Abstract: The present disclosure provides a determining device for the weathering resistant capability of clastic rocks in a tunnel based on feldspar features, which overcomes the shortcomings of current evaluation methods, is easy to operate, can be used to detect the type, content, and crystal structure of feldspar in a rock stratum, and integrates the information by combining a computer deep learning method to determine the weathering resistant capability of clastic rocks containing different types of feldspar in a tunnel, with high accuracy.

Abstract: A semiconductor structure includes a substrate and a word line (WL) structure. The WL structure includes: a work function stacking structure located in the substrate, where the work function stacking structure includes multiple sequentially and alternately stacked first work function layers and second work function layers, and a work function of the first work function layer is greater than a work function of the second work function layer; a WL conductive layer located in the substrate, and located on an upper surface of the work function stacking structure; and a gate oxide layer located between the work function stacking structure and the substrate as well as between the WL conductive layer and the substrate.

Abstract: A photothermal seawater desalination material with a multi-stage structure and a preparation method and application thereof. The photothermal seawater desalination material includes a light-absorbing material having a C/WO3-x heterogeneous junction, which is obtained by depositing a nano-C material on a porous metal foam material using plasma enhanced chemical vapor deposition (PECVD), and then synthesizing WO3-x with plasma resonance effect by a solvothermal reaction.

Abstract: The present disclosure proposes a modeling method and apparatus for diagnosing an ophthalmic disease based on artificial intelligence, and a storage medium. The modeling method includes: establishing a data collection of ophthalmic images and a data collection of non-image ophthalmic disease diagnosis questionnaires; training a first neural network model by employing the data collection of the ophthalmic images to obtain a first classification model; training a second classification model by employing the data collection of non-image ophthalmic disease diagnosis questionnaires; and merging the first classification model and the second classification model to obtain a target classification network model, in which, a test result outputted by the target classification network model is used as a diagnosis result of the ophthalmic disease.

Abstract: A method for multi-task-based predicting massive-user loads based on a multi-channel convolutional neural network, and belongs to the technical field of electric power systems. The method includes clustering all residential users into a plurality of clusters with different daily average electricity consumption modes by adopting an agglomerative hierarchical clustering method. Corresponding input data sets are constructed for various clusters by adopting a multi-channel-based multi-source input fusion method. Then, a multi-task-based load prediction model based on a convolutional neural network is established for each of the clusters. Load prediction values for different users in the corresponding cluster are output in parallel by each model to eventually obtain load prediction results of all of the residential users.

Abstract: The present disclosure proposes a modeling method and apparatus for diagnosing an ophthalmic disease based on artificial intelligence, and a storage medium. The modeling method includes: establishing a data collection of ophthalmic images and a data collection of non-image ophthalmic disease diagnosis questionnaires; training a first neural network model by employing the data collection of the ophthalmic images to obtain a first classification model; training a second classification model by employing the data collection of non-image ophthalmic disease diagnosis questionnaires; and merging the first classification model and the second classification model to obtain a target classification network model, in which, a test result outputted by the target classification network model is used as a diagnosis result of the ophthalmic disease.