Xiao Han, Huiqiang Wang, Guoliang Yang, Chengbo Wang
In vechcular networks, a promising approach to enhance vehicle task processing capabilities involves using a combination of roadside base stations or vehicles, there are two challenges when integrating the two offloading modeth: 1) the high mobility of vehicles can easily lead to connectivity interruptions between nodes, which in turn affects the processing of the tasks that are being offloaded; and 2) vehicles on the road are not completely trustworthy, and vehicle tasks that contain private information may suffer from result errors or privacy leakage and other problems. This paper investigates the computing offloading problem for minimizing task completion delay in vehicular networks. Specifically, we design a trust model for mobile in-vehicle networks and construct a migration decision problem to minimize the overall delay of task execution for all vehicle users. The simulation results show that the scheme proposed in this paper can effectively reduce the execution delay of the task compared to the baseline scheme.
{"title":"Efficient Task Offloading for Mobile Edge Computing in Vehicular Networks","authors":"Xiao Han, Huiqiang Wang, Guoliang Yang, Chengbo Wang","doi":"10.4018/ijdcf.349133","DOIUrl":"https://doi.org/10.4018/ijdcf.349133","url":null,"abstract":"In vechcular networks, a promising approach to enhance vehicle task processing capabilities involves using a combination of roadside base stations or vehicles, there are two challenges when integrating the two offloading modeth: 1) the high mobility of vehicles can easily lead to connectivity interruptions between nodes, which in turn affects the processing of the tasks that are being offloaded; and 2) vehicles on the road are not completely trustworthy, and vehicle tasks that contain private information may suffer from result errors or privacy leakage and other problems. This paper investigates the computing offloading problem for minimizing task completion delay in vehicular networks. Specifically, we design a trust model for mobile in-vehicle networks and construct a migration decision problem to minimize the overall delay of task execution for all vehicle users. The simulation results show that the scheme proposed in this paper can effectively reduce the execution delay of the task compared to the baseline scheme.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emad-ul-Haq Qazi, Tanveer A. Zia, Areej Muqbil Alotibi, Salem Yahya Altaleedi
Mobile phones and computers are widely used devices these days, with almost everyone carrying a smartphone and multiple personal computing devices at their homes. Unfortunately, the perpetrator exploits these devices for their unlawful activities. They employ various tactics such as sending phishing emails, and malicious links to harvest confidential information and exploit users. The perpetrators often leave traces on search engines, where they search for illegal materials and weapons, or send threatening emails to victims. This paper primarily focuses on locating and retrieving browsers' artifacts while considering the challenges posed by private browsing modes, which perpetrator may use to cover their tracks. The study also compares well-known search engines like Edge, Safari, and Firefox, analyzing the strengths and weaknesses of their directories. Moreover, it explores evidence extraction from smartphones, comparing the success rates between rooted or jailbroken phones and evidence obtained from browsers versus applications.
{"title":"Examining the Behavior of Web Browsers Using Popular Forensic Tools","authors":"Emad-ul-Haq Qazi, Tanveer A. Zia, Areej Muqbil Alotibi, Salem Yahya Altaleedi","doi":"10.4018/ijdcf.349218","DOIUrl":"https://doi.org/10.4018/ijdcf.349218","url":null,"abstract":"Mobile phones and computers are widely used devices these days, with almost everyone carrying a smartphone and multiple personal computing devices at their homes. Unfortunately, the perpetrator exploits these devices for their unlawful activities. They employ various tactics such as sending phishing emails, and malicious links to harvest confidential information and exploit users. The perpetrators often leave traces on search engines, where they search for illegal materials and weapons, or send threatening emails to victims. This paper primarily focuses on locating and retrieving browsers' artifacts while considering the challenges posed by private browsing modes, which perpetrator may use to cover their tracks. The study also compares well-known search engines like Edge, Safari, and Firefox, analyzing the strengths and weaknesses of their directories. Moreover, it explores evidence extraction from smartphones, comparing the success rates between rooted or jailbroken phones and evidence obtained from browsers versus applications.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problem that dangerous operation behaviors in the laboratory is difficult to identify by monitoring the video. An algorithm of dangerous operation behavior detection in multi-task laboratory based on improved YOLOv5 structure is proposed. Firstly, the algorithm enhances, adaptively scales, and adaptively anchors box computing on the input of YOLO network. Then convolution operation is carried out to strengthen the ability of network feature fusion. Finally, the GIoU_Loss function is used at the output to optimize the network parameters and accelerate the convergence of the model. The experimental results show that the algorithm performs well in real-time head localization, head segmentation, and population regression, with significant innovation and superiority. Compared with traditional methods, this algorithm has better accuracy and real-time performance and can more effectively achieve human operation behaviors detection in laboratory application environments.
