Abstract: A method for improving mechanical properties by changing a gradient nanotwinned structure of metallic materials is the technical field of nanostructured metallic materials. The method uses the inherent principles of microstructure and mechanical properties of metallic materials to improve materials mechanical properties. The metallic materials has a gradient nanotwinned structure. The principles of microstructure and mechanical properties of the metallic materials mean that the mechanical properties of the metallic materials are adjusted by changing the structural gradient scale of the nanotwinned structure. The method combines two strengthening methods of nanotwins and gradient structure, and can obviously improve the mechanical properties of the metallic materials. For pure copper materials of the gradient nanotwinned structure prepared by an electrodeposition technology: the yield strength is 481±15 MPa, the tensile strength is 520±12 MPa, the uniform elongation can be 7±0.

Abstract: A titanium alloy blade tip with a wear-resistant protective coating and a preparation method thereof are provided. The method includes: (1) spraying MCrAlY alloy powder on a surface of a titanium alloy blade tip by high velocity oxygen fuel (HVOF) spraying at a spraying distance of 300-400 mm to obtain the titanium alloy blade tip with a MCrAlY layer on the surface; where M is one of Ni and NiCo; (2) pre-plating Ni at a current density of 4-10 A/dm2; (3) placing the pre-plated titanium alloy blade tip in a Watt solution, covering a surface of the pre-plated titanium alloy blade tip obtained in the step (2) with abrasive particles, and then performing composite electroplating at a current density of 0.5-2 A/dm2. In the method, the wear-resistant protective coating with the high adhesion strength is prepared on the titanium alloy blade tip, and the wear-resistant protective coating has good wear resistance.

Abstract: A deep learning model is pre-trained with a large-scale of unsupervised data of code review tasks in order to learn the relationships between code changes and a code review. The pre-trained deep learning model predicts a code review given a code diff hunk in a code diff format. The code diff hunk includes the changed code and its surrounding context. The pre-trained deep learning model may then be fine-tuned with supervised data in order to make predictions for several code review activities, such as, code change quality estimation and code refinement.

Abstract: A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Abstract: The present invention relates to a carbide tool cleaning and coating production line and a method, including a cleaning device including a support frame, a cleaning mechanism and a drying mechanism are sequentially disposed under the support frame connected to a moving mechanism, the moving mechanism is connected to a lifting mechanism being capable of being connected to a tool fixture bracket being configured to accommodate the tool fixture; a coating device including a coating chamber which a plane target mechanism and a turntable assembly disposed in, the turntable assembly is capable of being connected to a plurality of tool fixtures being capable of rotating around an axial line of the coating chamber under the driving of the turntable assembly and rotating around an axial line thereof at the same time; and, a manipulator being disposed between the cleaning device and the coating device.

Abstract: A processing apparatus for overlay shift includes a storage unit and a control unit, and is applicable to a semiconductor wafer with several inspection regions. Each of the inspection regions has several sets of overlay marks for inspection. One set of overlay marks includes an original alignment mark without any overlay shift, and several split alignment marks with predetermined overlay shifts arranged near the original alignment mark. The original after-etch inspection (AEI) overlay data of the inspection regions is stored in the storage unit. The after-develop inspection (ADI) overlay data of the original alignment mark and the split alignment marks are compared with the original AEI overlay data by the control unit, thereby acquiring ADI pre-bias data of the original alignment mark and the split alignment marks. The control unit determines whether an overlay shift compensation is performed according to the acquired ADI pre-bias data.

Abstract: A multi-procedure integrated automatic production line for hard alloy blades under robot control is provided. The production line includes a rail-guided robot. A cutter passivation device and a blade cleaning and drying device are arranged on one side of the rail-guided robot. A blade-coating transfer table, a blade coating device, a blade boxing transfer table, a blade-tooling dismounting device and a blade boxing device are sequentially arranged on another side of the rail-guided robot. The blade-tooling dismounting device is arranged on one side of the blade boxing transfer table. The production line further includes squirrel-cage toolings for carrying the blades. The squirrel-cage tooling that are loaded with the blades can run among the cutter passivation device, the blade cleaning and drying device, the blade-coating transfer table and the blade boxing transfer table. The blades after being treated through the blade-tooling dismounting device are sent to the blade boxing device.

