive the basic reproduction numbers and outbreak thresholds for both homogeneous and heterogeneous networks. The epidemic model is then reconstructed and extensively analyzed using continuous-time Markov chain (CTMC) methods. This analysis includes the investigation of transition probabilities, transition rate matrices, steady-state distributions, and the transition probability matrix based on the embedded chain. In numerical simulations, a notable concordance exists between the outcomes of CTMC and mean-field (MF) simulations, thereby substantiating the efficacy of the CTMC model. Moreover, the CTMC-based model adeptly captures the inherent stochastic fluctuation in the disease transmission, which is consistent with the mathematical properties of Markov chains. We further analyze the relationship between the system’s steady-state infection density and the immunization rate through MCS. The results suggest that the infection density decreases with an increase in the immunization rate among susceptible individuals. The current research results will enhance our understanding of infectious disease transmission patterns in real-world scenarios, providing valuable theoretical insights for the development of epidemic prevention and control strategies.