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Taraxacum kok-saghyz (TKS), known as Russian dandelion, can produce high-quality natural rubber. The dry weight content of rubber in the TKS roots was found to be approximately 6–9%, of which 61% and 39% were stored in the root bark and root flesh, respectively. The content of lignin and holocellulose accounted for about 40% of the total root. Two new aqueous-based rubber extraction processes were proposed and optimized, namely, the strong water shearing process and the acid–base extraction process. These two processes made the purity of rubber reach about 91% and 94%, respectively. TKS rubber was characterized as having similar composition, and molecular structure to Hevea NR, and TKS rubber samples did not exhibit the strain-induced crystallization (SIC) phenomenon. TKS rubber was blended into a winter tire tread formulation and tested. The results showed no significant differences in processing and mechanical properties from the other formulations.

Visual or haptic feedback based on error modulation has been used to improve the effect of robot-assisted rehabilitation training. However, there are several investigations on the effects of error modulation-based visual and haptic feedback fusion strategies on motor learning and motivation. To observe the influence of different feedback fusion strategies on motor learning and motivation, a parallel controlled study was conducted, dividing 30 healthy subjects into three groups with similar skill levels. The no error modulation group received visual and haptic feedback without error modulation; the visual amplification haptic reduction group received visual error amplification combined with haptic error reduction, and the visual reduction haptic amplification (VRHA) group received visual error reduction combined with haptic error amplification. Each subject implemented a trajectory-tracking task with an upper limb rehabilitation robot. They went through baseline, training, assessment, and generalization tests and completed 340 consecutive tracking movements. To evaluate motor learning and motivation, the average tracking error, the root mean square (RMS) of surface electromyography (sEMG) signals, and the intrinsic motivation inventory scale were all examined. In the assessment tests, the average tracking error was significantly decreased in all three groups. In particular, the VRHA group had a larger reduction in average tracking error in the generalization test, lower RMS of sEMG signals both in the assessment and generalization tests and higher perceived competence in the assessment tests. The VRHA fusion strategy significantly improved the subjects’ motor learning and transfer ability, decreased muscle activation, and increased motor learning motivation. These findings may provide some new insights for multisensory feedback fusion technology in the application of rehabilitation robots.