Smart textiles consist of discrete devices fabricated from—or incorporated onto—fibres. Despite the tremendous progress in smart textiles for lighting/display applications, a large scale approach for a smart display system with integrated multifunctional devices in traditional textile platforms has yet to be demonstrated. Here we report the realisation of a fully operational 46-inch smart textile lighting/display system consisting of RGB fibrous LEDs coupled with multifunctional fibre devices that are capable of wireless power transmission, touch sensing, photodetection, environmental/biosignal monitoring, and energy storage. The smart textile display system exhibits full freedom of form factors, including flexibility, bendability, and rollability as a vivid RGB lighting/grey-level-controlled full colour display apparatus with embedded fibre devices that are configured to provide external stimuli detection. Our systematic design and integration strategies are transformational and provide the foundation for realising highly functional smart lighting/display textiles over large area for revolutionary applications on smart homes and internet of things (IoT).

In maximal sprint cycling, the power-cadence relationship to assess the maximal power output (P max ) and the corresponding optimal cadence (C opt ) has been widely investigated in experimental studies. These studies have generally reported a quadratic power-cadence relationship passing through the origin. The aim of the present study was to evaluate an equivalent method to assess P max and C opt for endurance cycling. The two main hypotheses were: (1) in the range of cadences normally used by cyclists, the power-cadence relationship can be well fitted with a quadratic regression constrained to pass through the origin; (2) P max and C opt can be well estimated using this quadratic fit. We tested our hypothesis using a theoretical and an experimental approach. The power-cadence relationship simulated with the theoretical model was well fitted with a quadratic regression and the bias of the estimated P max and C opt was negligible (1.0 W and 0.6 rpm). In the experimental part, eight cyclists performed an incremental cycling test at 70, 80, 90, 100, and 110 rpm to yield powercadence relationships at fixed blood lactate concentrations of 3, 3.5, and 4 mmol L -1 . The determined power outputs were well fitted with quadratic regressions (R 2 = 0.94-0.96, residual standard deviation = 1.7%). The 95% confidence interval for assessing individual P max and C opt was ±4.4 W and ±2.9 rpm. These theoretical and experimental results suggest that P max , C opt , and the power-cadence relationship around C opt could be well estimated with the proposed method.

In race cycling, the external power-cadence relationship at the performance level, that is sustainable for the given race distance, plays a key role. The two variables of interest from this relationship are the maximal external power output (P (max)) and the corresponding optimal cadence (C (opt)). Experimental studies and field observations of cyclists have revealed that when cycling uphill is compared to cycling on level ground, the freely chosen cadence is lower and a more upright body position seems to be advantageous. To date, no study has addressed whether P (max) or C (opt) is influenced by road incline or body position. Thus, the main aim of this study was to examine the effect of road incline (0 vs. 7%) and racing position (upright posture vs. dropped posture) on P (max) and C (opt). Eighteen experienced cyclists participated in this study. Experiment I tested the hypothesis that road incline influenced P (max) and C (opt) at the second ventilatory threshold ([Formula: see text] and [Formula: see text]). Experiment II tested the hypothesis that the racing position influenced [Formula: see text], but not [Formula: see text]. The results of experiment I showed that [Formula: see text] and [Formula: see text] were significantly lower when cycling uphill compared to cycling on level ground (P < 0.01). Experiment II revealed that [Formula: see text] was significantly greater for the upright posture than for the dropped posture (P < 0.01) and that the racing position did not affect [Formula: see text]. The main conclusions of this study were that when cycling uphill, it is reasonable to choose (1) a lower cadence and (2) a more upright body position.