Abstract: The present disclosure relates to an interaction method and apparatus, an electronic device, a storage medium, and a computer program product, wherein the method includes: presenting a virtual keyboard in an extended reality space; detecting a position and an action of a user finger relative to the virtual keyboard; and outputting a vibration signal based on the position and the action of the user finger relative to the virtual keyboard, wherein the vibration signal is used for prompting a position of the user finger in the virtual keyboard and/or an operation action of the user finger on a virtual key in the virtual keyboard.

Abstract: A method for blood plasma protein activity preservation is provided. The method comprises the steps of mixing blood plasma with two or more protectants selected from the group consisting of triglyceride, glycerol, propylene glycol, alanine, serine, glycine, alginate, and sucrose to obtain a mixture; and lyophilizing the mixture.

Abstract: A method for detecting a product for defects implemented in an electronic device includes detecting images of a product for defects by a first defect detection model in a preset period, and obtaining a detection result; when a ratio of the number of negative sample images is greater than a preset threshold, training an autoencoder model; obtaining historical positive sample images of the product, inputting the history positive sample images into the trained autoencoder model, and calculating a latent feature; inputting the latent feature of each history positive sample image into a decoding layer of the trained autoencoder model, and calculating newly added positive sample images; training the first defect detection model and obtain a second defect detection model; and inputting images of a product to be detected to the second defect detection model, and obtaining a detection result of the product.

Abstract: An image augmentation method applied to an electronic device is provided. The method includes constructing a variational learner and a discriminator based on a fully convolutional neural network. A target image is obtained by inputting a gas leakage image into the variational learner. A variational autoencoder model is obtained by training the variational learner based on a discrimination result of the discriminator on the target image. A reconstruction accuracy rate is calculated based on a test image, an augmented model is obtained by adjusting the variational autoencoder model based on the gas leakage image, in response that the reconstruction accuracy rate being less than a preset threshold; and an augmented image is obtained by inputting the image to be augmented into the augmented model.

Abstract: This invention discloses an injectable nanocomposite gel composition and the method of making the composition. The composition is composed of amphiphilic alginate nanoparticle, gel stabilizer, gel crosslinker, and gel structural modifiers. The nanocomposite gel can be manufactured into a form of highly-viscous gel or a solid-like gel, used as a vehicle to carry and deliver pharmaceutically active ingredients with high drug load via injection administration for medical uses.

Abstract: The present disclosure provides a dual-drag-containing nanoparticle, in which a first antiviral drag, such as remdesivir and a second antiviral drag are co-encapsulated and co-delivered by the nanoparticle, said nanoparticle comprising an alginate-oleic acid particle. The disclosure also relates to the therapeutic use of the nanoparticle in treating viral infections, such as SARS-CoV-2.

Abstract: A method of optimizing the detection of abnormalities in images of products generates a first image similar to training images and a second image similar to testing images with normal images and the images showing abnormalities inputted into a generative adversarial network (GAN). The GAN determines a first similarity ratio between the first image and the training image and generates a parameter based on the first similarity ratio for adjusting the GAN. A second similarity ratio between the second image and the testing image is determined. The testing image is deemed a normal image when the second similarity ratio is larger than the specified threshold value, and deemed to be an image revealing abnormalities when the second similarity ratio is less than or equal to the specified threshold value. A terminal device and a computer readable storage medium applying the method are also provided.

Abstract: A substance delivery mask includes a body, an exhaling valve, an inhaled valve, and a fog module. The fog module comprises a controlling module, a container, and an atomizer. The controlling module is disposed on the body. The container is disposed on the controlling module, the container is storing a mixed liquid, and the mixed liquid includes water and a plurality of nanoparticles. The atomizer is disposed on the container, the atomizer is used to atomize the water of the mixed liquid to form fog particles, and the fog particles are wrapped several of the nanoparticles respectively. The substance delivery mask can encapsulate the water-soluble or water-insoluble medicines, or nutritional products in the fog particles generated from the atomizer. The present invention can deliver medicine, nutritional products or vaccines by inhalation or oral.

Abstract: The present invention provides a new type of hydrophobically -modified sodium alginate, which is synthesized by alginate and oleic acid linked with a spacer. The AGO macromolecule as obtained therefrom is amphiphilic and has clinically-accessible molecular size, and anti- cancer activity. The AGO nanoparticle formed therefrom shows excellent structural stability, colloidal stability, and biocompatibility in-vitro and in-vivo, and is expected to be useful in biomedical area, for example, used as a drug delivery system.

Abstract: A method for optimizing a data model is used in a device. The device acquires data information and selecting at least two data models according to the data information, and utilizes the data information to train the at least two data models. The device acquires each accuracy of the at least two data models, determines a target data model which has greatest accuracy between the at least two data models, and optimizes the target data model.

