Abstract: One or more computing devices, systems, and/or methods are provided. For example, a controller of a dispenser may determine usage data of the dispenser. In association with a replenishment event in which a second refill component mounted to the dispenser is replaced by a first refill component, the controller may (i) determine a next replenishment time of the dispenser based upon the usage data, and/or (ii) transmit an indication of the next replenishment time to a first tag of the first refill component.

Abstract: A non-antimicrobial cleansing composition is disclosed comprising from about 10.0 wt. % to less than about 40 wt. % of one or more C1-C8 alcohols; about 0.5 wt. % to about 10.0 wt. % of at least one primary surfactant; 0 wt. % to about 10.0 wt. % of at least one secondary surfactant, with the primary and secondary surfactants having an HLB value greater than 8; a pH adjusting agent; and water. The composition does not achieve a microbial kill level greater than 2.0 log.

Abstract: A non-antimicrobial cleansing composition is disclosed comprising from about 10.0 wt. % to less than about 40 wt. % of one or more C1-C8 alcohols; about 0.5 wt. % to about 10.0 wt. % of at least one primary surfactant; 0 wt. % to about 10.0 wt. % of at least one secondary surfactant, with the primary and secondary surfactants having an HLB value greater than 8; a pH adjusting agent; and water. The composition does not achieve a microbial kill level greater than 2.0 log.

Abstract: A non-antimicrobial cleansing composition is disclosed comprising from about 10.0 wt. % to less than about 40 wt. % of one or more C1-C8 alcohols; about 0.5 wt. % to about 10.0 wt. % of at least one primary surfactant; 0 wt. % to about 10.0 wt. % of at least one secondary surfactant, with the primary and secondary surfactants having an HLB value greater than 8; a pH adjusting agent; and water. The composition does not achieve a microbial kill level greater than 2.0 log.

Abstract: A non-antimicrobial cleansing composition is disclosed comprising from about 10.0 wt. % to less than about 40 wt. % of one or more C1-C8 alcohols; about 0.5 wt. % to about 10.0 wt. % of at least one primary surfactant; 0 wt. % to about 10.0 wt. % of at least one secondary surfactant, with the primary and secondary surfactants having an HLB value greater than 8; a pH adjusting agent; and water. The composition does not achieve a microbial kill level greater than 2.0 log.

Abstract: A non-antimicrobial cleansing composition is disclosed comprising from about 10.0 wt. % to less than about 40 wt. % of one or more C1-C8 alcohols; about 0.5 wt. % to about 10.0 wt. % of at least one primary surfactant; 0 wt. % to about 10.0 wt. % of at least one secondary surfactant, with the primary and secondary surfactants having an HLB value greater than 8; a pH adjusting agent; and water. The composition does not achieve a microbial kill level greater than 2.0 log.

Abstract: A non-antimicrobial cleansing composition is disclosed comprising from about 10.0 wt. % to less than about 40 wt. % of one or more C1-C8 alcohols; about 0.5 wt. % to about 10.0 wt. % of at least one primary surfactant; 0 wt. % to about 10.0 wt. % of at least one secondary surfactant, with the primary and secondary surfactants having an HLB value greater than 8; a pH adjusting agent; and water. The composition does not achieve a microbial kill level greater than 2.0 log.

Abstract: A non-antimicrobial cleansing composition is disclosed comprising from about 10.0 wt. % to less than about 40 wt. % of one or more C1-C8 alcohols; about 0.5 wt. % to about 10.0 wt. % of at least one primary surfactant; 0 wt. % to about 10.0 wt. % of at least one secondary surfactant, with the primary and secondary surfactants having an HLB value greater than 8; a pH adjusting agent; and water. The composition does not achieve a microbial kill level greater than 2.0 log.

Abstract: A method of generating a tire recommendation includes receiving vehicle data and historical driving data of a user, retrieving, by a computer, a first set of tire names having associated vehicle fitment data that corresponds to the vehicle data, and selecting, by the computer, a first criteria based on the historical driving data. The method further includes identifying a final set of tire names, by identifying tire names in the first set of tire names that have associated tire performance data that meet the first criteria. The method also includes identifying, by the computer, a tire name from the final set of tire names having a highest associated sales history datum as a first tire recommendation, and displaying, by the computer, the first tire recommendation.

Abstract: A method of generating a tire recommendation includes receiving vehicle data and historical driving data of a user, retrieving, by a computer, a first set of tire names having associated vehicle fitment data that corresponds to the vehicle data, and selecting, by the computer, a first criteria based on the historical driving data. The method further includes identifying a final set of tire names, by identifying tire names in the first set of tire names that have associated tire performance data that meet the first criteria. The method also includes identifying, by the computer, a tire name from the final set of tire names having a highest associated sales history datum as a first tire recommendation, and displaying, by the computer, the first tire recommendation.