Abstract: A modular battery pack for a vehicle includes a scalable enclosure structure that includes a front enclosure rail, a rear enclosure rail, and a pair of rocker attachment rails extending between the front and rear enclosure rails along opposing sides of the enclosure structure. Each of the rocker attachment rails includes a length that is scalable to adjust a length of the battery pack. A bottom cover panel is coupled to respective first sides of the front enclosure rail, the rear enclosure rail, and each of the rocker attachment rails. A top cover panel is coupled to respective second sides of the front enclosure rail, the rear enclosure rail, and each of the rocker attachment rails. A battery module is supported on the scalable enclosure structure between the bottom cover panel and the top cover panel.

Abstract: A modular platform for a vehicle includes a scalable frame structure. The scalable frame structure includes a front frame portion configured to receive a front vehicle suspension, a rear frame portion configured to receive a rear vehicle suspension, and a pair of rails extending between the front frame portion and the rear frame portion along opposing sides of the frame structure. A length of the pair of rails is scalable to adjust a wheelbase of the vehicle between the front vehicle suspension and the rear vehicle suspension. A battery pack is supported on the frame structure.

Abstract: The present invention relates to peptides capable of forming a gel and to their use in tissue engineering and bioprinting. The present invention furthermore relates to a gel comprising a peptide in accordance with the present invention, to a method of preparing such gel and to the use of such gel. In one embodiment, such gel is a hydrogel. The present invention furthermore relates to a wound dressing or wound healing agent comprising a gel according to the present invention and to a surgical implant or stent comprising a peptide scaffold formed by a gel according to the present invention. Moreover, the present invention also relates to a pharmaceutical and/or cosmetic composition, to a biomedical device or an electronic device comprising the peptide according to the present invention.

Abstract: This disclosure relates generally to performing user segmentation, and more particularly to predicting attribute values for user segmentation. In one embodiment, the method includes segregating a user with an incomplete attribute value and a user with complete attribute values for an attribute into a first group and a second group respectively, computing prior probability for each suggestive attribute value, identified for the incomplete attribute value, based on number of users in second group having the suggestive attribute value as attribute value for the attribute. Computing likelihood for each suggestive attribute value based on similarity of the attribute values of the user of the first group with users of the second group, computing a posterior probability for each suggestive attribute value based on the prior probability and the likelihood, selecting a suggestive attribute value with the highest posterior probability as the attribute value for the incomplete attribute value of the user.

Abstract: The present invention relates to peptides capable of forming a gel and to their use in tissue engineering and bioprinting. The present invention furthermore relates to a gel comprising a peptide in accordance with the present invention, to a method of preparing such gel and to the use of such gel. In one embodiment, such gel is a hydrogel. The present invention furthermore relates to a wound dressing or wound healing agent comprising a gel according to the present invention and to a surgical implant or stent comprising a peptide scaffold formed by a gel according to the present invention. Moreover, the present invention also relates to a pharmaceutical and/or cosmetic composition, to a biomedical device or an electronic device comprising the peptide according to the present invention.

Abstract: Methods and systems for Assurance-enabled Linde Buzo Gray (ALBG) data clustering is described herein. In an implementation, a user model data from a database available to the processor is obtained. The user model data comprises data elements or users, each of which corresponds to features and feature values associated with the users. These data elements of the user model data are segmented into clusters using our segmentation approach with an initial accuracy criterion parametric value and the output is captured as segment data. The segment data output is checked for initial pareto validity. If successful, iterative segmentation run with incremental accuracy criterion using parameterized value is performed till the segmented clusters are determined valid against pareto validity check. The last successful pareto valid segmented cluster data is considered as the finalized segment output data.

Abstract: This disclosure relates generally to performing user segmentation, and more particularly to predicting attribute values for user segmentation. In one embodiment, the method includes segregating a user with an incomplete attribute value and a user with complete attribute values for an attribute into a first group and a second group respectively, computing prior probability for each suggestive attribute value, identified for the incomplete attribute value, based on number of users in second group having the suggestive attribute value as attribute value for the attribute. Computing likelihood for each suggestive attribute value based on similarity of the attribute values of the user of the first group with users of the second group, computing a posterior probability for each suggestive attribute value based on the prior probability and the likelihood, selecting a suggestive attribute value with the highest posterior probability as the attribute value for the incomplete attribute value of the user.

Abstract: Methods and systems for Assurance-enabled Linde Buzo Gray (ALBG) data clustering is described herein. In an implementation, a user model data from a database available to the processor is obtained. The user model data comprises data elements or users, each of which corresponds to features and feature values associated with the users. These data elements of the user model data are segmented into clusters using our segmentation approach with an initial accuracy criterion parametric value and the output is captured as segment data. The segment data output is checked for initial pareto validity. If successful, iterative segmentation run with incremental accuracy criterion using parameterized value is performed till the segmented clusters are determined valid against pareto validity check. The last successful pareto valid segmented cluster data is considered as the finalized segment output data.