Purpose This study (i) investigates the effect of recovery power (Prec) and duration (t rec) on the recovery of the curvature constant (W′) of the power–duration relationship, (ii) compares the experimentally measured W′ balance to that predicted (W′ bal) by two models (SK2 and BAR), and (iii) presents a case of real-time performance optimization using the critical power (CP) concept. Methods Seven competitive amateur cyclists performed a ramp test to determine their V˙O2peak and gas exchange threshold, two to four 3-min all-out tests to determine CP and W′, and nine intermittent cycling tests to investigate W′ recovery. The intermittent cycling tests involved a 2-min constant work-rate interval above CP, followed by a constant work-rate recovery interval below CP (Prec and t rec were varied), followed by a 3-min all-out interval. Results There was a significant two-way interaction between Prec and t rec on W′ recovery, P = 0.004 (η 2 = 0.52). Simple main effects were present only with respect to Prec at each t rec. The actual W′ balance at the end of the recovery interval was less than the W′ bal predicted by both SK2 (P = 0.035) and BAR (P = 0.015) models. The optimal strategy derived from the subject-specific recovery model reduced the race time by 55 s as compared with the self-strategy. Conclusions This study has shown that in a recovery interval, Prec has a greater influence than t rec on W′ recovery. The overprediction of W′ bal from SK2 and BAR suggests the need for individualized recovery parameters or models for sub-CP exercise. Finally, the optimal strategy results provide encouraging signs for real-time, model-based performance optimization.

This paper extends our previous work in [1], [2], on optimal scheduling of autonomous vehicle arrivals at intersections, from one to a grid of intersections. A scalable distributed Mixed Integer Linear Program (MILP) is devised that solves the scheduling problem for a grid of intersections. A computational control node is allocated to each intersection and regularly receives position and velocity information from subscribed vehicles. Each node assigns an intersection access time to every subscribed vehicle by solving a local MILP. Neighboring intersections will coordinate with each other in real-time by sharing their solutions for vehicles' access times with each other. Our proposed approach is applied to a grid of nine intersections and its positive impact on traffic flow and vehicles' fuel economy is demonstrated in comparison to conventional intersection control scenarios.

No abstract

Improving a cyclist performance during a timetrial effort has been a challenge for sport scientists for several decades. There has been a lot of work on understanding the physiological concepts behind it. The concepts of Critical Power (CP) and Anaerobic Work Capacity (AWC) have been discussed often in recent cycling performance related articles. CP is a power that can be maintained by a cyclist for a long time; meaning pedaling at or below this limit, theoretically, can be continued for infinite amount of time. However, there is a limited source of energy for generating power above CP. This limited energy source is AWC. After burning energy from this tank, a cyclist can recover some by pedaling below CP. In this paper we utilize the concepts of CP and AWC to mathematically model muscle fatigue and recovery of a cyclist. Then, the models are used to formulate an optimal control problem for a time trial effort on a 10.3 km course located in Greenville SC. The course is simulated in a laboratory environment using a CompuTrainer. At the end, the optimal simulation results are compared to the performance of one subject on CompuTrainer.

No abstract