Abstract: A display device and an operation method of the display device are provided in the present disclosure. The display device includes a display screen a sound-generation driving module, and further includes an acoustic scanning module, arranged on a non-display side of the display screen and configured to determine a spatial contour of a space where the display device is located through acoustic scanning; a viewer position detection module, configured to detect viewer coordinates in the space where the display device is located; and a control module, configured to control the sound-generation driving module to generate sound in accordance with the spatial contour and the viewer coordinates.

Abstract: A supramolecular nano-drug based on irinotecan and niraparib, and a preparation method and application thereof are provided. The nano-drug is a stable supramolecular nanostructure with a regular geometric appearance, which is formed by self-assembling irinotecan and niraparib through supramolecular acting forces, such as hydrogen bonds, and hydrophobic Van Der Waals force by a dynamic supramolecular self-assembly method. The supramolecular nano-drug formed in the present disclosure is capable of significantly improving the sensitivity of colorectal cells to the irinotecan, and the quantity of DNA damages induced in the colorectal cells is significantly higher than that induced by the irinotecan as a single chemotherapeutic drug. The supramolecular nano-drug of the present disclosure can not only induce death of colorectal cancer cells in vitro, but also play a role in treating the colorectal cancer in vivo after being administrated intravenously.

Abstract: A system for analyzing remotely sensed photos of a forest or other areas of interest uses a computer system to increase the variation in NIR data having values that represent items of interest. In one embodiment, a computer system applies a stretching function to the NIR data to increase their variation. The objective spectral stretched NIR data is used to differentiate different types of vegetation in the remotely sensed image. Objective-based Vegetation Index (OVI) values are calculated from the objective spectral stretched NIR data that allow different types of vegetation to be distinguished. In one embodiment, the OVI values are used to differentiate hardwoods from conifers in a digital aerial photo of a forest.

Abstract: A computer system is programmed to analyze data from aerial images and LiDAR data obtained from a region of interest in order to determine whether a group of LiDAR data points representing an individual item of vegetation (i.e. a blob) is a particular type of vegetation. Infrared data from aerial images of the region of interest is stretched and divided by red spectral data to compute an objective-stretched vegetation index value (OVI) for a pixel. The mean LiDAR intensity and the mean OVI for the LiDAR data points and the pixels in the area of a blob are used to predict what type of vegetation is represented in the area of the blob.

Abstract: A computer system is programmed to analyze data from aerial images and LiDAR data obtained from a region of interest in order to determine whether a group of LiDAR data points representing an individual item of vegetation (i.e. a blob) is a particular type of vegetation. Infrared data from aerial images of the region of interest is stretched and divided by red spectral data to compute an objective-stretched vegetation index value (OVI) for a pixel. The mean LiDAR intensity and the mean OVI for the LiDAR data points and the pixels in the area of a blob are used to predict what type of vegetation is represented in the area of the blob.

Abstract: A system for analyzing remotely sensed photos of a forest or other areas of interest uses a computer system to increase the variation in NIR data having values that represent items of interest. In one embodiment, a computer system applies a stretching function to the NIR data to increase their variation. The objective spectral stretched NIR data is used to differentiate different types of vegetation in the remotely sensed image. Objective-based Vegetation Index (OVI) values are calculated from the objective spectral stretched NIR data that allow different types of vegetation to be distinguished. In one embodiment, the OVI values are used to differentiate hardwoods from conifers in a digital aerial photo of a forest.