Abstract: The present invention discloses a composite-type nano-vaccine particle, which comprises an active ingredient selected from spike RBD protein of COVID-19, two adjuvants as aluminium salt nanoparticle and synthetic oligonucleotides, and an amphiphilic alginate-based nanocarrier encapsulating the active ingredient and the two adjuvants. The composite-type nano-vaccine particle has a particle size ranging from 300 nm to 1400 nm in diameter.

Abstract: A method, apparatus, and non-transitory computer readable medium for optimizing generative adversarial network includes determining a first weight of a generator and an equal second weight of a discriminator the first weight is configured to indicate a learning ability of the generator, the second weight is configured to indicate a learning ability of the discriminator; and alternative iteratively training the generator and the discriminator until the generator and the discriminator are convergent.

Abstract: A method for selecting a deep learning network which is optimal for solving an image processing task obtaining a type of the image processing task, selecting a data set according to the type of problem, and dividing selected data set into training data and test data. Similarities between different training data are calculated, and a batch size of the training data is adjusted according to the similarities of the training data. A plurality of deep learning networks is selected according to the type of problem, and the plurality of deep learning networks is trained through the training data to obtain network models. Each of the network models is tested through the test data, and the optimal deep learning network with the best test result is selected from the plurality of deep learning networks appropriate for image processing.

Abstract: A method, apparatus, and device for labeling images of PCBs includes obtaining an image to be tested; comparing the image to be tested to a reference image to generate an image mask, the image mask includes several connected domains; detecting defects of the image to be tested; when at least one defect detected in the image to be tested, obtaining a coordinate of the at least one defect; based on a central coordinate of the connected domains and the coordinate of the at least one defect, determining the connected domains to be defect connected domains or normal connected domains; generating a first image mask and a second image mask; and processing the first image mask and the second image mask with the image to be tested to obtain a defect element image corresponding to the defect connected domains and a normal element image corresponding to the normal connected domains.

Abstract: A method for loading multiple neural network model includes compiling at least two neural network models and generating at least two binary model files corresponding to the at least two neural network models. One of the at least two binary model files is an original model file which is taken as the basic model, and differences between the two files are calculated and recorded using preset difference calculation method. A differences file is generated, and the basic model and the differences file are compressed using a preset compression method, to generate an input file. Such input file is input into a neural network accelerator, the input file being decompressed to obtain the basic model and the differences file. The basic model and the differences file are loaded into the neural network accelerator. An electronic device and a non-volatile storage medium performing the above-described method are also disclosed.

Abstract: In an image processing method, a detection image and a marked image are obtained. An image segmentation model is applied to segment a first segmented image from the detection image. The first segmented image is corrected according to the marked image to obtain a second segmented image. A size of the second segmented image is adjusted to obtain an adjusted segmented image. The adjusted segmented image is used as a standard segmented image of the detection image. The method improves accuracy of image segmentation and recognition.

Abstract: A method for processing multiple modes of data with cross-learning and sharing to avoid duplicated learning generates a weighting when a neural network model is being trained with a plurality of multiple modes of training samples. The neural network model includes an input layer, a neural network backbone coupled to the input layer, and a plurality of different output layers coupled to the neural network backbone. Results of testing are output by inputting the obtained weighting into the neural network model and testing a multiple modes of test sample with the neural network model. The need for many neural network models is avoided. An electronic device and a non-transitory storage medium are also disclosed.

Abstract: In an image correction method, a distorted image and a standard image corresponding to the distorted image are obtained. The distorted image is divided into first image regions, the standard image into second image regions. For each first image region and corresponding second image region, first and second points respectively are selected. A relationship between the first image region and the second image region is determined according to differences between the first points and the second points. The first image region is corrected according to the relationship between the first image region and the second image region and corrected first image regions are stitched to obtain a corrected image of the distorted image. The image correction method improves the accuracy of image correction.

Abstract: In a method of distinguishing objects in images, a first image segmentation model is applied to segment a first segmented image including a first object from a test image. A second image segmentation model is applied to segment a second segmented image including a second object from the test image. A third segmented image marking the first object and the second object is obtained according to first coordinates of the first object in the first segmented image and/or second coordinates of the second object in the second segmented image. The method can segment different objects from an image quickly and accurately.

Abstract: A method for detecting a tumor from images which are required to be shrunken in resolution obtains one or more first images. Then, the method segments or divides the detection images into a number of detection image blocks according to an input size of training data of a convolutional neural network architecture, before segmenting, each of the plurality of detection image blocks comprising coordinate values. The detection image blocks are input into a preset tumor detection model to generate image blocks of a result of the detection images. The method merges the image blocks into a single image according to the coordinate values of each detection image block. Colors of normal areas, abnormal areas, and overlapping areas of the abnormal areas are all different. The method generates a final detection according to color depths in the image. A tumor detection device and a non-transitory storage medium are provided.