Traditional diagnostic framework consists of three parts: data acquisition, feature generation, and fault classification. However, manual feature extraction utilized signal processing technologies heavily depending on subjectivity and prior knowledge which affect the effectiveness and efficiency. To tackle these problems, an unsupervised deep feature learning model based on parallel convolutional autoencoder (PCAE) is proposed and applied in the stage of feature generation of diagnostic framework. Firstly, raw vibration signals are normalized and segmented into sample set by sliding window. Secondly, deep features are, respectively, extracted from reshaped form of raw sample set and spectrogram in time-frequency domain by two parallel unsupervised feature learning branches based on convolutional autoencoder (CAE). During the training process, dropout regularization and batch normalization are utilized to prevent over fitting. Finally, extracted representative features are feed into the classification model based on deep structure of neural network (DNN) with softmax. The effectiveness of the proposed approach is evaluated in fault diagnosis of automobile main reducer. The results produced in contrastive analysis demonstrate that the diagnostic framework based on parallel unsupervised feature learning and deep structure of classification can effectively enhance the robustness and enhance the identification accuracy of operation conditions by nearly 8%. As the key and principal component of the automobile transmission system, rear axle is of great significance to driving security and comfort of automobile. According to the previous investigation and experience of experts, the faults of the automobile rear axle mainly occur in the main reducer which is the core component of rear axle –3. ThereMachine health monitoring can be achieved by analyzing vibration signals, current signals, acoustical signals, and temperature data. In recent years, signal processing technique based on vibration signals is the most commonly used and effective approach for traditional condition monitoring and failure recognition –7. For rIn traditional fault diagnosis, signal processing techniques can be implemented in time domain, frequency domain, and time-frequency domain for feature extraction before failure recognition , 10. FeaBy introducing some machine learning algorithms and deep learning models into the mechanical field, condition monitoring and fault identification of mechanical equipment based on support vector machine (SVM) , artificFurthermore, deep belief network (DBN) is another typical unsupervised learning method which is widely used in feature extraction and dimensional reduction . Peng etTo utilize respective advantages of various modal features, in this research, we facilitate information fusion in feature extraction. Deep features are automatically extracted from segmentations of raw signals and spectrogram in time-frequency domain by two parallel feature learning branches based on convolutional autoencoder.During the training process of unsupervised deep feature extraction, we utilize dropout regularization technique to prevent overfitting. Additionally, batch normalization is utilized to sensibly adjust the output value of each layer in the deep structure so as to relieve gradient vanishing and improve training speed.In this research, we present an intelligent failure identification framework for main reducer based on PCAE and DNN. The performance and validity of this framework are verified by real data sets of automobile main reducer. Experimental comparison results demonstrate that the diagnostic framework is more advantageous to conventional models.With the purpose of handling the shortcomings of various feature extraction methods based on traditional signal processing and further improving the veracity of fault identification, a novel unsupervised deep feature learning approach based on parallel convolutional autoencoder (PCAE) is proposed, which combines with DNN together to achieve operation condition monitoring and failure identification of main reducer. The contributions of this research are summed up into the following three points:The remaining content is organized into 4 extra sections. In c in lower dimensionality can be learned from unlabeled original input data x by encode. Then, in the stage of decode, a reconstruction of input data c.Autoencoder is a typical unsupervised deep learning algorithm with asymmetrical neural network structure, and it is mainly utilized in deep feature extraction and dimension reduction –37. The x=T are transformed into a new vector of representation c=T as follows:W is the weight matrix between the first layer and the second layer of autoencoder and b is the bias vector of neural network. Then, the reconstruction vector c as follows:W, b, x and the reconstruction data With sigmoid function, the original input data represented as X, a convolutional layer with K convolutional filters maps X into K hidden feature maps. The kth feature map is represented as follows:Wk and bk are the kth kernel filter and bias and σ represents nonlinear function. By employing L convolutional layers, the final latent deep features of X are extracted recursively and expressed as follows:hL−1 is the hidden feature obtained from the (L − 1)th convolutional layer.As a special variant of basic autoencoder, convolutional autoencoder (CAE) is widely used in feature extraction of image and data in high dimensionality , 40. As hL in the process of decoding. The output of the last deconvolutional layer is as follows:A series of up-sampling layers and deconvolutional layers are used to reconstruct raw input data from X and reconstruction data Y as the loss function, and the convolutional filter and bias of convolutional layers are fine-tuning during the unsupervised feature learning. The loss function is optimized as follows:CAE utilizes mean square error (MSE)  between As shown in z-score normalization based on statistical properties of data, which utilizes the standard deviation σ and mean value  μ as follows:In intelligent fault diagnosis, the vibrational signal preprocessing contains three preparation steps: normalization, segmentation, and reshaping. After collecting the raw vibrational signals, data normalization should be implemented to restrain values within a limited range so as to reduce the fluctuation of training. A frequently used normalization approach is N points using sliding window. The segments with the length of N are used as sample set to train the unsupervised feature learning model.To enhance the size of dataset, the normalized data should be segmented into a series of fixed-size segments containing In order to perform unsupervised feature learning based on convolutional autoencoder, one-dimensional sample set obtained from the process of segmentation needs to be reshaped into two-dimensional image form. The reshaping procedure can be represented as follows:In order to extract deep and representative features which are sensitive to various fault modes, this research constructs a parallel unsupervised feature learning model with two branches. One branch is used to directly perform feature extraction from segmented data in time domain, while in the other branch features are extracted from spectrograms in time-frequency domain which are obtained by using continuous wavelet transform. Two parallel branches based on CAE with different structures concurrently analyze the raw time-domain data to realize data compression and feature learning. Finally, the output features of two branches are fused together as the input of fault classifier. The architecture of the unsupervised feature learning model based on PCAE is shown in As shown in ρ in the range of .This research utilizes the dropout regularization technique in the training process of deep networks to avoid the occurrences of overfitting. As a typical stochastic regularization technology, some neural network units are temporarily discarded from the hidden layers according to a certain probability which is denoted as In the encoding stage, dropout layers are inserted after each pair of convolutional layer and down-sampling layer. Generally speaking, dropout is only used after the pooling layer. Based on the sparse weight vector, network complexity is decreased and generalization performance of testing set can be obviously enhanced.In deep learning, as the numbers of layers gradually increase, the shift of data distribution will lead to the phenomenon of internal covariate shift, which will affect the training speed . Batch nIn batch normalization layers which are placed after convolutional layers, features are divided into several groups to update the parameters according to the group. The data in one group together determine the direction of the gradient and reduce the randomness when descending. On the other hand, because the sample number of batches is much smaller than that of the whole data set, the computational cost is much lower.After the latent features are extracted from the unsupervised feature learning model based on PCAE, a fault classification model is construced based on deep neural networks (DNNs). DNN is a typical supervised learning model containing a series of fully connected hidden layers. With the purpose of feature compression and fault recognition, the size of hidden layers is on the decrease layer-by-layer. The network weights in each layer are randomly initialized and are further trained by using the fused feature vectors obtained from PCAE and their corresponding fault labels. Errors between real labels and network outputs are back propagated to adjust the network weights iteratively.yi represents the target label and L2 regularization is an effective method to handle the problem of overfitting. By adding L2 regularization term, the cost function is as follows:λ denotes the decay hyperparameter.To improve the training efficiency, in this research, classification error rate is used as loss function as follows:The experiment analysis is implemented based on actual vibrational signals which are collected from a main reducer test rig. The test rig collects vibration signals of normal mode and various failure modes by simulating the practical working condition of the main reducer. As shown in fs is set to 12 kHz. In order to reserve useful information of failure modes, the sampling frequency should not be less than the meshing frequency of gear.As shown in By using the test rig to mimic the real running and operation of the faulty main reducer and the normal main reducer, we collect a series of vibration signals with the duration of 10 seconds. To ensure signal accuracy, it is better to start sampling with a delay of 0.5 seconds. Each sample corresponding to 7 condition modes contains 120000 data points. According to the rotation frequency and cycle of main reducer, each sample is segmented into 300 segmentations in which each segmentation contains 400 data points to cover a full cycle of rotation.For each condition mode, 10 tests are performed repeatedly to collect enough data to represent the mode. Therefore, there are 3000 segmentations for each condition mode. During the preprocessing, the data size is reshaped from 400 to 20 × 20 which can be inputted into CAE to extract the latent features from original time-domain data.The implementation of the experimental analysis is achieved in Matlab 8.0 on the PC with CPU of 3.4 GHz and RAM of 4 GB.The unsupervised feature learning model based on PCAE consists of two parallel CAE branches in which one CAE can automatically learn embedded features from reshaped images of original vibration signals in time domain and another CAE can automatically learn features from spectrogram of time-frequency domain. Each CAE contains convolutional layers, down-sampling layers or up-sampling layers, batch normalization layers, and dropout layers. In the architecture of CAE, the convolutional layers with ReLU activation function are utilized to extract feature maps, and the subsequent down-sampling layers are utilized to adjust the size of feature maps. The representative features extracted from two parallel branches of PCAE are fused together and inputted into the classifier based on DNN.The structure and parameters of the proposed model based on PCAE and DNN are given in In the fault classifier based on DNN, the fused features obtained from PCAE are flattened into one-dimensional and inputted into three full connection layers with randomly initial parameters. With a softmax layer, a predicted vector which can reflect the type of fault is finally outputted from the fault classifier.During the