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In this paper, the structural characteristic of urban multimodal transport system is fully analyzed and then a two-tier network structure is proposed to describe such a system, in which the first-tier network is used to depict the traveller’s mode choice behaviour and the second-tier network is used to depict the vehicle routing when a certain mode has been selected. Subsequently, the generalized travel cost is formulated considering the properties of both traveller and transport mode. A new link impedance function is proposed, in which the interferences between different vehicle flows are taken into account. Simultaneously, the bi-equilibrium patterns for multimodal transport network are proposed by extending Wardrop principle. Correspondingly, a bi-level programming model is then presented to describe the bi-equilibrium based assignment for multi-class multimodal transport network. The solution algorithm is also given. Finally, a numerical example is provided to illustrate the model and algorithm.

BackgroundInfluenza is an acute respiratory infectious disease with a high incidence rate in the Chinese army, which directly disturbs military training and affects soldiers’ health. Influenza surveillance systems are widely used around the world and play an important role in influenza epidemic prevention and control.MethodsAs a theater centers for disease prevention and control, we established an influenza monitoring platform (IMP) in 2014 to strengthen the monitoring of influenza-like illness and influenza virus infection. In this study, we introduced the constitution, influenza virus detection, and quality control for an IMP. The monitoring effect was also evaluated by comparing the monitoring data with data from national influenza surveillance systems. The experiences and problems associated with the platform also were summarized.ResultsA theater IMP was established based on 3 levels of medical units, including monitoring sites, testing laboratories and a checking laboratory. A series of measures were taken to guarantee the quality of monitoring, such as technical training, a unified process, sufficient supervision and timely communication. The platform has run smoothly for 3 monitoring years to date. In the 2014–2015 and 2016–2017 monitoring years, sample amount coincided with that obtained from the National Influenza Surveillance program. In the 2015–2016 monitoring year, due to the strict prevention and control measures, an influenza epidemic peak was avoided in monitoring units, and the monitoring data did not coincide with that of the National Influenza Surveillance program. Several problems, including insufficient attention, unreasonable administrative intervention or subordination relationships, and the necessity of detection in monitoring sites were still observed.ConclusionsA theater IMP was established rationally and played a deserved role in the prevention and control of influenza. However, several problems remain to be solved.

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The financial market has been developing rapidly in recent years, and the issue of credit risk concerning listed companies has become increasingly prominent. Therefore, predicting the credit risk of listed companies is an urgent concern for banks, regulators and investors. The commonly used models are the Z-score, Logit (logistic regression model), the kernel-based virtual machine (KVM) and neural network models. However, the results achieved could be more satisfactory. This paper proposes a credit-risk-prediction model for listed companies based on a CNN-LSTM and an attention mechanism, Our approach is based on the benefits of the long short-term memory network (LSTM) model for long-term time-series prediction combined with a convolutional neural network (CNN) model. Furthermore, the advantages of being integrated into a CNN-LSTM model include reducing the complexity of the data, improving the calculation speed and training speed of the model and solving the possible lack of historical data in the long-term sequence prediction of the LSTM model, resulting in prediction accuracy. To reduce problems, we introduced an attention mechanism to assign weights independently and optimize the model. The results show that our model has distinct advantages compared with other CNNs, LSTMs, CNN-LSTMs and other models. The research on the credit-risk prediction of the listing formula has significant meaning.