Online social networks have become important channels for spreading rumors, and the spreading process in online social networks is influenced by many factors. This study focuses on the problem of rumor spreading through indirect contact, such as non-following/non-friend relationships due to the role of social networks, and considers the phenomenon of rumor isolation from users due to information overload on social networks. The SMQIR rumor-spreading model with five user groups was developed by introducing two parameters: the probability of social network influence and the probability of rumor isolation. The laws of influence of the relevant parameters were investigated using numerical simulations. Furthermore, the reproduction number and equilibrium point of the model were calculated, and the stability of the equilibrium point was analyzed based on the Routh-Hurwitz criterion. The proposed model was evaluated through a comparative analysis with the SIR and SIHR models, while two authentic datasets were employed for validation purposes. This study revealed that online social networks not only intuitively increase the rate of rumor propagation and amplify its reach and magnitude, but also prolong the duration of rumor spreading. Moreover, a higher influence coefficient of online social media leads to an extended period for spreading rumors in terms of speed, scale, and longevity. Conversely, information overload hampers the speed and scale of rumor spreading while simultaneously extending its duration. Additionally, it was observed that information overload may result in the limited exposure to rumors for certain individuals.

Emergencies often lead to the impairment of infrastructure systems, including transportation systems. It is necessary to analyze the uncertainty and correlation of transportation network capacity caused by emergencies, aiming at the problems of emergency facilities’ location and matching in emergency contexts. This study introduces novel concepts, such as flow distribution betweenness centrality (FD-BC) and the transport capacity effect coefficient (TC-EC). Furthermore, we introduce the ellipsoidal uncertainty set to characterize uncertainties in transport capacity. We construct a multi-criteria decision-making (MCDM) model and a multi-strength elitist genetic algorithm (multi-SEGA) to ensure the lower limit of transport capacity between demand and emergency points while minimizing decision-making costs. By designing an uncertain scenario example, we analyze the effect of the perturbation ratio and the uncertainty level on the robust location model. The following results were drawn: (1) Indicators FD-BC and TC-EC effectively indicated the importance of each section in the emergency transportation network. (2) The optimal value of the model’s objective function changed more significantly as the perturbation ratio and uncertainty level increased. (3) After reaching a certain uncertainty level, the robust model with an ellipsoidal uncertainty set became more conservative than the robust model with a box uncertainty set, which lacked practical significance. The research results guarantee the robustness of the emergency support system in uncertain conditions.

Named entity recognition (NER) is a fundamental task for natural language processing, which aims to detect mentions of real-world entities from text and classifying them into predefined types. Recently, research on overlapped and discontinuous named entity recognition has received increasing attention. However, we note that few studies have considered both overlapped and discontinuous entities. In this paper, we proposed a novel sequence-to-sequence model that is capable of recognizing both overlapped and discontinuous entities based on machine reading comprehension. The model utilizes machine reading comprehension formulation to encode significant inferior information about the entity category. Then input sequence passes through a question-answering model to predict the mention relevance of the given source sentences to the query. Finally, we incorporate the mention relevance into the BART-based generation model. We conducted experiments on three type of NER datasets to show the generality of our model. The experimental results demonstrate that our model beats almost all the current top-performing baselines achieves a vast amount of performance boost over current SOTA models on overlapped and discontinuous NER datasets.