Abstract: The present application provides a lithium composite and a method for preparing the same. The lithium composite has a belt shape and comprises: a structure layer; a stress equalization layer disposed on at least one surface of the structure layer, the stress equalization layer comprising patterned metal lithium and/or lithium alloy and having a thickness in a range from 0.1 to 1 ?m; and a lithium film having a width in a range from 185 to 1500 mm and a uniform thickness in a range from 0.5 to 30 ?m, with a thickness tolerance within 20% of the thickness, wherein the lithium film is bound to the structure layer via the stress equalization layer to form the lithium composite.

Abstract: The present application provides a lithium carbon composite belt and a preparation method therefor. The lithium carbon composite belt comprises a substrate, a metal lithium transition layer on each side of the substrate, and a lithium carbon composite material layer on an outer layer of the metal lithium transition layer, wherein a mass fraction of carbon in the lithium carbon composite material layer is in a range from 5% to 90%. The substrate layer of the lithium carbon composite belt provides high tensile performance for the lithium carbon composite belt, which is suitable for large scale industrial production. The metal lithium transition layer makes the substrate layer tightly bound with the lithium carbon composite material layer, avoiding the problem of forming bumps during the production of the lithium carbon composite belt. The presence of the metal lithium transition layer can effectively reduce the internal resistance of the lithium carbon composite belt.

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: A first process remotely calls and executes, according to a first function readable and executable for a user-mode process, a second function in physical memory address space of a first system service process. The first function readable and executable for processes in user mode is used, so that the first process may remotely call, the second function stored in the physical memory address space of the first system service process.

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: An SVC compensation strategy optimization method, comprising: calculating a weak voltage node in a fault state based on risk measure; calculating the weak voltage node in a normal state based on a static stability margin; and determining an optimal SVC distribution point and calculating the optimal configuration of SVC capacity. The SVC compensation strategy optimization method overcomes the defects in the prior art, such as low reliability, low optimization precision, poor applicability, etc., and has the advantages of high reliability, high optimization precision, and good applicability.

Abstract: A mobile wireless bridge (MWB) (110) is able to bridge traffic in either direction between a wireless LAN (140) and a wireless WAN (130), and support roaming of LAN clients (141-143, 151-153) and of the MWB (110) itself. Through the MWB (110), wired (150) and wireless LAN (140) clients (141-143, 151-153) and users are provided Internet (190) connectivity even if the LAN client (141-143, 151-153) is not otherwise able to access the wireless WAN (120). Preferred MWBs (110) will utilize mobile cellular communication networks 100 as part of a WAN (120, 130) used to obtain access to Internet (190) resources. LAN clients (141-143, 151-153) and users can also access each other through the MWB (110) with the MWB (110) functioning as a hub, switch, gateway, and/or access point. Some MWBs (110) will be adapted to access multiple types of LANs (140, 150) and multiple types of WANs (120, 130).

Abstract: A mobile wireless bridge (MWB) (110) is able to bridge traffic in either direction between a wireless LAN (140) and a wireless WAN (130), and support roaming of LAN clients (141-143, 151-153) and of the MW (110) itself. Through the MWB (110), wired (150) and wireless LAN (140) clients (141-143, 151-153) and users are provided Internet (190) connectivity even if the LAN client (141-143, 151-153) is not otherwise able to access the wireless WAN (120). Preferred MWBs (110) will utilize mobile cellular communication networks 100 as part of a WAN (120, 130) used to obtain access to Internet (190) resources. LAN clients (141-143, 151-153) and users can also access each other through the MWB (110) with the MWB (110) functioning as a hub, switch, gateway, and/or access point. Some MWBs (110) will be adapted to access multiple types of LANs (140, 150) and multiple types of WANs (120, 130).