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31 results about "Regression modelling" patented technology

Define Regression Modeling: Regression model means an investment analysis tool used by investors to compare two or more stock variables.

Heavy-duty tool thermal error prediction method taking environmental temperature into account

The invention discloses a heavy-duty tool thermal deformation prediction method taking environmental temperature into account. The method specifically comprises the following steps of predicting the tool thermal deformation caused by the internal heat source of a tool, predicting tool thermal deformation caused by the external heat source of the tool, summing the tool thermal deformation caused by the internal and external heat sources to obtain the final tool thermal deformation. Time-lag thermal deformation error caused by the environmental temperature is taken into account during the prediction of the tool thermal deformation caused by the external heat source of the tool, and multiple regression modelling based on the least square method principle is performed during the prediction of the tool thermal deformation caused by the internal heat source of the tool. The joint action effects reflecting the influence of the environment temperature nonlinear lag and the influence of the internal heat source on the heavy-duty tool are combined by the method, and the thermal deformation error under any environmental condition and processing condition can be effectively predicted in real time.
Owner:WUHAN HENGLI HUAZHEN TECH CO LTD

Soft measurement method based on optimal selection and regression of orthogonal components

The invention discloses a soft measurement method based on optimal selection and regression of orthogonal components in order to predict the orthogonal components conducive to quality indexes by meansof optimal selection and utilize the selected orthogonal components to create a soft measurement model for optimal regression. Specifically, according to the method, principle component analysis (PCA), independent component analysis (ICA) and an partial least squares algorithm (PLSR) are parallelly and separately utilized to obtain the corresponding orthogonal components at first, then adjacent component analysis based on a genetic algorithm is utilized to optimally select the orthogonal components, and finally, the selected orthogonal components are utilized to conduct optimal regression molding based on a particle swarm algorithm. Compared with a traditional method, the method has the advantages that characteristic components conducive to prediction of the quality indexes are selected in an optimal mode, and final predicted values of the quality indexes are obtained according to an optimal regression vector. Therefore, the soft measurement performance of the method is fully ensured,and the method is a more optimized soft measurement method.
Owner:NINGBO UNIV

Combination optimization-based near-infrared non-invasive blood glucose detection wavelength variable screening method

The invention provides a combination optimization-based near-infrared non-invasive blood glucose detection wavelength variable screening method. The method comprises the following steps of firstly obtaining transmissivity spectral data of human blood glucose detection by adopting LED near-infrared light sources with different wavelengths; secondly selecting an optimal variable group in groups by using a continuous projection algorithm, a genetic algorithm and a gradual selection algorithm for multiple pieces of wavelength variable data by taking a root-mean-square error as an index in combination with linear regression modeling; thirdly performing scoring on each group of variables through a weighted scoring method; and finally recombining three groups of variables, sorting comprehensive scores in sequence from high to low, and selecting first few variables with highest scores as final auxiliary variables. According to an algorithm, a most effective wavelength combination is extracted through weighted combination optimization for the problem of an over-fitting phenomenon of a model due to excessive wavelength variables in regression modeling; and the variables are accurately selected, so that the model can be greatly simplified, the calculation complexity of the model is lowered, and the prediction capability and the real-timelines and robustness of non-invasive blood glucose detection are improved.
Owner:北京光巨力信息技术有限公司

Driver visual perception prediction model generation method based on regression learning

The present invention discloses a driver visual perception prediction model generation method based on regression learning. The method comprises: acquiring sample data, performing regression modeling on relationships between a vehicle speed and a fixation point, between the vehicle speed and dynamic vision, and between the vehicle speed and a visual field, wherein the regression modeling comprises logarithmic regression modeling, exponential regression modeling, polynomial regression modeling and linear regression modeling; and performing regression diagnosis on different regression models, to obtain a best prediction model. By adoption of the method provided by the technical scheme of the present invention, intelligent vehicles has higher autonomy and intelligence through knowledge acquisition and representation of driving behaviors, and with the theory of cognition applied to the driving behavior research field, the inherent mechanism of driving operations is analyzed, so that intelligent driving behaviors can be better explained and understood.
Owner:BEIJING UNION UNIVERSITY

Web application-oriented container cluster self-adaptive capacity expansion and shrinkage system and method

