Computers, materials & continua最新文献_第7页

Fusion-Based Deep Learning Model for Automated Forest Fire Detection 基于融合的森林火灾自动探测深度学习模型

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.024198

Mesfer Al Duhayyim, Majdy M. Eltahir, Ola Abdelgney Omer Ali, Amani Abdulrahman Albraikan, Fahd N. Al-Wesabi, Anwer Mustafa Hilal, Manar Ahmed Hamza, Mohammed Rizwanullah

{"title":"Fusion-Based Deep Learning Model for Automated Forest Fire Detection","authors":"Mesfer Al Duhayyim, Majdy M. Eltahir, Ola Abdelgney Omer Ali, Amani Abdulrahman Albraikan, Fahd N. Al-Wesabi, Anwer Mustafa Hilal, Manar Ahmed Hamza, Mohammed Rizwanullah","doi":"10.32604/cmc.2023.024198","DOIUrl":"https://doi.org/10.32604/cmc.2023.024198","url":null,"abstract":"Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development, regulatory measures, and their implementation elevating the environment. Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe. Therefore, the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent. Therefore, this paper focuses on the design of automated forest fire detection using a fusion-based deep learning (AFFD-FDL) model for environmental monitoring. The AFFD-FDL technique involves the design of an entropy-based fusion model for feature extraction. The combination of the handcrafted features using histogram of gradients (HOG) with deep features using SqueezeNet and Inception v3 models. Besides, an optimal extreme learning machine (ELM) based classifier is used to identify the existence of fire or not. In order to properly tune the parameters of the ELM model, the oppositional glowworm swarm optimization (OGSO) algorithm is employed and thereby improves the forest fire detection performance. A wide range of simulation analyses takes place on a benchmark dataset and the results are inspected under several aspects. The experimental results highlighted the betterment of the AFFD-FDL technique over the recent state of art techniques.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317688","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}

引用次数: 0

Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT 面向IoHT实时健康数据监测的缓存辅助分层检测

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.042403

Muhammad Tahir, Mingchu Li, Irfan Khan, Salman A. Al Qahtani, Rubia Fatima, Javed Ali Khan, Muhammad Shahid Anwar

引用次数: 0

Image Steganalysis Based on Deep Content Features Clustering 基于深度内容特征聚类的图像隐写分析

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.039540

Chengyu Mo, Fenlin Liu, Ma Zhu, Gengcong Yan, Baojun Qi, Chunfang Yang

{"title":"Image Steganalysis Based on Deep Content Features Clustering","authors":"Chengyu Mo, Fenlin Liu, Ma Zhu, Gengcong Yan, Baojun Qi, Chunfang Yang","doi":"10.32604/cmc.2023.039540","DOIUrl":"https://doi.org/10.32604/cmc.2023.039540","url":null,"abstract":"The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image. Then, the extracted features are clustered to obtain the corresponding class labels to achieve sample pre-classification. Finally, the steganalysis network is trained separately using samples in each subclass to achieve more reliable steganalysis. We experimented on publicly available combined datasets of Bossbase1.01, Bows2, and ALASKA#2 with a quality factor of 75. The accuracy of our proposed pre-classification scheme can improve the detection accuracy by 4.84% for Joint Photographic Experts Group UNIversal WAvelet Relative Distortion (J-UNIWARD) at the payload of 0.4 bits per non-zero alternating current discrete cosine transform coefficient (bpnzAC). Furthermore, at the payload of 0.2 bpnzAC, the improvement effect is minimal but also reaches 1.39%. Compared with the previous steganalysis based on deep learning, this method considers the differences between the training contents. It selects the proper detector for the image to be detected. Experimental results show that the pre-classification scheme can effectively obtain image subclasses with certain similarities and better ensure the consistency of training and testing images. The above measures reduce the impact of sample content inconsistency on the steganalysis network and improve the accuracy of steganalysis.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052436","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}

引用次数: 0

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems 连续时间非线性网络物理系统中缓解稀疏传感器攻击的随机模型

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.039466

Borja Bordel S醤chez, Ram髇 Alcarria, Tom醩 Robles

{"title":"Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems","authors":"Borja Bordel S醤chez, Ram髇 Alcarria, Tom醩 Robles","doi":"10.32604/cmc.2023.039466","DOIUrl":"https://doi.org/10.32604/cmc.2023.039466","url":null,"abstract":"Cyber-Physical Systems are very vulnerable to sparse sensor attacks. But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely. Therefore, in this paper, we propose a new non-linear generalized model to describe Cyber-Physical Systems. This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and random effects in the physical and computational worlds. Besides, the digitalization stage in hardware devices is represented too. Attackers and most critical sparse sensor attacks are described through a stochastic process. The reconstruction and protection mechanisms are based on a weighted stochastic model. Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics (such as the Fourier transform, first-return maps, or the probability density function). A decision algorithm calculates the final reconstructed value considering the previous error probability. An experimental validation based on simulation tools and real deployments is also carried out. Both, the new technology performance and scalability are studied. Results prove that the proposed solution protects Cyber-Physical Systems against up to 92% of attacks and perturbations, with a computational delay below 2.5 s. The proposed model shows a linear complexity, as recursive or iterative structures are not employed, just algebraic and probabilistic functions. In conclusion, the new model and reconstruction mechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks, even in dense or pervasive deployments and scenarios.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052437","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}

