Abstract: A two-component composition including a first component A including water or an aqueous solution, and a second component B including a self-setting adhesive powder including at least a multivalent metal salt and phosphoserine, and at least one monocarbonate selected from the group of sodium carbonate, ammonium carbonate and potassium carbonate, wherein the monocarbonate is present in a concentration between 4 and 12% by weight of component B.

Abstract: A two-component composition including a first component A including water or an aqueous solution and a second component B including a self-setting adhesive powder including at least a multivalent metal salt and phosphoserine, and at least one hydrogen carbonate, wherein the hydrogen carbonate is present in a concentration between 1 and 8% by weight of component B.

Abstract: Systems, devices, and/or methods may implement one or more machine learning models for finding specific data/items from one or more enterprise resource platforms. The one or more machine learning models may be running on any new (e.g., fresh) transactions that may be fed to/from the one or more enterprise payment systems. The one or more machine learning models may be configured to determine an association between software (e.g., application, program, code, etc.) subscription, purchase, and/or a license and at least one transaction. The one or more models may learn the association and/or may adjust one or more future matches, for example based on the learned associations. Perhaps as the one or more models run, at least one confidence score may be associated with one or more, or each, match made. The confidence score may indicate the confidence and/or reliability of the one or more associations to an end user.