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 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: The solid composite stabilizer for PCE includes the following components in parts by weight: 10 to 30 parts of a phenol-substituted ion-exchange resin, 50 to 80 parts of a basic anion-exchange resin, and 50 to 100 parts of a desiccating agent. The phenol-substituted ion-exchange resin is chloromethylated macroporous polystyrene-divinylbenzene (PS-DVB) substituted by a phenolic compound; and the basic anion-exchange resin is chloromethylated macroporous PS-DVB substituted by an amine compound. The preparation method includes the following step: thoroughly mixing the phenol-substituted ion-exchange resin, the basic anion-exchange resin, and the desiccating agent in a specified ratio to obtain the solid composite stabilizer for PCE. The solid composite stabilizer for PCE is packaged in a glass fiber bag and placed in PCE for storage and use.

Abstract: The solid composite stabilizer for PCE includes the following components in parts by weight: 10 to 30 parts of a phenol-substituted ion-exchange resin, 50 to 80 parts of a basic anion-exchange resin, and 50 to 100 parts of a desiccating agent. The phenol-substituted ion-exchange resin is chloromethylated macroporous polystyrene-divinylbenzene (PS-DVB) substituted by a phenolic compound; and the basic anion-exchange resin is chloromethylated macroporous PS-DVB substituted by an amine compound. The preparation method includes the following step: thoroughly mixing the phenol-substituted ion-exchange resin, the basic anion-exchange resin, and the desiccating agent in a specified ratio to obtain the solid composite stabilizer for PCE. The solid composite stabilizer for PCE is packaged in a glass fiber bag and placed in PCE for storage and use.

Abstract: An electrostatic discharge protection circuit includes a P.sup.- doped channel and an N.sup.- doped channel that form a serial path between a signal pad and a transistor. Holes are depleted from the P.sup.- doped channel in response to a negative electrostatic discharge on the input signal pad; and electrons are depleted from the N.sup.- doped channel in response to a positive electrostatic discharge on the input signal pad. When either depletion occurs, the path from the signal pad to its transistor is open circuited; and so the transistor is protected. Conversely, when no electrostatic charge exists on the signal pad, the path through the P.sup.- doped channel and the N.sup.- doped channel is highly conductive; and so signals pass between the pad and the transistor very quickly.