training process, some hyperparameters of the proposed model are set into optimal fixed values based on previous research and comparative analysis. Hyperparameters of the proposed model are given in To analyze the diagnostic performance of the proposed model, a series of comparative experiments are implemented. The sample set of 7 condition modes is divided into two subsets. Most of the sample set is used to be the training set, and the rest is regarded as testing set. With the purpose of analyzing the diagnostic capability of the proposed model on the whole sample set, 5-fold cross validation is utilized to generate five different combinations of training set and testing set. For each combination, the proposed model is trained on 80% data which are randomly selected and tested on the other 20% data. Experimental results of five individual trials are given in It can be seen from the results in To analyze the fault recognition capability of the proposed model for 7 different fault modes, a series of fault predictions on 7 fault modes of the testing set are implemented. The confusion matrix of the prediction results is given in It can be observed that the vast majority of samples of C1  are correctly predicted. It means that the differentiation degree of health mode against other fault modes is highest. Among the rest of 6 failure modes, C3 and C6 are more confusing and misclassified. Among 600 samples of C3, there are 52 samples which are predicted to C6. Overall, the proposed model can correctly recognize most of the faulty samples in testing set.To validate the superiority of the proposed model, a set of comparative experiments corresponding to several state-of-art models are carried out. In the field of condition monitoring and intelligent fault diagnosis, BPNN, SVM, and CNN are frequently used methods to build diagnostic models. For comparison, we construct diagnostic models based on BPNN, SVM, and CNN using raw time domain vibration signals. The proportion between training set and testing set is 4 to 1. Each model is tested for five trials. The average training accuracy and average testing accuracy of five trials are tallied as shown in It can be clearly concluded from It follows that deep structure of neural network has the natural advantage of automatic and adaptive feature learning. In addition, the proposed model combines two parallel branches to extract deep feature maps from time domain data and time-frequency domain data so that more comprehensive and pivotal features can be excavated. After that, the deep features are fed into another deep neural network with several fully connected layers to output the fault labels.For a diagnosis system, it is a significant advantage that using the limited training sample set can still obtain good diagnostic accuracy. Based on this, a series of comparative experiments with different datasets are carried out. Several models with the same parameters were built using 100%, 70%, 40%, 20%, and 5% of the original sample set, respectively. In the reduced dataset, 80% of the selected data are used to train the models and the rest data are used to test the models. Each situation is implemented for five times, and the average results of five trials were compared and analyzed as shown in It can be clearly displayed that for each situation, the precision of the proposed model based on PCAE-DNN is obviously better than that of other models. Meanwhile, when the dataset size is reduced, the accuracy of each model decreases more or less. With only 5% data, the diagnostic accuracy of the model based on BPNN is decreased from 89.54% to 61.91% and the diagnostic accuracy of the model based on SVM is decreased from 90.15 to 63.72%. The results demonstrate that the difference between models based on PCAE-DNN with 5% data and 100% data is relatively small, while the difference between models based on CNN, BPNN, and SVM which are built with 5% data and 100% data is huge. It is particularly noticeable that the proposed model based on PCAE-DNN can maintain good performance even with minimal dataset. The main reason for the superiority is that the proposed model is built with deep features which are fused from time-domain data and time-frequency domain data.With the purpose of analyzing the stability of these models with various sizes of training set, we plot the box-plots of diagnostic accuracy as illustrated in A key point of the proposed model is that the deep feature maps are learned from time domain data and time-frequency domain data in unsupervised way. Feature maps extracted from two parallel branches based on CAE are fused together and inputted into a structure based on DNN to achieve fault recognition. In order to measure the effectiveness of the unsupervised feature learning scheme, a series of experiments are performed to compare the capability of the proposed parallel branch feature extraction and single branch feature extraction.The proposed model is based on PCAE-CWT in which one branch employs CWT to obtain spectrograms of time-frequency domain as early manual feature extraction. To prove the superiority of CWT, an alternative parallel model based on PCAE-EMD is constructed. In addition, two single branch models based on CAE-CWT and CAE-signal are used to complete comparison and analysis. For each model, five trials are implemented, and the average accuracy is given in According to the results in With the purpose of visualizing the dispersion of the deep features extracted from each model more intuitively and clearly, we utilize principal component analysis (PCA) to reduce dimension of the features learned from basic CAE and retain two principal components. Then, these principal components are projected into two-dimensional feature space. The dispersion degree comparisons of principal features are shown in As shown in In this research, we propose a novel unsupervised feature learning model fault based on parallel convolutional autoencoder (PCAE) and by combining deep neural network (DNN) with PCAE to recognize fault. Firstly, deep features are, respectively, extracted from reshaped form of raw sample set in time domain and from spectrogram in time-frequency domain by two parallel unsupervised feature learning branches based on convolutional autoencoder (CAE). Furthermore, during the training process of PCAE, dropout regularization and batch normalization are utilized to prevent over fitting. Finally, extracted representative features are fed into the classification model based on DNN with softmax. The proposed model based on PCAE-DNN can learn more salient deep features from data. The results of comparison experiments show that the fault classification accuracy of the proposed model is better than that of other state-of-art models. Even with small data sets, the model based on PCAE-DNN can achieve better performance and stability. In the future, works on developing simultaneous fault diagnosis framework based on deep learning models and exploring new ways of converting vibrational signals into images will be carried out.


A typical Copy Number Variations (CNVs) detection process based on the depth of coverage in the Whole Exome Sequencing (WES) data consists of several steps: (I) calculating the depth of coverage in sequencing regions, (II) quality control, (III) normalizing the depth of coverage, (IV) calling CNVs. Previous tools performed one normalization process for each chromosome—all the coverage depths in the sequencing regions from a given chromosome were normalized in a single run.n sequencing regions, CNVind performs n independent depth of coverage normalizations. Before each normalization, the application selects the k most correlated sequencing regions with the depth of coverage Pearson’s Correlation as distance metric. Then, the resulting subgroup of n independent normalizations are combined; finally, the segmentation and CNV calling process is performed on the resultant dataset.Herein, we present the new CNVind tool for calling CNVs, where the normalization process is conducted separately for each of the sequencing regions. The total number of normalizations is equal to the number of sequencing regions in the investigated dataset. For example, when analyzing a dataset composed of We used WES data from the 1000 Genomes project to evaluate the impact of independent normalization on CNV calling performance and compared the results with state-of-the-art tools: CODEX and exomeCopy. The results proved that independent normalization allows to improve the rare CNVs detection specificity significantly. For example, for the investigated dataset, we reduced the number of FP calls from over 15,000 to around 5000 while maintaining a constant number of TP calls equal to about 150 CNVs. However, independent normalization of each sequencing region is a computationally expensive process, therefore our pipeline is customized and can be easily run in the cloud computing environment, on the computer cluster, or the single CPU server. To our knowledge, the presented application is the first attempt to implement an innovative approach to independent normalization of the depth of WES data coverage.The online version contains supplementary material available at 10.1186/s12859-022-04617-x. Copy Number Variation (CNV) has been identified as a major cause of structural variation in the genome, involving both duplications and deletions of sequences –3. RecenThere are many applications for CNVs detection in Whole Exome Sequencing (WES) data. Many of them use depth of coverage . ApplicaThe most important step in detecting CNVs based on WES depth of coverage is the normalization process, which estimates the ,,perfect” coverage table when there are no CNVs in investigated samples. The goal of obtaining the ,,perfect” coverage table is to eliminate the various sources of biases; the algorithms used for this task are different in other applications. For example, the CODEX  and CODEThere are also normalization methods that divide the investigated samples into groups. For example, in the  paper, wk most correlated sequencing regions are chosen, then the resultant set composed of https://github.com/wkusmirek/CNVind.The common element of all CNVs detection tools is the normalization process that occurs once for the entire group of sequencing regions, e.g., in the CODEX application for all sequencing regions from a given chromosome all samples set. Herein, we present a completely new approach to the process of normalizing the depth of coverage in sequencing regions. In the presented approach for each sequencing region, the In this section, we present the main data processing pipeline implemented in the CNVind tool. Herein, we presented the workflow of our approach, a detailed description of the processes for calculating the depth of coverage, independent read depth normalization, and CNV calling.The workflow of the CNVind tool is presented in Fig. k most correlated other sequencing regions are matched. The normalization module normalizes the resulting subgroups of sequencing regions; the results are combined into one table containing a normalized depth of coverage values. Finally, the resultant set of CNVs is detected based on the raw and normalized depth of coverage tables. The subsequent steps of the data processing listed here are described in detail later in this work.Then independent normalization of the depth of coverage in the sequencing region is performed—for each sequencing region, the The first step of CNVs detection is to calculate the depth of coverage in sequencing regions from the input BAM files. There are many applications for calculating the depth of coverage, such as: SeQuiLa-cov , samtoolTo overcome this issue, the CNVind application provides a default module for calculating the depth of coverage in sequencing regions. However, this module can be easily replaced—a ready-made raw depth of coverage table can be provided at the input of the CNVind application. This table can be prepared using different applications to calculate the depth of coverage with different values of the parameters filtering the DNA reads. We believe this approach allows for a maximum level of flexibility in terms of calculating the depth of coverage in sequencing regions.k most correlated sequencing regions, followed by normalization producing a list of normalized depth of coverage values for a considered sequencing region. The union of all partial results creates the final normalized depth of coverage table for the whole input sequencing regions set.Next, we carried out the process of normalizing the depth of coverage. The proposed approach follows fork-join  processik depicts the number of background modeling