Applied Intelligence最新文献_第4页

CMMTSE: Complex Road Network Map Matching Based on Trajectory Structure Extraction CMMTSE：基于轨迹结构提取的复杂路网地图匹配

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-27 DOI: 10.1007/s10489-024-05751-0

Xiaohan Wang, Pei Wang, Jing Wang, Yonglong Luo, Jiaqing Chen, Junze Wu

{"title":"CMMTSE: Complex Road Network Map Matching Based on Trajectory Structure Extraction","authors":"Xiaohan Wang, Pei Wang, Jing Wang, Yonglong Luo, Jiaqing Chen, Junze Wu","doi":"10.1007/s10489-024-05751-0","DOIUrl":"10.1007/s10489-024-05751-0","url":null,"abstract":"<div><p>Trajectory mapping onto a road network is a complex yet important task. This is because, in the presence of circular sections, Y-shaped intersections, and sections with elevated overlaps with the ground, the conditions of road networks become complicated. Consequently, trajectory mapping becomes challenging owing to the complexities of road networks and the noise generated by high positioning errors. In this study, in response to the difficulty in handling redundant noisy trajectory data in complex road network environments, a complex road network map-matching method based on trajectory structure extraction is proposed. The features of the structure are extracted from the original trajectory data to reduce the effects of redundancy and noise on matching. An adaptive screening candidate method is proposed using driver behavior to estimate the road density and reduce the matching time by selecting effective candidates. A spatiotemporal analysis function is redefined using speed and distance features, and a directional analysis function is proposed for use in combination with directional features to improve the matching accuracy of complex road networks. An experimental evaluation based on real-ground trajectory data collected using in-vehicle sensing devices is conducted to verify the effectiveness of the algorithm. Moreover, extensive experiments are performed on challenging real datasets to evaluate the proposed method, and its accuracy and efficiency are compared with those of two state-of-the-art map-matching algorithms. The experimental results confirm the effectiveness of the proposed algorithm.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 24","pages":"12676 - 12696"},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel stochastic algorithms for privacy-preserving utility mining 保护隐私的实用挖掘新随机算法

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-27 DOI: 10.1007/s10489-024-05826-y

Duc Nguyen, Bac Le

{"title":"Novel stochastic algorithms for privacy-preserving utility mining","authors":"Duc Nguyen, Bac Le","doi":"10.1007/s10489-024-05826-y","DOIUrl":"10.1007/s10489-024-05826-y","url":null,"abstract":"<div><p>High-utility itemset mining (HUIM) is a technique for extracting valuable insights from data. When dealing with sensitive information, HUIM can raise privacy concerns. As a result, privacy-preserving utility mining (PPUM) has become an important research direction. PPUM involves transforming quantitative transactional databases into sanitized versions that protect sensitive data while retaining useful patterns. Researchers have previously employed stochastic optimization methods to conceal sensitive patterns in databases through the addition or deletion of transactions. However, these approaches alter the database structure. To address this issue, this paper introduces a novel approach for hiding data with stochastic optimization without changing the database structure. We design a flexible objective function to let users restrict the negative effects of PPUM according to their specific requirements. We also develop a general strategy for establishing constraint matrices. In addition, we present a stochastic algorithm that applies the ant lion optimizer along with a hybrid algorithm, which combines both exact and stochastic optimization methods, to resolve the hiding problem. The results of extensive experiments are presented, demonstrating the efficiency and flexibility of the proposed algorithms.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 24","pages":"12725 - 12741"},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A group consensus reaching model balancing individual satisfaction and group fairness for distributed linguistic preference relations 兼顾分布式语言偏好关系的个人满意度和群体公平性的群体共识达成模型

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-27 DOI: 10.1007/s10489-024-05732-3

Yingying Liang, Tianyu Zhang, Yan Tu, Qian Zhao

{"title":"A group consensus reaching model balancing individual satisfaction and group fairness for distributed linguistic preference relations","authors":"Yingying Liang, Tianyu Zhang, Yan Tu, Qian Zhao","doi":"10.1007/s10489-024-05732-3","DOIUrl":"10.1007/s10489-024-05732-3","url":null,"abstract":"<div><p>In real-world complex group decision-making problems, preference inconsistency and opinion conflict are common and crucial challenges that need to be tackled. To promote consensus reaching, a novel group consensus reaching model is constructed considering individual satisfaction and group fairness. This study focuses on managing the group consensus reaching process based on flexible and adaptable information, modelled as distributed linguistic preference relations (DLPRs). First, a building process for DLPRs is discussed by integrating cumulative distribution functions converted from single linguistic term sets, hesitant fuzzy linguistic term sets, and comparative linguistic expressions. Furthermore, a two-stage consistency improvement method is proposed, which makes a trade-off between the frequency and magnitude of adjustments. Finally, we establish an improved group consensus model to balance individual satisfaction and group fairness, where individual satisfaction is measured by the deviation between the original and revised preferences and group fairness is measured by the deviation between individual satisfactions. The emergency response plan selection is conducted to show the validity and advantages of the proposed approach.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><img></picture></div></div></figure></div></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 24","pages":"12697 - 12724"},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic rib segmentation and sequential labeling via multi-axial slicing and 3D reconstruction 通过多轴切片和三维重建实现肋骨自动分割和顺序标记

