Abstract: The invention belongs to the technical field of nuclear reactor materials design, and discloses a method for improving the withstanding capability of the cladding material in the fast neutron irradiation environment, comprising the following steps: selecting the cladding material with the annular structure and placing it on the outer side of the metallic fuel slug, with leaving a 0.2-0.8 mm gap between the metallic fuel slug and the cladding material; processing the operation in a reactor subsequently, with an annealing process of the fast neutron reactor fuel during the operation of the reactor; improves the withstanding capability of the cladding material in the fast neutron irradiation environment.

Abstract: A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on an enterprise relationship network that includes nodes and edges. Each labeled sample includes a label indicating whether a corresponding node is a risky credit node. The graphical structure model is configured to iteratively calculate an embedding vector of at least one node in a hidden feature space based on an original feature of the at least one node and/or a feature of an edge associated with the at least one node. An embedding vector corresponding to a test-sample is calculated by using the graphical structure model. Credit risk analysis is performed on the test-sample. The credit risk analysis is performed based on a feature of the test-sample represented in the embedding vector. A node corresponding to the test-sample is labeled as a credit risk node.

Abstract: One or more implementations of the present specification provide risk control of transactions based on a graphical structure model. A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on a transaction data network that includes nodes representing entities in a transaction and edges representing relationships between the entities. Each labeled sample includes a label indicating whether a node corresponding to the labeled sample is a risky transaction node. The graphical structure model is configured to iteratively calculate an embedding vector of the node in a latent feature space based on an original feature of the node or a feature of an edge associated with the node. An embedding vector of an input sample is calculated by using the graphical structure model. Transaction risk control is performed on the input sample based on the embedding vector.

Abstract: The disclosure discloses a high-burnup fast reactor metal fuel, wherein the reactor core is loaded with metal fuel made of natural uranium alloy U-50Zr. The metal fuel manually controls the temperature to realize phase transition, increase burnup, and extend the service life of fuel; increases the fuel burnup to increase uranium utilization and reduce the pressure of disposing nuclear waste; extends the fuel life cycle to reduce nuclear power costs and improve the economy of nuclear energy; effectively carries out the timely release of fission gas and the periodic elimination of fuel defects, thus reducing the fuel-cladding mechanical interaction caused by swelling, and increasing the safety.

Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.

Abstract: A distributed computing framework and a distributed computing method are provided. A specific embodiment of the distributed computing framework includes: a parsing unit, configured to parse an expression of a distributed computing task, and determine an operator and a field corresponding to the operator; and an operator unit, configured to provide the operator, input parameters of the operator including: the field and a field-type distributed dataset. The type of parameters received and returned by any operator may be the field-type distributed dataset, and any operator may operate on the data corresponding to the field in the field-type distributed dataset. Therefore, any operator needs to be implemented once to realize the reuse of the operator. The distributed computing task is expressed in a simple expression, which simplifies the complexity of writing a distributed computing program with the distributed computing framework used by the user.

Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.

Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.

Abstract: One or more implementations of the present specification provide risk control of transactions based on a graphical structure model. A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on a transaction data network that includes nodes representing entities in a transaction and edges representing relationships between the entities. Each labeled sample includes a label indicating whether a node corresponding to the labeled sample is a risky transaction node. The graphical structure model is configured to iteratively calculate an embedding vector of the node in a latent feature space based on an original feature of the node or a feature of an edge associated with the node. An embedding vector of an input sample is calculated by using the graphical structure model. Transaction risk control is performed on the input sample based on the embedding vector.

Abstract: A graphical structure model trained by using labeled samples is obtained. The graphical structure model is defined based on an enterprise relationship network that includes nodes and edges. Each labeled sample includes a label indicating whether a corresponding node is a risky credit node. The graphical structure model is configured to iteratively calculate an embedding vector of at least one node in a hidden feature space based on an original feature of the at least one node and/or a feature of an edge associated with the at least one node. An embedding vector corresponding to a test-sample is calculated by using the graphical structure model. Credit risk analysis is performed on the test-sample. The credit risk analysis is performed based on a feature of the test-sample represented in the embedding vector. A node corresponding to the test-sample is labeled as a credit risk node.

Abstract: A graphical structure model trained with labeled samples is obtained. The graphical structure model is defined based on an account relationship network that comprises a plurality of nodes and edges. The edges correspond to relationships between adjacent nodes. Each labeled sample comprises a label indicating whether a corresponding node is an abnormal node. The graphical structure model is configured to iteratively calculate, for at least one node of the plurality of nodes, an embedding vector in a hidden feature space based on an original feature of the least one node and/or a feature of an edge associated with the at least one node. A first embedding vector that corresponds to a to-be-tested sample is calculated using the graphical structure model. Abnormal account prevention and control is performed on the to-be-tested sample based on the first embedding vector.

Abstract: A distributed computing framework and a distributed computing method are provided. A specific embodiment of the distributed computing framework includes: a parsing unit, configured to parse an expression of a distributed computing task, and determine an operator and a field corresponding to the operator; and an operator unit, configured to provide the operator, input parameters of the operator including: the field and a field-type distributed dataset. The type of parameters received and returned by any operator may be the field-type distributed dataset, and any operator may operate on the data corresponding to the field in the field-type distributed dataset. Therefore, any operator needs to be implemented once to realize the reuse of the operator. The distributed computing task is expressed in a simple expression, which simplifies the complexity of writing a distributed computing program with the distributed computing framework used by the user.