{"title":"Laboratory Dangerous Operation Behavior Detection System Based on Deep Learning Algorithm","authors":"Dawei Zhang","doi":"10.4018/ijdcf.340934","DOIUrl":"https://doi.org/10.4018/ijdcf.340934","url":null,"abstract":"Aiming at the problem that dangerous operation behaviors in the laboratory is difficult to identify by monitoring the video. An algorithm of dangerous operation behavior detection in multi-task laboratory based on improved YOLOv5 structure is proposed. Firstly, the algorithm enhances, adaptively scales, and adaptively anchors box computing on the input of YOLO network. Then convolution operation is carried out to strengthen the ability of network feature fusion. Finally, the GIoU_Loss function is used at the output to optimize the network parameters and accelerate the convergence of the model. The experimental results show that the algorithm performs well in real-time head localization, head segmentation, and population regression, with significant innovation and superiority. Compared with traditional methods, this algorithm has better accuracy and real-time performance and can more effectively achieve human operation behaviors detection in laboratory application environments.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To improve the security and privacy of audio data stored in third party servers, a novel watermarking scheme is proposed. Firstly, the authors split the host signal into frames and scramble each frame to get the encrypted signal. Secondly, they generate watermark bits by using the frame number and embed them into each frame of the encrypted signal, which is the data that will be uploaded to the third party servers. For the users, they can download the encrypted data and verify the data is intact or not. If the data is intact, the users decrypt the data to get the audio signal. If the audio signal is attacked in the process of transmission, they can also locate the location of the attacked frame. The experimental results show that the method proposed is effective not only for encrypted signals, but also for the encrypted signals after decryption.
{"title":"A Novel Watermarking Scheme for Audio Data Stored in Third Party Servers","authors":"Fuhai Jia, Yanru Jia, Jing Li, Zhenghui Liu","doi":"10.4018/ijdcf.340382","DOIUrl":"https://doi.org/10.4018/ijdcf.340382","url":null,"abstract":"To improve the security and privacy of audio data stored in third party servers, a novel watermarking scheme is proposed. Firstly, the authors split the host signal into frames and scramble each frame to get the encrypted signal. Secondly, they generate watermark bits by using the frame number and embed them into each frame of the encrypted signal, which is the data that will be uploaded to the third party servers. For the users, they can download the encrypted data and verify the data is intact or not. If the data is intact, the users decrypt the data to get the audio signal. If the audio signal is attacked in the process of transmission, they can also locate the location of the attacked frame. The experimental results show that the method proposed is effective not only for encrypted signals, but also for the encrypted signals after decryption.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140248845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The internet brings high efficiency and convenience to society; however, the issue of information security in network communication has significantly affected every aspect of the society. How to ensure the security of this network communication information has become an important research topic. This paper proposes a diagnosis and prediction method based on cyber-physical fusion and deep learning, such as LSTM and CNN, to diagnose and predict network security in a complex network environment. The experiment results showed that the accuracy of network security diagnosis of the LSTM method in the training set was approximately 80%/ After the CNN training process, it has the highest accuracy rate of 95% on the test data set. This paper analysed the nature of network security problems from the perspective of cyber-physical fusion. CNN-based method to diagnose network security can obtain results with a higher accuracy rate so that technicians can better take measures to protect network security.
{"title":"Assurance of Network Communication Information Security Based on Cyber-Physical Fusion and Deep Learning","authors":"Shi Cheng, Yan Qu, Chuyue Wang, Jie Wan","doi":"10.4018/ijdcf.332858","DOIUrl":"https://doi.org/10.4018/ijdcf.332858","url":null,"abstract":"The internet brings high efficiency and convenience to society; however, the issue of information security in network communication has significantly affected every aspect of the society. How to ensure the security of this network communication information has become an important research topic. This paper proposes a diagnosis and prediction method based on cyber-physical fusion and deep learning, such as LSTM and CNN, to diagnose and predict network security in a complex network environment. The experiment results showed that the accuracy of network security diagnosis of the LSTM method in the training set was approximately 80%/ After the CNN training process, it has the highest accuracy rate of 95% on the test data set. This paper analysed the nature of network security problems from the perspective of cyber-physical fusion. CNN-based method to diagnose network security can obtain results with a higher accuracy rate so that technicians can better take measures to protect network security.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135013624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Wang, Longxing Xing, Na Xu, Jiatao Su, Wenting Su, Jiarong Cao
When responding to emergencies such as sudden natural disasters, communication networks face challenges such as network traffic surge and complex geographic environments. Aiming at the problems of high transmission delay and insensitivity to user's preference in the current UAV edge caching strategy, this paper proposes a UAV caching content recommendation algorithm based on graph neural network. Firstly, the location of UAV is determined by clustering algorithm; secondly, the interest preferences of user nodes in the cluster are predicted by GCLRSAN model, and the UAV cache content is designed according to the result; finally, simulation experiments show that the model and algorithm proposed in this paper can effectively reduce the backhaul link overhead and outperform the comparison algorithms in the indexes such as accuracy rate, recall rate, cache hit rate, and transmission delay.
