Computers, materials & continua最新文献_第10页

NPBMT: A Novel and Proficient Buffer Management Technique for Internet of Vehicle-Based DTNs NPBMT:一种新颖而熟练的基于车联网DTNs的缓冲区管理技术

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.039697

Sikandar Khan, Khalid Saeed, Muhammad Faran Majeed, Salman A. AlQahtani, Khursheed Aurangzeb, Muhammad Shahid Anwar

{"title":"NPBMT: A Novel and Proficient Buffer Management Technique for Internet of Vehicle-Based DTNs","authors":"Sikandar Khan, Khalid Saeed, Muhammad Faran Majeed, Salman A. AlQahtani, Khursheed Aurangzeb, Muhammad Shahid Anwar","doi":"10.32604/cmc.2023.039697","DOIUrl":"https://doi.org/10.32604/cmc.2023.039697","url":null,"abstract":"Delay Tolerant Networks (DTNs) have the major problem of message delay in the network due to a lack of end-to-end connectivity between the nodes, especially when the nodes are mobile. The nodes in DTNs have limited buffer storage for storing delayed messages. This instantaneous sharing of data creates a low buffer/shortage problem. Consequently, buffer congestion would occur and there would be no more space available in the buffer for the upcoming messages. To address this problem a buffer management policy is proposed named “A Novel and Proficient Buffer Management Technique (NPBMT) for the Internet of Vehicle-Based DTNs”. NPBMT combines appropriate-size messages with the lowest Time-to-Live (TTL) and then drops a combination of the appropriate messages to accommodate the newly arrived messages. To evaluate the performance of the proposed technique comparison is done with Drop Oldest (DOL), Size Aware Drop (SAD), and Drop Larges (DLA). The proposed technique is implemented in the Opportunistic Network Environment (ONE) simulator. The shortest path map-based movement model has been used as the movement path model for the nodes with the epidemic routing protocol. From the simulation results, a significant change has been observed in the delivery probability as the proposed policy delivered 380 messages, DOL delivered 186 messages, SAD delivered 190 messages, and DLA delivered only 95 messages. A significant decrease has been observed in the overhead ratio, as the SAD overhead ratio is 324.37, DLA overhead ratio is 266.74, and DOL and NPBMT overhead ratios are 141.89 and 52.85, respectively, which reveals a significant reduction of overhead ratio in NPBMT as compared to existing policies. The network latency average of DOL is 7785.5, DLA is 5898.42, and SAD is 5789.43 whereas the NPBMT latency average is 3909.4. This reveals that the proposed policy keeps the messages for a short time in the network, which reduces the overhead ratio.","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":"135317299","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

Exercise Recommendation with Preferences and Expectations Based on Ability Computation 基于能力计算的偏好和期望运动推荐

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.041193

Mengjuan Li, Lei Niu

{"title":"Exercise Recommendation with Preferences and Expectations Based on Ability Computation","authors":"Mengjuan Li, Lei Niu","doi":"10.32604/cmc.2023.041193","DOIUrl":"https://doi.org/10.32604/cmc.2023.041193","url":null,"abstract":"In the era of artificial intelligence, cognitive computing, based on cognitive science; and supported by machine learning and big data, brings personalization into every corner of our social life. Recommendation systems are essential applications of cognitive computing in educational scenarios. They help learners personalize their learning better by computing student and exercise characteristics using data generated from relevant learning progress. The paper introduces a Learning and Forgetting Convolutional Knowledge Tracking Exercise Recommendation model (LFCKT-ER). First, the model computes studentsʼ ability to understand each knowledge concept, and the learning progress of each knowledge concept, and the model consider their forgetting behavior during learning progress. Then, studentsʼ learning stage preferences are combined with filtering the exercises that meet their learning progress and preferences. Then studentsʼ ability is used to evaluate whether their expectations of the difficulty of the exercises are reasonable. Then, the model filters the exercises that best match studentsʼ expectations again by studentsʼ expectations. Finally, we use a simulated annealing optimization algorithm to assemble a set of exercises with the highest diversity. From the experimental results, the LFCKT-ER model can better meet studentsʼ personalized learning needs and is more accurate than other exercise recommendation systems under various metrics on real online education public datasets.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"34 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":"135317300","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 Learning-Based Robust Morphed Face Authentication Framework for Online Systems 基于深度学习的在线系统鲁棒变形人脸认证框架