sequencing regions, The single normalization process is applied on the set of sequencing regions composed of DEX tool . BrieflyDEX tool , 27.k most strongly correlated other sequencing regions is selected, the resulting subgroups are normalized, the results are combined into the resulting normalized depth of coverage table. The k-nearest neighbors [k strongly correlated sequencing regions.The aspect that distinguishes CNVind applications from other state-of-the-art applications is the approach to the normalization process. Previous tools have only performed one normalization on the entire set of sequencing regions available, e.g. on all sequencing regions from a given chromosome. The presented approach is innovative, for each sequencing region a set of eighbors  algorithAfter the depth of coverage normalization step, a CNVs calling is performed. The process is iteratively performed for subsequent samples based on the raw and normalized depth of coverage values. If the normalized number of reads (the predicted number of reads in the absence of CNV) is much greater than the raw number of mapped reads, then deletion is likely to be present in that sequencing region. On the other hand, if the value of the normalized depth of coverage is significantly smaller than the raw value of the coverage depth, then there is a probable duplication in the given sequencing region. Thus, we have a ratio of raw coverage depth to normalized coverage depth for each sequencing region in a given sample. For ratios greater than 1, there is a duplication probability, for coefficients less than 1—a deletion probability. Finally, based on the mentioned ratios and the distances between sequencing regions, some of them are merged to avoid a large number of very small CNVs. For this purpose, the CNVind application implements the circular binary segmentation algorithm .k mostly correlated sequencing regions and normalization, (IV) segmentation and CNVs calling. Each of the mentioned modules is built and deployed as a separate docker [k most correlated sequencing regions and normalization of the depth of coverage, the calculations can be parallelized using the Kubernetes [The CNVind tool consists of four independent modules responsible for: (I) calculating depth of coverage, (II) removing some sequencing regions and samples in quality control process, (III) selecting e docker  image thbernetes  containeAt this point it should be mentioned that the most time-consuming stage of data processing in the CNVind application is the normalization of the depth and coverage. The input of this process is composed of two tables: (I) table with the raw depth of coverage values, (II) table with description of sequencing region coordinates. The sizes of both tables are not large, table (I) in our study for the entire chromosome 1 was 54 MB, and 29 MB for chromosome 11, table (II) is the size of a 7 MB. The normalization process is independent between regions, each region can be processed in parallel by a different docker/pod. After completing the docker/pod calculations, it creates a result file with normalized depth and coverage values for a given sequencing region. The input files are accessed through a shared directory—when running calculations on a computer cluster, depending on the cluster, the input files should be placed in a directory that is visible from all nodes—for example, for Kubernetes on the Microsoft Azure computing cloud, Storage Class structure and Persistent Volume Claim should be created, and then the input files should be placed in the appropriate directory. This way, each pod run under Persistent Volume Claim can access input files from Storage Class, there is only a single instance of the input file for all running pods. Similarly, after the computations are completed for the normalization results, each pod creates a result file with normalized values of the coverage depth for a given sequencing region. The results file is saved to the same Storage Class. When all pods finish their calculations, the resulting files are merged into a single file with a normalized coverage table. The duration of access to the input data and saving the results is negligibly small.This section describes the experiment results and CNVs detected by the CNVind tool presented in the paper. To evaluate the results, we compared the detected set of CNVs with the CNVs set golden record provided by 1000 Genomes Consortium  generateWe tested the performance of the CNVind tool on a public dataset consisting of 861 samples from 1000 Genomes Project . The invBefore the experiments, we carried out the quality control process to remove sequencing regions that did not pass the quality filters. In this process, we removed all sequencing regions (I) with GC content below 20% or greater than 80%, (II) with mappability factor below 0.9, (III) with median read depth across all samples below 20 or greater than 4000, and (IV) shorter than 20 bp or longer than 2000 bp. All previously mentioned filters and threshold values were transferred from the standard quality control process implemented in the CODEX tool. As a result of the quality control process, 2273 out of 20106 sequencing regions for chromosome 1 and 966 out of 10565 sequencing regions from chromosome 11 were removed.After the quality control process, we calculated the correlation between sequencing regions based on the depth of coverage across all investigated samples. The results proved that the correlation between the sequencing regions is not uniform—for each sequencing region, different sequencing regions can be designated either very strongly or very weakly correlated. The graphical representation of the depth of coverage across all investigated samples correlation between the different sequencing regions is presented in Fig. k should not be too small  or too large .Firstly, we examined how the number of neighbors in the knn algorithm affects the CNVs detection process results. The obtained results Fig. a presentSecondly, we compared the knn algorithm with the random selection of sequencing regions that model the background, and the results are presented in Fig. k most similar background modeling sequencing regions. This fact concludes that the depth of coverage Pearson’s Correlation as distance metric is the best metric to determine k strongly correlated sequencing regions.Thirdly, we checked whether other similarity metrics between the sequencing regions would yield better results. For this purpose, we tested three measures of similarity between the sequencing regions: (I) GC content, (II) the length of the sequencing region, and (III) the mean depth of coverage in the sequencing region. The obtained results are presented in Fig. k decreases in the knn algorithm for rare CNVs, the precision increases, while the sensitivity remains constant. Only for a very small value of k (below 100) the sensitivity for rare CNVs also decreases. The diagram also shows that changing the value of k for the random algorithm does not significantly affect the sensitivity and precision of rare CNVs detection process. The only exception is a very small k value where the detection sensitivity of rare CNVs decreases.Lastly, we examined how the number of neighbors in the knn and random algorithm affects the results’ precision and sensitivity; the results are presented in Fig. What is more, we compared the two previously presented methods of selecting background-modeling sequencing regions (knn and random) with the methods implemented in the CODEX and exomeCopy tools. There is only single normalization for all sequencing regions from a given chromosome. The results of our experiment are presented in Fig. Finally, we examined and compared the computation time of the presented approach; the results of the experiment are presented in Fig. In this paper, we presented the CNVind tool, the new application for calling CNVs based on the depth of coverage in WES data. The main innovation of the presented approach is the parallelized process of the independent depth of coverage normalization of each sequencing region. This stage is time-consuming, but the proposed pipeline is implemented in such a way that the application can be run on a computer cluster or in a cloud computing environment—the degree of parallelization depends on the available computing resources. In particular, the application can be run in the Kubernetes system, each pod normalizes the depth of coverage in a different sequencing region, only single instance of input files is stored on the cluster in a properly created Storage Class object with access via the Persistent Volume Claim structure. The same Storage Class represents the place where the results of the pods computations are stored.The main advantage of the presented application is the ability to improve the detection results of rare CNVs. In the experiments presented in the study, we showed that the use of the CNVind application allowed for a three-fold reduction in the number of rare FPs in relation to the results obtained from the original CODEX application. What is more, the significant reduction in the number of FPs did not reduce the number of rare TPs detected. Moreover, the paper presents the results for chromosome 11, additional results of the same experiments on chromosome 1 presented in Additional file 1 showed that this regularity is also maintained for another set of input data.k most correlated sequencing regions are selected for each sequencing region, which models the background. Therefore, it is important that the set of sequencing regions from which the final background modeling subset is selected should be large enough. For example, in this article, we presented the results for the analysis of WES data from chromosome 1 and chromosome 11, which contained the 20106 and 10565 regions of sequencing, respectively.There are two main disadvantages of the proposed approach. Firstly, the normalization of the sequencing regions is independent, which results in a longer computation time than one normalization of all sequencing regions simultaneously. This disadvantage is solved by the maximum dispersion of computations and adapting the application to computing in the cloud computing environment. Secondly, for the application to function properly, there must be a sufficiently large number of sequencing regions that are currently explored. In the presented application, the k parameter, i.e., the number of the most correlated sequencing regions used for background modeling during independent normalization. In the article, we present the research results for k assuming values in the range 50 to 500. However, the optimal value of k depends on the investigated data set. The planned approach to the effective determination of the value of k will be based on the approach presented in the Ximmer [k parameter; we plan to use the algorithm implemented in the SECNVs [One of the main directions of the application development is the addition of a module that would automatically select the e Ximmer  tool. The SECNVs  and Bamge SECNVs  tools foThe presence of rare CNVs causes many genetic diseases. However, detection methods for rare CNVs based on the depth of coverage of WES data are still unsatisfactory, mainly due to the presence of a very high number of false positives calls in the resulting CNVs dataset. Herein, we presented the new CNVind tool with the independent method of normalizing the depth of coverage in sequencing regions, which significantly improves the resulting set of detected CNVs, especially rare CNVs.Project name: CNVindhttps://github.com/wkusmirek/CNVindProject home page: Operating system(s): All (cross-platform)Programming language: bash, ROther requirements: dockerLicense: GNU GPL-3.0Any restrictions to use by non-academics: noneAdditional file 1. Additional results of thesame experiments on chromosome 1. Fig. S1. Correlation between depthof coverage in sequencing regions of benchmark dataset; Fig. S2.Effect of the size of the set of sequencing regions which modelsbackground on the number of CNVs detected by the CNVind tool; Fig.S3. Effect of using other metrics when selecting the k most similarsequencing regions: (A) GC content, (B) sequencing region length,(C) mean depth of coverage in the sequencing region; Fig. S4. Effectof the size of the set of sequencing regions which models backgroundon the results' sensitivity and precision; Fig. S5. Comparison ofthe results obtained by the CODEX, exomeCopy, and CNVindapplications in knn and random modes; Fig. S6. Comparison of thedepth of coverage normalization computation times for the CNVindapplication on a single machine with multiple cores and in MicrosoftAzure cloud.