{"title":"UAV Edge Caching Content Recommendation Algorithm Based on Graph Neural Network","authors":"Wei Wang, Longxing Xing, Na Xu, Jiatao Su, Wenting Su, Jiarong Cao","doi":"10.4018/ijdcf.332774","DOIUrl":"https://doi.org/10.4018/ijdcf.332774","url":null,"abstract":"When responding to emergencies such as sudden natural disasters, communication networks face challenges such as network traffic surge and complex geographic environments. Aiming at the problems of high transmission delay and insensitivity to user's preference in the current UAV edge caching strategy, this paper proposes a UAV caching content recommendation algorithm based on graph neural network. Firstly, the location of UAV is determined by clustering algorithm; secondly, the interest preferences of user nodes in the cluster are predicted by GCLRSAN model, and the UAV cache content is designed according to the result; finally, simulation experiments show that the model and algorithm proposed in this paper can effectively reduce the backhaul link overhead and outperform the comparison algorithms in the indexes such as accuracy rate, recall rate, cache hit rate, and transmission delay.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud computing involves transferring data to remote data centers for processing, which consumes significant network bandwidth and transmission time. Edge computing can effectively address this issue by processing tasks at edge nodes, thereby reducing the amount of data transmitted and enhancing the utilization of network bandwidth. This paper investigates intelligent task offloading under the three-layer architecture of cloud-edge-device to fully exploit the cloud-edge collaboration potential. Specifically, an optimization objective function is constructed by modelling the processing cost of all computing tasks. Additionally, asynchronous advantage actor-critic (A3C) algorithm is proposed under cloud-edge collaboration to solve the optimization problem of minimizing the sum of the weights of task offloading delay and energy consumption. Experimental results indicate that the algorithm can effectively utilize the computing resources of the cloud center, reduce task execution delay and energy consumption, and compare favourably with three existing task offloading methods.
{"title":"Task Offloading in Cloud-Edge Environments","authors":"Suzhen Wang, Yongchen Deng, Zhongbo Hu","doi":"10.4018/ijdcf.332066","DOIUrl":"https://doi.org/10.4018/ijdcf.332066","url":null,"abstract":"Cloud computing involves transferring data to remote data centers for processing, which consumes significant network bandwidth and transmission time. Edge computing can effectively address this issue by processing tasks at edge nodes, thereby reducing the amount of data transmitted and enhancing the utilization of network bandwidth. This paper investigates intelligent task offloading under the three-layer architecture of cloud-edge-device to fully exploit the cloud-edge collaboration potential. Specifically, an optimization objective function is constructed by modelling the processing cost of all computing tasks. Additionally, asynchronous advantage actor-critic (A3C) algorithm is proposed under cloud-edge collaboration to solve the optimization problem of minimizing the sum of the weights of task offloading delay and energy consumption. Experimental results indicate that the algorithm can effectively utilize the computing resources of the cloud center, reduce task execution delay and energy consumption, and compare favourably with three existing task offloading methods.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In medical data sharing, the data access control authorities of the sharing entities and computing capabilities of the sharing platforms are asymmetric. This asymmetry leads to poor patient control over their data, privacy disclosure, and difficulties in tracking data sharing. This aarticle proposes a cooperation model of cloud and chain (CMCC) for the secure sharing of medical data. In the CMCC, the power equivalence of blockchain nodes limits the control authority asymmetry between doctors and patients in medical data sharing. Moreover, a cloud server is used to store medical data, and some of the node-side computations are handed over to the cloud, which addresses the asymmetric computing capability asymmetry between the cloud and ordinary nodes. Based on the CMCC, a secure medical data sharing scheme based on proxy re-encryption mechanism is proposed. This scheme realizes secure medical data sharing, especially the patient's complete control of the data. The security and performance analysis show that the proposed scheme outperforms the existing ones.
