Abstract: A lighting device includes a base, a panel assembly, and a power supply assembly. The panel assembly is at least partially connected to the base and includes a luminous body. The power supply assembly is connected to the lighting device. The panel assembly includes a main panel and a plurality of auxiliary panels. The main panel is fixedly connected to the base, a first auxiliary panel among the plurality of auxiliary panels is rotatably connected to the main panel, and a second auxiliary panel among the plurality of auxiliary panels is rotatably connected to the first auxiliary panel.

Abstract: The present disclosure provides an antenna, a method for manufacturing an antenna and an antenna system. The antenna according to the present disclosure includes: a dielectric layer having a first surface and a second surface which are oppositely disposed along a thickness direction of the dielectric layer; a radiation patch disposed on the first surface of the dielectric layer; and a reference electrode layer disposed on the second surface of the dielectric layer and having an orthographic projection on the second surface at least partially overlapping an orthographic projection of the radiation patch on the second surface. The reference electrode layer has an opening penetrating therethrough along a thickness direction of the reference electrode layer, and an orthographic projection of at least a part of a radiation edge of the radiation patch on the first surface is located within an orthographic projection of the opening on the first surface.

Abstract: An electrochemical apparatus includes a positive electrode plate, a negative electrode plate, and a separator disposed between the positive electrode plate and the negative electrode plate, where the negative electrode plate includes a negative electrode current collector and a negative electrode active substance layer disposed on the negative electrode current collector, where the electrochemical apparatus has a first capacity a in the unit of mAh, the first capacity a is greater than 0, and the first capacity a represents a capacity when a battery that is prepared using the negative electrode plate and a lithium plate as a counter electrode of the negative electrode plate is charged to 2.0 V after the electrochemical apparatus has been discharged from a 100% state of charge (SOC) to 2.5 V.

Abstract: Provided is a construction method for a fully prefabricated multi-story concrete plant, the construction method may achieve full coverage of a hoisting operation of the large beam and column prefabricated components of a floor by employing a single intelligent hoisting robot, an angle change of the track devices, an angle change of a moving device, and a self-lifting device. It is not necessary to arrange a transition track at the turn of the installation route, which saves space and installation cost, and overcomes the disadvantage of high cost caused by the traditional prefabricated construction mode of multi-story concrete plant, which needs to arrange a plurality of large hoisting equipment. It may achieve the mechanization and intelligence of the whole construction process of the fully prefabricated multi-story concrete plant.

Abstract: A manual assembly tool (10) of the compound action type for ear clamps, including a pair of lever arms (12, 14), a pair of jaw members (16, 18). The lever arms (12, 14) are pivotably connected to each other at a pivot point (20) near a distal end of the lever arms (12, 14), where-in each lever arm (12, 14) includes a handle end (12a, 14a) on a proximal side and an extension arm (12b, 14b) extending beyond the pivot point (20). The jaw members (16, 18) include clamp edges (16a, 18a) forming a lip for receiving the clamp ears. Each of the jaw members (16, 18) is pivotably connected to the one of the extension arms (12b, 14b) at its proximal end, wherein the jaw members (16, 18) are pivotally interconnected at an intermediate portion of the jaw member (16, 18) so that closing the handle ends (12a, 14a) of the lever arms (12, 14) translates the jaw members (16, 18) towards a closed position thereof, thus squeezing the clamp ear.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

Abstract: A negative electrode material includes silicon composite particles. The silicon composite particles include amorphous silicon particles and a buffer phase. The amorphous silicon particles are dispersed in the buffer phase. A non-uniformity of the amorphous silicon particles dispersed in the buffer phase is less than or equal to 30%. Also, an electronic device including the negative electrode material.

Abstract: The disclosure provides a display panel and a display apparatus, and belongs to the field of display technology. The display panel includes a display backplane and an antenna structure; the display panel further includes a frequency selective surface on a side of the antenna structure close to a light exit surface of the display panel; the frequency selective surface is configured to transmit an electromagnetic wave with a specific frequency, so as to enhance a radiation gain of the antenna structure.

Abstract: A method protects a daemon in an operating system of a host computer. The operating system detects that there is an access of a plist file of a daemon by a process in the computer. If so, then it executes a callback function registered for the plist file. The callback function sends to a kernel extension a notification of the attempted access. The kernel extension returns a value to the operating system indicating that the access should be denied. The operating system denies access to the plist file of the daemon by the process. The extension may also notify an application which prompts the user for instruction. The kernel extension also protects itself by executing its exit function when a command is given to unload the extension, and the exit function determines whether or not the command is invoked by an authorized application, such as by checking a flag.

Abstract: An antenna unit, a manufacturing method thereof, a display device, and an electronic apparatus. The antenna unit includes a radiation main body, at least one feed line, and a plurality of grounding portions. The at least one feed line and the radiation main body are electrically connected, the radiation main body, the at least one feed line, and the plurality of grounding portions are provided in a same layer.

Abstract: The present disclosure provides a thin film sensor, a thin film sensor array and an electronic device. The thin film sensor has a functional area and a non-functional area surrounding the functional area, and includes: a dielectric substrate having a first surface and a second surface which are oppositely arranged; a first conductive layer located on the first surface of the dielectric substrate and including a first conductive structure arranged in the functional area; a second conductive layer located on the second surface of the dielectric substrate; a first light-shielding layer located on the first surface of the dielectric substrate, the first light-shielding layer includes a first light-shielding structure at least arranged in the non-functional area, the first light-shielding structure has the same pattern as the first conductive structure.

Abstract: An electrolyte includes a fluorine-containing lithium salt and lithium dicyanamide, and the fluorine-containing lithium salt includes lithium fluorosulfonyl(trifluoromethanesulfonyl)imide. According to embodiments of this application, the electrolyte is improved, so that a lithium salt includes the lithium fluorosulfonyl(trifluoromethanesulfonyl)imide and the lithium dicyanamide, thereby helping to enhance stability of a solid electrolyte interface (SEI) film of a negative electrode. In this way, cycling performance of the electrochemical apparatus is improved, and dynamic performance of the electrolyte is basically not affected.

Abstract: An electrochemical apparatus includes a positive electrode sheet including a positive electrode active material layer; a negative electrode sheet including a negative electrode active material layer; and a separator disposed between the positive electrode sheet and the negative electrode sheet. A mass ratio of fluorine to nitrogen on a surface layer of the positive electrode active material layer is A, a mass ratio of fluorine to nitrogen on a surface layer of the negative electrode active material layer is B, 3?A?50, and 30?B?300.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

Abstract: This application provides a feature processing method and apparatus for an artificial intelligence (AI) recommendation model, an electronic device, and a storage medium. The method includes obtaining input data, and converting a data structure of the input data into a uniform sample structure; determining a feature group identifier and a feature extraction function that correspond to the AI recommendation model; extracting a feature group identifier, a feature identifier and a feature value of the input data from the converted input data according to the feature extraction function; and constructing a sample of the AI recommendation model according to the feature group identifier, the feature identifier and the feature value of the input data.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

Abstract: A method protects a daemon in an operating system of a host computer. The operating system detects that there is an access of a plist file of a daemon by a process in the computer. If so, then it executes a callback function registered for the plist file. The callback function sends to a kernel extension a notification of the attempted access. The kernel extension returns a value to the operating system indicating that the access should be denied. The operating system denies access to the plist file of the daemon by the process. The extension may also notify an application which prompts the user for instruction. The kernel extension also protects itself by executing its exit function when a command is given to unload the extension, and the exit function determines whether or not the command is invoked by an authorized application, such as by checking a flag.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.