Modern intelligent and networked vehicles are increasingly equipped with electronic control units (ECUs) with increased computing power. These electronic devices form an in-vehicle network via the Controller Area Network (CAN) bus, the de facto standard for modern vehicles. Although many ECUs provide convenience to drivers and passengers, they also increase the potential for cyber security threats in motor vehicles. Numerous attacks on vehicles have been reported, and the commonality among these attacks is that they inject malicious messages into the CAN network. To close the security holes of CAN, original equipment manufacturers (OEMs) keep the Database CAN (DBC) file describing the content of CAN messages, confidential. This policy is ineffective against cyberattacks but limits in-depth investigation of CAN messages and hinders the development of in-vehicle intrusion detection systems (IDS) and CAN fuzz testing. Current research reverses CAN messages through tokenization, machine learning, and diagnostic information matching to obtain details of CAN messages. However, the results of these algorithms yield only a fraction of the information specified in the DBC file regarding CAN messages, such as field boundaries and message IDs associated with specific functions. In this study, we propose multiple linear regression-based frameworks for bit-level inversion of CAN messages that can approximate the inversion of DBC files. The framework builds a multiple linear regression model for vehicle behavior and CAN traffic, filters the candidate messages based on the decision coefficients, and finally locates the bits describing the vehicle behavior to obtain the data length and alignment based on the model parameters. Moreover, this work shows that the system has high reversion accuracy and outperforms existing systems in boundary delineation and filtering relevant messages in actual vehicles. The increasingly diverse features in today’s vehicles offer drivers and passengers a more relaxed driving experience and greater convenience. Vehicle connectivity provides real-time information and a variety of entertainment options. In addition, vehicle support features such as advanced driver assistance systems (ADAS), reduce driving stress and make driving safer. These capabilities have multiplied due to the increasing number of electronic control units (ECUs) and higher computing power. Current vehicles are equipped with up to 150 ECUs . HoweverMany examples of attacks on vehicles have confirmed that it is possible to attack the vehicle and perform negative control ,8,9. TheThe detailed specification of CAN messages is crucial for CAN network intrusion detection, fuzz testing, and automotive aftermarket products. To obtain the CAN message description in the DBC document, the security research field has proposed CAN bus reversion methods such as CAN message tokenization algorithm, machine learning-based inversion method, and onboard diagnostics II (OBD-II) diagnostic information matching. The earliest CAN message tokenization algorithm was the FBCA algorithm proposed by Markovitz et al. in 2017 , followeThe CAN frame data tags alone do not reveal any valuable information, and one needs to have DBC files to decode them. However, the DBC files are hidden and usually different for each model. Reverse engineering solutions for CAN information that are not constrained by the vehicle model and can access critical information in the DBC files are urgently needed. To achieve CAN message reversal close to the DBC file, this study innovatively proposes a multiple linear regression model after an in-depth analysis of the way the DBC file specifies the vehicle behavior. The model is built using each bit of the CAN message data field as the independent variable and the vehicle behavior data as the dependent variable. As the input of our framework additionally includes sensor data, our framework needs to be very useful. First, the framework uses the The structure of this study is as follows. The CAN bus is a serial communication bus originally developed by Bosch . Later tThe standard format of a CAN message is shown in For CAN message reversal work, the main targets of the reversal are the identifier (ID) and the data fields. When reversing CAN messages, the relevant message ID is usually locked first, and then the data fields are analyzed to obtain specific bit fields that characterize the vehicle behavior.The form and content of each type of CAN message are defined in the DBC file, so each OEM keeps it private to avoid leakage from the data source and prevent negative control and modification of the car. However, all CAN messages must be fully translated using the DBC file as a table, making sense for CAN reverse work. The contents defined in the DBC file are listed in The message with ID 0x198 is used to explain the correspondence between the DBC file and the CAN message content. As shown in In summary, the DBC file is vital to study the CAN messages in-depth, which makes the DBC file a realistic target for reverse work.In statistics, the multiple linear regression model describes the linear relationship ,42 betweThe estimation Based on the way the CAN messages are defined in the DBC file and the characteristics of the multiple linear regression model, this section presents the feasibility of a bit-level inverse CAN message.According to the definition of the DBC file, the vehicle behavior in the CAN message is expressed as a binary serial number of bits, and there is also a resolution and an offset between the actual vehicle behavior data and this value. As shown in According to the previous description, the DBC file defines the detailed content and form of each message, which is critical for both the research and aftermarket communities. For the scientific field, obtaining the specific meaning of CAN messages facilitates the construction of better Intrusion Detection Prevention Systems (IDPS), instead of just finding anomalies based on data variation patterns. In addition, fuzzy testing can also improve efficiency by performing more targeted data injections based on the content of CAN messages. For aftermarket manufacturers, DBC files can help produce more driver assistance products, such as head-up displays and driver assistance devices. However, for confidentiality and security reasons, OEMs keep DBC files private. In addition, most of the existing CAN message reversal solutions are focused on sorting and ID filtering of data fields. The current CAN message reversal results are limited, obtaining the tags of the data types, data boundaries, and the message IDs associated with some car behaviors.In this study, a bit-level automotive CAN message reverse framework is proposed by building a multiple linear regression model for CAN message data fields and actual physical measurements of the vehicle. Based on the optimal model parameters, the messages related to vehicle behavior are filtered. The data content, data boundary, encoding format, and linear relationship of CAN messages are extracted to maximize the recovery of the DBC file. This phase aims to acquire and process vehicle behavior measurements, as well as in-vehicle CAN traffic. The flowchart of this phase is shown in The basic data needed to execute the message reverse framework are the in-vehicle CAN bus traces, Since the raw data collected by the sensors is limited and does not provide a good picture of the various vehicle states, the collected When processing the raw CAN data collected through the OBD-II port, this framework classifies the raw CAN messages based on the ID and removes the constant data field CAN messages. Since the ID identifies the type of the CAN message, The next step of data processing is to synchronize the CAN messages with the vehicle behavior. In this study, the CAN messages in Finally, multiple linear regression described in ionality . In the Based on the results of data processing and resampling, the purpose of this stage is to build a linear regression model with Step 1: After processing, select a resampled vehicle behavior data Step 2: Build a multiple linear regression model with Step 3: Select the Step 4: Iterate through each Step 5: Execute step 1 to step 4 for all To obtain the relationship between each bit of the data field and the vehicle behavior, this step starts by expanding the data field in After the related message filtering phase, the most relevant candidate messages for the particular vehicle behavior and the corresponding linear regression models are determined. The linear regression models of Iterate through each If the filtered Analyzing the discrete In this stage, the specific details of how the data fields of candidate CAN messages describe the behavior of the vehicle are determined by analyzing the regression coefficient To evaluate the proposed bit-level CAN bus reverse framework, this study implements it on an actual vehicle and obtains specific details of the vehicle CAN message data fields depicting the vehicle behavior for that vehicle. Using the reverse results, the accuracy of the algorithm is evaluated for practical applications based on the available DBC files . In addihttp://49.232.218.41:8000/data.zip accessed on 23 January 2022) is quantitatively described in For the evaluation a 2017 Japanese B-Class sedan was used, whose internal network implements the standard CAN protocol and whose functionality is representative. A DBC file for this model has been obtained, which is used as ground truth for the reverse framework evaluation. To better represent the vehicle behavior, sensors are placed on the body, steering wheel, brake pedal, gas pedal, gear knob, and wiper switch to collect the behavioral data of the vehicle components, which are structured as shown in By analyzing the collected CAN traces, the frequency distribution of the messages is shown in The results of the multivariate linear regression of the collected continuous vehicle behavior with each type of message are shown in For the linear regression results of vehicle speed and CAN trace, according to the threshold value 0.6, three types of messages can be filtered out that directly record vehicle speed information with IDs 0x202, 0x215, and 0x217, as shown in As can be seen from the results of the message filtering, the By analyzing the linear regression result of the filtered messages, it is possible to reverse the portrayal of the vehicle behavior by the individual bits of the message.The reverse result for the speed-related messages is shown in The reverse results of the steering-related messages are shown in The results of the throttle-related message are shown in The results of the bit reverse for the brakes are shown in The reverse results for the gears are shown in The accuracy of the system proposed in this study is evaluated using the inverse results of the actual vehicles. The accuracy is evaluated using the DBC files of the test vehicle, which were determined to be the truth.The accuracy of message filtering is shown in The bit-reverse accuracy is shown in The overall bit-reverse accuracy of the proposed framework for vehicle behavior is shown in ® Xeon® Gold 6248 CPU @ 2.50 GHz and 8 GB of RAM