Abstract: A photogrammetric ground control point quality evaluation method based on Monte Carlo test relates to the field of photogrammetry technology. Firstly, aerial photographs and ground control points from a survey area are obtained, the ground control points are numbered, the aerial photographs are performed with point-placing and aerial triangulation densification. Secondly, a Monte Carlo test experiment is designed, a certain number of the ground control points are selected as control points with the rest as check points, ensuring each ground control point as the control point a certain number of times, and average errors of the ground control points are calculated. Thirdly, average values of the average errors of the ground control points are calculated, standard deviations of the average errors of the ground control points are calculated. Finally, a quality coefficient Q of each ground control point is calculated and evaluated according to quality evaluation standards.

Abstract: A sand dune extraction method based on fusion of positive-negative terrains and light shading includes: obtaining an original digital elevation model of a study region; using a neighborhood analysis method to process the original digital elevation model of the study region, thereby extracting a positive terrain region and a negative terrain region of the study region; using a lighting shading method to perform six directional lighting simulation calculations on each pixel of the original digital elevation model of the study region to obtain a lighting shading map, and extracting a lighting region and a shadow region of the study region from the lighting shading map; using an embedding method to fuse the positive terrain region and the negative terrain region with the lighting region and the shadow region. The sand dune extraction method is a method for remote sensing monitoring and governance of desertification regions.

Abstract: An organic light-emitting diode (OLED) display panel and a method of manufacturing the OLED display panel are provided. The OLED display panel includes: a substrate; an anode disposed on the substrate; and a pixel defining layer disposed on the substrate. The pixel defining layer is provided with a pixel opening. The anode extends into the pixel opening. A length of the pixel opening along a first direction is greater than a part of the anode disposed in the pixel opening along the first direction. Therefore, an operation space for inkjet printing can be enlarged without changing a length of the anode. As such, four or more nozzles can be simultaneously used to print an organic light-emitting layer in a same pixel in bottom-emitting OLED devices, and an uneven brightness issue can be solved.

Abstract: An embodiment of the present application provides a pixel structure, including first sub-pixels, second sub-pixels, and blue sub-pixels. In a repeating unit, the first sub-pixel and the blue sub-pixel are arranged around the second sub-pixel, and the first sub-pixel and the blue sub-pixel are spaced apart, and spacing distances between one of the first sub-pixels and the blue sub-pixels adjacent thereto are unequal and include at least the first distance and the second distance. By making the spacing distances between one of the first sub-pixels and the blue sub-pixels adjacent thereto be unequal and the spacing distances including at least the first distance and the second distance, the displayed dark lines are reduced by a more scattered arrangement of the blue sub-pixels.

Abstract: The present application provides a display panel. By arranging a plurality of sub-pixels in the display panel into the same pixel opening, the number of pixel openings in the display area can be reduced, and the number of ink droplets that can be sprayed in a single pixel opening can be increased, thereby the risk of color mixing of adjacent sub-pixels can be reduced, and at the same time, the number of times of inkjet printing can be reduced, the time of inkjet printing can be shortened, and the production efficiency can be improved.

Abstract: A display panel, a display screen, and an electronic device are disclosed. The display panel includes a plurality of auxiliary areas at least provided on adjoining two sides outside a display area. The auxiliary areas each include a positioning sub-area and a non-positioning sub-area. The positioning sub-area includes a geometric center, and the positioning sub-area has light transmittance opposite to that of a corresponding non-positioning area.

Abstract: A display panel and a display device are provided in embodiments of the present application. The display panel includes a first sub-pixel, a second sub-pixel, and a third sub-pixel with different light-emitting colors. The first sub-pixel and the second sub-pixel are sequentially arranged along a first direction. The third sub-pixel is arranged on at least two sides of an area where the first sub-pixel and the second sub-pixel are located. The third sub-pixel is a sub-pixel with a shortest light-emitting lifespan in the pixel unit. Such design can make a pixel area ratio of the third sub-pixel be increased in a unit pixel area, reducing a current density of the third sub-pixel and reducing its decay speed, so that the lifespan of the third sub-pixel matches with lifespan of the first sub-pixel or the second sub-pixel.

