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Over the past few years, there has been a significant increase in the performance of softball bats from the Olympics down to the recreational level. It is theorized that the introduction of composite-based material bats has been the largest contributor to the increase in batted-ball performance, which directly corresponds to a decrease in available pitcher reaction time that can lead to an increase in injury potential. To test this theory, a controlled field-test study comparing all of the different bat model types currently available was conducted. The performance of ten bat model types; two composite-based, two titanium-based, two aluminum-based multi-wall, two aluminum-based single-wall and two wood-based bats were measured using 1686 N/0.64 cm (379 lbs/0.25”) compression softballs in order to calculate available pitcher reaction times when using a specific bat model type. Over 1000 Batted-ball velocity measurements were analyzed using two calibrated radar devices and five experienced test subjects. The results of this study indicate that when titanium-based or composite-based softball bat performance results are compared to published safety studies in the sports of softball and baseball, available pitcher reaction times are unsafe, which can lead to a higher injury risk potential. The significance of this research is to provide experimental field-test data on the possible safety risks to pitchers that can be used to reduce the injury potential and promote safety awareness in the sport of softball. [ABSTRACT FROM AUTHOR]

Colloidal suspensions of monodisperse spheres are used as physical models of thermodynamic phase transitions and as precursors to photonic band gap materials. Current techniques for identifying the phase boundaries involve manually identifying the phase transitions, which is very tedious and time-consuming. In addition, current image analysis techniques are not able to distinguish between densely packed phases within conventional microscope images, which are mainly characterized by degrees of randomness or order with similar grayscale value properties. We have developed an intelligent machine vision technique that automatically identifies colloidal phase boundaries. The technique utilizes intelligent image processing algorithms that accurately identify and track phase changes vertically or horizontally for a sequence of colloidal hard sphere suspension images. This technique is readily adaptable to any imaging application wherein regions of interest are distinguished from the background by differing patterns of motion over time. Microsc. Res. Tech. 69:236-245, 2006. Published 2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]