Abstract: The present disclosure discloses a dispatching method and system for an electric vehicle battery swapping station. The dispatching method includes: acquiring battery data of electric vehicles and demand of battery swapping of users; determining total charging power and average charging power of a battery swapping station; determining a battery swapping price; determining an objective function of optimized dispatching of the electric vehicle battery swapping station according to the battery swapping price and the battery data; optimizing, according to a constraint condition and the objective function, a charging state of batteries in the battery swapping station and the demand of the battery swapping to obtain an optimized charging state of the batteries in the battery swapping station and optimized demand of the battery swapping; and performing the optimized dispatching on the electric vehicle battery swapping station.

Abstract: The present invention discloses an organic light-emitting display panel, a manufacturing method thereof, and an encapsulation film thereof. In this invention, an auxiliary encapsulation layer is disposed on an outer side of an inorganic layer, and the auxiliary encapsulation layer at least covers a bending region of the inorganic layer and a boundary region of the inorganic layer. Even if the inorganic layer cracks or peels in the bending region and the boundary region, a channel of water and oxygen generated at a cracked place or a peeling place is blocked by the auxiliary encapsulation layer, thereby ensuring an ability of the encapsulation film to block water and oxygen into an organic light-emitting device.

Abstract: This disclosure discloses a CT image generation method and apparatus, a computer device, and a computer-readable storage medium. The method includes: obtaining a first X-ray image and a second X-ray image, the first X-ray image and the second X-ray image being X-ray images acquired for a target object from two orthogonal viewing angles; calling a generator to perform three-dimensional reconstruction on the first X-ray image and the second X-ray image, to obtain a three-dimensional model of the target object; and obtaining a CT image of the target object according to the three-dimensional model of the target object.

Abstract: This application relates to a medical image segmentation method, a computer device, and a storage medium. The method includes: obtaining medical image data; obtaining a target object and weakly supervised annotation information of the target object in the medical image data; determining a pseudo segmentation mask for the target object in the medical image data according to the weakly supervised annotation information; and performing mapping on the medical image data by using a preset mapping model based on the pseudo segmentation mask, to obtain a target segmentation result for the target object. Because the medical image data is segmented based on the weakly supervised annotation information, there is no need to annotate information by using much labor during training of the preset mapping model, thereby saving labor costs. The preset mapping model is a model used for mapping the medical image data based on the pseudo segmentation mask.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container includes capturing a plurality of images of the specimen container from multiple viewpoints, stacking the multiple viewpoint images along a channel dimension into a single stacked input, and processing the stacked input with a single deep convolutional neural network (SDNN). The SDNN includes a segmentation convolutional neural network that receives the stacked input and outputs multiple label maps simultaneously. The SDNN also includes a classification convolutional neural network that processes the multiple label maps and outputs an HILN determination (Hemolysis, Icterus, and/or Lipemia, or Normal) of the serum or plasma portion of the specimen. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides a fine-grained HILN index (hemolysis, icterus, lipemia, normal) of the serum or plasma portion of the specimen, wherein the H, I, and L classes may each have five to seven sub-classes. The HILN index may also have one un-centrifuged class. Pixel data of an input image of the specimen container may be processed by a deep semantic segmentation network having, in some embodiments, more than 100 layers. A small front-end container segmentation network may be used to determine a container type and boundary, which may additionally be input to the deep semantic segmentation network. A discriminative network may be used to train the deep semantic segmentation network to generate a homogeneously structured output. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: Described herein are cyclic peptides, nanoparticles bound with cyclic peptides, and methods for using said cyclic peptides and/or said nanoparticles bound with cyclic peptides for intraoperative nerve tissue imaging.

Abstract: A method of training a neural network (Convolutional Neural Network-CNN) including reduced graphical annotation input is provided. The training method can be used to train a Testing CNN that can be used for determining Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N) of a serum or plasma portion of a test specimen. The training method includes capturing training images of multiple specimen containers including training specimens, generating region proposals of the serum or plasma portions of the training specimens; and selecting the best matches for the location, size and shape of the region proposals for the multiple training specimens. The obtained features (network and weights) from the training CNN can be used in a testing CNN. Quality check modules and testing apparatus adapted to carry out the training method, and characterization methods using abounding box regressor are described, as are other aspects.