The invention belongs to the technical field of web application deployment and resource scheduling. In order to comprehensively consider the use conditions of different resources to carry out capacityexpansion and shrinkage to guarantee the application work efficiency, the web application-oriented container cluster self-adaptive capacity expansion and shrinkage system comprises a resource monitoring module, a regression modeling module and a container expansion and shrinkage module, and the resource monitoring module is used for obtaining the use conditions of container resources; the regression modeling module is used for extracting container load data, wherein CPU utilization, memory utilization, network throughput rate and file system throughput rate are characteristic values, the number of requests processed per second is a label, multiple linear regression modeling is carried out so as to obtain the influence of independent variables Ccpu, Cmemory, Cnetwork _ io and Cfs _ io on dependent variables Cqps, and therefore the known independent variables are used for predicting the change of the dependent variables. The method and the device are mainly applied to web application occasions.
Owner:TIANJIN UNIV

Cross-border active user identification method and device

The invention discloses a cross-border active user identification method and device. The method comprises the steps of obtaining behavior data of historical users on a cross-border APP; based on the behavior and score mapping relationship, determining scores corresponding to the behavior data, and based on the scores, determining cross-border activeness of historical users; randomly selecting a seed user from all historical users; obtaining multi-dimensional feature data of the seed user, taking the multi-dimensional feature data as a training data set, and taking the cross-border activity ofthe seed user as a label of the training data set; cleaning and vectorizing the training data set to obtain a processed training data set; performing regression modeling based on the lightGBM and theprocessed training data set to obtain an activity regression prediction model; and obtaining multi-dimensional feature data of other channel users, and predicting the activity of using the cross-border APP by the other channel users based on the activity regression prediction model. The active customer mining work efficiency can be improved.
Owner:BANK OF CHINA

Displacement filling modeling method and related device

The invention discloses a displacement filling modeling method and device, and the method comprises the steps: carrying out the regression modeling based on the relative displacement sc and relative speed vc of a plurality of groups of other vehicles and a target vehicle, which are obtained through the recognition of historical moment point cloud data corresponding to a plurality of other vehicles, and the corresponding calculation time tc, and obtaining a first displacement filling model; and determining a target displacement filling model based on the determinable coefficient R2 of the firstdisplacement filling model and a preset determinable coefficient value. Thus, the method is based on the relative displacement sc and the relative speed vc of a plurality of groups of other vehiclesand a target vehicle and corresponding calculation time tc. The target displacement filling model obtained by adopting the regression modeling mode is used for realizing displacement filling, and thepoint cloud data can more accurately predict the relative displacement of other vehicles and the target vehicle through the target displacement filling model, so that the delay difference is reduced,and the problem of inaccurate lag is avoided.
Owner:NEUSOFT REACH AUTOMOBILE TECH (SHENYANG) CO LTD

Rejection inference method based on Cox regression and logistic regression and electronic equipment

InactiveCN111798310AEasy to handleAvoid bias caused by unreasonable selectionFinanceCharacter and pattern recognitionAlgorithmRegression analysis
The invention relates to the technical field of financial science, in particular to a rejection inference method based on Cox regression and logistic regression and electronic equipment. The method comprises the following steps of: S1, collecting all application user data in a preset period, and defining two sets of tags for each information user, i.e., a binary classification target variable anda survival analysis target variable; S2, performing Cox regression modeling on useful information user data based on variables defined by survival analysis; S3, based on a Cox regression result, respectively calculating a probability P(G|A) that a rejected sample is a 'good' sample and a probability P(B|A) that the rejected sample is a 'bad' sample after credit granting and loan passing; and S4, based on the binary classification label and the rejected sample inference result, training a model by using a binary classification algorithm to complete the development of a score card model. According to the method, the survival analysis model is used for deducing a rejected sample, a non-parametric method Cox regression analysis is selected, a distribution function of the survival duration doesnot need to be judged, the process is simplified, deviation caused by unreasonable selection of the distribution function is avoided, and it is guaranteed that the model effect is more accurate.
Owner:睿智合创(北京)科技有限公司

Face key point detection method and system based on local principal component analysis

