Abstract: A power generation system includes a hydrogen sulfide separator, a hydrocarbon fractionator, a hydrogen sulfide processor, a methane processor, and a hydrogen power generator. The hydrogen sulfide separator separates a gaseous flowback stream into a stream including hydrogen sulfide and a stream including hydrocarbons. The hydrocarbon fractionator fractionates hydrocarbons into methane, ethane and natural gas. The hydrogen sulfide processor converts hydrogen sulfide into hydrogen and sulfur, and the methane processor converts methane into hydrogen and carbon. The hydrogen power generator reacts hydrogen with oxygen to generate electricity.

Abstract: The system obtains a recording of an interaction between a UE and a representative of a network. The system obtains a summary associated with the interaction, where the summary includes an indication of a topic representing a portion of the interaction, a time when the topic was present in the interaction, and a generator of the portion of the interaction associated with the topic. The system obtains multiple inputs associated with the multiple UEs and multiple representatives. The system provides the summary of the interaction and the multiple inputs to an AI configured to predict a likelihood of a predetermined action associated with the UE. The system receives from the AI the likelihood of the predetermined action. Based on the likelihood of the predetermined action, the system performs an action within a network prior to an occurrence of the predetermined action associated with the UE.

Abstract: A wellbore fluid composition includes an aqueous base fluid and mesoporous silica nanoparticles (MSNs). The MSNs are encapsulated in a surfactant. A method of drilling a wellbore includes circulating an aqueous drilling fluid into the wellbore while drilling and recovering shale cuttings from the aqueous drilling fluid. The aqueous drilling fluid includes mesoporous silica nanoparticles encapsulated by a surfactant.

Abstract: The system obtains an undesirable action associated with the UE from multiple undesirable actions, including disassociating from the wireless telecommunication network or seeking another interaction with a representative of the wireless telecommunication network. The system obtains multiple key performance indices (KPIs) associated with the UE or the representative of the wireless telecommunication network. The system obtains multiple groups of representatives configured to connect to the UE, where a first group among the multiple groups is associated with fewer occurrences of the undesirable action than a second group among the multiple groups. Based on the KPIs associated with the UE, the system determines whether a likelihood of the undesirable action occurring is above a predetermined threshold. Upon determining that the likelihood of the undesirable action occurring is above the predetermined threshold, the system connects the UE with a representative from the first group.

Abstract: The endodontic point containing ultrasonic deformable material is a filler for root canal therapy that decreases in viscosity when subjected to ultrasonic waves. Once the point is inserted into a prepared tooth space, such as a prepared and cleaned root canal, the point is subjected to ultrasonic waves. The ultrasonic waves cause the viscosity of the point to lower, thus allowing the point to flow into voids in the tooth space and allow air to escape from the voids. The point then hardens and remains in the shape acquired when at low viscosity once application of the ultrasonic waves is stopped.

Abstract: A system and a method for accepting a payment on an application is provided. The method is implemented by a processor. The processor receives login information from a client to activate a banking application; selects quick accept entry points associated with the client's business banking demand deposit account (DDA) based on determining that the client is eligible for the quick accept entry points in response to the login information; sets up a payment receiving option for receiving a payment via the banking application; causes the banking application to receive transaction amount data corresponding to a transaction between the client and a customer; causes the banking application to process the transaction amount data; and automatically updates the DDA to reflect receipt of the transaction amount data.

Abstract: The disclosed system obtains KPIs and configuration parameters associated with the mature and the immature network. A physical layer of the mature wireless telecommunication network corresponds to a physical layer of the immature wireless telecommunication network. The system combines KPIs of the immature network and KPIs of the mature network to obtain multiple KPIs. The system predicts an attribute associated with the immature wireless telecommunication network, where the attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the configuration parameters to a machine learning model trained on data associated with the mature wireless telecommunication network. The machine learning model predicts the value of the attribute associated with the immature wireless telecommunication network based on the multiple key performance indicators and the configuration parameters.