Abstract: An organic light-emitting diode (OLED) display panel and a method of manufacturing the same are disclosed. The OLED display panel includes a phase-compensated liquid crystal layer and a linear polarizer disposed on an organic light-emitting device layer. The phase-compensated liquid crystal layer is disposed between the organic light-emitting device layer and the linear polarizer. Polarized light is generated after external light passes through a linear polarizer and the phase-compensated liquid crystal layer and is reflected to go through again the phase-compensated liquid crystal layer in a direction perpendicular to a polarization direction of the linear polarizer. By providing a liquid crystal layer to achieve compensation for phase difference, the present application can overcome a problem of visual interference and glare effects brought about by the display panel due to external light.

Abstract: Provided are inorganic nanocages. The inorganic nanocages may be non-metal nanocages, transition metal oxide nanocages, or transition metal nanocages. Non-metal nanocages may include metal oxides. The inorganic nanocages can be made using micelles formed using pore expander molecules. The inorganic nanocages may be used as catalysts, drug delivery agents, diagnostic agents, therapeutic agents, and theranostic agents.

Abstract: A display panel including a substrate, multiple display pixels, an encapsulation structure, and an auxiliary layer is provided. The substrate includes a display region, a bendable region, and a buffer region positioned therebetween. The display pixels are disposed in the display region. The encapsulation structure is overlapped with the display region and covers the display pixels. The auxiliary layer is overlapped with the bendable region and has a top surface. A first height is included between a top surface of the auxiliary layer and a surface of the substrate. The auxiliary layer and the encapsulation structure define a recess overlapped with the buffer region. A second height is included between a bottom surface of the recess positioned in the buffer region and the surface of the substrate. A difference between the first height and the second height is greater than 0 ?m and less than or equal to 4 ?m.

Abstract: The present disclosure provides a film stack structure formed on a substrate and methods for forming the film stack structure on the substrate. In one embodiment, the method for forming a film stack structure on a substrate includes depositing a first adhesion layer on an oxide layer formed on the substrate and depositing a metal layer on the first adhesion layer, wherein the first adhesion layer and the metal layer form a stress neutral structure.

Abstract: Disclosed herein are nanoparticle immunoconjugates useful for therapeutics and/or diagnostics. The immunoconjugates have diameter (e.g., average diameter) no greater than 20 nanometers (e.g., as measured by dynamic light scattering (DLS) in aqueous solution, e.g., saline solution). In certain embodiments, the conjugates are silica-based nanoparticles with single chain antibody fragments attached thereto.

Abstract: A method for identifying a feature in a first image comprises establishing an initial database of image triplets, and in a pose estimation processor, training a deep learning neural network using the initial database of image triplets, calculating a pose for the first image using the deep learning neural network, comparing the calculated pose to a validation database populated with images data to identify an error case in the deep learning neural network, creating a new set of training data including a plurality of error cases identified in a plurality of input images and retraining the deep learning neural network using the new set of training data. The deep learning neural network may be iteratively retrained with a series of new training data sets. Statistical analysis is performed on a plurality of error cases to select a subset of the error cases included in the new set of training data.

Abstract: Described herein is a method of induced cell death via ferroptosis by nanoparticle ingestion. Moreover, the present disclosure describes the administration of high concentrations of ultrasmall nanoparticles at multiple times over the course of treatment in combination with a nutrient-depleted environment, thereby modulating cellular metabolic pathways to induce cell death by the mechanism ferroptosis. Ferroptosis involves iron, reactive oxygen species, and a synchronous mode of cell death execution.

Abstract: For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.

Abstract: A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality.

Abstract: Described herein are nanoparticle drug conjugates (NDCs), which, in certain embodiments, comprise a non-toxic, multi-modality, clinically proven silica-based nanoparticle platform with covalently attached drug molecules/moieties. The nanoparticle drug conjugates (NDCs) demonstrate imaging capability and targeting ligands which efficiently clear through the kidneys. Furthermore, the conjugates incorporate therapeutic agents for cancer detection, prevention, and/or treatment.

Abstract: A neural network-based method for quantifying a volume of a specimen. The method includes providing a specimen, capturing images of the specimen, and directly classifying to one of a plurality of volume classes or volumes using a trained neural network. Quality check modules and specimen testing apparatus adapted to carry out the volume quantification method are described, as are other aspects.

Abstract: A method for training a learning-based medical scanner including (a) obtaining training data from demonstrations of scanning sequences, and (b) learning the medical scanner's control policies using deep reinforcement learning framework based on the training data.