ActiveCN110826534AHigh precisionSmall scaleCharacter and pattern recognitionLocal principal component analysisRegression modelling
The invention discloses a face key point detection method and system based on local principal component analysis. The method comprises the steps of S1, collecting a large amount of human face image sample data, and marking the face key points; S2, dividing the face key points into a plurality of local key points, adopting the principal component analysis to process the local key points, and obtaining the principal component features of the local key points; S3, calculating a combination coefficient of the key points of each face image under the principal component characteristics; S4, constructing a regression model, training the model through the combination coefficient, and generating a combination coefficient regression model; S5, inputting a to-be-detected face image into the combination coefficient regression model, and predicting to obtain a combination coefficient; and S6, restoring the face key points based on the predicted combination coefficient and the principal component features. According to the present invention, the local principal component analysis is carried out on the key points, and the local principal component coefficients are predicted, so that the complexity of directly carrying out principal component analysis on all the key points is reduced, and the regression modeling precision is improved.
Owner:HANGZHOU QUWEI SCI & TECH

Infrared spectrum modeling method based on consensus selection technique

The invention discloses an infrared spectrum modeling method based on a consensus selection technique. The infrared spectrum modeling method comprises the steps: according to original infrared spectroscopy data of samples, building a plurality of derivative spectrum spaces with different orders; in the plurality of derivative spectrum spaces with different orders, building respective calibration sets; treating the calibration sets of the derivative spectrum spaces by using the consensus selection technique, to obtain a basic calibration set; according to the basic calibration set, treating remaining samples in the derivative spectrum spaces with different orders, to obtain an extending calibration set; according to the basic calibration set and the extending calibration set, forming a final calibration set; and using the final calibration set and a validation set, and carrying out regression modeling. Through building the plurality of derivative spectrum spaces with different orders, then the derivative spectrum spaces with different orders are subjected to calibration set partitioning by using the consensus selection technique, the final calibration set formed from the basic calibration set and the extending calibration set is used for regression modeling, the model prediction accuracy is high, and the stability is good.
Owner:NORTHEASTERN UNIV

A manufacturing industry big data-oriented regression modeling method

The invention discloses a manufacturing industry big data-oriented regression modeling method, which comprises the following steps: S1, obtaining low-dimensional features suitable for modeling through data preprocessing; s2, converting low-dimensional data of different service domains into a latent variable form; s3, establishing a regression equation among different latent variables through partial least square regression analysis, calculating to obtain the latent variables according to the maximum covariance among the latent variables, and determining the number of the latent variables by adopting a predicted residual sum of squares, so as to realize simultaneous regression analysis of multiple dependent variables on multiple independent variables; and S4, establishing a binomial regression equation between the latent variables to obtain a standard regression coefficient beta of each independent variable acting on each dependent variable, and further obtaining a single service predicted value. According to the invention, the latent structure model among the business domains is established, the influence relationship among different business domain data is mined, and different types of data of a plurality of business domains are communicated, so that the modeling effect of a single business is better, and the business is helped to improve quality and efficiency.
Owner:GUANGDONG UNIV OF TECH

Natural gas demand prediction method and device, electronic equipment and medium

The invention discloses a natural gas demand prediction method and device, electronic equipment and a medium. The method can comprise the following steps: constructing a gray equal-dimension progressive complement model; according to a gray equal-dimension progressive complementation model, performing preliminary prediction on the influence factors to obtain predicted values of the influence factors; taking natural gas historical consumption data as dependent variables, taking historical values of influence factors as independent variables, and constructing a partial least square regression model; and predicting the natural gas demand according to the predicted values of the influence factors and a partial least square regression model. According to the method, the system noise is eliminated through the gray equal-dimension progressive complement model, and the development trend of various influence factors is predicted and analyzed; the partial least square model is used for carrying out regression modeling on the natural gas historical consumption and various influence factors, so that not only can the natural gas demand be reasonably and accurately predicted, but also the development trend and the change rule of the natural gas demand can be accurately mastered; the method has important guiding significance in natural gas production management, scheduling optimization, development planning, strategy making and the like.
Owner:CHINA PETROLEUM & CHEM CORP +1

Blind regression modeling method for protecting data privacy in mobile group perception, and updating method

