Background The use of extracorporeal membrane oxygenation (ECPR) in refractory out-of-hospital cardiac arrest (OHCA) patients is usually implemented in-hospital. As survival in ECPR patients is critically time-dependent, alternative models in ECPR delivery could improve equity of access. Objectives To identify the best strategy of ECPR delivery to provide optimal patient access, to examine the time-sensitivity of ECPR on predicted survival and to model potential survival benefits from different delivery strategies of ECPR. Methods We used transport accessibility frameworks supported by comprehensive travel time data, population density data and empirical cardiac arrest time points to quantify the patient catchment areas of the existing in-hospital ECPR service and two alternative ECPR strategies: rendezvous strategy and pre-hospital ECPR in Sydney, Australia. Published survival rates at different time points to ECMO flow were applied to predict the potential survival benefit. Results With an in-hospital ECPR strategy for refractory OHCA, five hospitals in Sydney (Australia) had an effective catchment of 811,091 potential patients. This increases to 2,175,096 under a rendezvous strategy and 3,851,727 under the optimal pre-hospital strategy. Assuming earlier provision of ECMO flow, expected survival for eligible arrests will increase by nearly 6% with the rendezvous strategy and approximately 26% with pre-hospital ECPR when compared to the existing in-hospital strategy. Conclusion In-hospital ECPR provides the least equitable access to ECPR. Rendezvous and pre-hospital ECPR models substantially increased the catchment of eligible OHCA patients. Traffic and spatial modelling may provide a mechanism to design appropriate ECPR service delivery strategies and should be tested through clinical trials.

Theoretical modeling and the sliding mode control (SMC) of an active trailing-edge flap of a wind turbine blade based on the adaptive reaching law are investigated. The blade is a single-celled thin-walled composite structure using circumferentially asymmetric stiffness (CAS) design, exhibiting displacements of flap-wise/twist coupling. A reduced structural model originated from the variation method is used to model the structure of the blade, the structural damping of which is computed. The trailing-edge flap is a rigid structure that is embedded in and hinged to the blade host structure, and it is driven by two pairs of pneumatic cylinders moving in reverse. Flutter suppression for the large-amplitude vibration of the blade tip is investigated based on an active trailing-edge flap structure and SMC algorithm using the adaptive reaching law. The controlled responses of flap-wise/twist displacements and control inputs (the angles of the trailing-edge flap) are illustrated, with obvious simulation effects demonstrated. An experimental platform for driving the pneumatic cylinders verifies the effectiveness of the control algorithm using the adaptive reaching law and the effectiveness of the selected pneumatic transmission scheme controlled by another adaptive SMC based on the minimum parameter learning of neural networks.

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Traffic congestion is largely due to the high proportion of solo drivers during peak hours. Ridesharing, in the sense of carpooling, has emerged as a travel mode with the potential to reduce congestion by increasing the average vehicle occupancy rates and reduce the number of vehicles during commuting periods. In this study, we propose a simulation-based optimization framework to explore the potential of subsidizing ridesharing users, drivers, and riders, so as to improve social welfare and reduce congestion. We focus our attention on a realistic case study representative of the morning commute on Sydney’s M4 Motorway in Australia. We synthesize a network model and travel demand data from open data sources and use a multinomial logistic model to capture users’ preferences across different travel roles, including solo drivers, ridesharing drivers, ridesharing passengers, and a reserve option that does not contribute to congestion on the freeway network. We use a link transmission model to simulate traffic congestion on the freeway network and embed a fixed-point algorithm to equilibrate users’ mode choice in the long run within the proposed simulation-based optimization framework. Our numerical results reveal that ridesharing incentives have the potential to improve social welfare and reduce congestion. However, we find that providing too many subsidies to ridesharing users may increase congestion levels and thus be counterproductive from a system performance standpoint. We also investigate the impact of transaction fees to a third-party ridesharing platform on social welfare and traffic congestion. We observe that increasing the transaction fee for ridesharing passengers may help in mitigating congestion effects while improving social welfare in the system.