Abstract: A method for determining a reasonable sampling interval with three-dimensional (3D) laser scanning includes: collecting rock joints; acquiring point cloud data of the rock joints with 3D laser scanning; performing preprocessing on acquired point cloud data; conducting indoor mechanical tests to determine rock mechanics parameters; calculating, with preprocessed point cloud data of the rock joints, joint roughness coefficients (JRCs) under different intervals obtained from statistical roughness parameters; fitting different relational curves according to a scatter plot between the different intervals and the JRCs to obtain a fitting equation; reversely calculating a JRC of the rock joint sample with the rock mechanics parameters to obtain a reversely calculated JRC; and substituting, according to a relational equation between the different intervals and the JRCs, the reversely calculated JRC into the fitting equation to determine a reasonable sampling interval for roughness evaluation.

Abstract: Provided are a protection board and a battery. The protection board includes an access board, a diverter board, a rigid board, and at least one connection board. The rigid board, the diverter board and the connection board are all connected to the access board. The rigid board and the access board are stacked to each other. The diverter board is located on one side of the access board and is bent around the rigid board, and at least one connection board is located at one end of the access board along its extension direction. The present application provides a protection board with small size and a battery with large capacity.

Abstract: A method and a system for automatic machine learning-based prediction of new energy power with cloud-edge collaboration are disclosed. The method includes: obtaining, in response to a power prediction demand for a target new energy station, future numerical weather prediction data of the target new energy station in a future period and historical output power of a historical period corresponding to the future period; selecting, based on missing of the future numerical weather prediction data and a data amount of the historical output power, a target power prediction model corresponding to the target new energy station; and adjusting the target power prediction model according to the target working mode, and predicting, by the adjusted target power prediction model, a target output power of the target new energy station in the future period, based on the future numerical weather prediction data and the historical output power.

Abstract: A planning method for a power distribution automation system based on a reliability constraint is provided. The method includes: defining installation states of components and principles of a fault isolation, a load transfer and a fault recovery after a branch fault occurs; establishing a reliability evaluation optimization model for a power distribution network based on a mixed integer linear programming, the reliability evaluation optimization model comprising a target function and constraint conditions; solving the reliability evaluation optimization model to obtain an optimum planning scheme; and planning the power distribution automation system based on the optimum planning scheme.

Abstract: A method for determining a reasonable sampling interval with three-dimensional (3D) laser scanning includes: collecting rock joints; acquiring point cloud data of the rock joints with 3D laser scanning; performing preprocessing on acquired point cloud data; conducting indoor mechanical tests to determine rock mechanics parameters; calculating, with preprocessed point cloud data of the rock joints, joint roughness coefficients (JRCs) under different intervals obtained from statistical roughness parameters; fitting different relational curves according to a scatter plot between the different intervals and the JRCs to obtain a fitting equation; reversely calculating a JRC of the rock joint sample with the rock mechanics parameters to obtain a reversely calculated JRC; and substituting, according to a relational equation between the different intervals and the JRCs, the reversely calculated JRC into the fitting equation to determine a reasonable sampling interval for roughness evaluation.

Abstract: The present disclosure provides a method for optimizing transformation of automation equipment in a power distribution network based on reliability, including determining installation states of respective components in the power distribution network and operation criterions for fault isolation, load transfer and fault recovery after a fault occurred in a feeder segment; determining a target function which is a target function for minimizing a total transformation cost of the power distribution network; determining constraint conditions including reliability constraints; establishing an optimization model for evaluating the reliability of the power distribution network based on the reliability constraints in accordance with the target function and the constraints; and solving the established optimization model for evaluating the reliability of the power distribution network based on the reliability constraints to obtain optimal solutions as optimization results of the automation transformation state of the cir

Abstract: The disclosure provides power distribution network reliability index calculation method based on mixed integer linear programming. The method includes: establishing a model for optimizing reliability indexes of a power distribution network based on a mixed integer linear programming model, wherein the model comprises an objective function and constraint conditions, the objective function is for minimizing a system average interruption duration index (SAIDI); solving the model based on the objective function and the constraint conditions to obtain reliability indexes of the power distribution network; and controlling operation of the power distribution network based on the reliability indexes.