Abstract: A high-temperature disaster forecast method based on a directed graph neural network is provided, and the method includes the following steps: S1, performing standardization processing on meteorological elements respectively to scale the meteorological elements into a same value range; S2, taking the meteorological elements as nodes in the graph, and describing relationships among the nodes by an adjacency matrix of graph; then learning node information by a stepwise learning strategy and continuously updating a state of the adjacency matrix; S3, training the directed graph neural network model after determining a loss function, obtaining a model satisfying requirements by adjusting a learning rate, an optimizer and regularization parameters as a forecast model, and saving the forecast model; and S4, inputting historical multivariable time series into the forecast model, changing an output stride according to demands, and thereby obtaining high-temperature disaster forecast for a future period of time.

Abstract: The present disclosure provides a meteorological big data fusion method based on deep learning, including the following steps: constructing multi-source meteorological data samples; according to an original resolution of different climate variables, selecting a corresponding super-resolution multiple to obtain an optimized super-resolution module under the constraint of maximizing information retention efficiency; constructing a spatial-temporal attention module using a focused attention mechanism, and selecting a corresponding time stride according to periodic characteristics of different climate variables; constructing a meteorological data fusion model in combination with the optimized super-resolution model and the spatial-temporal attention module; taking a minimum resolution of climate variables as a loss function, and training the meteorological data fusion model with the multi-source meteorological data samples; and importing the acquired real-time meteorological data from multiple data sources into t

Abstract: A high-temperature disaster forecast method based on a directed graph neural network is provided, and the method includes the following steps: S1, performing standardization processing on meteorological elements respectively to scale the meteorological elements into a same value range; S2, taking the meteorological elements as nodes in the graph, and describing relationships among the nodes by an adjacency matrix of graph; then learning node information by a stepwise learning strategy and continuously updating a state of the adjacency matrix; S3, training the directed graph neural network model after determining a loss function, obtaining a model satisfying requirements by adjusting a learning rate, an optimizer and regularization parameters as a forecast model, and saving the forecast model; and S4, inputting historical multivariable time series into the forecast model, changing an output stride according to demands, and thereby obtaining high-temperature disaster forecast for a future period of time.

Abstract: The present disclosure provides a meteorological big data fusion method based on deep learning, including the following steps: constructing multi-source meteorological data samples; according to an original resolution of different climate variables, selecting a corresponding super-resolution multiple to obtain an optimized super-resolution module under the constraint of maximizing information retention efficiency; constructing a spatial-temporal attention module using a focused attention mechanism, and selecting a corresponding time stride according to periodic characteristics of different climate variables; constructing a meteorological data fusion model in combination with the optimized super-resolution model and the spatial-temporal attention module; taking a minimum resolution of climate variables as a loss function, and training the meteorological data fusion model with the multi-source meteorological data samples; and importing the acquired real-time meteorological data from multiple data sources into t

Abstract: An evaluation method for evaluating a precipitation-induced landslide disaster loss under climate change is provided. The evaluation method belongs to the technical field of geological disaster prevention and treatment. The evaluation method uses a physical process based model, in considering of spatial heterogeneity of land-surface features of grids in the area, to obtain precipitation thresholds corresponding to the respective grids in the area having the spatial heterogeneity. Historical data and climate model data are taken in combination to select suitable climate models, and the model then is used to simulate landslide prone zones and possible influence zones caused by landslides. An influence zones simulated by the evaluation method can better match disaster loss grid data, which can solve the problem that climate change scenarios and influence of landslide are difficult to be evaluated in landslide disaster evaluation.

Abstract: An image processing method for removal of glare spots appearing on images captured is disclosed. The method comprises transmitting, by way of a first illumination source, a first light ray towards a first gaze and by way of a second illumination source, a second light ray towards a second gaze. An image sensor captures a first gaze image comprising an image of the first gaze and captures a second gaze image comprising an image of the second gaze. A saturation spot removal algorithm selects a primary gaze image between the first gaze image captured and the second gaze image captured, in which the primary gaze image selected is suitable for removing a glare spot to yield a resultant gaze image for ascertaining a gaze direction, for eye tracking functions. A system, a computer program product, and a computer-readable medium of the same is also disclosed.

