Recently, it has been demonstrated that hypertrophic training with CLU （cluster） sets produces greater strength and power following a 12-week periodized program. The results suggest possible differences in neuromusc...Recently, it has been demonstrated that hypertrophic training with CLU （cluster） sets produces greater strength and power following a 12-week periodized program. The results suggest possible differences in neuromuscular adaptations. Therefore, we sought to compare the acute effect of TRD （traditional） and CLU set configurations during the parallel back squat on mean power output and integrated EMG （electromyography） activity of the VL （vastus lateralis） and BF （biceps femoris）. Ten males （23 ~ 2.4 years; height 182.9 ~ 6.1 cm; weight 86.2 ~ 4.2 kg; 5 ~ 2 years training） performed the parallel back squat using TRD and CLU with 75% 1RM （one-repetition maximum） in a randomized crossover design. Data was analyzed by a repeated measures--ANOVA （analysis of variance）. A significant effect of set （P = 0.006） was observed in mean power output. Mean power output decreased over each successive set when collapsed for condition. Clusters resulted in greater mean power output during latter repetitions of each set （repetition 4, 6-10; P 〈 0.05）. A significant effect of set （P = 0.049） was observed in VL EMG. VL EMG increased over each successive set when collapsed for condition. TRD training produced significantly greater VL EMG during latter repetitions of each set （repetition 6-8; P 〈 0.05）. An interaction was observed in BF EMG. No significant differences were observed in post-hoc. Thus, cluster sets can be used to achieve greater power output, but greater neuromuscular activity should not be expected relative to traditional training.展开更多
in this paper we present a method for detecting and determining the characteristicpoints on the surfacelsurface intersection. At first , criteria for detecting the characteristic pointsare derived by aid of a theorem ...in this paper we present a method for detecting and determining the characteristicpoints on the surfacelsurface intersection. At first , criteria for detecting the characteristic pointsare derived by aid of a theorem on algebraic curves , and then ar algorithm is presented for locaringthe characteristic points which is coupled with the numerical tracing techniques. instances are alsopresented for illustrating the capability of our algorithm.展开更多
文摘Recently, it has been demonstrated that hypertrophic training with CLU （cluster） sets produces greater strength and power following a 12-week periodized program. The results suggest possible differences in neuromuscular adaptations. Therefore, we sought to compare the acute effect of TRD （traditional） and CLU set configurations during the parallel back squat on mean power output and integrated EMG （electromyography） activity of the VL （vastus lateralis） and BF （biceps femoris）. Ten males （23 ~ 2.4 years; height 182.9 ~ 6.1 cm; weight 86.2 ~ 4.2 kg; 5 ~ 2 years training） performed the parallel back squat using TRD and CLU with 75% 1RM （one-repetition maximum） in a randomized crossover design. Data was analyzed by a repeated measures--ANOVA （analysis of variance）. A significant effect of set （P = 0.006） was observed in mean power output. Mean power output decreased over each successive set when collapsed for condition. Clusters resulted in greater mean power output during latter repetitions of each set （repetition 4, 6-10; P 〈 0.05）. A significant effect of set （P = 0.049） was observed in VL EMG. VL EMG increased over each successive set when collapsed for condition. TRD training produced significantly greater VL EMG during latter repetitions of each set （repetition 6-8; P 〈 0.05）. An interaction was observed in BF EMG. No significant differences were observed in post-hoc. Thus, cluster sets can be used to achieve greater power output, but greater neuromuscular activity should not be expected relative to traditional training.
文摘in this paper we present a method for detecting and determining the characteristicpoints on the surfacelsurface intersection. At first , criteria for detecting the characteristic pointsare derived by aid of a theorem on algebraic curves , and then ar algorithm is presented for locaringthe characteristic points which is coupled with the numerical tracing techniques. instances are alsopresented for illustrating the capability of our algorithm.
