The potential to use the vertical jump (VJ) to assess both athletic performance and risk of anterior cruciate ligament (ACL) injury could have widespread clinical implications since VJ is broadly used in high school, university, and professional sport settings. Although drop jump (DJ) and VJ observationally exhibit similar lower extremity mechanics, the extent to which VJ can also be used as screening tool for ACL injury risk has not been assessed. This study evaluated whether individuals exhibit similar knee joint frontal plane kinematic and kinetic patterns when performing VJs compared with DJs. Twentyeight female collegiate athletes performed DJs and VJs. Paired t-tests indicated that peak knee valgus angles did not differ significantly between tasks (p = 0.419); however, peak knee internal adductor moments were significantly larger during the DJ vs. VJ (p < 0.001). Pearson correlations between the DJ and VJ revealed strong correlations for knee valgus angles (r = 0.93, p < 0.001) and for internal knee adductor moments (r = 0.82, p < 0.001). Our results provide grounds for investigating whether frontal plane knee mechanics during VJ can predict ACL injuries and thus can be used as an effective tool for the assessment of risk of ACL injury in female athletes.
A method which incorporates modeling of equipment deterioration, inspection, and minor & major maintenance for optimal maintenance decision is proposed in this paper. Optimal inspection and maintenance policy to maximize equipment availability and minimize the cost is evaluated by Semi-Markov Processes and Semi-Markov Decision Processes. An example illustrates the implementation of proposed method for maintenance evaluation of circuit breakers. Also the results of how inspection and maintenance rate impact equipment availability and benefit are discussed. This study is valuable for utilities to determine optimal maintenance policy of equipment. 1
The requisite combination of speed, power, and strength necessary for a bobsled push athlete coupled with the difficulty in directly measuring pushing ability makes selecting effective push crews challenging. Current practices by USA Bobsled and Skeleton (USABS) utilize field combine testing to assess and identify specifically selected performance variables in an attempt to best predict push performance abilities. Combine data consisting of 11 physical performance variables were collected from 75 subjects across two winter Olympic qualification years (2009 and 2013). These variables were sprints of 15-, 30-, and 60 m, a flying 30 m sprint, a standing broad jump, a shot toss, squat, power clean, body mass, and dry-land brake and side bobsled pushes. Discriminant Analysis (DA) in addition to Principle Component Analysis (PCA) was used to investigate two cases (Case 1: Olympians vs. non-Olympians; Case 2: National Team vs. non-National Team). Using these 11 variables, DA led to a classification rule that proved capable of identifying Olympians from non-Olympians and National Team members from non-National Team members with 9.33% and 14.67% misclassification rates, respectively. The PCA was used to find similar test variables within the combine that provided redundant or useless data. After eliminating the unnecessary variables, DA on the new combinations showed that 8 (Case 1) and 20 (Case 2) other combinations with fewer performance variables yielded misclassification rates as low as 6.67% and 13.33% respectively. Utilizing fewer performance variables can allow governing bodies in many other sports to create more appropriate combine testing that maximize accuracy, while minimizing irrelevant and redundant strategies.
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