We present results from an experiment in which human subjects interact with an unknown dynamic system 40 times during a two-week period. During each interaction, subjects are asked to perform a command-following (i.e., pursuit tracking) task. Each subject's performance at that task improves from the first trial to the last trial. For each trial, we use subsystem identification to estimate each subject's feedforward (or anticipatory) control, feedback (or reactive) control, and feedback time delay. Over the 40 trials, the magnitudes of the identified feedback controllers and the identified feedback time delays do not change significantly. In contrast, the identified feedforward controllers do change significantly. By the last trial, the average identified feedforward controller approximates the inverse of the dynamic system. This observation provides evidence that a fundamental component of human learning is updating the anticipatory control until it models the inverse dynamics.

A parallel hybrid electric vehicle (PHEV) is used to investigate the fuel economy effect of the equivalent fuel consumption minimization strategy (ECMS) with the equivalent factor as the core, where the equivalent factor is the conversion coefficient between fuel thermal energy and electric energy. In the conventional ECMS strategy, the battery cannot continue to discharge when the state of charge (SOC) is lower than the target value. At this time, the motor mainly works in the battery charging mode, making it difficult to adjust the engine operating point to the high-efficiency zone during the acceleration process. To address this problem, a relationship model of the battery SOC, vehicle acceleration a, and equivalent factor S was established. When the battery SOC is lower than the target value and the vehicle demand torque is high, which makes the engine operating point deviate from the high-efficiency zone, the time that the motor spends in the power generation mode during the driving process is reduced. This enables the motor to drive the vehicle at the appropriate time to reduce the engine output torque, and helps the engine operate in the high-efficiency zone. The correction function under US06 condition was optimized by genetic algorithm (GA). The best equivalent factor MAP was obtained with acceleration a and battery SOC as independent variables, and the improved global optimal equivalent factor of ECMS was established and simulated offline. Simulation results show that compared with conventional ECMS, the battery still has positive power output even when the SOC is less than the target value. The SOC is close to the target value after the cycle condition, and fuel economy improved by 1.88%; compared with the rule-based energy management control strategies, fuel economy improved by 10.17%. These results indicate the effectiveness of the proposed energy management strategy.

No abstract

Aims Poly (ADP-ribose) polymerase (PARP) inhibitor has been reported to attenuate inflammatory response in rat models of inflammation. This study was designed to investigate the effect of PARP signalling in osteoarthritis (OA) cartilage inflammatory response in an OA rat model. Methods The OA model was established by anterior cruciate ligament transection with medial meniscectomy in Wistar rats. The poly (ADP-ribose) polymerase 1 (PARP-1) shRNA (short hairpin (sh)-PARP-1) and negative control shRNA (sh-NC) were delivered using a lentiviral vector and were intra-articularly injected into rats after surgery. The weight-bearing distribution of the hind limbs and the knee joint width were measured every two weeks. The expression levels of PARP-1, inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2) in cartilage were determined using real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) and Western blot. The serum concentrations of inflammatory cytokines were detected using enzyme-linked immunosorbent assay (ELISA). Results PARP-1 expression level significantly increased in the cartilage of the established OA rat model. sh-PARP-1 treatment suppressed PARP-1 levels, decreased the Δ Force (the difference between the weight on ipsilateral limb and contralateral limb) and the knee joint width, inhibited cartilage matrix catabolic enzymes, and ameliorated OA cartilage degradation and attenuated inflammatory response. Conclusion PARP-1 inhibition attenuates OA cartilage inflammatory response in the OA rat model. Cite this article: Bone Joint Res 2021;10(7):401–410.