Computers, materials & continua最新文献_第3页

Solar Power Plant Network Packet-Based Anomaly Detection System for Cybersecurity 基于数据包的太阳能电站网络安全异常检测系统

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.039461

Ju Hyeon Lee, Jiho Shin, Jung Taek Seo

{"title":"Solar Power Plant Network Packet-Based Anomaly Detection System for Cybersecurity","authors":"Ju Hyeon Lee, Jiho Shin, Jung Taek Seo","doi":"10.32604/cmc.2023.039461","DOIUrl":"https://doi.org/10.32604/cmc.2023.039461","url":null,"abstract":"As energy-related problems continue to emerge, the need for stable energy supplies and issues regarding both environmental and safety require urgent consideration. Renewable energy is becoming increasingly important, with solar power accounting for the most significant proportion of renewables. As the scale and importance of solar energy have increased, cyber threats against solar power plants have also increased. So, we need an anomaly detection system that effectively detects cyber threats to solar power plants. However, as mentioned earlier, the existing solar power plant anomaly detection system monitors only operating information such as power generation, making it difficult to detect cyberattacks. To address this issue, in this paper, we propose a network packet-based anomaly detection system for the Programmable Logic Controller (PLC) of the inverter, an essential system of photovoltaic plants, to detect cyber threats. Cyberattacks and vulnerabilities in solar power plants were analyzed to identify cyber threats in solar power plants. The analysis shows that Denial of Service (DoS) and Man-in-the-Middle (MitM) attacks are primarily carried out on inverters, aiming to disrupt solar plant operations. To develop an anomaly detection system, we performed preprocessing, such as correlation analysis and normalization for PLC network packets data and trained various machine learning-based classification models on such data. The Random Forest model showed the best performance with an accuracy of 97.36%. The proposed system can detect anomalies based on network packets, identify potential cyber threats that cannot be identified by the anomaly detection system currently in use in solar power plants, and enhance the security of solar plants.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"29 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":"135317309","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

Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features 基于细粒度特征的多分支深度伪造检测算法

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.042417

Wenkai Qin, Tianliang Lu, Lu Zhang, Shufan Peng, Da Wan

{"title":"Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features","authors":"Wenkai Qin, Tianliang Lu, Lu Zhang, Shufan Peng, Da Wan","doi":"10.32604/cmc.2023.042417","DOIUrl":"https://doi.org/10.32604/cmc.2023.042417","url":null,"abstract":"With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance the extraction of forgery details in deep forgery samples, we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained classification. First, to address the critical problem in locating discriminative feature regions in fine-grained classification tasks, we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature map. Second, using information complementation, we introduce a correlation-guided fusion module to enhance the discriminative feature information of different branches. Finally, we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature channels. We conduct sufficient ablation experiments and comparative experiments. The experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++ and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years, which can achieve better detection results.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"2015 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":"135317497","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

LSTDA: Link Stability and Transmission Delay Aware Routing Mechanism for Flying Ad-Hoc Network (FANET) LSTDA:飞行Ad-Hoc网络(FANET)链路稳定性和传输延迟感知路由机制

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.040628

Farman Ali, Khalid Zaman, Babar Shah, Tariq Hussain, Habib Ullah, Altaf Hussain, Daehan Kwak

{"title":"LSTDA: Link Stability and Transmission Delay Aware Routing Mechanism for Flying Ad-Hoc Network (FANET)","authors":"Farman Ali, Khalid Zaman, Babar Shah, Tariq Hussain, Habib Ullah, Altaf Hussain, Daehan Kwak","doi":"10.32604/cmc.2023.040628","DOIUrl":"https://doi.org/10.32604/cmc.2023.040628","url":null,"abstract":"The paper presents a new protocol called Link Stability and Transmission Delay Aware (LSTDA) for Flying Ad-hoc Network (FANET) with a focus on network corridors (NC). FANET consists of Unmanned Aerial Vehicles (UAVs) that face challenges in avoiding transmission loss and delay while ensuring stable communication. The proposed protocol introduces a novel link stability with network corridors priority node selection to check and ensure fair communication in the entire network. The protocol uses a Red-Black (R-B) tree to achieve maximum channel utilization and an advanced relay approach. The paper evaluates LSTDA in terms of End-to-End Delay (E2ED), Packet Delivery Ratio (PDR), Network Lifetime (NLT), and Transmission Loss (TL), and compares it with existing methods such as Link Stability Estimation-based Routing (LEPR), Distributed Priority Tree-based Routing (DPTR), and Delay and Link Stability Aware (DLSA) using MATLAB simulations. The results show that LSTDA outperforms the other protocols, with lower average delay, higher average PDR, longer average NLT, and comparable average TL.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"30 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":"135317685","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

Efficient Technique for Image Cryptography Using Sudoku Keys 使用数独密钥的高效图像加密技术

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.035856

M. A. P. Manimekalai, M. Karthikeyan, I. Thusnavis Bella Mary, K. Martin Sagayam, Ahmed A Elngar, Unai Fernandez-Gamiz, Hatıra Günerhan