Abstract: The disclosed system obtains KPIs and configuration parameters associated with the mature and the immature network. A physical layer of the mature wireless telecommunication network corresponds to a physical layer of the immature wireless telecommunication network. The system combines KPIs of the immature network and KPIs of the mature network to obtain multiple KPIs. The system predicts an attribute associated with the immature wireless telecommunication network, where the attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the configuration parameters to a machine learning model trained on data associated with the mature wireless telecommunication network. The machine learning model predicts the value of the attribute associated with the immature wireless telecommunication network based on the multiple key performance indicators and the configuration parameters.

Abstract: Methods of preparing a crosslinked hydraulic fracturing fluid include combining a hydraulic fracturing fluid comprising a polyacrylamide polymer with a plurality of coated proppants. The plurality of coated proppants include a proppant particle and a resin proppant coating on the proppant particle. The resin proppant coating includes resin and a zirconium oxide crosslinker. The resin includes at least one of phenol, furan, epoxy, urethane, phenol-formaldehyde, polyester, vinyl ester, and urea aldehyde. Methods further include allowing the zirconium oxide crosslinker within the resin proppant coating to crosslink the polyacrylamide polymer within the hydraulic fracturing fluid at a pH of at least 10, thereby forming the crosslinked hydraulic fracturing fluid.

Abstract: A method of preparing a polymer-sand nanocomposite for water shutoff. The method includes applying a surface polymerization to sand particles. The surfaced polymerization formed by combining a polymerization initiator dissolved in a solvent with the sand particles to form a precursor sand mixture, combining a co-monomer and additional polymerization initiator in the presence of graphene, where the graphene is not functionalized, to form a precursor polymer mixture, and combining the precursor sand mixture and the precursor polymer mixture to form a sand-copolymer-graphene nanocomposite. The method further includes drying the sand-copolymer-graphene nanocomposite, preparing a polymer hydrogel, and combining the polymer hydrogel and the sand-copolymer-graphene nanocomposite to crosslink the components and form the polymer-sand nanocomposite.

Abstract: A computer-implemented method of designing at least a portion of an electronic circuit schematic is described herein. The method comprises receiving requirements for an electronic circuit or at least a portion of an electronic circuit, creating a set of variables and constraints based on the requirements for the electronic circuit, wherein the constraints limit the possible value that may be assigned to the variables, assigning values to the variables using a solver such that the values of the variables satisfy the constraints, and outputting at least a portion of a designed electronic circuit schematic or circuit schematic specification that meets the requirements for the electronic circuit based on the assigned values of the variables.

Abstract: The disclosed system obtains KPIs and configuration parameters associated with the mature and the immature network. A physical layer of the mature wireless telecommunication network corresponds to a physical layer of the immature wireless telecommunication network. The system combines KPIs of the immature network and KPIs of the mature network to obtain multiple KPIs. The system predicts an attribute associated with the immature wireless telecommunication network, where the attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the configuration parameters to a machine learning model trained on data associated with the mature wireless telecommunication network. The machine learning model predicts the value of the attribute associated with the immature wireless telecommunication network based on the multiple key performance indicators and the configuration parameters.

Abstract: The system obtains multiple KPIs and multiple configuration parameters directly from a wireless telecommunication network. The multiple KPIs indicate an observed performance associated with the wireless telecommunication network. The multiple configuration parameters indicate a configuration of the wireless telecommunication network. The system predicts a value of a difficult to predict attribute of the wireless telecommunication network, where the difficult to predict attribute depends on multiple other attributes associated with the wireless telecommunication network. To make the prediction, the system provides the multiple key performance indicators and the multiple configuration parameters to a machine learning model. The machine learning model predicts the value of the difficult to predict attribute associated with the wireless telecommunication network based on multiple key performance indicators and the multiple configuration parameters.

Abstract: Coated particles include a particulate substrate, a surface copolymer layer surrounding the particulate substrate, and a resin layer surrounding the surface copolymer layer. The surface copolymer layer includes a copolymer of at least two monomers chosen from styrene, methyl methacrylate, ethylene, propylene, butylene, imides, urethanes, sulfones, carbonates, and acrylamides. The resin layer includes a cured resin. Methods of preparing the coated particles include preparing a first mixture including at least one polymerizable material, an initiator, and optionally a solvent; contacting the first mixture to a particulate substrate to form a polymerization mixture; heating the polymerization mixture to cure the polymerizable material and form a polymer-coated particulate; preparing a second mixture including the polymer-coated substrate, an uncured resin, and a solvent; and adding a curing agent to the second mixture to cure the uncured resin and form the coated particle.