Abstract: An evaluation method for evaluating a precipitation-induced landslide disaster loss under climate change is provided. The evaluation method belongs to the technical field of geological disaster prevention and treatment. The evaluation method uses a physical process based model, in considering of spatial heterogeneity of land-surface features of grids in the area, to obtain precipitation thresholds corresponding to the respective grids in the area having the spatial heterogeneity. Historical data and climate model data are taken in combination to select suitable climate models, and the model then is used to simulate landslide prone zones and possible influence zones caused by landslides. An influence zones simulated by the evaluation method can better match disaster loss grid data, which can solve the problem that climate change scenarios and influence of landslide are difficult to be evaluated in landslide disaster evaluation.

Abstract: A display panel, a display device, and a method of manufacturing the display panel are provided. The method of manufacturing the display panel includes the following steps: manufacturing a plurality of first electrodes arranged in an array on the array substrate, wherein a first interval is formed between adjacent first electrodes in a first direction, and wherein a second interval is formed between adjacent first electrodes in a second direction; covering a whole layer of the negative material on the first electrodes and the array substrate; exposing and developing the whole layer covered of the negative material, wherein the negative material in the first interval forms a first pixel definition layer, and wherein the negative material in the second interval forms a second pixel definition layer; exposing the first pixel definition layer again.

Abstract: An organic light-emitting diode substrate and a manufacturing method thereof are provided. The organic light-emitting diode substrate includes a substrate, a thin-film transistor element layer, an anode layer, a pixel definition layer, a light-emitting functional layer, and a cathode layer, wherein, a plurality of pixel electrodes, the light-emitting functional layer, and the cathode layer form a plurality of pixel unit rows under definition of a plurality of pixel grooves, and wherein a plurality of dummy pixel units are arranged outside at least one side of an upper side, a lower side, a left side, or a right side of the plurality of pixel unit rows.

Abstract: The present application provides a method and system for identifying extreme climate events. The method acquires climate index (CI) grid data of a to-be-identified region within an extreme climate time period, and gradually expands each of event centers in the to-be-identified region, until CI values of all grids adjacent to the event center are not greater than a CI threshold. The method can obtain extreme climate impacted areas of extreme climate events in the to-be-identified region, and can further obtain CI intensities of the extreme climate events by average calculation. The method can obtain three pieces of dimension information on each of the extreme climate events in the to-be-identified region, including an extreme climate impacted area, a CI intensity and a duration. Therefore, the method can identify the extreme climate events more comprehensively.

Abstract: A display panel, a display screen, and an electronic device are disclosed. The display panel includes a plurality of auxiliary areas at least provided on adjoining two sides outside a display area. The auxiliary areas each include a positioning sub-area and a non-positioning sub-area. The positioning sub-area includes a geometric center, and the positioning sub-area has light transmittance opposite to that of a corresponding non-positioning area.

Abstract: A landslide recognition method based on Laplacian pyramid remote sensing image fusion includes: performing original remote sensing image reconstruction based on extracted local features and global features of remote sensing images through a Laplacian pyramid fusion module to generate a fused image, constructing a deep learning semantic segmentation model through a semantic segmentation network, labeling the fused image to obtain a dataset of landslide disaster label map, and training the deep learning semantic segmentation model by the dataset, and then storing when a loss curve is fitted and a landslide recognition accuracy of remote sensing image of the deep learning semantics segmentation model meets a requirement by modifying a structure of the semantic segmentation network and adjusting parameters of the deep learning semantics segmentation model.

Abstract: A method for flood disaster monitoring and disaster analysis based on vision transformer is provided. It includes: step (1), constructing a bi-temporal image change detection model based on vision transformer; step (2), selecting bi-temporal remote sensing images to make flood disaster labels; and step (3), performing flood monitoring and disaster analysis according to the bi-temporal image change detection model constructed in the step (1). In combination with the bi-temporal image change detection model based on an advanced vision transformer in deep learning and radar data which is not affected by time and weather and has strong penetration ability, data when floods occur can be obtained and recognition accuracy is improved.