Abstract: A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of: a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a procurement action to be taken in the value chain network based upon, at least in part, an output of the set of AI-based learning models. A VCN process may execute the procurement action.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may provide a computer code instruction set to a machine to execute the task to facilitate an improvement in the operation of the value chain network.

Abstract: A VCN process may receive information associated with a set of value chain network entities. A VCN process may provide the information to a first set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the first set of AI-based learning models is trained to generate a prediction of future demand for an item. A VCN process may provide the information to a second set of AI-based learning models, wherein at least one member of the second set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity. A VCN process may determine a potential risk in the value chain network associated with the at least one value chain network entity based upon, at least in part, an output of the AI-based learning models.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A VCN process may determine a task to be completed for the value chain network based upon, at least in part, on an output of the set of AI-based learning models. A VCN process may execute the task to facilitate an improvement in the value chain network.

Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a potential risk in the value chain based upon, at least in part, an output of the AI-based learning classification. A VCN process may execute an action to mitigate the potential risk in the value chain network.

Abstract: There is provided a solder bump structure comprising an under bump metallurgy (UBM) layer, a first solder portion over the UBM layer, the first solder portion having a first composition, a barrier layer encapsulating the first solder portion, and a second solder portion over the barrier layer, the second solder portion having a second composition different from the first composition.

Abstract: System and method for displaying reaction animations that represent audience reactions to content. A reaction measurement module receives video data featuring at least one audience user from at least one video capture device. The reaction measurement module processes the video data to determine reaction data based on characteristics of the audience user's head. The reaction data is passed to a server and an animation-generation module generates animation data based on the reaction data. The animation data is passed to a user device for display. In some embodiments, the reaction animation(s) are displayed with the content in real or near real time. In other embodiments, the reaction animations are recorded for later viewing. In some embodiments, the animation(s) each represent multiple audience users.

Abstract: Three-dimensional (3D) visualization of multi-layer networks include receiving data associated with a network, wherein the network includes interconnected network elements that operate at a plurality of levels that include any of network layers and encapsulations; displaying a plurality of three-dimensional icons each represent one of a plurality of network elements of the interconnected network elements; and displaying links between the plurality of three-dimensional icons each link representing one of a network layer connection and an encapsulation connection.

Abstract: Three-dimensional (3D) visualization of multi-layer networks include receiving data associated with a network, wherein the network includes interconnected network elements that operate at a plurality of levels that include any of network layers and encapsulations; displaying a plurality of three-dimensional icons each represent one of a plurality of network elements of the interconnected network elements; and displaying links between the plurality of three-dimensional icons each link representing one of a network layer connection and an encapsulation connection.

Abstract: Systems and methods include receiving data from a network that includes a plurality of network elements that operate at a plurality levels that include any of network layers and encapsulations; displaying any of the plurality of network elements as a three-dimensional icon on a network map; illustrating a plinth in the three-dimensional icon for each of the plurality of levels for each network element of the any of the plurality of network elements, wherein a plinth at each level indicates a corresponding network element participates in that level; and illustrating horizontal connectivity as links between any of the network elements via associated plinths.

Abstract: It is provided new chemical entities allowing simultaneous diagnosis and treatment of cancers. More specifically, it is provided a theranostic compound consisting of a stabilized peptide ligand (agonists and antagonists) for the kinin B1 receptors (B1R) conjugated to specific radionuclides (e.g. 64Cu) suitable for dual imaging/radiotherapy applications.

Abstract: A method of manufacturing an optical fiber is provided. The method involves providing a fiber preform with an active core and a pump-guiding cladding, and assembling one or more side rods to the fiber preform. The side rods extend longitudinally along an outer surface of the pump-guiding cladding. The resulting fiber preform assembly is drawn into the optical fiber. Each side rod defines a longitudinal protrusion extending along the optical fiber. Each longitudinal protrusion may have a cross-section forming a middle bump projecting radially away from the outer surface of the pump-guiding cladding and smooth transition regions with this outer surface of the pump-guiding cladding on opposite sides of the middle bump.

Abstract: It is provided new chemical entities allowing simultaneous diagnosis and treatment of cancers. More specifically, it is provided a theranostic compound consisting of a stabilized peptide ligand (agonists and antagonists) for the kinin B1 receptors (B1R) conjugated to specific radionuclides (e.g. 64Cu) suitable for dual imaging/radiotherapy applications.

Abstract: The present invention provides for novel kinin B1 receptors peptide agonists of formula (1) having very good to excellent affinities and selectivity for the B1 receptor, in vitro and in vivo increased resistance to enzymatic degradation, superior pharmacokinetic properties to those of naturally occurring agents, capacity to significantly enhance delivery of chemotherapeutic substances across the blood brain barrier and within peripheral tissues for the treatment of tumors, capacity to protect and restore kidney, heart, brain and other organ functions, when given alone or in combination with other therapies in the treatment of hypertension, diabetes and other cardiovascular diseases particularly, but not limited to, atherosclerosis and arteriosclerosis.

Abstract: The present invention provides for novel kinin B1 receptors peptide agonists of formula (I) having very good to excellent affinities and selectivity for the B1 receptor, in vitro and in vivo increased resistance to enzymatic degradation, superior pharmacokinetic properties to those of naturally occurring agents, capacity to significantly enhance delivery of chemotherapeutic substances across the blood brain barrier and within peripheral tissues for the treatment of tumors, capacity to protect and restore kidney, heart, brain and other organ functions, when given alone or in combination with other therapies in the treatment of hypertension, diabetes and other cardiovascular diseases particularly, but not limited to, atherosclerosis and arteriosclerosis. Formula (I): aay-aax-aa0-aa1-aa2-aa3-aa4-aa5-Ser6-Pro7-D-Phe8-X.

Abstract: The present invention provides for novel kinin B1 receptors peptide agonists of formula (I) having very good to excellent affinities and selectivity for the B1 receptor, in vitro and in vivo increased resistance to enzymatic degradation, superior pharmacokinetic properties to those of naturally occurring agents, capacity to significantly enhance delivery of chemotherapeutic substances across the blood brain barrier and within peripheral tissues for the treatment of tumors, capacity to protect and restore kidney, heart, brain and other organ functions, when given alone or in combination with other therapies in the treatment of hypertension, diabetes and other cardiovascular diseases particularly, but not limited to, atherosclerosis and arteriosclerosis. Formula (I): aay-aax-aa0-aa1-aa2-aa3-aa4-aa5-Ser6-Pro7-D-Phe8-X.