Abstract: The embodiments of the present disclosure provide a transparent display panel, including: a first substrate; a second substrate; and a liquid crystal layer; a light guide plate; a light source assembly; and a plurality of pixel units, wherein the plurality of pixel units includes a first pixel unit and a second pixel unit, a distance from a center of an orthographic projection of the first pixel unit on a light exit surface of the light guide plate to the light incident surface of the light guide plate is less than that of the second pixel unit, and an area of an orthographic projection of an effective modulation area of the liquid crystal layer in the first pixel unit on the light exit surface of the light guide plate is less than that in the second pixel unit.

Abstract: A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.

Abstract: A storage card is capable of sharing a card slot with a standard SIM card. A missing corner edge used for defining a mistake-proof missing corner is disposed on each of the storage card and the standard SIM card. When another edge of the storage card overlaps another edge of the standard SIM card, at least a part of the missing corner edge of the storage card is located on an outer side of a projection line that is of the missing corner edge of the standard SIM card and that is on the storage card. A storage wafer is disposed in the storage card, and a vertex angle of the storage wafer falls between the missing corner edge of the storage card and the projection line.

Abstract: A mesh structure is provided. The mesh structure includes a first insulating layer; one or more mesh lines on a first side of the first insulating layer; and one or more protruding structures on a second side of the first insulating layer, the second side being opposite to the first side. An orthographic projection of a respective protruding structure on a projection plane containing a surface of the first insulating layer at least partially overlaps with an orthographic projection of a respective mesh line on the projection plane. A refractive index of the one or more protruding structures is greater than a refractive index of the first insulating layer.

Abstract: One or more computing devices, systems, and/or methods for video encoding are provided. For example, a video file may be segmented into at least a first portion and a second portion. The first portion may be analyzed to determine that the first portion is associated with a first level of complexity, and the second portion may be analyzed to determine that the second portion is associated with a second level of complexity. A first bitrate associated with the first level of complexity may be determined, and a second bitrate associated with the second level of complexity may be determined. The first portion may be encoded at the first bitrate to generate a first encoded portion, and the second portion may be encoded at the second bitrate to generate a second encoded portion. The first encoded portion and the second encoded portion may be assembled to generate an optimized video file.

Abstract: The present application relates to a filter module, a color filter, an image sensor and an imaging device. The filter module includes: a plurality of color filters and a control component. Each of the color filters includes: a first substrate; a metasurface structure located on the first substrate and including a plurality of microstructures periodically arranged; a dielectric layer located on a side of the metasurface structure away from the first substrate and covering the metasurface structure, wherein a refractive index of the dielectric layer is different from a refractive index of the metasurface structure; a second substrate located on a side of the dielectric layer away from the first substrate. The control component is configured to adjust the refractive index of the dielectric layer so as to adjust wavelengths of visible light passing through the color filter.

Abstract: A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.

Abstract: The disclosure provides an ultralow-carbon clinker-free cement, prepared from the following raw materials: granulated blast-furnace slag, gypsum and calcium oxide-based materials. The granulated blast-furnace slag accounts for 65%-95% of the total weight of the raw materials, the gypsum accounts for 4.5%-34.5% of the total weight of the raw materials, and the balance is the calcium oxide-based material. A weight percentage of calcium oxide and/or calcium hydroxide in the total weight of the raw materials is controlled to be 0.05%-0.75%. The disclosure further provides a method for preparing the ultralow-carbon clinker-free cement and application of the ultralow-carbon clinker-free cement in the preparation of concrete, mortar or cement products. The ultralow-carbon clinker-free cement of the disclosure has the advantages of high early strength, ultrahigh long-term strength, low shrinkage, carbonation resistance, low carbon emissions, etc.

Abstract: An information processing method processes an x-ray image including the steps of: obtaining first lower-radiation dose three-dimensional image data during a first scan of a patient; and detecting, using a trained neural network, a presence of an artifact (e.g., a metal artifact) in the first lower-radiation dose three-dimensional image data. An information processing apparatus includes processing circuitry for performing the detection method, and computer instructions stored in a non-transitory computer readable storage medium cause a computer processor to performing the detection method.

Abstract: Disclosed is an optimal scheduling method for peak regulation of a cascade hydro-photovoltaic complementary power generation system. The method includes: establishing an objective function of optimal scheduling for peak regulation of the cascade hydro-photovoltaic complementary power generation system; establishing a photovoltaic power station output constraint condition considering uncertainty; optimizing a mixed integer linear model by performing linear processing on the constraint condition; and obtaining a scheduling solution by solving the mixed integer linear model. According to the present disclosure, a unit commitment of a hydro-power station and an operational solution of a reservoir are considered, so that photovoltaic output can be consumed by fully using a characteristic that the hydro-power unit is easy to regulate, and a demand for peak regulation of a power grid can be satisfied.