{"title":"MD-S3C3","authors":"Heng Pan, Yaoyao Zhang, Jianmei Liu, Xueming Si, Zhongyuan Yao, Liang Zhao","doi":"10.4018/ijdcf.329219","DOIUrl":"https://doi.org/10.4018/ijdcf.329219","url":null,"abstract":"In medical data sharing, the data access control authorities of the sharing entities and computing capabilities of the sharing platforms are asymmetric. This asymmetry leads to poor patient control over their data, privacy disclosure, and difficulties in tracking data sharing. This aarticle proposes a cooperation model of cloud and chain (CMCC) for the secure sharing of medical data. In the CMCC, the power equivalence of blockchain nodes limits the control authority asymmetry between doctors and patients in medical data sharing. Moreover, a cloud server is used to store medical data, and some of the node-side computations are handed over to the cloud, which addresses the asymmetric computing capability asymmetry between the cloud and ordinary nodes. Based on the CMCC, a secure medical data sharing scheme based on proxy re-encryption mechanism is proposed. This scheme realizes secure medical data sharing, especially the patient's complete control of the data. The security and performance analysis show that the proposed scheme outperforms the existing ones.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49139483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mathew Nicho, Maha Alblooki, Saeed AlMutiwei, Christopher D. McDermott, O. Ilesanmi
The abundance of digital data within modern vehicles makes digital vehicle forensics (DVF) a promising subfield of digital forensics (DF), with significant potential for investigations. In this research, the authors apply DVF methodology to a SUV, simulating a real case by extracting and analyzing the data in the period leading up to an incident to evaluate the effectiveness of DVF in solving crime. The authors employ DVF approach to extract data to reveal evidential information for judicial evaluation and verdict. This data helped determine whether the incident represented an accident or an act of crime. This simulated case and the assumptions supported by the DVF evidence provides a compelling example of how law enforcement agencies can leverage DVF to collect and present evidence to relevant authorities. This form of forensics can assist government in planning for and regulating the deployment of DVF data, the judiciary in assessing the nature and admissibility of evidence, and vehicle manufacturers in complying with the regulations relating to the harvesting and retrieval of data.
{"title":"A Crime Scene Reconstruction for Digital Forensic Analysis","authors":"Mathew Nicho, Maha Alblooki, Saeed AlMutiwei, Christopher D. McDermott, O. Ilesanmi","doi":"10.4018/ijdcf.327358","DOIUrl":"https://doi.org/10.4018/ijdcf.327358","url":null,"abstract":"The abundance of digital data within modern vehicles makes digital vehicle forensics (DVF) a promising subfield of digital forensics (DF), with significant potential for investigations. In this research, the authors apply DVF methodology to a SUV, simulating a real case by extracting and analyzing the data in the period leading up to an incident to evaluate the effectiveness of DVF in solving crime. The authors employ DVF approach to extract data to reveal evidential information for judicial evaluation and verdict. This data helped determine whether the incident represented an accident or an act of crime. This simulated case and the assumptions supported by the DVF evidence provides a compelling example of how law enforcement agencies can leverage DVF to collect and present evidence to relevant authorities. This form of forensics can assist government in planning for and regulating the deployment of DVF data, the judiciary in assessing the nature and admissibility of evidence, and vehicle manufacturers in complying with the regulations relating to the harvesting and retrieval of data.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42579618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problem that abnormal behavior is difficult to distinguish from normal behavior, a retrieval method for abnormal behavior of laboratory security surveillance video based on deep automatic encoder is proposed. Firstly, the fuzzy median filtering algorithm is used to reduce the noise of the collected laboratory security surveillance video, and then the YUV spatial chromaticity difference method is used to divide the foreground and background of the video, and the illumination degree in the video is determined. The diagonal model and codebook clustering idea are used to compensate for global and local lighting mutations. Finally, the preprocessed video is input into the mixture model, which is based on the deep automatic encoder and combined with the Gaussian mixture model, and the abnormal behavior retrieval results are output. The experimental results show that the proposed method has good security surveillance video preprocessing effect, large AUC, small error rate of abnormal behavior retrieval, and high operation efficiency.
{"title":"Abnormality Retrieval Method of Laboratory Surveillance Video Based on Deep Automatic Encoder","authors":"Dawei Zhang","doi":"10.4018/ijdcf.325224","DOIUrl":"https://doi.org/10.4018/ijdcf.325224","url":null,"abstract":"Aiming at the problem that abnormal behavior is difficult to distinguish from normal behavior, a retrieval method for abnormal behavior of laboratory security surveillance video based on deep automatic encoder is proposed. Firstly, the fuzzy median filtering algorithm is used to reduce the noise of the collected laboratory security surveillance video, and then the YUV spatial chromaticity difference method is used to divide the foreground and background of the video, and the illumination degree in the video is determined. The diagonal model and codebook clustering idea are used to compensate for global and local lighting mutations. Finally, the preprocessed video is input into the mixture model, which is based on the deep automatic encoder and combined with the Gaussian mixture model, and the abnormal behavior retrieval results are output. The experimental results show that the proposed method has good security surveillance video preprocessing effect, large AUC, small error rate of abnormal behavior retrieval, and high operation efficiency.","PeriodicalId":44650,"journal":{"name":"International Journal of Digital Crime and Forensics","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42831407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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