引用次数: 0

Siamese Dense Pixel-Level Fusion Network for Real-Time UAV Tracking 面向无人机实时跟踪的暹罗密集像素级融合网络

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.039489

Zhenyu Huang, Gun Li, Xudong Sun, Yong Chen, Jie Sun, Zhangsong Ni, Yang Yang

{"title":"Siamese Dense Pixel-Level Fusion Network for Real-Time UAV Tracking","authors":"Zhenyu Huang, Gun Li, Xudong Sun, Yong Chen, Jie Sun, Zhangsong Ni, Yang Yang","doi":"10.32604/cmc.2023.039489","DOIUrl":"https://doi.org/10.32604/cmc.2023.039489","url":null,"abstract":"Onboard visual object tracking in unmanned aerial vehicles (UAVs) has attracted much interest due to its versatility. Meanwhile, due to high precision, Siamese networks are becoming hot spots in visual object tracking. However, most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs. To meet the tracking precision and real-time requirements, this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL. Specifically, the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network, then performs correlation matching to obtain the candidate region with high similarity. To improve the matching effect of template and search features, this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection. An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions. In addition, a target localization module is designed to improve target location accuracy. Compared with other advanced trackers, experiments on two public benchmarks, which are UAV123@10fps and UAV20L from the unmanned air vehicle123 (UAV123) dataset, show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"110 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":"136052709","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

Speech Recognition via CTC-CNN Model 基于CTC-CNN模型的语音识别

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.040024

Wen-Tsai Sung, Hao-Wei Kang, Sung-Jung Hsiao

{"title":"Speech Recognition via CTC-CNN Model","authors":"Wen-Tsai Sung, Hao-Wei Kang, Sung-Jung Hsiao","doi":"10.32604/cmc.2023.040024","DOIUrl":"https://doi.org/10.32604/cmc.2023.040024","url":null,"abstract":"In the speech recognition system, the acoustic model is an important underlying model, and its accuracy directly affects the performance of the entire system. This paper introduces the construction and training process of the acoustic model in detail and studies the Connectionist temporal classification (CTC) algorithm, which plays an important role in the end-to-end framework, established a convolutional neural network (CNN) combined with an acoustic model of Connectionist temporal classification to improve the accuracy of speech recognition. This study uses a sound sensor, ReSpeaker Mic Array v2.0.1, to convert the collected speech signals into text or corresponding speech signals to improve communication and reduce noise and hardware interference. The baseline acoustic model in this study faces challenges such as long training time, high error rate, and a certain degree of overfitting. The model is trained through continuous design and improvement of the relevant parameters of the acoustic model, and finally the performance is selected according to the evaluation index. Excellent model, which reduces the error rate to about 18%, thus improving the accuracy rate. Finally, comparative verification was carried out from the selection of acoustic feature parameters, the selection of modeling units, and the speaker’s speech rate, which further verified the excellent performance of the CTCCNN_5 + BN + Residual model structure. In terms of experiments, to train and verify the CTC-CNN baseline acoustic model, this study uses THCHS-30 and ST-CMDS speech data sets as training data sets, and after 54 epochs of training, the word error rate of the acoustic model training set is 31%, the word error rate of the test set is stable at about 43%. This experiment also considers the surrounding environmental noise. Under the noise level of 80∼90 dB, the accuracy rate is 88.18%, which is the worst performance among all levels. In contrast, at 40–60 dB, the accuracy was as high as 97.33% due to less noise pollution.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"11 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":"136053006","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 Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification 急性出血检测与亚型分型的双分支异常结构

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.041855

Muhammad Naeem Akram, Muhammad Usman Yaseen, Muhammad Waqar, Muhammad Imran, Aftab Hussain

{"title":"A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification","authors":"Muhammad Naeem Akram, Muhammad Usman Yaseen, Muhammad Waqar, Muhammad Imran, Aftab Hussain","doi":"10.32604/cmc.2023.041855","DOIUrl":"https://doi.org/10.32604/cmc.2023.041855","url":null,"abstract":"This study presents a deep learning model for efficient intracranial hemorrhage (ICH) detection and subtype classification on non-contrast head computed tomography (CT) images. ICH refers to bleeding in the skull, leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis. It is classified as intra-axial hemorrhage (intraventricular, intraparenchymal) and extra-axial hemorrhage (subdural, epidural, subarachnoid) based on the bleeding location inside the skull. Many computer-aided diagnoses (CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels. However, these approaches perform only binary classification and suffer from a large number of parameters, which increase storage costs. Further, the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical applications. To overcome these problems, a fast and efficient system for the automatic detection of ICH is needed. We designed a double-branch model based on xception architecture that extracts spatial and instant features, concatenates them, and creates the 3D spatial context (common feature vectors) fed to a decision tree classifier for final predictions. The data employed for the experimentation was gathered during the 2019 Radiologist Society of North America (RSNA) brain hemorrhage detection challenge. Our model outperformed benchmark models and achieved better accuracy in intraventricular (99.49%), subarachnoid (99.49%), intraparenchymal (99.10%), and subdural (98.09%) categories, thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"30 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":"136053961","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

Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports 基于异构图关注网络的中文投诉报告主题感知抽象摘要

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.040492

Yan Li, Xiaoguang Zhang, Tianyu Gong, Qi Dong, Hailong Zhu, Tianqiang Zhang, Yanji Jiang

{"title":"Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports","authors":"Yan Li, Xiaoguang Zhang, Tianyu Gong, Qi Dong, Hailong Zhu, Tianqiang Zhang, Yanji Jiang","doi":"10.32604/cmc.2023.040492","DOIUrl":"https://doi.org/10.32604/cmc.2023.040492","url":null,"abstract":"Automatic text summarization (ATS) plays a significant role in Natural Language Processing (NLP). Abstractive summarization produces summaries by identifying and compressing the most important information in a document. However, there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics. In particular, Chinese complaint reports, generated by urban complainers and collected by government employees, describe existing resident problems in daily life. Meanwhile, the reflected problems are required to respond speedily. Therefore, automatic summarization tasks for these reports have been developed. However, similar to traditional summarization models, the generated summaries still exist problems of informativeness and conciseness. To address these issues and generate suitably informative and less redundant summaries, a topic-based abstractive summarization method is proposed to obtain global and local features. Additionally, a heterogeneous graph of the original document is constructed using word-level and topic-level features. Experiments and analyses on public review datasets (Yelp and Amazon) and our constructed dataset (Chinese complaint reports) show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"108 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":"136053963","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

Deep-Net: Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition 深度网络:用于脑肿瘤识别的微调深度神经网络多特征融合

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.038838

Muhammad Attique Khan, Reham R. Mostafa, Yu-Dong Zhang, Jamel Baili, Majed Alhaisoni, Usman Tariq, Junaid Ali Khan, Ye Jin Kim, Jaehyuk Cha

{"title":"Deep-Net: Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition","authors":"Muhammad Attique Khan, Reham R. Mostafa, Yu-Dong Zhang, Jamel Baili, Majed Alhaisoni, Usman Tariq, Junaid Ali Khan, Ye Jin Kim, Jaehyuk Cha","doi":"10.32604/cmc.2023.038838","DOIUrl":"https://doi.org/10.32604/cmc.2023.038838","url":null,"abstract":"Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the original images for contrast enhancement. Next, transfer learning (TL) is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling layer. Then, using a multiset canonical correlation analysis (CCA) method, features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better classification. Although the information was increased, computational time also jumped. This issue is resolved using a hybrid feature optimization algorithm that chooses the best classification features. The experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8% and 95.7%, respectively. The proposed method is compared with several recent studies and outperformed in accuracy. In addition, we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"108 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":"136052435","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

Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking 车辆命名数据网络中基于推送的内容传播和面向机器学习的错觉攻击检测

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.040784

Arif Hussain Magsi, Ghulam Muhammad, Sajida Karim, Saifullah Memon, Zulfiqar Ali

{"title":"Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking","authors":"Arif Hussain Magsi, Ghulam Muhammad, Sajida Karim, Saifullah Memon, Zulfiqar Ali","doi":"10.32604/cmc.2023.040784","DOIUrl":"https://doi.org/10.32604/cmc.2023.040784","url":null,"abstract":"Recent advancements in the Vehicular Ad-hoc Network (VANET) have tremendously addressed road-related challenges. Specifically, Named Data Networking (NDN) in VANET has emerged as a vital technology due to its outstanding features. However, the NDN communication framework fails to address two important issues. The current NDN employs a pull-based content retrieval network, which is inefficient in disseminating crucial content in Vehicular Named Data Networking (VNDN). Additionally, VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous vehicles. Although various solutions have been proposed for detecting vehicles’ behavior, they inadequately addressed the challenges specific to VNDN. To deal with these two issues, we propose a novel push-based crucial content dissemination scheme that extends the scope of VNDN from pull-based content retrieval to a push-based content forwarding mechanism. In addition, we exploit Machine Learning (ML) techniques within VNDN to detect the behavior of vehicles and classify them as attackers or legitimate. We trained and tested our system on the publicly accessible dataset Vehicular Reference Misbehavior (VeReMi). We employed five ML classification algorithms and constructed the best model for illusion attack detection. Our results indicate that Random Forest (RF) achieved excellent accuracy in detecting all illusion attack types in VeReMi, with an accuracy rate of 100% for type 1 and type 2, 96% for type 4 and type 16, and 95% for type 8. Thus, RF can effectively evaluate the behavior of vehicles and identify attacker vehicles with high accuracy. The ultimate goal of our research is to improve content exchange and secure VNDN from attackers. Thus, our ML-based attack detection and prevention mechanism ensures trustworthy content dissemination and prevents attacker vehicles from sharing misleading information in VNDN.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"1 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":"136052439","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