Abstract: Devices, systems, and methods obtain scan data that were generated by scanning a scanned region, wherein the scan data include groups of scan data that were captured at respective angles; generate partial reconstructions of at least a part of the scanned region, wherein each partial reconstruction of the partial reconstructions is generated based on a respective one or more groups of the groups of scan data, and wherein a collective scanning range of the respective one or more groups is less than the angular scanning range; input the partial reconstructions into a machine-learning model, which generates one or more motion-compensated reconstructions of the at least part of the scanned region based on the partial reconstructions; calculate a respective edge entropy of each of the one or more motion-compensated reconstructions of the at least part of the scanned region; and adjust the machine-learning model based on the respective edge entropies.

Abstract: A multilayer sheet includes a core layer including a composition including about (60) to about (90) weight percent of polyphenylene, about (0) to about (20) weight percent of polystyrene, about (10) to about (20) weight percent of a hydrogenated block copolymer of an alkenyl aromatic compound and a conjugated diene, and optionally, a flame retardant additive; and a first cap layer disposed on a surface of the core layer, wherein the first cap layer includes polypropylene.

Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produce uniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.

Abstract: A tire mold cleaning device includes a protective cover, a cleaning mechanism, a loading mechanism and a limiting mechanism. The cleaning mechanism is disposed in the protective cover; the loading mechanism includes linear sliding rails and a loading platform, the linear sliding rails are mounted at a bottom end of an interior of the protective cover, and the loading platform is connected to movable ends of the linear sliding rails. The limiting mechanism includes a base plate and limiting rods, and the base plate is disposed on a bottom end of the interior of the protective cover. A tire mold is cleaned through cooperation of the linear sliding rails and the loading platform, the limiting rods can limit a moving range of the tire mold, thereby preventing the tire mold from sliding under the action of inertia.

Abstract: Microphones, microphone systems, and methods for capturing and processing sound are described. The microphones and microphone systems may adaptively change the direction from which sound is captured. The microphones and microphone systems avoid the need to provide arrays of microphones, while providing adaptive beamforming without a time delay between each channel of information, and multi-directional sound capture. A dependency between the frequency response and system size is also avoided.

Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produceuniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.

Abstract: A method of processing information acquired by imaging performed by a medical image diagnostic apparatus, the method including but not limited to at least one of (A) acquiring a training image volume including at least one three-dimensional object having an embedded three-dimensional feature having a first cross-sectional area in a first three-dimensional plane; selecting a second cross-sectional area in a second three-dimensional plane containing the embedded three-dimensional feature, wherein the second cross-sectional area is larger than the first cross-sectional area; and training an untrained neural network with an image of the second cross-sectional area generated from the training image volume; and (B) acquiring a first set of training data; determining a first distribution of tissue density information from the first set of training data; generating from the first set of training data a second set of training data by performing at least one of a tissue-density shifting process and a tissue-density sca

Abstract: Provided are a heterojunction solar cell film deposition apparatus, method and system, a solar cell, a module, and a power generation system. The heterojunction solar cell film deposition apparatus is configured for amorphous silicon-based film deposition, and comprises a loading cavity, a preheating cavity, intrinsic process cavities, doping process cavities and an unloading cavity that are linearly arranged in sequence, the cavities being isolated from each other by means of an isolating valve. At least two intrinsic process cavities are provided and are configured for deposition by means of an intrinsic layer silicon film process; and at least one doping process cavity is provided and is configured for deposition by means of an N-type silicon film or P-type silicon film process. The preheating cavity comprises a heating preheating chamber and a preheating buffer chamber that is configured for adjusting the gas and pressure atmosphere.

Abstract: The present disclosure provides technical solutions related to intelligent cloud management based on profile. Artificial intelligent is applied to cloud management and cloud management suggestion may be proposed intelligently. In daily work, behaviors in using cloud resources may show characteristics of cloud users or cloud tenants themselves. The technical solution of intelligent cloud management of the present disclosure generates profile identifying cloud using characteristics by extracting behavior data in using cloud and intelligently proposes cloud management suggestions based on the profile.

Abstract: The present invention relates to a kit for diagnosing hepatocellular carcinoma consisting of plasma microRNA, a kit containing the same, and a new method therefor. The marker for diagnosing hepatocellular carcinoma consists of a plurality of nucleic acid molecules, each nucleic acid molecule encoding at least one microRNA sequence, preferably consists of nucleic acid molecules encoding hsa-miR-122, hsa-miR-192, hsa-miR-21, hsa-miR-223, hsa-miR-26a, hsa-miR-27a, hsa-miR-801 and hsa-miR-1228. The kit can be used for diagnosing hepatocellular carcinoma, especially early hepatocellular carcinoma, and also for discriminating plasma of at least one hepatocellular carcinoma patient from that of at east one healthy individual, at east one chronic hepatitis B patient, or at east one cirrhosis patient.