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.038556

Harsh Mankodiya, Priyal Palkhiwala, Rajesh Gupta, Nilesh Kumar Jadav, Sudeep Tanwar, Osama Alfarraj, Amr Tolba, Maria Simona Raboaca, Verdes Marina

{"title":"Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems","authors":"Harsh Mankodiya, Priyal Palkhiwala, Rajesh Gupta, Nilesh Kumar Jadav, Sudeep Tanwar, Osama Alfarraj, Amr Tolba, Maria Simona Raboaca, Verdes Marina","doi":"10.32604/cmc.2023.038556","DOIUrl":"https://doi.org/10.32604/cmc.2023.038556","url":null,"abstract":"The amalgamation of artificial intelligence (AI) with various areas has been in the picture for the past few years. AI has enhanced the functioning of several services, such as accomplishing better budgets, automating multiple tasks, and data-driven decision-making. Conducting hassle-free polling has been one of them. However, at the onset of the coronavirus in 2020, almost all worldly affairs occurred online, and many sectors switched to digital mode. This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business. This paper proposes a three-layered deep learning (DL)-based authentication framework to develop a secure online polling system. It provides a novel way to overcome security breaches during the face identity (ID) recognition and verification process for online polling systems. This verification is done by training a pixel-2-pixel <i>Pix2pix</i> generative adversarial network (GAN) for face image reconstruction to remove facial objects present (if any). Furthermore, image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome, thus checking the electorate credentials.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"101 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":"135317503","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

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net EMU-Net:基于高效改进U-Net的脑肿瘤自动分割与分类

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.042493

Mohammed Aly, Abdullah Shawan Alotaibi

{"title":"EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net","authors":"Mohammed Aly, Abdullah Shawan Alotaibi","doi":"10.32604/cmc.2023.042493","DOIUrl":"https://doi.org/10.32604/cmc.2023.042493","url":null,"abstract":"Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning methods have emerged as key players in automating brain tumor segmentation. In this paper, we propose an efficient modified U-Net architecture, called EMU-Net, which is applied to the BraTS 2020 dataset. Our approach is organized into two distinct phases: classification and segmentation. In this study, our proposed approach encompasses the utilization of the gray-level co-occurrence matrix (GLCM) as the feature extraction algorithm, convolutional neural networks (CNNs) as the classification algorithm, and the chi-square method for feature selection. Through simulation results, the chi-square method for feature selection successfully identifies and selects four GLCM features. By utilizing the modified U-Net architecture, we achieve precise segmentation of tumor images into three distinct regions: the whole tumor (WT), tumor core (TC), and enhanced tumor (ET). The proposed method consists of two important elements: an encoder component responsible for down-sampling and a decoder component responsible for up-sampling. These components are based on a modified U-Net architecture and are connected by a bridge section. Our proposed CNN architecture achieves superior classification accuracy compared to existing methods, reaching up to 99.65%. Additionally, our suggested technique yields impressive Dice scores of 0.8927, 0.9405, and 0.8487 for the tumor core, whole tumor, and enhanced tumor, respectively. Ultimately, the method presented demonstrates a higher level of trustworthiness and accuracy compared to existing methods. The promising accuracy of the EMU-Net study encourages further testing and evaluation in terms of extrapolation and generalization.","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":"135317507","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

Survey on Deep Learning Approaches for Detection of Email Security Threat 电子邮件安全威胁检测的深度学习方法研究

