Abstract: Aspects of the disclosure relate to using computer vision methods for asset evaluation. A computing platform may receive historical images of a plurality of properties and corresponding historical inspection results. Using the historical images and historical inspection results, the computing platform may train a roof waiver model (which may be a computer vision model) to output inspection prediction information directly from an image. The computing platform may receive a new image corresponding to a particular residential property. Using the roof waiver model, the computing platform may analyze the new image to output of a likelihood of passing inspection. The computing platform may send, to a user device and based on the likelihood of passing inspection, inspection information indicating whether or not a physical inspection should be performed and directing the user device to display the inspection information, which may cause the user device to display the inspection information.

Abstract: Aspects of the disclosure relate to using computer vision methods to forecast damage. A computing platform may receive historical images comprising aerial images of residential properties and historical loss data corresponding to the residential properties. Using the historical images and the historical loss data, the computing platform may train a computer vision model, which may configure the computer vision model to output loss prediction information directly from an image. The computing platform may receive a new image corresponding to a particular residential property, and may analyze the new image, using the computer vision model, which may directly result in a likelihood of damage score. Based on the likelihood of damage score, the computing platform may send likelihood of damage information and one or more commands directing a user device to display the likelihood of damage information, which may cause the user device to display the likelihood of damage information.

Abstract: Aspects of the disclosure relate to using computer vision methods for asset evaluation. A computing platform may receive historical images of a plurality of properties and corresponding historical inspection results. Using the historical images and historical inspection results, the computing platform may train a roof waiver model (which may be a computer vision model) to output inspection prediction information directly from an image. The computing platform may receive a new image corresponding to a particular residential property. Using the roof waiver model, the computing platform may analyze the new image to output of a likelihood of passing inspection. The computing platform may send, to a user device and based on the likelihood of passing inspection, inspection information indicating whether or not a physical inspection should be performed and directing the user device to display the inspection information, which may cause the user device to display the inspection information.

Abstract: Aspects of the disclosure relate to using computer vision methods to forecast damage. A computing platform may receive historical images comprising aerial images of residential properties and historical loss data corresponding to the residential properties. Using the historical images and the historical loss data, the computing platform may train a computer vision model, which may configure the computer vision model to output loss prediction information directly from an image. The computing platform may receive a new image corresponding to a particular residential property, and may analyze the new image, using the computer vision model, which may directly result in a likelihood of damage score. Based on the likelihood of damage score, the computing platform may send likelihood of damage information and one or more commands directing a user device to display the likelihood of damage information, which may cause the user device to display the likelihood of damage information.

Abstract: Example implementations relate to software updates of a plurality of computing systems. An example system includes a plurality of computing systems including a first computing system and a second computing system. The first computing system includes a first board management controller and the second computing system includes a second board management controller. The first computing system and the second computing system are capable of staging from each other through the first board management controller and the second board management controller via a network protocol.

Abstract: Example implementations relate to software updates of a plurality of computing systems. An example system includes a plurality of computing systems including a first computing system and a second computing system. The first computing system includes a first board management controller and the second computing system includes a second board management controller. The first computing system and the second computing system are capable of staging from each other through the first board management controller and the second board management controller via a network protocol.

Abstract: The present invention relates to a method of producing a biologically active polypeptide having insulinotropic activity, the method comprising steps of: (a) transforming a genetically modified host cell that has protease gene knockout, with a polynucleotide vector encoding the polypeptide; and (b) growing the transformed host cell to produce the biologically active polypeptide; and a method of producing a biologically active polypeptide having an N-terminal recognition site His-Gly with insulinotropic activity, the method comprising steps of: (a) transforming a genetically modified Pichia pastoris that has protease gene STE13 knockout, with a polynucleotide vector encoding the polypeptide; and (b) growing the transformed Pichia pastoris to produce the biologically active polypeptide.

Abstract: The present invention relates to a method of producing a biologically active polypeptide having insulinotropic activity, the method comprising steps of: (a) transforming a genetically modified host cell that has protease gene knockout, with a polynucleotide vector encoding the polypeptide; and (b) growing the transformed host cell to produce the biologically active polypeptide; and a method of producing a biologically active polypeptide having an N-terminal recognition site His-Gly with insulinotropic activity, the method comprising steps of: (a) transforming a genetically modified Pichia pastoris that has protease gene STE13 knockout, with a polynucleotide vector encoding the polypeptide; and (b) growing the transformed Pichia pastoris to produce the biologically active polypeptide.

Abstract: Provided are a method of applying fast mobile IPv6 (FMIPv6) to a mobile node in order to prevent loss of packets transmitted during a handover of a mobile router from a first access router to a second access router, and a mobile router and a mobile network therefor. Specifically, the mobile router receives, from the first access router, a message containing a prefix corresponding to the second access router, transmits, to the mobile node, a message containing the prefix and information indicating that the prefix is received from the first access router, transmits, to the first access router, a message for FMIPv6, and transmits, to the mobile node, a message to set a zero lifetime for a prefix corresponding to the first access router. Furthermore, the mobile node transmits a message for FMIPv6 to the first access router when the mobile node receives the message containing the prefix.

Abstract: A method and an apparatus to speed up fast mobile internet protocol version 6 (FMIPv6) by using a relatively fast layer 2 handover. A method and an apparatus to use FMIPv6 to trigger a relatively fast layer 2 handover of a mobile node from an initial access point associated with a previous access router to a neighboring access point associated with a next access router.