In this paper, the development of compact transmission soft x-ray microscopy (XM) with sub-50 nm spatial resolution for biomedical applications is described. The compact transmission soft x-ray microscope operates at lambda = 2.88 nm (430 eV) and is based on a tabletop regenerative x-ray source in combination with a tandem ellipsoidal condenser mirror for sample illumination, an objective micro zone plate and a thinned back-illuminated charge coupled device to record an x-ray image. The new, compact x-ray microscope system requires the fabrication of proper x-ray optical devices in order to obtain high-quality images. For an application-oriented microscope, the alignment procedure is fully automated via computer control through a graphic user interface. In imaging studies using our compact XM system, a gold mesh image was obtained with 45 nm resolution at x580 magnification and 1 min exposure. Images of a biological sample (Coscinodiscus oculoides) were recorded.

Chae, JS, Park, J, and So, W-Y. Ranking prediction model using the competition record of ladies professional golf association players. J Strength Cond Res 32(8): 2363-2374, 2018-The purpose of this study was to suggest a ranking prediction model using the competition record of the Ladies Professional Golf Association (LPGA) players. The top 100 players on the tour money list from the 2013-2016 US Open were analyzed in this model. Stepwise regression analysis was conducted to examine the effect of performance and independent variables (i.e., driving accuracy, green in regulation, putts per round, driving distance, percentage of sand saves, par-3 average, par-4 average, par-5 average, birdies average, and eagle average) on dependent variables (i.e., scoring average, official money, top-10 finishes, winning percentage, and 60-strokes average). The following prediction model was suggested:(Equation is included in full-text article.)(Equation is included in full-text article.)(Equation is included in full-text article.)(Equation is included in full-text article.)(Equation is included in full-text article.)Scoring of the above 5 prediction models and the prediction of golf ranking in the 2016 Women's Golf Olympic competition in Rio revealed a significant correlation between the predicted and real ranking (r = 0.689, p < 0.001) and between the predicted and the real average score (r = 0.653, p < 0.001). Our ranking prediction model using LPGA data may help coaches and players to identify which players are likely to participate in Olympic and World competitions, based on their performance.

This study aims to identify the most accurate prediction model for the possibility of victory from the annual average data of 25 seasons (1993–2017) of the Ladies Professional Golf Association (LPGA), and to determine the importance of the predicting factors. The four prediction models considered in this study were a decision tree, discriminant analysis, logistic regression, and artificial neural network analysis. The mean difference in the classification accuracy of these models was analyzed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA) and the one-way analysis of variance (ANOVA). When the prediction was based on technical variables, the most important predicting variables for determining victory were greens in regulation (GIR) and putting average (PA) in all four prediction models. When the prediction was based on the output of the technical variables, the most important predicting variable for determining victory was birdies in all four prediction models. When the prediction was based on the season outcome, the most important predicting variables for determining victory were the top 10 finish% (T10) and official money. A significant mean difference in classification accuracy was observed while performing the one-way ANOVA, and the least significant difference post-hoc test showed that artificial neural network analysis exhibited higher accuracy than the other models, especially, for larger data sizes. From the results of this study, it can be inferred that the player who wants to win the LPGA should aim to increase GIR, reduce PA, and improve driving distance and accuracy through training to increase the birdies chance at each hole, which can lead to lower average strokes and increased possibility of being within T10.