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.036894

Mozamel M. Saeed, Zaher Al Aghbari

{"title":"Survey on Deep Learning Approaches for Detection of Email Security Threat","authors":"Mozamel M. Saeed, Zaher Al Aghbari","doi":"10.32604/cmc.2023.036894","DOIUrl":"https://doi.org/10.32604/cmc.2023.036894","url":null,"abstract":"Emailing is among the cheapest and most easily accessible platforms, and covers every idea of the present century like banking, personal login database, academic information, invitation, marketing, advertisement, social engineering, model creation on cyber-based technologies, etc. The uncontrolled development and easy access to the internet are the reasons for the increased insecurity in email communication. Therefore, this review paper aims to investigate deep learning approaches for detecting the threats associated with e-mail security. This study compiles the literature related to the deep learning methodologies, which are applicable for providing safety in the field of cyber security of email in different organizations. Relevant data were extracted from different research depositories. The paper discusses various solutions for handling these threats. Different challenges and issues are also investigated for e-mail security threats including social engineering, malware, spam, and phishing in the existing solutions to identify the core current problem and set the road for future studies. The review analysis showed that communication media is the common platform for attackers to conduct fraudulent activities via spoofed e-mails and fake websites and this research has combined the merit and demerits of the deep learning approaches adaption in email security threat by the usage of models and technologies. The study highlighted the contrasts of deep learning approaches in detecting email security threats. This review study has set criteria to include studies that deal with at least one of the six machine models in cyber security.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"163 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":"135317691","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

GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture GMLP-IDS:一种新的基于深度学习的智能农业入侵检测系统

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.041667

Abdelwahed Berguiga, Ahlem Harchay, Ayman Massaoudi, Mossaad Ben Ayed, Hafedh Belmabrouk

{"title":"GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture","authors":"Abdelwahed Berguiga, Ahlem Harchay, Ayman Massaoudi, Mossaad Ben Ayed, Hafedh Belmabrouk","doi":"10.32604/cmc.2023.041667","DOIUrl":"https://doi.org/10.32604/cmc.2023.041667","url":null,"abstract":"Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture system. GMLP-IDS is evaluated with the CIC-DDoS2019 dataset, which contains various Distributed Denial-of-Service (DDoS) attacks. The paper first uses the Pearson’s correlation coefficient approach to determine the correlation between the CIC-DDoS2019 dataset characteristics and their corresponding class labels. Then, the CIC-DDoS2019 dataset is divided randomly into two parts, i.e., training and testing. 75% of the data is used for training, and 25% is employed for testing. The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating, loss rating, recall, and F1 score. Comparisons are handled on both binary and multi-class classification problems. The results revealed that the proposed GMLP-IDS system achieved more than 99.99% detection accuracy and a loss of 0.02% compared to traditional MLP. Furthermore, evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"39 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":"135317882","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

VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity VGWO:具有高精度和低时间复杂度的变种灰狼优化器

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.041973

Junqiang Jiang, Zhifang Sun, Xiong Jiang, Shengjie Jin, Yinli Jiang, Bo Fan

引用次数: 0

Research on Multi-Blockchain Electronic Archives Sharing Model 多区块链电子档案共享模型研究

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.028330

Fang Yu, Wenbin Bi, Ning Cao, Jun Luo, Diantang An, Liqiang Ding, Russell Higgs

{"title":"Research on Multi-Blockchain Electronic Archives Sharing Model","authors":"Fang Yu, Wenbin Bi, Ning Cao, Jun Luo, Diantang An, Liqiang Ding, Russell Higgs","doi":"10.32604/cmc.2023.028330","DOIUrl":"https://doi.org/10.32604/cmc.2023.028330","url":null,"abstract":"The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric encryption. Aiming at the problem of dynamic permission management in common access control methods, a new access control method based on smart contract under blockchain is proposed, which improves the intelligence level under blockchain technology. Firstly, the Internet attribute access control model based on smart contract is established. For the dynamic access of heterogeneous devices, the management contract, permission judgment contract and access control contract are designed; Secondly, the access object credit evaluation algorithm based on particle swarm optimization radial basis function (PSO-RBF) neural network is used to dynamically generate the access node credit threshold combined with the access policy, so as to realize the intelligent access right management method. Finally, combined with the above models and algorithms, the workflow of electronic archives sharing and utilization model of multi blockchain is constructed. The experimental results show that the time-consuming of the process increases linearly with the number of continuous access to electronic archives blocks, and the secure access control of sharing and utilization is feasible, secure and effective.","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":"136053658","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-Agent Deep Reinforcement Learning for Efficient Computation Offloading in Mobile Edge Computing 移动边缘计算中高效计算卸载的多智能体深度强化学习

