The service quality of public transit, such as comfort and convenience, is an important factor influencing ridership and fare revenue, which also reflects the passengers’ perception to the transit performance. Passengers are frustrated while waiting to board a crowded train especially during the peak hours, while the fail-to-board (FtB) situation commonly exists. The service performance measures determined by deterministic passenger demand and service frequency cannot reflect the perceived service of passengers. With the automatic fare collection system data provided by Chengdu Metro, we develop a data-driven approach considering the joint probability of spatiotemporal passenger demand at stations based on posted train schedule to approximate passenger travel time (e.g., in-vehicle and out-of-vehicle times). It was found that the estimated wait time can reflect the actual situation as passengers FtB. The proposed modeling approach and analysis results would be useful and beneficial for transit providers to improve system performance and service planning.

In this study, we aimed to develop a simple gene model to identify bacterial infection, which can be implemented in general clinical settings. Methods: We used a clinically availablereal-time quantitative polymerase chain reaction platform to conduct focused gene expression assays on clinical blood samples. Samples were collected from 2 tertiary hospitals. Results: We found that the 8 candidate genes for bacterial infection were significantly dysregulated in bacterial infection and displayed good performance in group classification, whereas the 2 genes for viral infection displayed poor performance. A two-gene model (S100A12 and CD177) displayed 93.0% sensitivity and 93.7% specificity in the modeling stage. In the independent validation stage, 87.8% sensitivity and 96.6% specificity were achieved in one set of case-control groups, and 93.6% sensitivity and 97.1% specificity in another set. Conclusions: We have validated the signature genes for bacterial infection and developed a two-gene model to identify bacterial infection in general clinical settings.

No abstract

No abstract

Multi-attribute emergency decision-making problems have the characteristics of incomplete information and shortage of response time. Evidence theory can effectively express uncertain information in the decision-making process. However, evidence theory requires that the condition of evidence independence is met, and the evaluation information of experts is often vague. Therefore, an emergency decision-making method based on intuitionistic fuzzy sets and evidence theory is proposed. First, each expert gives an intuitionistic fuzzy evaluation of each emergency plan. Secondly, the proposed intuitionistic fuzzy similarity calculation method is used to obtain the similarity between experts and determine the expert weight. The attribute weight is known, the intuitionistic fuzzy evaluation is converted into a Mass function, and the evaluation of expert's decision-making plan is revised and fused using evidence theory to obtain the final decision. Finally, an example analysis proves that the model is feasible and effective.