Abstract: Techniques to suggest one or more sets of ingredients that can be used to recreate or mimic a target sensory description using artificial intelligence are disclosed. An artificial intelligence model includes a transformer inspired neural network architecture that learns from ingredients, recipes, and sensory profiles. The artificial intelligence model includes a sensory transformer model that generates a probability distribution of source ingredients based on an embedding associated with first digital data representing ingredients and the second digital data representing sensory description, and a selector that selects at least one candidate ingredient from the probability distribution of source ingredients for the embedding. A complete set of ingredients generated based on the at least one candidate ingredient when combined become a food product that has or achieves the sensory description.

Abstract: A wheel assembly includes a wheel having a surface and defining a plurality of slots and a cap having a cap surface. The cap includes a plurality of positioning posts extending from the cap surface, each positioning post including a pillar and a stop fixed to the pillar. The pillar extends farther from the cap surface than the stop. The stop has a distal end. Each pillar is received by one of the slots of the wheel and the distal end of each stop is engaged with the surface of the wheel.

Abstract: A wheel assembly includes a wheel having a surface and defining a plurality of slots and a cap having a cap surface. The cap includes a plurality of positioning posts extending from the cap surface, each positioning post including a pillar and a stop fixed to the pillar. The pillar extends farther from the cap surface than the stop. The stop has a distal end. Each pillar is received by one of the slots of the wheel and the distal end of each stop is engaged with the surface of the wheel.

Abstract: A computer-implemented method comprising: receiving the first set of words and the second set of words, wherein each of the first and second sets of words; calculating a first likelihood-based measure representing how well a first model can be fit to the first and second sets of words, the first model comprising a shared parametric distribution representing both the first and second sets of words; calculating a second likelihood-based measure representing how well a second model can be fit to the first and second sets of words, the second model comprising a first parametric distribution representing the first set of words and a second parametric distribution representing the second set of words; calculating a similarity score based on a ratio of the first likelihood measure to the second likelihood measure, the similarity score being representative of the similarity between the first and second sets of words; and outputting the similarity score.

Abstract: Brake rotor apparatus for use with vehicles are disclosed. An example apparatus includes a brake rotor having braking surfaces. The apparatus includes first fins coupled to the brake rotor and disposed between the braking surfaces. The first fins are distributed along a first radius of the brake rotor and extend radially outward. The apparatus includes second fins coupled to the brake rotor and disposed between the braking surfaces. The second fins are distributed along a second radius of the brake rotor smaller than the first radius and adjacent the first fins.

Abstract: Brake rotor apparatus for use with vehicles are disclosed. An example apparatus includes a brake rotor having braking surfaces. The apparatus includes first fins coupled to the brake rotor and disposed between the braking surfaces. The first fins are distributed along a first radius of the brake rotor and extend radially outward. The apparatus includes second fins coupled to the brake rotor and disposed between the braking surfaces. The second fins are distributed along a second radius of the brake rotor smaller than the first radius and adjacent the first fins.

Abstract: An apparatus and method for testing adhesion of a laminate, particularly a foam-film laminate, includes an inclined ramp to which the laminate is secured with an adhesive element and a weight that travels down the ramp and over the laminate and adhesive element. After the weight has been applied, the adhesive element is removed and the percentage separation of laminate layer(s) from the laminate surface is assessed. The weight may be a roller that is guided with a guide element along its path of travel.