The invention provides a blind regression modeling method for protecting data privacy in a mobile group perception system. Blind regression modeling is achieved by the interaction between a mobile perception node and a mobile perception server and can be summarized as the following steps: selecting a ''clean'' perception data subset, constructing a rough global model and performing global regression model refinement. The invention further provides an updating method of a blind regression model built by the blind regression modeling method. Model updating performed by using new perception datacan be summarized as the following steps: constructing a new rough global model and performing new regression model refinement. According to the method provided by the invention, an aggregation resultis exchanged between the mobile perception server and the mobile perception node to ensure that the contents of the perception data are not disclosed; and the incremental model updating is adopted toreduce the communication and calculation costs of the mobile perception node. By adoption of the method provided by the invention, the effects of protecting the perception data privacy, weakening theinfluence of abnormal data on the regression model, improving the model accuracy and realizing lightweight model updating are achieved.
Owner:DONGHUA UNIV

Regression modeling method based on regression attention generative adversarial network data enhancement

The invention discloses a regression modeling method based on regression attention generative adversarial network data enhancement. A regression attention generative adversarial network adds attentionmechanisms to a generator and a discriminator; an attention module 1 in the generator constructs regression loss by utilizing independent variables and dependent variables of generated data output bythe generator; meanwhile, the attention module 1 is finely adjusted through real data; an attention module 2 in the discriminator constructs a new loss by using a difference value between the real data and the generated data regression loss; according to the invention, the feature containing the maximum regression information is extracted by minimizing the loss, and the feature contains the regression difference information between the maximized real data and the generated data, so that consideration of a discriminator on the regression information is facilitated. Based on the regression attention generative adversarial network, the original data is enhanced by utilizing the generated data, and then regression modeling is carried out by utilizing a data driving method, so that the performance and the prediction precision of the regression model are effectively improved.
Owner:ZHEJIANG UNIV

Automatic regression diagnosis method on big data platform

The invention relates to an automatic regression diagnosis method on a big data platform, which is characterized in that it includes the following steps: importing data sources into the big data platform; performing random sampling of the data imported into the big data platform; On the node, the core algorithm is used to calculate each sample obtained by random sampling to obtain the regression model corresponding to each sample; the obtained regression models are cross-validated to obtain the final regression model. After adopting the method provided by the present invention, the regression modeling can be carried out completely automatically, that is, the work of regression diagnosis is directly completed by the algorithm without investing a large amount of modeling cost, and the efficiency of regression modeling and diagnosis is improved.
Owner:上海派拉软件股份有限公司

Item popularity analysis method based on mixed effect linear regression model

ActiveCN108647863BComprehensive assessment of popularityIncreased description diversityOffice automationResourcesStatistical analysisEngineering
Aiming at the problem that the evaluation of the project popularity has one-sidedness since the defect report and the feature report are separately explored for the existing research, the invention provides a project popularity analysis method based on a mixed effect linear regression model, comprising: by collecting project data from GitHub and then using statistical analysis and regression modeling, giving the influence relation on the project popularity by the number of defect reports and the number of characteristic reports in the project, analyzing the relation between the improvement ofthe project popularity and the defect reports and feature reports through the difference of the influence factors of the defect reports and feature reports in the project to the project popularity; and progressively performing four dimensions of analysis on the description diversity of the defect reports and feature reports to find out the difference between the defect reports and the feature reports in describing the diversity. The invention comprehensively studies the project popularity by analyzing the difference between the number of defect reports and the number of feature reports in theproject, and may comprehensively evaluate the popularity of the project.
Owner:NAT UNIV OF DEFENSE TECH

Project popularity analysis method based on mixed effect linear regression model

ActiveCN108647863AComprehensive assessment of popularityIncreased description diversityOffice automationResourcesStatistical analysisMixed effects
Aiming at the problem that the evaluation of the project popularity has one-sidedness since the defect report and the feature report are separately explored for the existing research, the invention provides a project popularity analysis method based on a mixed effect linear regression model, comprising: by collecting project data from GitHub and then using statistical analysis and regression modeling, giving the influence relation on the project popularity by the number of defect reports and the number of characteristic reports in the project, analyzing the relation between the improvement ofthe project popularity and the defect reports and feature reports through the difference of the influence factors of the defect reports and feature reports in the project to the project popularity; and progressively performing four dimensions of analysis on the description diversity of the defect reports and feature reports to find out the difference between the defect reports and the feature reports in describing the diversity. The invention comprehensively studies the project popularity by analyzing the difference between the number of defect reports and the number of feature reports in theproject, and may comprehensively evaluate the popularity of the project.
Owner:NAT UNIV OF DEFENSE TECH