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.040068

Tianzhe Jiao, Xiaoyue Feng, Chaopeng Guo, Dongqi Wang, Jie Song

{"title":"Multi-Agent Deep Reinforcement Learning for Efficient Computation Offloading in Mobile Edge Computing","authors":"Tianzhe Jiao, Xiaoyue Feng, Chaopeng Guo, Dongqi Wang, Jie Song","doi":"10.32604/cmc.2023.040068","DOIUrl":"https://doi.org/10.32604/cmc.2023.040068","url":null,"abstract":"Mobile-edge computing (MEC) is a promising technology for the fifth-generation (5G) and sixth-generation (6G) architectures, which provides resourceful computing capabilities for Internet of Things (IoT) devices, such as virtual reality, mobile devices, and smart cities. In general, these IoT applications always bring higher energy consumption than traditional applications, which are usually energy-constrained. To provide persistent energy, many references have studied the offloading problem to save energy consumption. However, the dynamic environment dramatically increases the optimization difficulty of the offloading decision. In this paper, we aim to minimize the energy consumption of the entire MEC system under the latency constraint by fully considering the dynamic environment. Under Markov games, we propose a multi-agent deep reinforcement learning approach based on the bi-level actor-critic learning structure to jointly optimize the offloading decision and resource allocation, which can solve the combinatorial optimization problem using an asymmetric method and compute the Stackelberg equilibrium as a better convergence point than Nash equilibrium in terms of Pareto superiority. Our method can better adapt to a dynamic environment during the data transmission than the single-agent strategy and can effectively tackle the coordination problem in the multi-agent environment. The simulation results show that the proposed method could decrease the total computational overhead by 17.8% compared to the actor-critic-based method and reduce the total computational overhead by 31.3%, 36.5%, and 44.7% compared with random offloading, all local execution, and all offloading execution, respectively.","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":"136053952","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

PAN-DeSpeck: A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling 泛去噪:SAR图像去噪的轻量级金字塔和基于注意力的网络

Computers, materials & continua Pub Date : 2023-01-01 DOI: 10.32604/cmc.2023.041195

Saima Yasmeen, Muhammad Usman Yaseen, Syed Sohaib Ali, Moustafa M. Nasralla, Sohaib Bin Altaf Khattak

{"title":"PAN-DeSpeck: A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling","authors":"Saima Yasmeen, Muhammad Usman Yaseen, Syed Sohaib Ali, Moustafa M. Nasralla, Sohaib Bin Altaf Khattak","doi":"10.32604/cmc.2023.041195","DOIUrl":"https://doi.org/10.32604/cmc.2023.041195","url":null,"abstract":"SAR images commonly suffer from speckle noise, posing a significant challenge in their analysis and interpretation. Existing convolutional neural network (CNN) based despeckling methods have shown great performance in removing speckle noise. However, these CNN-based methods have a few limitations. They do not decouple complex background information in a multi-resolution manner. Moreover, they have deep network structures that may result in many parameters, limiting their applicability to mobile devices. Furthermore, extracting key speckle information in the presence of complex background is also a major problem with SAR. The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling (PAN-Despeck) network. The primary objective is to enhance image quality and enable improved information interpretation, particularly on mobile devices and scenarios involving complex backgrounds. The PAN-Despeck network leverages domain-specific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis. By utilizing this approach, complex background information can be effectively decoupled, leading to enhanced despeckling performance. Furthermore, the attention mechanism selectively focuses on key speckle features and facilitates complex background removal. The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed, making it lightweight while maintaining high performance. Through comprehensive evaluations, it is demonstrated that PAN-Despeck outperforms existing image restoration methods. With an impressive average peak signal-to-noise ratio (PSNR) of 28.355114 and a remarkable structural similarity index (SSIM) of 0.905467, it demonstrates exceptional performance in effectively reducing speckle noise in SAR images. The source code for the PAN-DeSpeck network is available on .","PeriodicalId":93535,"journal":{"name":"Computers, materials & continua","volume":"116 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":"136053955","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