using Python 3. The time is taken to compute the three critical stages of data resampling, multiple linear regression modeling, and a bitwise inversion was calculated separately during the evaluation. The framework’s time performance analysis was performed on a CentOS server with an IntelThis section presents the performance comparison results between the bit-level reverse framework proposed in this study and other CAN message reverse methods. Nowadays, the effective CAN message reversal algorithms are READ , LibreCAIn terms of boundary delineation, we compare the linear regression framework of this paper with the bit-flip rate algorithm used by READ, ReCAN, and LibreCAN. The performance of the methods in this study and the bit-flip rate method in delineating CAN messages with discrete states and continuous vehicle behavior is shown in The reasons for the different performance of existing methods in delineating boundaries are explained in This section describes the outstanding performance of the framework in this paper compared to existing schemes in related message filtering, where existing schemes mainly use correlation coefficients  to filter related messages. In addition, as shown in We compare the algorithms in this section concerning the devices needed for their execution, the data requirement, the algorithm execution time, and the reverse results. As shown in The bit-level automotive CAN bus reverse framework proposed in this study can be used in almost all commercially available vehicles, independent of vehicle make and model. According to To verify the applicability of the framework, an electric car with completely a different power and brand was chosen to apply the framework. The reverse results are shown in In this study, we propose an innovative bit-level reverse framework for automotive CAN messages. This framework builds a multiple linear regression model between CAN traces and sensor data, uses decision coefficients to filter candidate messages, and uses model parameters to determine how data fields represent vehicle behavior and maximally recover DBC files. In the test vehicle, this framework has high accuracy in both message screening and bit-inversion. However, the limitation of the test environment results in the unavailability of the extreme vehicle behavior data, leading to less than perfect results in bit-reversion. In addition, the framework reverses the candidate messages correctly in a short time, which improves the reversal efficiency. Our study proposes the only CAN message translator that can achieve bit-level reversal and has significant advantages over other existing methods for boundary delineation and message verification. Finally, the framework can be applied to any standard-compliant commercially available vehicle.This study examines the bit-level CAN bus reverse framework using a multiple linear regression model. This framework is the only method that can achieve bit-level reversion. It uses sensor data as the dependent variable and each bit of the CAN message data field as the dependent variable to build a multiple linear regression model to obtain the carving of vehicle behavior for each bit based on the The present study has three significant limitations that can be addressed in future studies.First, the lack of extreme data affected the correctness of the experiment. When CAN traffic and vehicle behavior data were acquired, CAN data and sensor data could not cover extreme data, such as vehicle speed reaching 255 km/h, maximum steering wheel angle, and pedal reaching maximum angle. The lack of extreme data departs the highest position in the experimental results, resulting in unsatisfactory experimental results. Future research can obtain extreme data in closed scenarios to optimize the experimental results.Second, insufficient DBC files. We use open-source DBC descriptions as truth when testing the results of validation experiments in vehicles. However, most of the current open-source DBC files are obtained by extracting the ECU firmware, resulting in a minimal number. This study can obtain the description of CAN messages without firmware, which provides a new idea to obtain DBC files for subsequent studies.Finally, application limitations. Due to the limited number of test vehicles used, this framework validated its reverse effect in a subset of vehicles. According to the devices and data on which the framework relies, it can be applied to almost all vehicles. To address the difficulty of testing in actual vehicles, software and hardware simulations  of the i


Extreme climate shocks cause agricultural yield reductions and increase long-term climate risk, altering farmers’ long-term production decisions and affecting green agricultural development (GAD). We take the 2008 snow disaster in China as an extreme climate shock, calculate the GAD index by the entropy weighting method, and use the difference-in-difference method to study the extreme climate shock’s impact on GAD. The results show that: (1) Extreme climate shocks are detrimental to GAD, with the snow disaster decreasing China’s GAD level by 3.07%. (2) The impacts of extreme climate shocks are heterogeneous across climate and economic zones, with greater impact in humid and developed regions. (3) Extreme climate shocks affect GAD mainly by reducing farmers’ willingness to cultivate, and increasing energy consumption, fertilizer, and pesticide input. (4) Extreme climate shocks do not reduce agricultural yields in the long run. Still, they reduce the total value of agricultural production and decrease the quality of agricultural products expressed in terms of unit value. The findings of this study have policy implications for developing countries in coping with extreme climate shocks and promoting GAD. With the increase in global warming and pollution, green development has become a common goal pursued by many countries. Agriculture is constrained as a sector closely related to the natural environment, while its production methods can significantly affect the environment and climate. Pursuing GAD and reduced land and water resources by fertilizers and pesticides are important issues for developing countries, including China.An important way to achieve a green revolution in agriculture is to increase the long-term benefits of green agriculture for farmers . In the The 2008 snow disaster in China is one of the major meteorological disasters suffered by China in the 21st century. The abnormal atmospheric circulation caused by the La Niña phenomenon in 2007 led to intense rainfall in southern China and triggered snow disasters in early 2008 ,6. The sWe conduct an empirical analysis using a sample of 2086 counties in China in the period of 2000–2018 to study the impact of extreme climate shocks on GAD. To measure the level of GAD, we refer to the existing literature ,8,9 and This paper has the following innovations: (1) We examined the impact factors of GAD from an extreme climate perspective and provided empirical evidence that extreme climate shocks impede GAD in the long term. (2) We found heterogeneity in climate extremes’ impact on GAD due to climate and economic development differences. In humid zones and economically developed areas, extreme climates have a greater impact on GAD. (3) We investigated the impact mechanism of extreme climate shocks on GAD and found that extreme climate shocks mainly hinder GAD by reducing farmers’ willingness to cultivate, increasing energy consumption in agricultural production, and increasing fertilizer and pesticide inputs. At the same time, we found that farmers increase fertilizer and pesticide inputs to make up for short-term losses, which protects yields but reduces the proportion of green products in the long run and reduces the quality of agricultural products.There is little literature on the impact of extreme climate shocks on GAD. The relevant studies mainly focus on three areas: GAD, climate change and green development, and the impact of climate change on agricultural production.2O emissions are also an important indicator for measuring the level of GAD [In terms of GAD, scholars believe that the connotation of GAD is the combination of agricultural productivity improvement and environmental protection ,10,11,12l of GAD . Some scl of GAD . In contl of GAD . In terml of GAD ,18.In the area of climate change and green development, scholars believe that climate change exacerbates the risk of agricultural production, thus it is harmful to water resources , hygieneCurrent research remains diverse on the impact of climate change on agricultural production. Most scholars believe climate change will increase extreme weather ,28 and rThe existing literature is deficient in the following aspects. First, there is a lack of literature directly studying the impact of extreme climate shocks on GAD. Only a few scholars have studied the impact of extreme weather on agricultural efficiency from a sustainable development perspective ,37. HoweExtreme climate shocks reduce agricultural output in the short term and increase long-term climate risks . Since aExtreme weather shocks first affect farmers’ willingness to cultivate. Unlike climate change, which affects agricultural production in the long term, extreme climate shocks can wreak havoc on agricultural production in the short term by reducing agricultural yields and damaging some agricultural facilities  and reduFor agricultural production, water is the most important input resource. Due to the scarcity of water resources, developing water-saving agriculture in areas lacking large amounts of rainfall is conducive to protecting groundwater resources and the environment, preventing land desertification. It is one of the most important measures for GAD. Water-saving agriculture is conducive to sustainable development in the long term but can reduce yields in the short term. After facing extreme weather shocks, farmers may abandon water-saving agriculture to compensate for losses and turn to large amounts of river and groundwater extraction for irrigation , resultiReducing fertilizer and pesticide inputs to protect the environment is one of the core connotations of GAD. Although reducing fertilizer and pesticide inputs will reduce agricultural yields in the short term, in the long term, it will help safeguard soil fertility and provide green agricultural products, thus improving product quality and prices and ultimately increasing farmers’ income. An important factor in the promotion of green agriculture is the increase in long-term benefits . When exWe use the difference-in-difference method to analyze the impact of the snow disaster on the level of GAD, and the regression equation is as follows:terature , we use We use the entropy weighting method (EWM) to calculate a comprehensive index of the level of GAD. Combining previous scholars’ studies ,46 and aReferring to previous scholars , we calcThe first step is to standardize all metrics, where The second step is to calculate the entropy value.The third step is to calculate the entropy weights.The fourth step is to calculate the comprehensive index of GAD.We take the DID variable Although the 2008 snow disaster was a manifestation of climate