Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence.

No abstract

This study suggests that overall physical performance level is not different between women with a distal radial fracture and those without a distal radial fracture. Differences in chair stand test scores and grip strength may imply an early subtle decrease in physical performance level in patients with distal radial fracture.

Energy management strategy is an important factor in determining the fuel economy of hybrid electric vehicles; thus, much research on how to distribute the required power to engines and motors of hybrid vehicles is required. Recently, various studies have been conducted based on reinforcement learning to optimally control the hybrid electric vehicle. In fact, the fundamental control approach of reinforcement learning shares many control frameworks with the control approach by using deterministic dynamic programming or stochastic dynamic programming. In this study, we compare the reinforcement learning based strategy by using these dynamic programming-based control approaches. For optimal control of hybrid electric vehicle, each control method was compared in terms of fuel efficiency by performing simulation by using various driving cycles. Based on our simulations, we showed the reinforcement learning-based strategy can obtain global optimality in the optimal control problem with an infinite horizon, which can also be obtained by stochastic dynamic programming. We also showed that the reinforcement learning-based strategy can present a solution close to the optimal one using deterministic dynamic programming, while a reinforcement learning-based strategy is more appropriate for a time variant controller with boundary value constraints. In addition, we verified the convergence characteristics of the control strategy based on reinforcement learning, when transfer learning was performed through value initialization using stochastic dynamic programming. INDEX TERMS Dynamic programming, hybrid electric vehicle, optimal control, reinforcement learning, power management.