Abstract: Implementations are described herein for automatically labeling synthetic plant parts in synthetic training images, where the synthetic training images and corresponding labels can be used as training data for training machine learning models to detect, segment, and/or classify various parts of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The synthetic training image may be generated to depict one or more three-dimensional synthetic plants in the area. In many implementations, a plant mask, identifying individual plants as a whole in the synthetic training image, as well as a part mask, uniquely identifying one or more parts of the synthetic plant models, can be overlaid on the synthetic training image to label the one or more parts of the synthetic plant models.

Abstract: A power distribution subrack and power distribution equipment, and is related to the field of circuit technologies. The power distribution subrack includes a subrack body and a collection board, the collection board is disposed inside the subrack body, and the collection board includes a plurality of connection terminals and a plurality of controllers. Second ends of at least two of the plurality of connection terminals are connected to a same controller. First ends of at least some of the plurality of connection terminals are plug-connected to jacks of one or more circuit breakers, and the at least some connection terminals are in a one-to-one correspondence with the one or more circuit breakers. Each of the at least some connection terminals obtains a status signal of a corresponding circuit breaker, and transmits the status signal to a corresponding controller.

Abstract: The present disclosure provides a method for planning a distribution network with reliability constraints based on a feeder corridor, including determining installation states of respective elements in the distribution network; determining an objective function, the objective function being an objective function of minimizing a total investment cost of the distribution network; obtaining fault-isolation-and-load-transfer time and fault recovery time in a case where the feeder segment of each feeder line that is contained in each feeder corridor fails; determining constraint conditions including reliability constraints; building a distribution network planning model according to the objective function and the constraints; and solving the distribution network planning model built to obtain optimal solutions as planning states and reliability indexes to plan the distribution network.

Abstract: A method for analyzing cell epigenomics from multiple dimensions. The method comprises the following steps: by using ChiTag transposase and conventional Tn5 transposase in cells, respectively embedding different linker sequences, and achieving common analysis of information of a chromatin open region and information of a specific protein binding sequence on a cellular level. The method has important application prospects in aspects such as study of development and/or disease related cell population heterogeneity, drawing of a cell map, analysis of tumor cells having different clinical characteristics, and clinical study of evolution and/or metastasis of tumor cells.

Abstract: A method for measuring disaster resilience of urban public services includes the following steps: constructing a residence-service-transportation space network under normal conditions; removing, through disaster simulation, failed road segments and function nodes to construct a damaged residence-service-transportation space network; calculating a per capita accessible public service of each residential node to represent network performance; calculating a change rate of the per capita accessible public service level before and after the disaster; and drawing a relation curve between the change rate and the disaster intensity to measure the disaster resilience of the urban public services.

Abstract: A multi-water-channel hot stamping die includes an upper die base and insert, a lower die base insert. The upper and lower die bases are provided with a water channel, which is provided with die cavities, in which water through openings are arranged and in communication. A caliber of water through openings is smaller than die cavities, introducing turbulence into the cooling medium in the water channels and the die cavities thus improving cooling efficiency. Die cavities are arranged in the main water channel in a non-single straight line, water retaining blocks are placed at water through openings of the die cavities. A plurality of cooling zones is formed A plurality of inserts is correspondingly arranged in cooling zones, so that the of inserts are cooled simultaneously. Targeted control over cooling water flow of the main water channel is effected by supplying certain inserts with water.

Abstract: A method for diagnosing transformer fault based on a deep coupled dense convolutional neural network, includes: obtaining datasets of dissolved gas in oil of a transformer in normal and fault states; expanding the datasets by using an adaptive synthetic oversampling method; performing, in a form of a two-dimensional matrix, feature reconstruction on characteristic gas dissolved in the oil; building a transformer fault diagnosis model based on a deep coupled dense convolutional neural network; and dividing an expanded dataset into a training set and a test set, and taking the two-dimensional matrix as an input of the deep coupled dense convolutional neural network and a set label as an output to train the network to obtain a fault diagnosis model. The present disclosure can resolve a problem that a fault diagnosis accuracy rate of the transformer is low due to insufficient and unbalanced fault samples in the dissolved gas in the oil.