change, the degree of disaster in different regions may still be related to their economic and social characteristics. Developed regions may have better responses to reduce losses. Therefore, we consider factors that are potentially relevant to both disaster and GAD as control variables: Total population (POP), agricultural machinery power (Machinery), primary industry value-added (First), secondary industry value-added (Second), total government expenditure (Expand), and total social investment (Invest). All control variables are taken as logarithms in the empirical analysis. County-level fixed effects control factors that do not change with time during the sample period, such as terrain and slope. Time fixed effects control factors that do not change with county-level factors such as sea level rise and the world economic crisis.We use a sample of 2086 counties in China from 2000 to 2018 for empirical analysis and use the entropy weighting method (EWM) to construct the variables in Rainfall data are from the FLDAS Noah Land Surface Model L4 Global Monthly 0.1 × 0.1 degree (FLDAS). We first obtain the monthly rainfall for each county from 2000–2018 based on latitude and longitude matching and then use the z-score to calculate the annual rainfall fluctuations for each county.Since we identified the degree of damage in each county based on the rainfall fluctuations during the snow disaster (January 2008), to better demonstrate the balance of the sample, we treated the counties whose rainfall fluctuates above the median (0.3) during the snow disaster as the treatment group and the rest of the counties as the control group. Descriptive statistics are shown in The baseline estimations are shown in Column (1) reports the results that do not contain the control variables and only control the time fixed effects. The results show that the coefficient of The baseline regression results show that the snow disaster has significantly reduced the level of GAD. Taking the result of column (5) as an example, it shows that the snow disaster caused an average decrease of 0.012 in the GAD index in the long run. Based on the average value of the GAD index of 0.39 in the total sample, the level of GAD has dropped by 3.07%. At the same time, we found that other control variables have little effect on the level of green agriculture except for agriculture-related variables. This result shows that the GAD does not completely depend on the overall economic level and government intervention is relatively independent.To further verify the robustness of the baseline estimation, we conduct robust checks from the following ten aspects.To test the effectiveness of the DID method, we conducted a parallel trend test on the sample, and the results are shown in The results in Some scholars believe that the time trend item is of great significance in the time series , and addAs China’s four municipalities  have been highly urbanized, the proportion of agriculture and the employed population are limited. To avoid the impact of sample differences on the results, we excluded the county samples belonging to these four municipalities. The results are shown in column (2) of During the sample period (2000–2018), the Chinese government also implemented a land transfer policy that promoted land resource optimization. As the land transfer policy has improved the agricultural scale, it is conducive to long-term agricultural investment and green production. If the counties affected by the snow disaster also implemented the land transfer policy later, it would have a confounding effect. To eliminate this confounding effect, we constructed the land transfer policy variable based on when each province promulgated the Law on Land Contract as the cut-off point and added it to the regression. The estimated results are shown in Although the snow disaster is a purely exogenous climate shock, the extent of the disaster in each region is closely related to its rainfall fluctuations. Recent studies by some scholars have found that rainfall fluctuations are no longer a random process due to climate change but have a time trend . The rairate see  and educrate see  on GAD, The main areas affected by the 2008 snow disaster in China were southern provinces. These places are mostly hilly areas that are not conducive to large-scale production and are traditional family farming areas. Therefore, there may be sample self-selection, that is, the affected counties are also counties with a low degree of green agriculture. To avoid self-selection bias, we selected samples by the PSM. We used Machinery, First, Second, Expend, and Invest as matching variables and then matched the treatment group and the control group for 1:1 neighbor matching to obtain new samples. The results of the PSM are shown in column (1) of The results in column (1) of The baseline estimate is to identify the affected area based on the rainfall fluctuations of the affected month. This guarantees the exogeneity of the independent variables, but it cannot fully reflect the disaster situation in different regions. Some regions may have large rainfall fluctuations but are not affected. To test the validity of the baseline estimation, in this section, we change the DID identification method. Based on the actual disaster situation of the Chinese provinces in the 2008 snow disaster, we analyzed the seven most severely affected provinces  as the treatment group and the others as the control group. The results are shown in column (2) of To test the exogeneity of DID identification, in this section, we use instrumental variable estimation for discussion. We use the 2007 rainfall fluctuations and the latitude and longitude as the instrumental variables (IVs) of the January 2008 rainfall fluctuations, and use the two-stage least squares method (2SLS) for analysis.First of all, the feasibility of using the 2007 rainfall fluctuations as the IV is shown by the La Niña phenomenon that appeared in 2007 ,6, whichSecondly, the reason for adopting latitude and longitude as an IV is that the snow disaster in 2008 was mainly concentrated in central China and south China. The snow disaster is regional, so the disaster areas are significantly related to the geographical location. The latitude and longitude are a completely exogenous factor, which guarantees the exogeneity of the IV.The results using 2007 rainfall fluctuations as an IV are shown in column (3) of In this part, we adopt another calculation method and construct a new GAD index as the dependent variable for analysis. Specifically, we used two variables of agricultural production consumption and agricultural pollution in After adopting the new GAD index, the coefficient of To test whether the baseline results were caused by some missing random variables, we randomly divided the sample counties into a treatment group and a control group for placebo tests. The sample includes 2086 counties from 31 provinces in China, of which seven provinces were the most affected in the 2008 snow disaster. We randomly selected 11 from 31 provinces as the treatment group, and the others as the control group, constructed a new DID variable for the placebo test, and repeated this process 500 times and 800 times, respectively. The results of the placebo test are shown in The baseline results show that the snow disaster will reduce the level of GAD in the long run. GAD levels have fallen by an average of 3.07% in the 10 years since the snow disaster. Due to China’s vast territory, there are significant differences in climate and economic factors in different regions, which will cause differences in the impact of snow disaster on GAD. Therefore, we analyzed the heterogeneity from the four aspects of climate, poverty, industrial structure, and economic development differences.Due to the impact of climate shocks, there is heterogeneity in different precipitation areas ,17,49 anColumns (1)–(3) in Agricultural development is greatly affected by government policies. In China, poor and non-poor counties will receive varying degrees of government subsidies. Therefore, the impact of the snow disaster on the GAD may be heterogeneous due to differences in government subsidies and policies. According to the list of poor counties announced by the Chinese government, we divided the sample into poor counties and non-poor counties for heterogeneity analysis. The results are shown in Counties with different industrial structures may be affected differently when faced with extreme climate shock . To testThe results show that in both agricultural and non-agricultural counties, the impact of the snow disaster on GAD is significantly negative, with a coefficient of −0.01 for agricultural counties and −0.014 for non-agricultural counties, and the impact is similar. These results show that the impact of extreme climate shocks on GAD does not have significant heterogeneity in agricultural and non-agricultural counties. The reason for this is that the extent of the snow disaster is not influenced by the industrial structure and therefore there is no heterogeneity in the impact of the snow disaster.Finally, considering the different levels of development in different economic regions, we divided the sample into eastern, central, and western regions for heterogeneity analysis. The eastern part has the highest level of economic development and the western part has the lowest level . The resThe above results indicate that the impact of the snow disaster on GAD has significant heterogeneity in regions with different economic development levels. The snow disaster reduced the GAD index by 0.031 and 0.015 in the eastern and central regions, respectively, and had no effect on the GAD index in the western region. The reason is that farmers are more bound by land, and climate shocks affect agricultural output more than the green level in the western region.The baseline results show that the snow disaster has significantly reduced the level of GAD. According to the conclusions of the theoretical analysis part, extreme climate shocks lead to lower agricultural incomes, causing agricultural production to be exposed to long-term climate risks, changing farmers’ production decisions. To verify this mechanism, we conducted empirical tests from three aspects of farming willingness, production energy consumption, and agricultural pollution. The results are shown in Extreme climate shocks have led to a decline in agricultural output and damage to agricultural infrastructure, resulting in a widening GAD between agricultural income and non-agricultural income. The opportunity cost of engaging in agricultural production has increased. Therefore, the snow disaster will reduce the farming willingness of farmers in the disaster-stricken areas, causing them to transfer or abandon farmland, thereby reducing the utilization rate of cultivated land. The reduction in the utilization rate of arable land has reduced the degree of farmland improvement, resulting in soil erosion, and may lead to the occupation of part of the arable land, which is not conducive to the GAD. Based on the above analysis, we use the utilization rate of cultivated land as a mechanism variable to test this effect, and the results