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-26 DOI: 10.1007/s10489-024-05785-4

Hyunsung Kim, Seonghyeon Ko, Junghyun Bum, Duc-Tai Le, Hyunseung Choo

{"title":"Automatic rib segmentation and sequential labeling via multi-axial slicing and 3D reconstruction","authors":"Hyunsung Kim, Seonghyeon Ko, Junghyun Bum, Duc-Tai Le, Hyunseung Choo","doi":"10.1007/s10489-024-05785-4","DOIUrl":"10.1007/s10489-024-05785-4","url":null,"abstract":"<div><p>Radiologists often inspect hundreds of two-dimensional computed-tomography (CT) images to accurately locate lesions and make diagnoses, by classifying and labeling the ribs. However, this task is repetitive and time consuming. To effectively address this problem, we propose a multi-axial rib segmentation and sequential labeling (MARSS) method. First, we slice the CT volume into sagittal, frontal, and transverse planes for segmentation. The segmentation masks generated for each plane are then reconstructed into a single 3D segmentation mask using binarization techniques. After separating the left and right rib volumes from the entire CT volume, we cluster the connected components identified as bones and sequentially assign labels to each rib. The segmentation and sequential labeling performance of this method outperformed existing methods by up to 4.2%. The proposed automatic rib sequential labeling method enhances the efficiency of radiologists. In addition, this method provides an extended opportunity for advancements not only in rib segmentation but also in bone-fracture detection and lesion-diagnosis research.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 24","pages":"12644 - 12660"},"PeriodicalIF":3.4,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A framework based on physics-informed graph neural ODE: for continuous spatial-temporal pandemic prediction 基于物理信息图神经 ODE 的框架：用于连续时空流行病预测

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-26 DOI: 10.1007/s10489-024-05834-y

Haodong Cheng, Yingchi Mao, Xiao Jia

{"title":"A framework based on physics-informed graph neural ODE: for continuous spatial-temporal pandemic prediction","authors":"Haodong Cheng, Yingchi Mao, Xiao Jia","doi":"10.1007/s10489-024-05834-y","DOIUrl":"10.1007/s10489-024-05834-y","url":null,"abstract":"<div><p>Physics-informed spatial-temporal discrete sequence learning networks have great potential in solving partial differential equations and time series prediction compared to traditional fully connected PINN algorithms, and can serve as the foundation for data-driven sequence prediction modeling and inverse problem analysis. However, such existing models are unable to deal with inverse problem scenarios in which the parameters of the physical process are time-varying and unknown, while usually failing to make predictions in continuous time. In this paper, we propose a continuous time series prediction algorithm constructed by the physics-informed graph neural ordinary differential equation (PGNODE). Proposed parameterized GNODE-GRU and physics-informed loss constraints are used to explicitly characterize and solve unknown time-varying hyperparameters. The GNODE solver integrates this physical parameter to predict the sequence value at any time. This paper uses epidemic prediction tasks as a case study, and experimental results demonstrate that the proposed algorithm can effectively improve the prediction accuracy of the spread of epidemics in the future continuous time.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 24","pages":"12661 - 12675"},"PeriodicalIF":3.4,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FMCF: Few-shot Multimodal aspect-based sentiment analysis framework based on Contrastive Finetuning FMCF：基于对比微调的少镜头多模态方面情感分析框架

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-25 DOI: 10.1007/s10489-024-05841-z

Yongping Du, Runfeng Xie, Bochao Zhang, Zihao Yin

{"title":"FMCF: Few-shot Multimodal aspect-based sentiment analysis framework based on Contrastive Finetuning","authors":"Yongping Du, Runfeng Xie, Bochao Zhang, Zihao Yin","doi":"10.1007/s10489-024-05841-z","DOIUrl":"10.1007/s10489-024-05841-z","url":null,"abstract":"<div><p>Multimodal aspect-based sentiment analysis (MABSA) aims to predict the sentiment of aspect by the fusion of different modalities such as image, text and so on. However, the availability of high-quality multimodal data remains limited. Therefore, few-shot MABSA is a new challenge. Previous works are rarely able to cope with low-resource and few-shot scenarios. In order to address the above problems, we design a <b>F</b>ew-shot <b>M</b>ultimodal aspect-based sentiment analysis framework based on <b>C</b>ontrastive <b>F</b>inetuning (FMCF). Initially, the image modality is transformed to the corresponding textual caption to achieve the entailed semantic information and a contrastive dataset is constructed based on similarity retrieval for finetuning in the following stage. Further, a sentence encoder is trained based on SBERT, which combines supervised contrastive learning and sentence-level multi-feature fusion to complete MABSA. The experiments demonstrate that our framework achieves excellent performance in the few-shot scenarios. Importantly, with only 256 training samples and limited computational resources, the proposed method outperforms fine-tuned models that use all available data on the Twitter dataset.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 24","pages":"12629 - 12643"},"PeriodicalIF":3.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models 利用基于预训练大型语言模型的机器学习方法，在自由对话中检测可解释的认知能力下降情况