Abstract: A system and a method for accepting a payment on an application is provided. The method is implemented by a processor. The processor receives login information from a client to activate a banking application; selects quick accept entry points associated with the client's business banking demand deposit account (DDA) based on determining that the client is eligible for the quick accept entry points in response to the login information; sets up a payment receiving option for receiving a payment via the banking application; causes the banking application to receive transaction amount data corresponding to a transaction between the client and a customer; causes the banking application to process the transaction amount data; and automatically updates the DDA to reflect receipt of the transaction amount data.

Abstract: A method of preparing a polymer-sand nanocomposite for water shutoff. The method includes applying a surface polymerization to sand particles. The surfaced polymerization formed by combining a polymerization initiator dissolved in a solvent with the sand particles to form a precursor sand mixture, combining a co-monomer and additional polymerization initiator in the presence of graphene, where the graphene is not functionalized, to form a precursor polymer mixture, and combining the precursor sand mixture and the precursor polymer mixture to form a sand-copolymer-graphene nanocomposite. The method further includes drying the sand-copolymer-graphene nanocomposite, preparing a polymer hydrogel, and combining the polymer hydrogel and the sand-copolymer-graphene nanocomposite to crosslink the components and form the polymer-sand nanocomposite.

Abstract: A method of preparing a polymer-sand nanocomposite for water shutoff. The method includes applying a surface polymerization to sand particles. The surfaced polymerization formed by combining a polymerization initiator dissolved in a solvent with the sand particles to form a precursor sand mixture, combining a co-monomer and additional polymerization initiator in the presence of graphene, where the graphene is not functionalized, to form a precursor polymer mixture, and combining the precursor sand mixture and the precursor polymer mixture to form a sand-copolymer-graphene nanocomposite. The method further includes drying the sand-copolymer-graphene nanocomposite, preparing a polymer hydrogel, and combining the polymer hydrogel and the sand-copolymer-graphene nanocomposite to crosslink the components and form the polymer-sand nanocomposite.

Abstract: Methods of preparing a crosslinked hydraulic fracturing fluid include combining a hydraulic fracturing fluid comprising a polyacrylamide polymer with a plurality of coated proppants. The plurality of coated proppants include a proppant particle and a resin proppant coating on the proppant particle. The resin proppant coating includes resin and a zirconium oxide crosslinker. The resin includes at least one of phenol, furan, epoxy, urethane, phenol-formaldehyde, polyester, vinyl ester, and urea aldehyde. Methods further include allowing the zirconium oxide crosslinker within the resin proppant coating to crosslink the polyacrylamide polymer within the hydraulic fracturing fluid at a pH of at least 10, thereby forming the crosslinked hydraulic fracturing fluid.

Abstract: One example method includes receiving an IO request from an application, determining if an affinity policy applies to the application that transmitted the IO request, when an affinity policy applies to the application, directing the IO request to a specified site of a replication system, when no affinity policy applies to the application, determining if a lag in replication of the IO request from a primary site to a replication site is acceptable, if a lag in replication of the IO request is acceptable, processing the IO request using performance based parameters and/or load balancing parameters, and if a lag in replication of the IO request is not acceptable, either directing the IO request to a most up to date replica site, or requesting a clone copy of a volume to which the IO request was initially directed and directing the IO request to the cloned copy.

Abstract: A method of preparing a polymer-sand nanocomposite for water shutoff. The method includes applying a surface polymerization to sand particles. The surfaced polymerization formed by combining a polymerization initiator dissolved in a solvent with the sand particles to form a precursor sand mixture, combining a co-monomer and additional polymerization initiator in the presence of graphene, where the graphene is not functionalized, to form a precursor polymer mixture, and combining the precursor sand mixture and the precursor polymer mixture to form a sand-copolymer-graphene nanocomposite. The method further includes drying the sand-copolymer-graphene nanocomposite, preparing a polymer hydrogel, and combining the polymer hydrogel and the sand-copolymer-graphene nanocomposite to crosslink the components and form the polymer-sand nanocomposite.