Relationship between anaerobic capacity and changes in speed in the 100-m breaststroke stroke in short-distance breaststroke swimmers: the mediating role of technical change

Objective:By analyzing and summarizing the relationship between anaerobic capacity, technical changes of 100m breaststroke en route and speed changes of short distance breaststroke athletes, the interrelationship and internal pathways between the three are revealed to provide reference for improving athletic performance of short distance breaststroke athletes and provide theoretical basis for anaerobic capacity training.Method:Fifteen male short-distance breaststroke athletes (age 19.67±2.61 years, height 178.4±7.04 cm, weight 71.6±7.79 kg) were selected to perform anaerobic power cycling and 100 m breaststroke tests on the upper and lower extremities. The correlations and intrinsic linkage pathways between the three were explored by calculating Pearson correlation coefficients and using a mediating effects model.Result:Significant differences existed in speed, stroke rate, cycle time per stroke, and swim efficiency index in the 100 m breaststroke all-out test. There were significant correlations between the rate of anaerobic power decrease in the upper limb and the changes in stroke amplitude, cycle time per stroke, and speed. There were significant correlations between the change in mean stroke rate, the change in cycle time per stroke, the change in swim efficiency index and the change in speed. Anaerobic power indirectly influenced the speed variation during the en-route swim, which was mediated by the technical variation in cycle time per stroke.Conclusion:The upper limb anaerobic fatigue resistance of short distance breaststroke athletes is a key factor affecting the technique and speed stability of the 100m breaststroke en route, and the rate of decline in upper limb anaerobic power leads to a decrease in speed by affecting the change in time per stroke cycle. (AU)

Discovering Speed Changes of Vehicles from Audio Data.

In this paper, we focus on detection of speed changes from audio data, representing recordings of cars passing a microphone placed near the road. The goal of this work is to observe the behavior of drivers near control points, in order to check whether their driving is safe both when approaching the speed camera and after passing it. The audio data were recorded in controlled conditions, and they are publicly available for downloading. They represent one of three classes: car accelerating, decelerating, or maintaining constant speed. We used SVM, random forests, and artificial neural networks as classifiers, as well as the time series based approach. We also tested several approaches to audio data representation, namely: average values of basic audio features within the analyzed time segment, parametric description of the time evolution of these features, and parametric description of curves (lines) in the spectrogram. Additionally, the combinations of these representations were used in classification experiments. As a final step, we constructed an ensemble classifier, consisting of the best models. The proposed solution achieved an accuracy of almost 95%, without mistaking acceleration with deceleration, and very rare mistakes between stable speed and speed changes. The outcomes of this work can become a basis for campaigns aiming at improving traffic safety.