Abstract: The invention is measurement of two categories of medical attributes that proceed the causes previously measured to cause lower musculoskeletal (MSK) injuries including stress fractures. The innovation is the measurement of increase in the risk for lower musculoskeletal (MSK) injuries, including stress fractures, due to genetic factors that cause disease (pre-cursors of the disease) as an independent cause for an increase in the risk of MSK injuries, and the diseased states themselves as a cause of increased risk for lower MSK injuries. The method to be used for this measurement will be through statistical and quasi-experimental design. The innovation of measuring the genetic pre-cursor of diseases and these diseases themselves includes them as antecedents for the previously measured causes of stress fractures; such as biomechanical and anatomical factors; muscle power; muscle, ligament and tendon flexibility; equipment used; nutritional; bone mineral density; smoking; amenorrhea; estrogen, among others.

Abstract: A method and computer product is disclosed for arbitrating the outputs of a plurality of recognizers to select a hypothesis associated with a given input from a plurality of hypotheses. Fuzzy membership states are assigned to respective candidate outputs of the plurality of recognizers. At least one rule is selected according to the assigned fuzzy membership states. A selected rule provides a determined amount of support mass for an associated one of the plurality of hypotheses and a determined amount of uncertainty mass. A support value for each hypothesis and an overall uncertainty are determined from the provided mass values. A hypothesis having a highest support value while having an acceptable low level of uncertainty is selected.

Abstract: A method and computer program product are disclosed for determining an optimal classifier model for a pattern recognition system and updating the determined model to recognize new output classes. An initial plurality of classifier models are selected from a set of generated classifier models. An optimal representative classifier for each classifier model is selected according to an iterative optimization routine. The representative classifier having the highest associated value for a fitness function is accepted.

Abstract: An aqueous pigment dispersion containing at least one FD&C and/or D&C pigment dispersed in water and a polysaccharide resin optionally in combination with a styrene-acrylic resin and ethylenediaminetetraacetic acid. The dispersion offers excellent viscosity and settling stability to pH-sensitive FD&C and D&C pigments together with good color value.