are shown in The results show that the coefficient of Based on theoretical analysis, farmers who continue to engage in agricultural production will be more inclined to pursue short-term gains due to greater uncertainty. One way to pursue short-term gains in agriculture is to increase production input, such as water resources, electricity, and other consumable resources, which leads to an increase in the energy consumption of agricultural production. Based on this, we use the variables of diesel, electricity, and water resource input per unit of agricultural output, and use the entropy method to construct the agricultural production energy consumption index as a mechanism variable for testing. The results are shown in The results show that the coefficient of Based on theoretical analysis, the snow disaster will increase the long-term climate risk and decrease farmers’ willingness to protect the environment. This risk causes farmers to invest more fertilizers and pesticides to ensure short-term benefits. We use the logarithm of the input of fertilizer and pesticide per hectare as the mechanism variable for analysis to test this mechanism. The results are shown in The results show that the snow disaster increased the fertilizer input per hectare by 1.3% and the pesticide input by 4.7% on average, which led to increased pollution in agricultural production and lowered the level of GAD. Increased inputs of fertilizers and pesticides indicate that farmers are more inclined to guarantee short-term harvests rather than pursue long-term gains when facing greater climate risks in the future. This result further validates the conclusion that extreme climate shocks affect the GAD by changing farmers’ risk expectations.As farmers are more inclined to guarantee short-term harvests after extreme weather shocks, extreme weather may not reduce the total output of agricultural products. However, due to the long-term decline in the level of GAD, the reduction in green agricultural products will reduce the overall quality of agricultural products. To verify this conjecture, we use the total output of major agricultural products and primary industry value-added as the explained variables to study the impact of extreme climate shocks on the output and quality of agricultural products. The relevant results are shown in Columns (1)–(3) in Extreme climate shocks cause agricultural yield reductions and increase long-term climate risk, altering farmers’ long-term production decisions and affecting GAD. This impact is more significant with the increased levels of climate change. We use the difference-in-difference method to study the impact of extreme climate shocks on GAD based on the context of the 2008 snow disaster in China.The conclusion shows that extreme climate shocks are one of the most important factors hindering GAD. Additionally, the impact of extreme climate shocks is heterogeneous across climate and economic zones. Taking targeted measures to cope with extreme climate shocks is an important guarantee to promote GAD in China against the background of intensifying climate change and more frequent extreme weather. Meanwhile, we find that the mechanism by which extreme climate shocks reduce GAD is mainly to change farmers’ income and expectations and extreme climate shocks have reduced the quality of agricultural products in the long term. Therefore, government subsidies and agricultural insurance can mitigate this effect and improve the quality of GAD.Based on the above findings, we propose the following policy recommendations: (1) Increasing investment in agricultural infrastructure to enhance farmers’ capacity to cope with climate disasters. (2) Adapting to local conditions and making appropriate agricultural policies in different regions, focusing on post-disaster reconstruction and security in areas with plentiful rainfall and developed regions. (3) Providing agricultural disaster insurance to protect against future extreme weather disasters, reducing farmers’ expected risks, and protecting the income of green agriculture in the long term. (4) Subsidizing farmers who adopt green production and increase the purchase price of green agricultural products. Subsidies can compensate for the losses caused by extreme climate shocks. Increasing the purchase price of green agricultural products can encourage farmers to continue to adopt green production methods, thus safeguarding the quality of agricultural products and promoting GAD.


The strategic perspective of management policies gained utmost importance during the post-Covid era. The researchers are trying to introduce strategies that can help organizations cope with post-crisis destruction. Yet, the research on the topic is fragmented, mainly related to the hospitality and tourism industry. This manuscript aims to present scholarly research findings dealing with the post-Covid-19 management strategies in the hospitality and tourism industry from January 1, 2020, to July 28, 2021. These strategies can play an essential role in the survival and growth of the sectors. The study identified and acknowledged the core contributing authors, journals, countries, affiliation, corresponding authors through bibliometric, citation, and keyword analysis. It also conducted the co-occurrence analysis and reported three significant research streams and bibliometric coupling to identify four research themes for management strategies of the tourism and hospitality industry in the post-Covid era. With the help of an influential and conceptual framework, the study highlights the future challenges managers could face and suggests the possible area for reviewing and revising the existing policies by proposing future directions. Consequently, this study contributes to the current literature on post-Covid-19 management strategies and policies by developing the critical analysis of the extant literature and highlighting the understudy areas that future studies must explore to expand the scope of the research. Covid-19 becomes an unprecedented world challenge for the organizations that impact core business activities nationally and internationally . AlthougScholars are publishing a significant number of articles to device solutions to the problem and evaluating the consequences of the pandemic for the hospitality and tourism industry. For that purpose, there is a need to conduct a bibliometric analysis to synthesize the findings of a large number of scholars in the field. Specifically, they are more interested in the two issues: first in future solutions of the crisis through better management and second in a quick recovery of the sector. At the same time, most of the attention is paid to the recovery process. The hospitality industry is switching from traditional to innovative business models to facilitate their customers . CompaniAlthough the researchers discussed the post-Covid strategies relevant to the field. But still, no comprehensive review has been conducted to report the growth of scholarly publications in the hospitality and tourism industry that identified the main management strategies and policies to handle the consequences of a virus outbreak effectively in the future. This approach helps to specify the current advancement in the field that help to reveal the strategic perspective and behavioral changes that occurred in these sectors. In light of the arguments mentioned earlier, the present study aims to: (1) find out the growth of publications on the post-Covid management strategies in the hospitality and tourism sector, indexed in the Scopus database between January 1, 2020, to July 28, 2021; (2) identify the core contributors  that are worth considering for devising the future management strategies; (3) suggest the key emerging themes and research streams for the prospective stakeholders; and (4) help to develop pandemic resistance strategies that provide future solutions to the unexpected setbacks.The first two research objectives are academically essential to understanding the directions of hospitality and tourism literature after the pandemic. The third objective is intended to provide an in-depth insight into the main research streams through co-occurrence network and bibliometric analysis. The last research objective discusses the future avenues for prospective stakeholders of the hospitality and tourism industry and suggests solutions for unpredictable future situations. All and all, this study contributes to the emerging literature on post-Covid-19 management strategies related to the hospitality and tourism industry through a comprehensive analysis of the extant literature and suggest research gaps that future scholars must address to validate the proposed solutions.The remaining manuscript is arranged in four sections. In the first stage, the source selection process is defined; in stage 2, bibliometric analysis is conducted; stage 3 explains the science mapping technique; finally, stage 4 includes the discussion on results, followed by the study implications, limitations, and future directions. Further, the information about the flow of research is elaborated in r-studio to analyze various aspects of the extracted data related to the topic under study. The query consultation included: TITLE-ABS-KEY { AND (strategies OR policies) AND  AND [(post OR after) AND (covid OR sars OR pandemic OR corona∗)]} AND [LIMIT-TO  OR LIMIT-TO ]. However, the results may likely change with the increased publications related to the post-Covid-19 management strategies for the hospitality and tourism industry in future.Bibliometric analysis is a tool for extracting scholarly information  from well-reputed databases  . The relThis research aims to establish a conceptual framework for the prospective scholars that can lead them to explore the new research areas for the post-Covid-19 policies for the hospitality and tourism industry by giving credit to the prior researches that contribute to generate future knowledge in the field. For this purpose, firstly, an extensive literature review is conducted within the scope of the topic under study. Later on, strict criteria for selecting the final data are followed so that only relevant information is included for the analysis. The sorted information can help to improve the understanding of the researchers, practitioners, and policymakers of the management strategies in the post-crisis era. n = 47; 81%). However, the percentage also includes conference papers ; letter ; note ; and review .Initially, we extracted 121 documents, of which 63 were duplicated, irrelevant topics and, therefore, deleted them before final analysis. We ended up with 58 documents from different sources . The current study also included the documents  written in the “English” language. The bibliometric analysis aims to develop the framework that can assist hospitality and tourism industry managers to design their post-Covid-19 business strategies. It also helps the researchers to delineate current findings for creating future knowledge. For this purpose, firstly, a systematic literature review is conducted to identify the top sources, journals, scholars, corresponding authors, countries, searching keywords, and citations. This