Abstract: The present disclosure is related to a file processing method, electronic apparatus and storage medium, the file processing method including: updating a file list according to file access information for a file in a storage device, wherein the file list contains file inode numbers of files to be defragmented; defragmenting the files to be defragmented corresponding to the file inode numbers in the storage device, according to the updated file list, in response to the occurrence of a triggering event.

Abstract: The present disclosure provides a multi-modal multi-view classification method for echocardiograms based on a deep learning algorithm, including: collecting videos and images of multi-modal multi-view adult echocardiograms, and preprocessing the videos and images; annotating the preprocessed videos and images of adult echocardiograms to generate an adult echocardiogram dataset; dividing the adult echocardiogram dataset into a training set, a validation set, and a test set; constructing an adult echocardiogram view classification model based on a ResNet network, training the model using the training set, and selecting an optimal classification model using the validation set; evaluating performance of the optimal adult echocardiogram view classification model based on the test set; and inputting a to-be-tested image or video into the adult echocardiogram view classification model to obtain a classification result.

Abstract: A defragmentation method may include: acquiring at least one chunk information of data in response to a writing request for the data, wherein the at least one chunk information may include an identification of at least one chunk and a stream identification assigned to data of the at least one chunk; updating a fragmentation degree of each of the at least one chunk and a stream to which the each of the at least one chunk belongs in a chunk index structure to obtain an updated chunk index structure, based on the at least one chunk information, wherein the chunk index structure may include identifications of a plurality of chunks, stream identifications corresponding to the plurality of chunks, and fragmentation degrees of the plurality of chunks; and determining a first chunk to be defragmented, based on the fragmentation degrees of the plurality of chunks in the updated chunk index structure.

Abstract: A synchronous write method includes: receiving a synchronous write command of a process; if a process state table indicates that another synchronous write command of the process has not been added to a command queue, adding the synchronous write command to the command queue; if the process state table indicates that the other synchronous write command has been added to the command queue, adding an order preserving command to the command queue, and then adding the synchronous write command to the command queue; and sending commands in the command queue to a storage device according to the order of the commands in the command queue. The order preserving command is used to indicate that a synchronous write commands located before the order preserving command in the command queue is to be executed prior to the synchronous write command by the storage device.

Abstract: A method for providing a piece of data in a communication system. The method includes: allocating a memory means for updating data in the memory means, in particular by a producer, in particular in response to a request signal of the producer; updating the data in the provided memory means with the piece of data; providing the memory means for the purpose of being read out, in particular by a consumer, wherein, in the allocation step, the memory means is allocated as a function of a status of the memory means.

Abstract: A synchronous write method includes: receiving a synchronous write command of a process; if a process state table indicates that another synchronous write command of the process has not been added to a command queue, adding the synchronous write command to the command queue; if the process state table indicates that the other synchronous write command has been added to the command queue, adding an order preserving command to the command queue, and then adding the synchronous write command to the command queue; and sending commands in the command queue to a storage device according to the order of the commands in the command queue. The order preserving command is used to indicate that a synchronous write commands located before the order preserving command in the command queue is to be executed prior to the synchronous write command by the storage device.

Abstract: A synchronous write method includes: receiving a synchronous write command of a process; if a process state table indicates that another synchronous write command of the process has not been added to a command queue, adding the synchronous write command to the command queue; if the process state table indicates that the other synchronous write command has been added to the command queue, adding an order preserving command to the command queue, and then adding the synchronous write command to the command queue; and sending commands in the command queue to a storage device according to the order of the commands in the command queue. The order preserving command is used to indicate that a synchronous write commands located before the order preserving command in the command queue is to be executed prior to the synchronous write command by the storage device.

Abstract: A method for providing a piece of data in a communication system. The method includes: allocating a memory means for updating data in the memory means, in particular by a producer, in particular in response to a request signal of the producer; updating the data in the provided memory means with the piece of data; providing the memory means for the purpose of being read out, in particular by a consumer, wherein, in the allocation step, the memory means is allocated as a function of a status of the memory means.

Abstract: A method and a device for determining a strategy for data placement within an SSD are provided. The method for determining a strategy for data placement within a Solid State Drive SSD, includes acquiring an optimization target and workload metric data that is pre-collected for a time period; selecting a machine learning model and a training data strategy according to the optimization target; selecting feature data from the workload metric data for the time period according to the selected training data strategy, and training the selected machine learning model based on the selected feature data; determining, in each subsequent predicting time, a strategy for data placement for a predicting time period corresponding to the predicting time according to the workload metric data for the time period and the workload metric data that is collected in the predicting time by using the trained machine learning model.

Abstract: The present invention belongs to the field of microbial technology, and specifically relates to a strain of Hericium erinaceus HT-3, with a deposit number of CCTCC No: M 2018567. The present invention also relates to a screening method for Hericium erinaceus HT-3. The screening method comprises the steps of purifying the strain of Hericium erinaceus from a tissue block, fermenting and culturing the strain, soak extracting ergothioneine from the mycelium cells in the fermentation broth, detecting the ergothioine content in the fermentation broth. The Hericium erinaceus strain screened according to the present invention has high ergothioneine yield, and the screening method is simple in process, easy to be operated, and low in production cost.

Abstract: A method and a device for determining a strategy for data placement within an SSD are provided. The method for determining a strategy for data placement within a Solid State Drive SSD, includes acquiring an optimization target and workload metric data that is pre-collected for a time period; selecting a machine learning model and a training data strategy according to the optimization target; selecting feature data from the workload metric data for the time period according to the selected training data strategy, and training the selected machine learning model based on the selected feature data; determining, in each subsequent predicting time, a strategy for data placement for a predicting time period corresponding to the predicting time according to the workload metric data for the time period and the workload metric data that is collected in the predicting time by using the trained machine learning model.

Abstract: The present invention relates to a method for creating nanostructures in and on organic or inorganic substrates comprising at least the following steps: a) providing a primary substrate having a predetermined refractive index; b) coating the primary substrate with one or more mediating layers each having a predetermined refractive index different from that of the primary substrate, wherein the sequence of the layers is arranged so that a predetermined gradient of the refractive index is generated between the primary substrate and the uppermost layer of the one or more mediating layers; c) optionally coating the uppermost layer of the one or more mediating layers with an additional top layer; d) depositing a nanostructured etching mask onto the uppermost layer of the composite substrate obtained after steps a)-b) or a)-c); e) generating protruding structures, in particular conical or pillar structures, or recessed structures, in particular holes, in at least the uppermost layer of the composite substrate by me