Abstract: The present invention is directed to a value chain return process for polyurethane and polyisocyanurate rigid foams containing at least one additive (A1) which is not chemically bonded to the polymer chain selected from the group consisting of phosphorous ester-based flame retardants, polymerization catalysts and surfactants comprising the steps of providing a composition comprising a comminuted polyurethane or polyisocyanurate rigid foam, wherein the comminuted foam has a content of intact cells of less than 10%, based on the number of intact cells of the not comminuted polyurethane or polyisocyanurate rigid foam, and extraction of the additive (A1) with a solvent at a temperature below 190° C.

Abstract: The present invention is directed to a value chain return process comprising the depolymerization of a polyurethane and polyisocyanurate rigid foam based on polymeric methylenediphenyl diisocyanate (pMDI) to give a mixture (M1); distillative removal of volatile compounds from mixture (M1) to give a mixture (M2) comprising pMDA and at least one polyol; dissolution of the mixture (M2) in an aprotic organic solvent (S1) with a dipol moment in the range from 0.5*10-30 Cm to 7.8*10-30 Cm; and subsequent addition of HCl and separation of the pMDA-HCl salt. The present invention is also directed to the polymeric methylene phenylene amine (pMDA) and the polymeric methylenediphenyl diisocyanate (pMDI) obtained or obtainable according to said process as well as the use thereof for the preparation of polyurethanes or polyisocyanurates.

Abstract: Polyurethane rigid foams containing at least one phosphorous ester-based flame retardant are returned to the value chain by hydrogenating the polyurethane rigid foam, to obtain a hydrogenation product containing a polyamine and a polyol from the polyurethane rigid foam, and recovering the flame retardant from the hydrogenation product. The hydrogenation is carried out in an organic aprotic solvent, in a hydrogen atmosphere, in the presence of at least one homogeneous transition metal catalyst complex, wherein the transition metal is selected from metals of groups 7, 8, 9 and 10 of the periodic table of elements according to IUPAC, and at a reaction temperature of at least 120° C.

Abstract: A method is provided that includes receiving a data request from a requesting device requesting data from a data source, looking up, in a blockchain ledger, a transaction corresponding to the data request to validate the data request, forwarding the validated data request to a data server, receiving a data response from the data server in response to the data request, wherein the data response comprises the requested data from the data source, and forwarding the data response to the requesting device.

Abstract: A method for welding at least two parts including a thermoplastic material and having respective surfaces to be welded, including: inserting an insert between the surfaces to be welded of the two parts; generating heat via the insert; wherein the insert moves in relation to the parts to be welded in a welding direction. Also, an installation adapted for implementation of the method.

Abstract: A test piece configured for the implementation of a method for evaluating an assembly by welding of parts made of thermoplastic materials, wherein the test piece includes at least two parts based on thermoplastic materials having surfaces to be welded that are at least partially placed opposite one another and respective free surfaces, wherein at least one of the parts is a perforated reference part including at least one perforation.

Abstract: The present invention relates to a method for evaluating an assembly by welding of parts made of thermoplastic materials, to a test piece and its associated uses, to an installation for implementing this method and to the associated welding system.

Abstract: A method is provided that includes receiving a data request from a requesting device requesting data from a data source, looking up, in a blockchain ledger, a transaction corresponding to the data request to validate the data request, forwarding the validated data request to a data server, receiving a data response from the data server in response to the data request, wherein the data response comprises the requested data from the data source, and forwarding the data response to the requesting device.

Abstract: Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

Abstract: A network may include multiple allocations. The allocations may include: a first allocation encompassing central infrastructure, such as central office servers, data centers, or other core infrastructure; an second allocation encompassing gateway elements or other central consumer premises network infrastructure; and a third allocation encompassing nodes, such as client devices, terminals, or other nodes. A virtualization management engine may coordinate resources from the various allocations to support virtual functions distributed over multiple allocations of the network. The virtualization management engine may determine the distribution across the allocations for the virtual functions. The virtualization management engine may be implemented as a virtual function and be distributed across the allocations of the network.

Abstract: A method for welding at least two parts including a thermoplastic material and having respective surfaces to be welded, including: inserting an insert between the surfaces to be welded of the two parts; generating heat via the insert; wherein the insert moves in relation to the parts to be welded in a welding direction. Also, an installation adapted for implementation of this method.

Abstract: Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

Abstract: The present invention relates to a method for evaluating an assembly by welding of parts made of thermoplastic materials, to a test piece and its associated uses, to an installation for implementing this method and to the associated welding system.

Abstract: Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

Abstract: The invention first relates to a method for welding at least two parts comprising a thermoplastic material and having respective surfaces to be welded, comprising: inserting an insert between the surfaces to be welded of the two parts; generating heat via said insert; wherein the insert moves in relation to the parts to be welded in a welding direction. The invention also relates to an installation adapted for implementation of this method.

Abstract: Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

Abstract: A network may include multiple allocations. The allocations may include: a first allocation encompassing central infrastructure, such as central office servers, data centers, or other core infrastructure; an second allocation encompassing gateway elements or other central consumer premises network infrastructure; and a third allocation encompassing nodes, such as client devices, terminals, or other nodes. A virtualization management engine may coordinate resources from the various allocations to support virtual functions distributed over multiple allocations of the network. The virtualization management engine may determine the distribution across the allocations for the virtual functions. The virtualization management engine may be implemented as a virtual function and be distributed across the allocations of the network.

Abstract: A network may include multiple allocations. The allocations may include: a first allocation encompassing central infrastructure, such as central office servers, data centers, or other core infrastructure; an second allocation encompassing gateway elements or other central consumer premises network infrastructure; and a third allocation encompassing nodes, such as client devices, terminals, or other nodes. A virtualization management engine may coordinate resources from the various allocations to support virtual functions distributed over multiple allocations of the network. The virtualization management engine may determine the distribution across the allocations for the virtual functions. The virtualization management engine may be implemented as a virtual function and be distributed across the allocations of the network.

Abstract: Implementations provide for extending an authentication protocol to dynamically create a per user end to end encryption over a multi-hop path for data traffic, which provides an automatic triggering of authentication on each hop of a path when a client joins the network. A device includes a processor that is configured to, in response to receipt of a request for authentication from an end device, perform an authentication protocol to authenticate with an authentication server via an authenticator device. When the authentication protocol is successfully performed, the processor is configured to receive a message indicating that the device was successfully authenticated by the authentication server. The processor is configured to create a pairwise master key (PMK) from the parameters, and derive a pairwise temporary key (PTK) from a key derivation function seeded by the PMK. The processor is configured to encrypt, using the PTK, a message from the end device.

Abstract: A system includes service management circuitry to dynamically deploy a plurality of service agents in response to dynamic assembly of a corresponding chain of services that each provide different service functionality. The different service functionality can be provided to an operational network device by different respective network devices over a network. The system can include network interface circuitry to transmit the service agents over the network to monitor performance of the respective network devices providing respective services included in the chain of services. The network interface circuitry can receive service performance information from each of the service agents. The performance information can include information indicative of the performance of the respective network devices. The service management circuitry can analyze the received service performance information and manage the chain of services.