Abstract: Provided is a display substrate, including: a silicon-based substrate having a display area, a binding area located on one side of the display area, and a trace area located between the display area and the binding area; a trace protection structure is arranged on the silicon-based substrate in the trace area, and a pad assembly is integrated in the silicon-based substrate in the binding area; and a minimum distance between an edge of an orthographic projection of the trace protection structure on the silicon-based substrate and an edge of an orthographic projection of an opening of the pad assembly on the silicon-based substrate is smaller than a maximum size of one subpixel.

Abstract: A retrieval-augmented code completion system uses the context of a partially-formed source code snippet of a source code program and a hint to predict the source code tokens needed to complete the partially-formed source code snippet. The hint is a source code segment that completes a semantically-similar source code segment of the partially-formed source code snippet. The hint is found in a retrieval source code database using a hybrid retrieval technique. A deep learning decoder model uses the context of the partially-formed source code snippet and the hint to predict the most likely candidate sequence of source code tokens to complete the partially-formed source code snippet.

Abstract: A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Abstract: A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Abstract: A dispatching method for an electric-hydrogen energy system considering flexible hydrogen demand includes establishing an electric load flexibility equation, a power purchase and sale constraint equation, a renewable energy output constraint equation, a hydrogen load flexibility equation, an electricity-to-hydrogen production safety operation constraint equation and an electric power balance constraint equation, establishing an electric-hydrogen energy system dispatching model with the lowest operating cost of the electric-hydrogen energy system within the dispatching cycle as an objective function, and solving the electric-hydrogen energy system dispatching model to obtain an optimal dispatching result. As compared with the prior art, the present invention can effectively solve the problem of coordination between electric and hydrogen energy flows, while taking into account the flexibility of electric and hydrogen loads, further providing additional flexibility to the operation of the system.

Abstract: A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Abstract: A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Abstract: A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Abstract: Disclosed is an integrated energy system operational optimization method considering thermal inertia of district heating networks and buildings, comprising the following steps. Step 10: respectively establish a district heating network model considering transmission delay and heat loss and a building model considering thermal storage capacity. Step 20: establish an integrated energy system optimization model consisting of a combined cooling, heat and power system model, the district heating network model and the building model. Step 30: solve the integrated energy system optimization model to obtain an optimal scheduling plan, control outputs of a gas turbine and a gas boiler per hour according to the optimal scheduling plan, and purchase electricity from a power grid and a wind power. According to the method, both the district heating network and buildings are included in a scheduling scope, so that the load adjustment with multiple degrees of freedom can be achieved.

Abstract: A cross-border account splitting method based on an intelligent internally-connected electronic account, an account splitting platform, and a payment platform.

Abstract: An in-depth payment splitting method and an in-depth payment splitting system for dynamic network balance are provided. In the method, a node from which a fund inflows is determined as a primary node, dynamic fund flow relationships with the primary node in a time window are summarized, and a payment network at a preset payment depth is generated based on the summarized fund flow relationships. Under the condition of balance between fund inflows and fund outflows at nodes, spaced nodes in a fund flow having a multi-level link or a duplicate link in the payment network are directly connected and adjacent nodes in the fund flow are pruned to shorten payment paths. A new payment network is generated based on new fund flow relationships. Splitting payment is performed in accordance with fund flow paths in the new payment network.

Abstract: Disclosed is an integrated energy system operational optimization method considering thermal inertia of district heating networks and buildings, comprising the following steps. Step 10: respectively establish a district heating network model considering transmission delay and heat loss and a building model considering thermal storage capacity. Step 20: establish an integrated energy system optimization model consisting of a combined cooling, heat and power system model, the district heating network model and the building model. Step 30: solve the integrated energy system optimization model to obtain an optimal scheduling plan, control outputs of a gas turbine and a gas boiler per hour according to the optimal scheduling plan, and purchase electricity from a power grid and a wind power. According to the method, both the district heating network and buildings are included in a scheduling scope, so that the load adjustment with multiple degrees of freedom can be achieved.

Abstract: The application discloses a rare-earth permanent magnetic powder, a bonded magnet, and a device using the bonded magnet. The rare-earth permanent magnetic powder comprises 4 to 12 at. % of Nd, 0.1 to 2 at. % of C, 10 to 25 at. % of N and 62.2 to 85.9 at. % of T, wherein T is Fe or FeCo and the main phase of the rare-earth permanent magnetic powder is a hard magnetic phase with a TbCu7 structure. Material volatilization can be avoided effectively during a preparation process of the rare earth permanent magnetic powder, thus improving the wettability with a water-cooling roller during the preparation process and final prepared materials are provided with good magnetic properties.