Abstract: An energy absorbing material includes a multi-cellular structure formed from a plurality of interconnected cells having a lattice structure. Each of the plurality of interconnected cells includes at least four nodes and at least one lattice element extending between each of the at least four nodes. The at least one lattice element has a diameter no greater than 2.5 mm.

Abstract: In some embodiments, a method for controlling a learning machine used in a drilling operation to drill a well into a subsurface formation includes receiving, via a video stream, an image of debris including cuttings from the drilling operation. The method may further include generating a first mask on the image to identify the cuttings in the debris, generating, via instance segmentation, a second mask for each of the identified cuttings, determining, based on the second masks, one or more properties of each of the cuttings, and associating each of the identified cuttings to a depth interval of the subsurface formation based, at least in part, on the properties of each of the identified cuttings and at least one property of the drilling operation.

Abstract: Aspects of a storage device are provided that handle host commands associated with active and inactive zones using a hybrid L2P mapping system. The storage device includes a NVM, a controller, a first volatile memory and a second volatile memory. The controller allocates, as a superblock, one or more physical blocks respectively in one or more memory dies of the NVM, receives write commands including logical addresses associated with active zones, and stores in an L2P mapping table L2P address mappings of these logical addresses to physical addresses associated with either volatile memory or the superblock. The controller refrains from storing L2P address mappings for inactive zones, instead storing in a superblock mapping table a mapping of superblocks to inactive zones in response to respective zone finish commands. As a result, L2P mapping table sizes are reduced, zone read, reset, and TTR performance are increased, and reduced WAF is achieved.

Abstract: A hood assembly of a vehicle includes a hood outer panel defining an outer surface of the hood assembly, and a hood inner panel defining an inner surface of the hood assembly, such that a hood cavity is defined between the hood inner panel and the hood outer panel. An energy absorbing material is located at the hood inner panel. The energy absorbing material includes a multi-cellular structure formed from a plurality of interconnected cells having a lattice structure. Each of the plurality of interconnected cells includes at least four nodes and at least one lattice element extending between each of the at least four nodes. The at least one lattice element has a diameter no greater than 2.5 mm.

Abstract: At least some embodiments are directed to an insights inference system that produces multiple insights associated with an entity. The insights inference system generates a decision tree machine learning model, assigning a first insight to a parent node of a decision tree machine learning model and assigning at least one second insight to child nodes of the decision tree machine learning model. Each child node is associated with a sequence number and a rank number. The sequence number and the rank number are indicative of a significance associated with the at least one second insight. The insight inference system responds to queries by traversing the decision tree machine learning model to compute at least one response insight based on the sequence number and the rank number associated with each child node and outputs the at least one response insight to a client terminal.

Abstract: An energy absorbing material includes a multi-cellular structure formed from a plurality of interconnected cells having a lattice structure. Each of the plurality of interconnected cells includes at least four nodes and at least one lattice element extending between each of the at least four nodes. The at least one lattice element has a diameter no greater than 2.5 mm.

Abstract: A computer-implemented method for reducing spot welds in a parameterized workpiece model includes generating a new design space (DS) from an original spot weld DS. The new DS includes a plurality of spot weld locations from the parameterized workpiece model with the original spot weld DS. The new DS is optimized to have a fewer number of spot weld locations than the original DS. The processor identifies a plurality of offending spot weld locations of the remaining spot weld locations in the new DS and a plurality of conforming spot weld locations of the remaining spot weld locations. The processor removes all but one optimized extension candidate from processor-selected groupings of welds while enforcing a minimum distance requirement. The processor outputs an inter-distance constrained parameterized workpiece model with an optimized extension candidate in each of the plurality of extended DSs to an operatively connected output processor.

Abstract: A computer-implemented method for reducing spot welds in a parameterized workpiece model includes generating a new design space (DS) from an original spot weld DS. The new DS includes a plurality of spot weld locations from the parameterized workpiece model with the original spot weld DS. The new DS is optimized to have a fewer number of spot weld locations than the original DS. The processor identifies a plurality of offending spot weld locations of the remaining spot weld locations in the new DS and a plurality of conforming spot weld locations of the remaining spot weld locations. The processor removes all but one optimized extension candidate from processor-selected groupings of welds while enforcing a minimum distance requirement. The processor outputs an inter-distance constrained parameterized workpiece model with an optimized extension candidate in each of the plurality of extended DSs to an operatively connected output processor.