Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: In order to facilitate entity resolution, systems and methods include a processor receiving first records associated with one or more entities, and second records associated with the one or more entities. The processor generates candidate pairs based on a similarity between first entity data and second entity data. The processor generates features for each candidate pair based on similarity measures between the first entity record and the second entity record. The processor utilizes a scoring machine learning model to determine a match score for each candidate pair based on each feature. The processor determines clusters of candidate pairs based on the match score of each feature for each candidate pair. The processor merges records of candidate pairs of each cluster into a respective entity record. The processor determines an entity associated with each entity record and updates an entity database with the entity record.

Abstract: A multiprocessor computer system includes a plurality of processor nodes and at least a three-tier hierarchical network interconnecting the processor nodes. The hierarchical network includes a plurality of routers interconnected such that each router is connected to a subset of the plurality of processor nodes; the plurality of routers are arranged in a hierarchy of n?3 tiers (T1, . . . , Tn); the plurality of routers are partitioned into disjoint groups at the first tier T1, the groups at tier Ti being partitioned into disjoint groups (of complete Ti groups) at the next tier Ti+1 and a top tier Tn including a single group containing all of the plurality of routers; and for all tiers 1?i?n, each tier-Ti?1 subgroup within a tier Ti group is connected by at least one link to all other tier-Ti?1 subgroups within the same tier Ti group.

Abstract: A multiprocessor computer system includes a plurality of processor nodes and at least a three-tier hierarchical network interconnecting the processor nodes. The hierarchical network includes a plurality of routers interconnected such that each router is connected to a subset of the plurality of processor nodes; the plurality of routers are arranged in a hierarchy of n?3 tiers (T1, . . . , Tn); the plurality of routers are partitioned into disjoint groups at the first tier T1, the groups at tier Ti being partitioned into disjoint groups (of complete Ti groups) at the next tier Ti+1 and a top tier Tn including a single group containing all of the plurality of routers; and for all tiers 1?i?n, each tier-Ti?1 subgroup within a tier Ti group is connected by at least one link to all other tier-Ti?1 subgroups within the same tier Ti group.

Abstract: A method of assembling an inflatable shell of a structure comprises folding a plurality of shell sections about a set of fold lines and integrating the plurality of shell sections together with one another to form the shell. In another embodiment, an inflatable shell comprises a plurality of shell sections, each shell section having two pairs of fold lines for folding into stowage comprising a first gore section having a plurality of first gore panels layered and collectively folded about at a first set of fold lines. Each layer of the first gore panels and second gore panels are configured such that, once the first gore panel and second gore panel are attached to one another at the respective side edges of each panel, the lines of attachment forming a second set of fold lines for the shell section. A system and method for fabricating gore panels is also disclosed.

Abstract: A method of assembling an inflatable shell of a structure comprises folding a plurality of shell sections about a set of fold lines and integrating the plurality of shell sections together with one another to form the shell. In another embodiment, an inflatable shell comprises a plurality of shell sections, each shell section having two pairs of fold lines for folding into stowage comprising a first gore section having a plurality of first gore panels layered and collectively folded about at a first set of fold lines. Each layer of the first gore panels and second gore panels are configured such that, once the first gore panel and second gore panel are attached to one another at the respective side edges of each panel, the lines of attachment forming a second set of fold lines for the shell section. A system and method for fabricating gore panels is also disclosed.

Abstract: An inflatable structure comprises at least two generally toroidal, inflatable modules. When in a deployed mode, the first, inner module has a major diameter less than that of a second, outer module and is positioned within the inner circumference of the outer module such that the first module is nested circumferentially alongside the second module. The inflatable structure, in a non-deployed, non-inflated mode, is of compact configuration and adapted to be transported to a site of deployment. When deployed, the inflatable structure is of substantially increased interior volume. In one embodiment, access between the interior of the first module and the second module is provided by at least one port or structural pass-through. In another embodiment, the inflatable structure includes at least one additional generally toroidal module external of and circumferentially surrounding the second module.

Abstract: A connecting node comprises a polyhedral structure comprising a plurality of panels joined together at its side edges to form a spherical approximation, wherein at least one of the plurality of panels comprises a faceted surface being constructed with a passage for integrating with one of a plurality of elements comprising a docking port, a hatch, and a window that is attached to the connecting node. A method for manufacturing a connecting node comprises the steps of providing a plurality of panels, connecting the plurality of panels to form a spherical approximation, wherein each edge of each panel of the plurality is joined to another edge of another panel, and constructing at least one of the plurality of panels to include a passage for integrating at least one of a plurality of elements that may be attached to the connecting node.

Abstract: An inflatable structure comprises at least two generally toroidal, inflatable modules. When in a deployed mode, the first, inner module has a major diameter less than that of a second, outer module and is positioned within the inner circumference of the outer module such that the first module is nested circumferentially alongside the second module. The inflatable structure, in a non-deployed, non-inflated mode, is of compact configuration and adapted to be transported to a site of deployment. When deployed, the inflatable structure is of substantially increased interior volume. In one embodiment, access between the interior of the first module and the second module is provided by at least one port or structural pass-through. In another embodiment, the inflatable structure includes at least one additional generally toroidal module external of and circumferentially surrounding the second module.

Abstract: The present invention provides a device and method for heating a whole blood sample for use in an assay, comprising irradiating said whole blood sample with monochromatic visible light of between 380 nm and 740 nm to raise the temperature of said whole blood sample to a target temperature between about 35° C. and 40 ° C. An apparatus for heating whole blood samples stored in a cartridge is further described comprising a housing assembly disposed to receive the cartridge one or more visible light optical emitters disposed in the housing apparatus and configured to selectively illuminate a staging area of the cartridge a temperature sensor disposed in the housing apparatus and configured to sense a temperature of the cartridge and a control circuit coupled to the temperature sensor and the array of visible light optical emitters so as to control a heating profile of the whole blood sample.

Abstract: A connecting node comprises a polyhedral structure comprising a plurality of panels joined together at its side edges to form a spherical approximation, wherein at least one of the plurality of panels comprises a faceted surface being constructed with a passage for integrating with one of a plurality of elements comprising a docking port, a hatch, and a window that is attached to the connecting node. A method for manufacturing a connecting node comprises the steps of providing a plurality of panels, connecting the plurality of panels to form a spherical approximation, wherein each edge of each panel of the plurality is joined to another edge of another panel, and constructing at least one of the plurality of panels to include a passage for integrating at least one of a plurality of elements that may be attached to the connecting node.

Abstract: An improved process for separating and isolating individual polar protic monomer(s) and/or oligomer(s) on the basis of degree of polymerization. A liquid sample containing polar protic monomer(s) and/or oligomer(s) is introduced into a liquid chromatography (LC) column packed with a polar bonded stationary chromatographic phase. The individual polar protic monomer(s) and/or oligomer(s) are separated via a binary mobile phase elution. One or more individual fractions containing the monomer(s) and/or oligomer(s) are eluted. The polar protic monomer(s) and/or oligomer(s) may be proanthocyanidins, hydrolyzable tannins, oligosaccharides, oligonucleotides, peptides, acrylamides, polysorbates, polyketides, poloxarners, polyethylene glycols, polyoxyethylene alcohols or polyvinyl alcohols. The binary mobile phase comprises an A phase consisting essentially of a polar aprotic solvent and a B phase consisting essentially of a polar protic solvent. A process for separating and isolating xanthine(s) (e.g.