引用次数: 0

A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks 一种区块链辅助的分布式边缘智能用于保护隐私的汽车网络

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.039487

Muhammad Firdaus, Harashta Tatimma Larasati, Kyung-Hyune Rhee

{"title":"A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks","authors":"Muhammad Firdaus, Harashta Tatimma Larasati, Kyung-Hyune Rhee","doi":"10.32604/cmc.2023.039487","DOIUrl":"https://doi.org/10.32604/cmc.2023.039487","url":null,"abstract":"The enormous volume of heterogeneous data from various smart device-based applications has growingly increased a deeply interlaced cyber-physical system. In order to deliver smart cloud services that require low latency with strong computational processing capabilities, the Edge Intelligence System (EIS) idea is now being employed, which takes advantage of Artificial Intelligence (AI) and Edge Computing Technology (ECT). Thus, EIS presents a potential approach to enforcing future Intelligent Transportation Systems (ITS), particularly within a context of a Vehicular Network (VNets). However, the current EIS framework meets some issues and is conceivably vulnerable to multiple adversarial attacks because the central aggregator server handles the entire system orchestration. Hence, this paper introduces the concept of distributed edge intelligence, combining the advantages of Federated Learning (FL), Differential Privacy (DP), and blockchain to address the issues raised earlier. By performing decentralized data management and storing transactions in immutable distributed ledger networks, the blockchain-assisted FL method improves user privacy and boosts traffic prediction accuracy. Additionally, DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing transactions. Our model has been deployed in two strategies: First, DP-based FL to strengthen user privacy by masking the intermediate data during model uploading. Second, blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular networks. The simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget (ε) of 4.03, 1.18, and 0.522, achieving model accuracy of 95.8%, 93.78%, and 89.31%, respectively.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052438","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}

引用次数: 0

FedTC: A Personalized Federated Learning Method with Two Classifiers 基于两个分类器的个性化联邦学习方法

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.039452

Yang Liu, Jiabo Wang, Qinbo Liu, Mehdi Gheisari, Wanyin Xu, Zoe L. Jiang, Jiajia Zhang

{"title":"FedTC: A Personalized Federated Learning Method with Two Classifiers","authors":"Yang Liu, Jiabo Wang, Qinbo Liu, Mehdi Gheisari, Wanyin Xu, Zoe L. Jiang, Jiajia Zhang","doi":"10.32604/cmc.2023.039452","DOIUrl":"https://doi.org/10.32604/cmc.2023.039452","url":null,"abstract":"Centralized training of deep learning models poses privacy risks that hinder their deployment. Federated learning (FL) has emerged as a solution to address these risks, allowing multiple clients to train deep learning models collaboratively without sharing raw data. However, FL is vulnerable to the impact of heterogeneous distributed data, which weakens convergence stability and suboptimal performance of the trained model on local data. This is due to the discarding of the old local model at each round of training, which results in the loss of personalized information in the model critical for maintaining model accuracy and ensuring robustness. In this paper, we propose FedTC, a personalized federated learning method with two classifiers that can retain personalized information in the local model and improve the model’s performance on local data. FedTC divides the model into two parts, namely, the extractor and the classifier, where the classifier is the last layer of the model, and the extractor consists of other layers. The classifier in the local model is always retained to ensure that the personalized information is not lost. After receiving the global model, the local extractor is overwritten by the global model’s extractor, and the classifier of the global model serves as an additional classifier of the local model to guide local training. The FedTC introduces a two-classifier training strategy to coordinate the two classifiers for local model updates. Experimental results on Cifar10 and Cifar100 datasets demonstrate that FedTC performs better on heterogeneous data than current studies, such as FedAvg, FedPer, and local training, achieving a maximum improvement of 27.95% in model classification test accuracy compared to FedAvg.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052702","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}

引用次数: 0

Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model 基于双向长短时记忆模型的注入攻击高效安全技术

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.040121

Abdulgbar A. R. Farea, Gehad Abdullah Amran, Ebraheem Farea, Amerah Alabrah, Ahmed A. Abdulraheem, Muhammad Mursil, Mohammed A. A. Al-qaness

