Abstract: A vehicle interior component configured to provide an illuminated display/image and comprising a composite structure is disclosed. The component may comprise a cover providing a surface for the illuminated display/image and a substrate; the cover may comprise a cover layer, bottom layer and/or intermediate layer. The cover layer may comprise a surface layer and a base layer. The base layer and/or bottom layer may comprise a reinforcing layer. The surface layer, cover layer and/or base layer may comprise a pigment. The surface layer, cover layer and/or bottom layer may comprise a colorant (or pigment, dye, etc.). The bottom layer may comprise a pattern region comprising the image, a mask and/or light-blocking layer. The composite structure may comprise the cover, a functional layer, spacer/buffer layer, diffuser layer, sensor grid, capacitive sensor, textile and/or base/substrate. The cover surface may comprise a skin.

Abstract: A fire engine including a vehicle frame, a liquid nitrogen storage tank, a liquid nitrogen conveying pipeline, a gasification device, a plurality of electric valves, a water pipe adapter, a liquid nitrogen spray gun, and a mixed spray gun. The liquid nitrogen conveying pipeline includes a first pipeline and a second pipeline. The first pipeline connects the lower part of the liquid nitrogen storage tank, the gasification device, and the upper part of the liquid nitrogen storage tank sequentially in that order. The second pipeline connects the liquid nitrogen storage tank, an input end of the liquid nitrogen spray gun, and a first input end of the mixed spray gun. The mixed spray gun includes a first input end, a second input end, a liquid nitrogen nozzle, and a spray pipe. The spray pipe includes a contraction section, an expansion section, and an acceleration section.

Abstract: A thermionic emission device comprises a first electrode, a second electrode, a single carbon nanotube, an insulating layer and a gate electrode. The gate electrode is located on a first surface of the insulating layer. The first electrode and the second electrode are located on a second surface of the insulating layer and spaced apart from each other. The carbon nanotube comprises a first end, a second end opposite to the first end, and a middle portion located between the first end and the second end. The first end of the carbon nanotube is electrically connected to the first electrode, and the second end of the carbon nanotube is electrically connected to the second electrode.

Abstract: A system and computer-implemented method for facilitating a user of a mobile device obtaining image data of damage to a vehicle for damage assessment includes capturing image data of a vehicle with the mobile device. The mobile device may include an orientation model for capturing the image data. The captured image data is analyzed, and a determination is made of the orientations of the images of the captured image data. In addition, a determination is made as to whether the captured image data can be used for the damage assessment. The captured image data may then be transmitted to a damage estimator computing device for estimating an amount of damage to the vehicle.

Abstract: A system and computer-implemented method for processing image data of a vehicle to identify one or more damaged parts includes receiving the image data of the vehicle from a policyholder. The image data is processed to determine whether the image data includes images of one or more damaged parts of the vehicle. One or more damaged parts are identified. A parts database is used to receive data corresponding to the identified one or more damaged parts. A parts list is generated including the identified one or more damaged parts and the data corresponding to the identified one or more damaged parts.

Abstract: A system for capturing images of damage to an object configured to (i) store an orientation model associated with an object; (ii) receive, from a user, a request to analyze damage to the object; (iii) instruct the user to position a camera at a first position relative to the object; (iv) receive an image of the object from the camera; (v) determine whether the received image is properly framed; (vi) if the received image is not properly framed, instruct the user to adjust the position of the camera; and (vii) if the received image is properly framed, instruct the user to position the camera at a second position relative to the object. As a result, obtaining images of sufficient quality for proper processor analysis may be facilitated.

Abstract: A system and computer-implemented method for facilitating a user of a mobile device obtaining image data of damage to a vehicle for damage assessment includes capturing image data of a vehicle with the mobile device. The mobile device may include an orientation model for capturing the image data. The captured image data is analyzed, and a determination is made of the orientations of the images of the captured image data. In addition, a determination is made as to whether the captured image data can be used for the damage assessment. The captured image data may then be transmitted to a damage estimator computing device for estimating an amount of damage to the vehicle.

Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: The present invention discloses a method of preparing oligosaccharide using biomass sugar as raw material. The method places a biomass sugar in a quartz tubular reactor, places the quartz tubular reactor at a temperature-controlled zone of a tubular thermal conversion reactor under the protection of nitrogen gas, controls a thermal conversion time, rapidly quenches a material to obtain an oligosaccharide precursor after thermal conversion ends, then dissolves by adding pure water and purifies through an inorganic ultrafiltration membrane, and finally obtain an oligosaccharide after drying.

Abstract: A field effect transistor comprises a source electrode, a drain electrode, a single carbon nanotube, an insulating layer and a gate electrode. The gate electrode is located on a first surface of the insulating layer. The source electrode and the drain electrode are located on a second surface of the insulating layer and spaced away from each other. The single carbon nanotube comprises a first end, a second end opposite to the first end, and a middle portion located between the first end and the second end. The first end of the single carbon nanotube is electrically connected to the source electrode, and the second end of the single carbon nanotube is electrically connected to the drain electrode. The middle portion of the single carbon nanotube comprises a plurality of defects.

Abstract: A thermionic emission device comprises a first electrode, a second electrode, a single carbon nanotube, an insulating layer and a gate electrode. The gate electrode is located on a first surface of the insulating layer. The first electrode and the second electrode are located on a second surface of the insulating layer and spaced apart from each other. The carbon nanotube comprises a first end, a second end opposite to the first end, and a middle portion located between the first end and the second end. The first end of the carbon nanotube is electrically connected to the first electrode, and the second end of the carbon nanotube is electrically connected to the second electrode.

Abstract: A method of making a carbon nanotube bundle is provided. A plurality of carbon nanotubes is provided. A plurality of sulfur nanoparticles is disposed on the plurality of carbon nanotubes to form at least two visible carbon nanotubes. The at least two visible carbon nanotubes are stacked to form a carbon nanotube bundle preparation body. The plurality of sulfur nanoparticles in the carbon nanotube bundle preparation body is removed to obtain the carbon nanotube bundle.

Abstract: An electron emission source is provided. The electron emission source comprises a first electrode, an insulating layer, and a second electrode, The first electrode, the insulating layer, and the second electrode are successively stacked with each other. the second electrode is a graphene layer, and the graphene layer is an electron emission end to emit electron. A thickness of the graphene layer ranges from about 0.1 nanometers to about 50 nanometers.

Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: An electron emission source is provided. The electron emission source comprises a first electrode, an insulating layer, a semiconductor layer, and a second electrode. The first electrode, the insulating layer, the semiconductor layer, and the second electrode are successively stacked with each other. The second electrode is a graphene layer, and the graphene layer is an electron emission end to emit electrons.

Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Abstract: A system for capturing images of damage to an object configured to (i) store an orientation model associated with an object; (ii) receive, from a user, a request to analyze damage to the object; (iii) instruct the user to position a camera at a first position relative to the object; (iv) receive an image of the object from the camera; (v) determine whether the received image is properly framed; (vi) if the received image is not properly framed, instruct the user to adjust the position of the camera; and (vii) if the received image is properly framed, instruct the user to position the camera at a second position relative to the object. As a result, obtaining images of sufficient quality for proper processor analysis may be facilitated.

Abstract: In a computer-implemented method and associated tangible non-transitory computer-readable medium, an image of a damaged vehicle may be analyzed to generate a repair estimate. A dataset populated with digital images of damaged vehicles and associated claim data may be used to train a deep learning neural network to learn damaged vehicle image characteristics that are predictive of claim data characteristics, and a predictive similarity model may be generated. Using the predictive similarity model, one or more similarity scores may be generated for a digital image of a newly damaged vehicle, indicating its similarity to one or more digital images of damaged vehicles with known damage level, repair time, and/or repair cost. A repair estimate may be generated for the newly damaged vehicle based on the claim data associated with images that are most similar to the image of the newly damaged vehicle.