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.

Effect of Nalmefene on Delayed Neurocognitive Recovery in Elderly Patients Undergoing Video-assisted Thoracic Surgery with One Lung Ventilation

The intravenous use of nalmefene has been found to exert neuroprotective effect in patients with severe traumatic brain injury and acute cerebral infarction;nonetheless,it is unknown whether nalmefene alleviates delayed neurocognitive recovery.Our purpose of the current research was to clarify the impact of nalmefene on delayed neurocognitive recovery in aged patients experiencing video-assisted thoracic surgery(VATS)with intraoperative use of one lung ventilation(OLV).The present study involved 120 paticents undergoing selective VATS,randomized to accept low-dose nalmefene(N1 group,n=40),high-dose nalmefene(N2 group,1n=40)or equal volume of physiologic saline(control group,1=40).A battery of neuropsychological tests were used to estimate cognitive function I day before surgery(o)and 10 days after surgery or before discharge(t).Regional cerebral oxygen saturation(rSO2)was detected 5 min before induction(t),5 min after induction(1),15 and 60 min after onset of OLV(62 and 13),and 15 min after termination of OLV(4).The plasma values of interleukin(IL)-1β,IL-6,tumor necrosis factor(TNF)-a1 and adiponectin(ADP)were also detected prior to induction of anesthesia(T0),1 h,2 h and 6 h after surgery(TI,T2,T3).On 1,delayed neurocognitive recovery occurred in 5/40(12.5%)patients of NI group,in 5/40(12.5%)patients of N2 group and in 13/40(32.5%)patients of control group(P<0.05).There were no statistical differences in rSO2 among three groups at different time points.At Tl,T2 and T3,IL-1β,IL-6 and TNF-a values significantly increased and ADP value significantly decreased(P<0.05)in control group.In contrast,at TI,T2 and T3,IL-1β,IL-6 and TNF-a values decreased and ADP value decreased less in N1 and N2 groups(P<0.05).At TI,T2 and T3,IL-1β,IL-6 and TNF-a concentrations presented a trend of N2 group<N1 group<control group and ADP presented a trend of N2 group>Nl group>control group(P<0.05).The result of our present research supports the hypothesis that the perioperative intravenous treatment with nalmefene to VATS with OLV ameliorates postoperative cognitive function and decreases the incidence of delayed neurocognitive recovery,most likely by suppression of inflammatory responses.

An Adaptive Data-driven Method Based on Fuzzy Logic for Determining Power System Voltage Status

This paper proposes an adaptive method based on fuzzy logic that utilizes data from phasor measurement units(PMUs) to assess and classify generating-side voltage trajectories. The voltage variable and its associated derivatives are used as the input variables of a fuzzy-logic block. In addition, the voltage trajectory is compared with the pre-selected pilot-bus voltage to make a reliable decision about the voltage operational state. Different types of short-term voltage dynamics are considered in the proposed method. The fuzzy membership functions are determined using a systematic method that considers the current situation of the voltage trajectory. Finally, the voltage status is categorized into four classes to determine appropriate remedial actions. The proposed method is validated on a IEEE 73-bus power system in a MATLAB environment.