Power customer multi-incoming call service risk early warning method

The invention relates to a power customer multi-incoming call service risk early warning method. The implementation process of the method comprises the following steps: firstly, acquiring 95598 service caller order data; analyzing the caller order data of each customer; by using XGBoost and Logistic regression modeling, the probability that a user calls again in the next three days is predicted in advance, and a client with a high probability of calling again is early warned in advance. Compared with the prior art, the multiple-incoming-call service risk early-warning method can reduce service risk events, accurately position risk reasons, improve the early-warning capability of an electric power customer service center for customer complaints, remarkably improve the service response speed and improve the service quality. And technical guarantee is provided for reducing service cost of an electric customer service center and improving enterprise management level.
Owner:国家电网有限公司客户服务中心

A Soft Sensor Method Based on Optimal Selection and Optimal Regression of Orthogonal Components

The invention discloses a soft sensor method based on the optimal selection and optimal regression of orthogonal components, aiming at optimally selecting the orthogonal components that are beneficial to the prediction quality index, and using the selected orthogonal components to establish a soft sensor for optimal regression. Measurement model. Specifically, the method of the present invention at first utilizes principal component analysis (PCA), independent component analysis (ICA) and partial least squares (PLSR) algorithms to obtain corresponding orthogonal components respectively at first, and then utilizes the nearest neighbor component based on genetic algorithm The optimal choice of orthogonal components is analyzed, and the optimal regression modeling based on the particle swarm optimization algorithm is implemented most effectively using the selected orthogonal components. Compared with the traditional method, the method of the present invention selects the feature components that are beneficial to the prediction quality index in an optimized manner and obtains the final quality index prediction value through the optimal regression vector. Therefore, the soft sensing performance of the method of the present invention is fully guaranteed, and it is a more preferred soft sensing method.
Owner:NINGBO UNIV

Blind Regression Modeling and Update Method for Data Privacy Protection in Mobile Crowd Sensing

The invention provides a blind regression modeling method for protecting data privacy in a mobile group perception system. Blind regression modeling is achieved by the interaction between a mobile perception node and a mobile perception server and can be summarized as the following steps: selecting a ''clean'' perception data subset, constructing a rough global model and performing global regression model refinement. The invention further provides an updating method of a blind regression model built by the blind regression modeling method. Model updating performed by using new perception datacan be summarized as the following steps: constructing a new rough global model and performing new regression model refinement. According to the method provided by the invention, an aggregation resultis exchanged between the mobile perception server and the mobile perception node to ensure that the contents of the perception data are not disclosed; and the incremental model updating is adopted toreduce the communication and calculation costs of the mobile perception node. By adoption of the method provided by the invention, the effects of protecting the perception data privacy, weakening theinfluence of abnormal data on the regression model, improving the model accuracy and realizing lightweight model updating are achieved.
Owner:DONGHUA UNIV

Position coordinate estimation method based on partial least square regression

The invention relates to a position coordinate estimation method based on partial least square regression, and belongs to the technical field of indoor positioning. The method comprises the following steps: S1, constructing initial independent variable and dependent variable matrixes X0 and Y0 by using the position fingerprints, wherein position fingerprints and position coordinates of Nf reference points are known; S2, preprocessing X0 and Y0 to obtain X and Y; S3, constructing an optimization problem according to the first pair of principal components t1 and u1 of X and Y and the axial vectors w1 and c1, and solving t1 and u1 according to a covariance maximum principle under certain constraints; S4, performing regression modeling on the principal components t1 and u1; S5, taking residual matrixes E1 and G1 in X and Y as new X and Y, continuing to extract new principal components, and repeating the steps until the number of the principal components reaches an upper limit; S6, based on a PLSR algorithm, obtaining a regression equation of mapping X to Y; and S7, obtaining position fingerprints of the test points, and then obtaining position coordinates of the test points by using the independent variable coefficient matrix A in the regression equation. According to the method, the operation complexity is reduced, and meanwhile, the estimation precision of the position coordinates is ensured.
Owner:CHONGQING UNIV OF POSTS & TELECOMM

Optimization method of vod service cache based on xgboost algorithm in edge network environment

