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Using the Adapted Youth Track and Field Athletes' Participation Motivation Factors Questionnaire (AYTFAPMFQ), this study examined and analyzed the characteristics and relationships among highschool track and field (T&F) athletes' participation motivation Factors (PMFs), and four healthy-related elements. Participants were 133 youth athletes from fifteen high-school T&F teams of New York City public schools. Data analyses involved 2x2x2x2 factorial MANOVA, exploratory factor analysis (EFA), and multiple regression analyses (MRA). Results included: 1.) The top four motivation factors (MFs) were: MF3 (For a Healthier Body); MF11 (To Foster Self-Esteem); MF1(High Technical Content and Unique Value); MF2 (For Fun and Happy). These four MFs possess the highest impact on these participants' PMs. 2.) Genders (male/female) and Supporting (by parents or by school) had significant impacts on these athletes' PMs, but Goalsettings ('for professional' or 'for non-professional') and 'Years engage in T&F competition' (one to two years or three and four years) did not. 3.) Through the analyses of EFA and MRA, the characteristics and relationships among these high-school T&F athletes' participation motivations were correlations were identified. Further, the traits of the four healthy related elements associated with their basic psychological needs were also identified, and verified these traits were predictable. Last, meaningful recommendations on how to apply these findings to youth sports education were also provided. [ABSTRACT FROM AUTHOR]

There is an ongoing debate regarding the necessity for sex‐segregated sports particularly in youth. However, there has been minimal evaluation of prepubertal sex‐based differences in the events of shot put, javelin throw, and long jump. Therefore, the top eight performances from the USA Track and Field National Youth Outdoor Championships and National Junior Olympic Championships during the years 2016–2023 for shot put, javelin throw, and long jump in the 8‐and‐under and 9–10‐year‐old age groups were analyzed for sex‐based differences. The 8‐and‐under males threw the shot put farther (P < 0.0001 and Hedges' g = 0.922) than females by 19.3% and the 9–10‐year‐old males threw the shot put farther (P = 0.016 and Hedges' g = 0.332) than females by 6.5%. The 8‐and‐under males threw the javelin farther (P < 0.0001 and Hedges' g = 1.269) than females by 32.6% and 9–10‐year‐old males threw the javelin farther (P < 0.0001 and Hedges' g = 1.169) than females by 23.5%. The 8‐and‐under males long jumped farther (P = 0.010 and Hedges' g = 0.359) than females by 4.7% and 9–10‐year‐old age males long jumped farther (P = 0.007 and Hedges' g = 0.552) than females by 3.9%. The average between sex differences were larger than the within sex differences between the first through fourth place finishers in all but 9–10‐year‐old shot put. In all events, the greatest individual distance for a male exceeded that for a female. Therefore, the present data indicate that, in elite competition, males in the 8‐and‐under and 9–10‐year‐old age groups typically performed long jump and throw the shot put and javelin farther than females of the same age. Highlights: Males in the 8‐and‐under age group outperformed females of the same ages by 19.3% in shot put, 32.6% in javelin throw, and 4.7% in long jump.Males in the 9–10‐year‐old age group outperformed females of the same ages by 6.5% in shot put, 23.5% in javelin throw, and 3.9% in long jump.The average differences between the first through fourth place finishers within males and within females were smaller than the sex‐based differences between males and females in all but 9–10‐year‐old shot put. [ABSTRACT FROM AUTHOR]

Prediction and identification of faults in track circuits are crucial for improving the safety and efficiency of railway transportation. However, due to the absence of real data, the task of track circuit fault prediction through deep learning methods facing significant challenges. This paper proposed a novel heterogeneous transfer learning network structure for track circuit deep learning fault prediction. The proposed transfer learning network can reduce the reliance on track circuit data in the process of deep learning models training by utilizing public datasets from other similar tasks. In this paper, an index describing the data distribution is used to demonstrate the transferability between heterogeneous data firstly. Then a heterogeneous transfer learning network structure is proposed to help the deep learning model training on the track circuit fault prediction task. Finally, the effect of transfer learning is comprehensively examined. The simulation experimental results show that the proposed heterogeneous transfer learning network structure can transfer useful knowledge in other similar fields for tasks in track circuit fault prediction, and the resulting model can distinguish between nine different classes with a high accuracy level over 99% on the test dataset while reducing the amount of required training data to 10% of the traditional training methods. [ABSTRACT FROM AUTHOR]

Athlete burnout potentially has negative consequences for sport performance. However, to the best of our knowledge, empirical studies have yet to examine the relationship between athlete burnout and objective sport performance. Consequently, we aimed to provide a first such examination. To do so, we recruited three samples. We used Sample 1 (n = 106: track and field athletes) to examine the predictive utility of athlete burnout on a single performance, Sample 2 (n = 181: swimmers) to examine whether the findings from Sample 1 can be replicated in a different sport, and Sample 3 (n = 169: track and field athletes) to examine the predictive utility of athlete burnout on peak performance in a three-month period. Finally, having captured three samples each assessing the athlete burnout-performance relationship, we also sought to quantify the combined effect across these samples using a relatively new analytical technique – mini meta-analysis. Results from regression analyses showed that in at least two of the three samples total burnout, reduced sense of accomplishment, and sport devaluation each negatively predicted performance, whereas emotional and physical exhaustion was unrelated to performance. When we combined the samples, mini meta-analysis showed that total burnout, reduced sense of accomplishment, and sport devaluation each displayed a small-to-medium negative and significant meta-correlation with performance. The findings suggest that certain athlete burnout symptoms may indeed have negative consequences for sport performance, and that this is the case when considering a single performance and peak performance. • Total burnout was shown to negatively predict sport performance for a one-off competition and peak competition performance in a three-month period. • Both reduced sense of accomplishment and sport devaluation were each shown to negatively predict sport performance for a one-off competition and peak competition performance in a three-month period. • Emotional and physical exhaustion did not predict sport performance for a one-off competition or peak competition performance in a three-month period. [ABSTRACT FROM AUTHOR]