Impact of a Bundle on Prevention and Control of Healthcare associated Infections in Intensive Care Unit

Inpatients in the intensive care unit（ICU） are at high risk for healthcare-associated infections（HAIs）. In the current study, a bundle of interventions and measures for preventing and controlling HAIs were developed and implemented in the ICU by trained personnel, and the impact of the bundle was evaluated. The incidence of HAIs, the adjusted daily incidence of HAIs and the incidence of three types of catheter-related infections before and after the bundle implementation were compared. The execution rate of the bundle for preventing and controlling ventilator-associated pneumonia（VAP） was increased from 82.06% in 2012 to 96.88% in 2013. The execution rate was increased from 83.03% in 2012 to 91.33% in 2013 for central line-associated bloodstream infection（CLABSI）, from 87.00% to 94.40% for catheter-associated urinary tract infection（CAUTI）, and from 82.05% to 98.55% for multidrug-resistant organisms（MDROs）, respectively. In total, 136 cases（10.37%） in 2012 and 113 cases（7.72%） in 2013 involved HAIs, respectively. Patients suffered from infection of the lower respiratory tract, the most common site of HAIs, in 134 cases（79.29%） in 2012 and 107 cases（74.30%） in 2013 respectively. The incidence of VAP was 32.72‰ and 24.60‰, the number of strains of pathogens isolated was 198 and 173, and the number of MDROs detected in the ICU was 91 and 74 in 2012 and 2013, respectively. The percentage of MDROs among the pathogens causing HAIs was decreased in each quarter of 2013 as compared with the corresponding percentage in 2012. In 2013, the execution rate of the bundle for preventing and controlling HAIs was increased, whereas the incidence of HAIs and VAP decreased as compared with that in 2012.

Delay recovery model for high-speed trains with compressed train dwell time and running time

Modeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of dispatchers.In this study,the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train timetables.First,the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions,namely the compression of the train dwell time at stations and the compression of the train running time in sections.Stepwise regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time,namely the delay time,the scheduled supplement time,the running interval,the occurrence time,and the place where the delay occurred,under the two train operation adjustment actions.Finally,the gradient-boosted regression tree(GBRT)algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions.A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.

Study on Development of Train Running Diagram Optimization

This article from a whole new perspective on existing studies was classified, from the specific issues addressed by starting on two key issues of recent train scheduling plan: train running diagram and train operation adjustment. Solutions, basic ideas and researches are discussed in detail. Among them, the train working diagram is divided into single and double lane sections and Passenger Train Running Diagram. And the train operation adjustment includes the real-time adjustment, the service of train insert and the conflict prediction. This paper introduces automatic train operation dispatching system in scheduling applications, and discusses the train scheduling problems and the future direction of development.