The invention discloses a VOD service cache optimization method based on an XGBoost algorithm in an edge network environment, comprising the following steps: collecting video data; taking the average visit volume as the prediction target, performing regression modeling with the XGBoost algorithm to obtain a prediction model; using the prediction model Predict the average access volume; establish a cache optimization model based on the prediction results; use the knapsack algorithm to solve the optimization model to obtain the final cache solution. The present invention considers that the edge server needs to process a large amount of video information, and the excellent data analysis ability of machine learning in big data processing, so that the edge server can minimize the service access delay, improve the cache efficiency of the edge server, and the The scheme is very simple and easy to implement, and has a good application prospect.
Owner:NANJING UNIV OF POSTS & TELECOMM

Soft measurement modeling method based on modal common feature separation

The invention relates to a soft measurement modeling method based on modal common feature separation. According to the method, data characteristics of an industrial process are fully considered, modal common characteristics of the multi-modal industrial process are extracted through a beta variational auto-encoder and a gradient inversion method, meanwhile, modal unique coefficients are generated through input, a modal unique system and the modal common characteristics are multiplied, and the method is used for soft measurement modeling of the multi-modal process. And combining the modal basic characteristics with modal unique parameters for regression modeling of soft measurement, and estimating and forecasting key quality variables. Compared with other existing methods, the method has the advantages that the multi-modal and non-linear characteristics in the industrial data can be effectively extracted, automatic modal division is performed on the to-be-measured multi-modal input data, modeling is performed by utilizing common characteristics and unique characteristics of modals, and the prediction precision of the soft measurement model on the multi-modal industrial process is greatly improved.
Owner:HANGZHOU NORMAL UNIVERSITY

A face key point detection method and system based on local principal component analysis

ActiveCN110826534BHigh precisionSmall scaleCharacter and pattern recognitionLocal principal component analysisRegression modelling
The invention discloses a face key point detection method and system based on local principal component analysis. The method includes steps: S1, collecting a large amount of face image sample data, and marking the face key points; S2, dividing the face key points Form a plurality of local key points, adopt principal component analysis to process each local key point respectively, obtain the principal component feature of each local key point; S3, calculate the combination of each key point of each piece of face image under the described principal component feature Coefficient; S4, build regression model, train model by described combination coefficient, generate combination coefficient regression model; S5, input the combination coefficient regression model of face image to be detected, predict and obtain described its combination coefficient; S6, based on prediction The obtained combination coefficients and the principal component features restore the key points of the face. The invention performs local principal component analysis on key points, predicts local principal component coefficients, reduces the complexity of directly performing principal component analysis on all key points, and improves regression modeling precision.
Owner:HANGZHOU QUWEI SCI & TECH

Regression modeling method based on Monte Carlo algorithm

The invention discloses a regression modeling method based on a Monte Carlo algorithm, and relates to the field of medicine and pharmacology, and the Monte Carlo algorithm is suitable for carrying out a calculation simulation test on a discrete system. In the calculation simulation, a probability model similar to the system performance is constructed, and a random test is performed on a digital computer, so that the random characteristics of the system can be simulated. Through the method, the efficiency of combination optimization can be improved.
Owner:SHANGHAI JIAO TONG UNIV

Deep integrated forest regression modeling method for measuring concrete compressive strength

The invention discloses a deep integrated forest regression-based modeling method for measuring the compressive strength of concrete. The method comprises the following steps of preprocessing originalhigh-dimensional features by adopting a dimension reduction strategy suitable for an industrial process to obtain a reduction feature vector; then, training a plurality of sub-forest models by takingthe reduction feature vector as an input, selecting predicted values of a plurality of sub-forests through a KNN neighbor method to combine to obtain a layer regression vector, and combining the layer regression vector with the reduction feature vector to obtain an enhancement layer regression vector, thereby obtaining an output of the layer; secondly, inputting the enhancement layer regression vector of the input layer to obtain the output of the second-layer forest model, and repeating the steps in sequence until the output of the (K-1)th-layer forest model is completed; and finally, training a plurality of sub-forest models by taking the output of the (K-1)th layer of middle-layer forest model as the input of an output-layer forest model module, and performing arithmetic average on theprediction output of the sub-forest model to obtain a final prediction result.
Owner:BEIJING UNIV OF TECH
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