{"title":"Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model","authors":"Abdulgbar A. R. Farea, Gehad Abdullah Amran, Ebraheem Farea, Amerah Alabrah, Ahmed A. Abdulraheem, Muhammad Mursil, Mohammed A. A. Al-qaness","doi":"10.32604/cmc.2023.040121","DOIUrl":"https://doi.org/10.32604/cmc.2023.040121","url":null,"abstract":"E-commerce, online ticketing, online banking, and other web-based applications that handle sensitive data, such as passwords, payment information, and financial information, are widely used. Various web developers may have varying levels of understanding when it comes to securing an online application. Structured Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the Open Web Application Security Project (OWASP) for its 2017 Top Ten List Cross Site Scripting (XSS). An attacker can exploit these two flaws and launch malicious web-based actions as a result of these flaws. Many published articles focused on these attacks’ binary classification. This article described a novel deep-learning approach for detecting SQL injection and XSS attacks. The datasets for SQL injection and XSS payloads are combined into a single dataset. The dataset is labeled manually into three labels, each representing a kind of attack. This work implements some pre-processing algorithms, including Porter stemming, one-hot encoding, and the word-embedding method to convert a word’s text into a vector. Our model used bidirectional long short-term memory (BiLSTM) to extract features automatically, train, and test the payload dataset. The payloads were classified into three types by BiLSTM: XSS, SQL injection attacks, and normal. The outcomes demonstrated excellent performance in classifying payloads into XSS attacks, injection attacks, and non-malicious payloads. BiLSTM’s high performance was demonstrated by its accuracy of 99.26%.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052705","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}

引用次数: 0

An Intelligent Algorithm for Solving Weapon-Target Assignment Problem: DDPG-DNPE Algorithm 求解武器目标分配问题的智能算法:DDPG-DNPE算法

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.041253

Tengda Li, Gang Wang, Qiang Fu, Xiangke Guo, Minrui Zhao, Xiangyu Liu

{"title":"An Intelligent Algorithm for Solving Weapon-Target Assignment Problem: DDPG-DNPE Algorithm","authors":"Tengda Li, Gang Wang, Qiang Fu, Xiangke Guo, Minrui Zhao, Xiangyu Liu","doi":"10.32604/cmc.2023.041253","DOIUrl":"https://doi.org/10.32604/cmc.2023.041253","url":null,"abstract":"Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper, compared to the traditional RELU algorithm, the agent trained with reinforcement learning algorithms, such as Deep Deterministic Policy Gradient algorithm, Asynchronous Advantage Actor-Critic algorithm, Deep Q Network algorithm performs better. It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific. In contrast to other reinforcement learning algorithms, the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation, and the use of weapon resources is more efficient. It shows that the model and algorithm have certain superiority and rationality. The results of this paper provide new ideas for solving the problem of weapon-target assignment in air defense combat command decisions.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136053970","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}

引用次数: 0

A Comprehensive Analysis of Datasets for Automotive Intrusion Detection Systems 汽车入侵检测系统数据集的综合分析

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.039583

Seyoung Lee, Wonsuk Choi, Insup Kim, Ganggyu Lee, Dong Hoon Lee

{"title":"A Comprehensive Analysis of Datasets for Automotive Intrusion Detection Systems","authors":"Seyoung Lee, Wonsuk Choi, Insup Kim, Ganggyu Lee, Dong Hoon Lee","doi":"10.32604/cmc.2023.039583","DOIUrl":"https://doi.org/10.32604/cmc.2023.039583","url":null,"abstract":"Recently, automotive intrusion detection systems (IDSs) have emerged as promising defense approaches to counter attacks on in-vehicle networks (IVNs). However, the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation. Despite the availability of several datasets for automotive IDSs, there has been a lack of comprehensive analysis focusing on assessing these datasets. This paper aims to address the need for dataset assessment in the context of automotive IDSs. It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs, to evaluate the quality of datasets. These metrics take into consideration various aspects such as dataset description, collection environment, and attack complexity. This paper evaluates eight commonly used datasets for automotive IDSs using the proposed metrics. The evaluation reveals biases in the datasets, particularly in terms of limited contexts and lack of diversity. Additionally, it highlights that the attacks in the datasets were mostly injected without considering normal behaviors, which poses challenges for training and evaluating machine learning-based IDSs. This paper emphasizes the importance of addressing the identified limitations in existing datasets to improve the performance and adaptability of automotive IDSs. The proposed metrics can serve as valuable guidelines for researchers and practitioners in selecting and constructing high-quality datasets for automotive security applications. Finally, this paper presents the requirements for high-quality datasets, including the need for representativeness, diversity, and balance.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054180","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}

引用次数: 0

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion RF-Net:基于视网膜和曝光融合的无监督微光图像增强

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.042416

Tian Ma, Chenhui Fu, Jiayi Yang, Jiehui Zhang, Chuyang Shang

{"title":"RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion","authors":"Tian Ma, Chenhui Fu, Jiayi Yang, Jiehui Zhang, Chuyang Shang","doi":"10.32604/cmc.2023.042416","DOIUrl":"https://doi.org/10.32604/cmc.2023.042416","url":null,"abstract":"Low-light image enhancement methods have limitations in addressing issues such as color distortion, lack of vibrancy, and uneven light distribution and often require paired training data. To address these issues, we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network (RF-Net), which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms. This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images. In the first stage, we design a multi-scale feature extraction module based on Retinex theory, capable of extracting details and structural information at different scales to generate high-quality illumination and reflection images. In the second stage, an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features, and the generated images are fused with the original input images to complete the low-light image enhancement. Experiments show the effectiveness and rationality of each module designed in this paper. And the method reconstructs the details of contrast and color distribution, outperforms the current state-of-the-art methods in both qualitative and quantitative metrics, and shows excellent performance in the real world.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317506","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}

引用次数: 1