sorted information provides a brief introduction about the core contributors in the field. In the second phase of the study, the researchers aim to identify the key themes and streams through the science mapping tools of the conceptual structure and by following a holistic keywords approach as input data. These approaches help to provide an in-depth understanding of the data and suggest a future research agenda to the prospective researchers in the field of hospitality and tourism management.In the first stage, the scientific performance of the core contributors  is identified.Sustainability in 2020, respectively. Whereas Resources, Conservation and Recycling received the highest citations (49) in 2021.We use Bradford law and source impact to find the top journals publishing the literature relevant to post Covid-19 management strategies for the hospitality and tourism industry. Sustainability is a top journal for publishing the literature regarding post-Covid-19 business strategies and policies. Recently, Worldwide hospitality and tourism themes stand at second rank. In his recent publication, The third important source of publication is Current Issues in Tourism. In line with the previous study by The keyword analysis highlights the core concepts used in the main body of the articles. In all the sections, “Covid-19” remains the most commonly used keyword. As the WHO declared Covid-19 a pandemic in 2019, therefore, “pandemic” and “viral disease” are also found in all categories of keywords. This virus is originated in China, so “China” is also included in the author’s keyword. The major consequences of Covid-19 are related to public health and crisis in all most all the segments of the economy. The same is reflected by the keywords of “crisis,” “health” or “public health.” The current study specifically focused on the post-crisis pandemic period strategies in the hospitality and tourism industry. So, the keywords like “industry,” “tourism,” “strategies,” “post Covid,” “management” are also found in the selected documents. This section highlights the leading publications based on total citations and citations per year related to the management policies and strategies for the hospitality and tourism industry after the Covid-19 breakout see . Ibn-MohWe are considering devising specific underlying theories, themes, and streams under which management strategies for the tourism and hospitality industry are converging. We have devised the bibliometric coupling and co-occurrence network to identify post-pandemic theories, themes, and streams of management strategies.From 58 scholarly publications on the underlying topic, it was cumbersome to identify the two or more papers with duplicate citations. Bibliometric coupling checks the similarity between two documents. It occurs when two documents cite the same references . There cCluster 1, shown in red color in Cluster 2 in green is more related to post-pandemic risk factors of the tourism and hospitality industry. With total link strength of 6, Blue cluster number 3 is related to the ecosystem and economic strategies. This cluster possess the article with the highest number of citations. Cluster 4 is close to cluster 1, and it is shown in yellow . This clA co-occurrence network is the holistic keyword analysis to identify patterns of various themes in literature. This study provides various themes that can be used as the future directions for developing the management strategies of the hospitality and tourism industry. It helps in reviving the industry and provides directions for more profitability and a systematically developed industrial base.While using Vosviewer, we have developed a co-occurrence analysis based on keywords with parameters set to normalization method of association strength with a maximum of one thousand iterations, step size convergence of 0.001 and reduction of 0.75. We have set the minimum cluster to one with a clustering resolution of 0.90, and association weights are based on keyword occurrences The red cluster is the largest, and it represents the keywords that represent the research stream of environmental and public health protection management strategies. As a result of pandemics and the implementation of lockdowns in many countries, most industrial activities were halted because of public health concerns. For industrial survival, eco-protection regulations are required . As mostGreen cluster is related to strategic and sustainable tourism and hospitality industry measures after the pandemic. Government policies play a significant role in developing the resilient future of tourism and the hospitality industry with new business design and working capital availability . DigitalThe research stream of the blue cluster is suggested to be crisis management, innovative marketing and resilient tourism destinations. Post pandemic tourism development is dependent on dynamic inbound tourism programs . GovernmAlthough the rapid increase in scholarly publications addresses the post-Covid management strategies for the hospitality and tourism industry, it is surprising that no research has yet created the synthesis in the area. The current study conducts a bibliometric analysis to report the core contributors  in the field. It also constitutes the citations, keyword, and co-occurrence analysis to identify the main concepts and research streams for the future solutions of the industry. Data were extracted from the Scopus database for the period of January 1, 2020, to July 28, 2021. After defining the selection criteria and removing the duplicates, the final sample ended up at 58 scholarly documents, taken from 39 sources, covering 239 authors appearances. “Sustainability” is the major journal that has grown the most over the years. China is one of the main contributing countries that publish the most significant articles that received the highest citations and also ranked at the top in the correspondence. Spain and China are the countries that reported the highest institutional affiliation.The study reports two methods under a conceptual framework and suggests four research themes and three future research streams regarding post-Covidmanagement strategies of the hospitality and tourism industry. The first method this study report is the bibliometric coupling that divides the post-Covid management strategies literature into four clusters. Each cluster represents a theme. The first theme is “post-Covid recovery of tourism sector” which represents various management strategies that needed to be altered for recovery after the pandemic. Furthermore, cluster 2 suggests a theme titled “post-pandemic risk factors of tourism and hospitality industry”. This theme discusses various risk factors affecting the development of the tourism and hospitality industry and proposes management measures to curb these uncertainties. The third theme is related to “ecosystem and economic strategies” which suggests post-Covid management strategies for environmental and macroeconomy challenges. Cluster 4 represents the theme that deals with crises and disaster management. The study deploys a co-occurrence network to provide three favorable research streams for post-Covid management strategies for the tourism and hospitality industry. First, researchers and academicians can take the route of environmental and public health protection management under post-Covid management strategies of the tourism and hospitality industry. Secord research stream is related to Sustainable Tourism and hospitality industry measures after the pandemic. The third direction for post-Covid management strategies is crisis management, innovative marketing and resilient tourist destinations.Theoretically, this research serves as a foundation for future studies in the hospitality and tourism industry by providing an avenue to understand the development of a new paradigm and modification of the existing theories under the disruption caused by Covid-19. The research streams identified in this study can help the scholars to devise post-Covid management strategies and can contribute to the emergence body of knowledge in the field. Second, the current study is beneficial for the hospitality and tourism industry management, who are more anxious to adopt a proactive approach to understand which policies, strategies, and practices are required to survive in an unprecedented situation. The Covid-19 crisis is a new and complex scenario for the organizations that give a practical lesson to the managers to be the forward thinker. The firms can gain even a competitive advantage if they restructure their existing business models according to the transition of the economy, society, and technology. Third, it will guide the managers to apply advanced technologies, artificial intelligence tools and modern disaster management practices as coping up strategies that will benefit them to achieve sustainable development. Fourth, it will help the policymakers to introduce new policies and identify the potential changes in the existing rules to deal with the major economic issues and restore the shuttered services, like travel, tourism, hospitality.The present study faces certain limitations that provide guidelines to future scholars to broader the canvas of the research. First, it enriches the current research on management strategies in the epidemic-induced hospitality and tourism industry and the methodological literature by employing a bibliometric and co-occurrence analysis. Further, it invites future researchers to investigate the topics such as stakeholder’s sentiment, promotional strategies during the industry revival phase, application of artificial intelligence techniques, etc., in developing the management strategies. Second, the data is retrieved from the Scopus database, considered one of the most reputable databases. Still, some other databases like Web of Sciences and other non-indexed journals are not considered in the analysis. Future researchers may include the suggested databases to recommend a better solution to the industry. Third, the current study only focuses on the hospitality and tourism industry. Future researchers can also explore the crisis response strategies in other sectors by employing the same methodological technique. It will help to increase scholarly contributions that can potentially change future research trends. Fourth, it ignores the population factor while analyzing the geographic distribution of the publications. The result may be different if the correction effect is implied. Future scholars also consider it in their investigations. Fifth for future direction, managers, policymakers, researchers and academicians can take notes from the conceptual framework of this study. This study suggests research streams and themes which provide literature segregation and established future direction.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.KK contributed significantly in influential aspects of the article. AN contributed to conceptual aspects of the article. SS contributed to structure, implications, future directions and review of the article. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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