Abstract: The present invention relates to a method for the in situ synthesis of of MOFs (metal organic frameworks), COFs (covalent organic frameworks), and ZIFs (Zeolitic imidazolate framework), onto and within different types of porous substrates, and their applications. The present invention provides a unique and easy to utilize method by which a number of MOFs, COFs and ZIFs can be synthesized directly onto and within a porous substrate with high performance efficiency ensuring higher grammage in formation of adsorbent product onto and within the substrate.

Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction and modeling of one or more channels and relevant conditions through which resources may be directed to users in an environment where resource demand, utility, and perceived value vary over time. Some example implementations employ predictive, machine-learning modeling to facilitate the use of multiple disparate and unrelated data sets to extrapolate and otherwise predict the future needs for certain resources and identify the channels and conditions that may be employed to meet such future needs. An apparatus, method, system, and computer program product are provided for improved generating, adjusting, and/or facilitating approval of a resource offer set. Some example implementations employ one or more predictive models.

Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction and modeling of one or more channels and relevant conditions through which resources may be directed to users in an environment where resource demand, utility, and perceived value vary over time. Some example implementations employ predictive, machine-learning modeling to facilitate the use of multiple disparate and unrelated data sets to extrapolate and otherwise predict the future needs for certain resources and identify the channels and conditions that may be employed to meet such future needs. An apparatus, method, system, and computer program product are provided for improved generating, adjusting, and/or facilitating approval of a resource offer set. Some example implementations employ one or more predictive models.

Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction and modeling of one or more channels and relevant conditions through which resources may be directed to users in an environment where resource demand, utility, and perceived value vary over time. Some example implementations employ predictive, machine-learning modeling to facilitate the use of multiple disparate and unrelated data sets to extrapolate and otherwise predict the future needs for certain resources and identify the channels and conditions that may be employed to meet such future needs. An apparatus, method, system, and computer program product are provided for improved generating, adjusting, and/or facilitating approval of a resource offer set. Some example implementations employ one or more predictive models.

Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction and modeling of one or more channels and relevant conditions through which resources may be directed to users in an environment where resource demand, utility, and perceived value vary over time. Some example implementations employ predictive, machine-learning modeling to facilitate the use of multiple disparate and unrelated data sets to extrapolate and otherwise predict the future needs for certain resources and identify the channels and conditions that may be employed to meet such future needs. An apparatus, method, system, and computer program product are provided for improved generating, adjusting, and/or facilitating approval of a resource offer set. Some example implementations employ one or more predictive models.

Abstract: Examples provide order fulfillment that includes receiving an online order from a user. An initial distribution hub for delivery of the order is identified and the order routed from the initial distribution hub to an intermediate hub. A notification of receipt of the order at the intermediate hub is received and a user location is determined. A plurality of options for routing the order to a next hub based on the user location and a cost associated with routing the order to the next hub are identified. The cost is based on a bidding process performed among a plurality of possible hubs to which the order will be routed. The order is routed to the next hub in response to a user confirmation of acceptance of a change in delivery location for the order.

Abstract: The present invention relates to method for the synthesis in-situ of the class of compounds known generally as MOFs (metal organic frameworks or organometallic compounds), COFs (covalent organic frameworks), and ZIFs (Zeolitic imidazolate framework), within and onto different types of substrates, and to the applications of such substrates having in-situ synthesized MOFs, COFs and ZIFs.

Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction and modeling of one or more channels and relevant conditions through which resources may be directed to users in an environment where resource demand, utility, and perceived value vary over time. Some example implementations employ predictive, machine-learning modeling to facilitate the use of multiple disparate and unrelated data sets to extrapolate and otherwise predict the future needs for certain resources and identify the channels and conditions that may be employed to meet such future needs. An apparatus, method, system, and computer program product are provided for improved generating, adjusting, and/or facilitating approval of a resource offer set. Some example implementations employ one or more predictive models.

Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction and modeling of one or more channels and relevant conditions through which resources may be directed to users in an environment where resource demand, utility, and perceived value vary over time. Some example implementations employ predictive, machine-learning modeling to facilitate the use of multiple disparate and unrelated data sets to extrapolate and otherwise predict the future needs for certain resources and identify the channels and conditions that may be employed to meet such future needs. An apparatus, method, system, and computer program product are provided for improved generating, adjusting, and/or facilitating approval of a resource offer set. Some example implementations employ one or more predictive models.

Abstract: An apparatus, method, and computer program product are provided for the improved and automatic prediction and modeling of one or more channels and relevant conditions through which resources may be directed to users in an environment where resource demand, utility, and perceived value vary over time. Some example implementations employ predictive, machine-learning modeling to facilitate the use of multiple disparate and unrelated data sets to extrapolate and otherwise predict the future needs for certain resources and identify the channels and conditions that may be employed to meet such future needs. An apparatus, method, system, and computer program product are provided for improved generating, adjusting, and/or facilitating approval of a resource offer set. Some example implementations employ one or more predictive models.

Abstract: Examples provide order fulfillment that includes receiving an online order from a user. An initial distribution hub for delivery of the order is identified and the order routed from the initial distribution hub to an intermediate hub. A notification of receipt of the order at the intermediate hub is received and a user location is determined. A plurality of options for routing the order to a next hub based on the user location and a cost associated with routing the order to the next hub are identified. The cost is based on a bidding process performed among a plurality of possible hubs to which the order will be routed. The order is routed to the next hub in response to a user confirmation of acceptance of a change in delivery location for the order.