METHODS: Participants (N.=27) with the mean age of 16.95±0.8 years, height of 165.6±6.1 cm and weight of 54.19±8.1 kg were matched into either modified exponential taper (N.=7), normal exponential taper (N.=7), or control (N.=7) groups using their initial VO2max values. Both experimental groups followed a 12-week progressive endurance training program and subsequently, a 2-week tapering phase. A simulated 20-km time trial performance along with VO2max, power output, heart rate and rating of perceived exertion were measured at baseline, pre and post-taper. One way ANOVA was used to analyze the difference between groups before the start of the intervention while mixed factorial ANOVA was used to analyze the difference between groups across measurement sessions. When homogeneity assumption was violated, the Greenhouse-Geisser Value was used for the corrected values of the degrees of freedom for the within subject factor the analysis.
RESULTS: Significant interactions between experimental groups and testing sessions were found in VO2max (F=6.67, df=4, P<0.05), power output (F=5.02, df=4, P<0.05), heart rate (F=10.87, df=2.51, P<0.05) rating of perceived exertion (F=13.04, df=4, P<0.05) and 20KM time trial (F=4.64, df=2.63, P<0.05). Post-hoc analysis revealed that both types of taper exhibited positive effects compared to the non-taper condition in the measured performance markers at post-taper while no different were found between the two taper groups.
CONCLUSIONS: It was concluded that both taper protocols successfully inducing physiological adaptations among the junior cyclists by reducing the volume and maintaining the intensity of training.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.