Athletic training researchers and scholarly clinicians can use the information presented in this article to better conduct and interpret the results of clinical trials. Implementing these techniques will increase the power and validity of findings of athletic medicine clinical trials, which will ultimately improve the quality of care provided.

Bioimpedance analysis (BIA) has been demanded for the assessment of appendicular skeletal muscle mass (ASM) in clinical and epidemiological settings. This study aimed to validate BIA equations for predicting ASM in the standing and supine positions; externally to cross-validate the new and published and built-in BIA equations for group and individual predictive accuracy; and to assess the overall agreement between the measured and predicted ASM index as sarcopenia diagnosis. In total, 199 healthy older adults completed the measurements of multifrequency BIA (InBody770 and InBodyS10) and dual-energy X-ray absorptiometry (DXA). Multiple regression analysis was used to validate the new multifrequency bioelectrical impedance analysis (MF-BIA) prediction equations. Each MF-BIA equation in the standing and supine position developed in the entire group included height2/resistance, sex, and reactance as predictors (R2 = 92.7% and 92.8%, SEE = 1.02 kg and 1.01 kg ASM for the standing and supine MF-BIA). The new MF-BIA equations had a specificity positive predictive value and negative predictive value of 85% or more except for a sensitivity of about 60.0%. The new standing and supine MF-BIA prediction equation are useful for epidemiological and field settings as well as a clinical diagnosis of sarcopenia. Future research is needed to improve the sensitivity of diagnosis of sarcopenia using MF-BIA.

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Flow of steam, different from other gas flows, involves droplet generation in flow expansion process. This phase transition affects not only the flow fields, but also machine performance including efficiency. In addition, it is totally harmful for machine structures as blades and casing. Therefore, prevention or preparation of droplet generation in steam flows is dreadfully important in stable machine operation. Nowadays, Computational Fluid Dynamics (CFD) is widely used in machine design and optimization process. Thus, simulation with CFD should consider this droplet generation phenomena to predict internal flows precisely. Many studies that analyze steam condensing flow in nozzles, cascades and steam turbines were carried out. Though, the flows of wet-steam which include non-equilibrium phase-transition phenomena are still difficult to predict, especially in the 3D rotating cases as steam turbines. Therefore, more studies are required to get comparable results with experiment. In this study, non-equilibrium wet-steam model was implemented on T-Flow to simulate realistic non-equilibrium steam condensing flow. In the cases of White cascade, characteristics of wet-steam flow were studied and pressure distributions were compared with experimental results for model validation. To use implemented wet-steam model for calculating flows in rotation, especially in steam turbines, a study of steam condensing flow in single stage steam turbine was conducted. Interaction between the stator and rotor using frozen rotor or mixing plane method in steady calculations were compared in order to find the effects of used interface on flow fields and steam condensation. As a result, condensing flows were predicted well even in the rotating cases by using non-equilibrium wet-steam model. The wet-steam parameters (nucleation, droplet size, wetness) are differed throughout the spans due to 3D effects and influenced by selection of interface as expected. In addition, droplet generation enhances entropy rise throughout the domain. The case using mixing plane seems to be overestimate the size of high wetness zone and it is recommended to use frozen rotor in multi-phase calculations. However, to apply this model in general cases, comparison with experimental data from real steam turbines should be conducted in further studies.

The purpose of this study was to classify sports match fixing based on the scope of human network and financial involvement. We analyze the concept of match fixing as defined by the International Olympic Committee and create a classification scheme based on four types of match fixing behavior: type 1organization-based match fixing with money involved within an internal-external human network, type 2-relationship-based match fixing with the involvement of an internal-external human network without monetary involvement, type 3-sustenance-based match fixing with money involved within an internal human network, and type 4-goal-based match fixing with the involvement of an internal human network without monetary involvement. Lastly, we need to consider variables intrinsic and extrinsic to a sports game, which should be carefully examined and managed depending on the match fixing type to prevent match fixing behavior.