Abstract: A method for preparing dihydromyricetin nanocrystals includes: uniformly dispersing dihydromyricetin in a good solvent to obtain a dihydromyricetin solution; uniformly dispersing a precipitator and a stabilizer in water to obtain a mixed solution, and then adding the dihydromyricetin solution into the mixed solution and mixing uniformly under a stirring condition to obtain a dihydromyricetin nanocrystal solution; and drying the dihydromyricetin nanocrystal solution to obtain the dihydromyricetin nanocrystals. The method for preparing the dihydromyricetin nanocrystals by an anti-solvent method is simple and easy to implement, low in cost, good in nanocrystal stability, small in organic solvent consumption, and safe and environment-friendly in preparation process.

Abstract: A method for preparing dihydromyricetin nanocrystals includes: uniformly dispersing dihydromyricetin in a good solvent to obtain a dihydromyricetin solution; uniformly dispersing a precipitator and a stabilizer in water to obtain a mixed solution, and then adding the dihydromyricetin solution into the mixed solution and mixing uniformly under a stirring condition to obtain a dihydromyricetin nanocrystal solution; and drying the dihydromyricetin nanocrystal solution to obtain the dihydromyricetin nanocrystals. The method for preparing the dihydromyricetin nanocrystals by an anti-solvent method is simple and easy to implement, low in cost, good in nanocrystal stability, small in organic solvent consumption, and safe and environment-friendly in preparation process.

Abstract: Implementations are described herein for automatically labeling synthetic plant parts in synthetic training images, where the synthetic training images and corresponding labels can be used as training data for training machine learning models to detect, segment, and/or classify various parts of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The synthetic training image may be generated to depict one or more three-dimensional synthetic plants in the area. In many implementations, a plant mask, identifying individual plants as a whole in the synthetic training image, as well as a part mask, uniquely identifying one or more parts of the synthetic plant models, can be overlaid on the synthetic training image to label the one or more parts of the synthetic plant models.

Abstract: The disclosure provides a method for planning a power distribution network, an apparatus for planning a power distribution network, and a storage medium. The method includes: establishing a model for planning the power distribution network, the model including a target function and constraints, the target function for minimizing a cost of the power distribution network when branches and nodes are installed into the power distribution network, the nodes including transformers and substations, the constraints including a power balance constraint of the power distribution network, a power constraint of the branches, a power constraint of the transformers, a radial operation constraint of the power distribution network, a fault constraint, a calculation constraint of indices of a reliability, a constraint of the indices of the reliability, and a logic constraint; and solving the model to determine whether the branches and the nodes are installed into the power distribution network.

Abstract: Implementations are described herein for realistic plant growth modeling and various applications thereof. In various implementations, a plurality of two-dimensional (2D) digital images that capture, over time, one or more of a particular type of plant based on one or more machine learning models to generate output, may be processed. The output may be analyzed to extract temporal features that capture change over time to one or more structural features of the particular type of plant. Based on the captured temporal features, a first parameter subspace of whole plant parameters may be learned, wherein the whole plant parameters are usable to generate a three-dimensional (3D) growth model that realistically simulates growth of the particular type of plant over time. Based on the first parameter subspace, one or more 3D growth models that simulate growth of the particular type of plant may be non-deterministically generated and used for various purposes.

Abstract: A planning method for a power distribution automation system based on a reliability constraint is provided. The method includes: defining installation states of components and principles of a fault isolation, a load transfer and a fault recovery after a branch fault occurs; establishing a reliability evaluation optimization model for a power distribution network based on a mixed integer linear programming, the reliability evaluation optimization model comprising a target function and constraint conditions; solving the reliability evaluation optimization model to obtain an optimum planning scheme; and planning the power distribution automation system based on the optimum planning scheme.