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-24 DOI: 10.1007/s10489-024-05808-0

Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera, Francisco J. González-Castaño

{"title":"Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models","authors":"Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera, Francisco J. González-Castaño","doi":"10.1007/s10489-024-05808-0","DOIUrl":"10.1007/s10489-024-05808-0","url":null,"abstract":"<div><p>Cognitive and neurological impairments are very common, but only a small proportion of affected individuals are diagnosed and treated, partly because of the high costs associated with frequent screening. Detecting pre-illness stages and analyzing the progression of neurological disorders through effective and efficient intelligent systems can be beneficial for timely diagnosis and early intervention. We propose using Large Language Models to extract features from free dialogues to detect cognitive decline. These features comprise high-level reasoning content-independent features (such as comprehension, decreased awareness, increased distraction, and memory problems). Our solution comprises (<i>i</i>) preprocessing, (<i>ii</i>) feature engineering via Natural Language Processing techniques and prompt engineering, (<i>iii</i>) feature analysis and selection to optimize performance, and (<i>iv</i>) classification, supported by automatic explainability. We also explore how to improve Chat<span>gpt</span>’s direct cognitive impairment prediction capabilities using the best features in our models. Evaluation metrics obtained endorse the effectiveness of a mixed approach combining feature extraction with Chat<span>gpt</span> and a specialized Machine Learning model to detect cognitive decline within free-form conversational dialogues with older adults. Ultimately, our work may facilitate the development of an inexpensive, non-invasive, and rapid means of detecting and explaining cognitive decline.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 24","pages":"12613 - 12628"},"PeriodicalIF":3.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-label feature selection for missing labels by granular-ball based mutual information 基于颗粒球互信息的缺失标签多标签特征选择

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-23 DOI: 10.1007/s10489-024-05809-z

Wenhao Shu, Yichen Hu, Wenbin Qian

{"title":"Multi-label feature selection for missing labels by granular-ball based mutual information","authors":"Wenhao Shu, Yichen Hu, Wenbin Qian","doi":"10.1007/s10489-024-05809-z","DOIUrl":"10.1007/s10489-024-05809-z","url":null,"abstract":"<p>Multi-label feature selection serves an effective dimensionality reduction technique in the high-dimensional multi-label data. However, most feature selection methods regard the label as complete. In fact, in real-world applications, labels in a multi-label dataset may be missing due to various difficulties in collecting sufficient labels, which enables some valuable information to be overlooked and leads to an inaccurate prediction in the classification. To address these issues, a feature selection algorithm based on the granular-ball based mutual information is proposed for the multi-label data with missing labels in this paper. At first, to improve the classification ability, a label recovery model is proposed to calculate some labels, which utilizes the correlation between labels, the properties of label specific features and global common features. Secondly, to avoid computing the neighborhood radius, a granular-ball based mutual information metric for evaluating candidate features is proposed, which well fits the data distribution. Finally, the corresponding feature selection algorithm is developed for selecting a subset from the multi-label data with missing labels. Experiments on the different datasets demonstrate that compared with the state-of-the-art algorithms the proposed algorithm considerably improves the classification accuracy. The code is publicly available online at https://github.com/skylark-leo/MLMLFS.git</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 23","pages":"12589 - 12612"},"PeriodicalIF":3.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain adaptation of time series via contrastive learning with task-specific consistency 通过具有特定任务一致性的对比学习实现时间序列的领域适应性

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-20 DOI: 10.1007/s10489-024-05799-y

Tao Wu, Qiushu Chen, Dongfang Zhao, Jinhua Wang, Linhua Jiang

{"title":"Domain adaptation of time series via contrastive learning with task-specific consistency","authors":"Tao Wu, Qiushu Chen, Dongfang Zhao, Jinhua Wang, Linhua Jiang","doi":"10.1007/s10489-024-05799-y","DOIUrl":"10.1007/s10489-024-05799-y","url":null,"abstract":"<div><p>Unsupervised domain adaptation (UDA) for time series analysis remains challenging due to the lack of labeled data in target domains. Existing methods rely heavily on auxiliary data yet often fail to fully exploit the intrinsic task consistency between different domains. To address this limitation, we propose a novel time series UDA framework called CLTC that enhances feature transferability by capturing semantic context and reconstructing class-wise representations. Specifically, contrastive learning is first utilized to capture contextual representations that enable label transfer across domains. Dual reconstruction on samples from the same class then refines the task-specific features to improve consistency. To align the cross-domain distributions without target labels, we leverage Sinkhorn divergence which can handle non-overlapping supports. Consequently, our CLTC reduces the domain gap while retaining task-specific consistency for effective knowledge transfer. Extensive experiments on four time series benchmarks demonstrate state-of-the-art performance improvements of 0.7-3.6% over existing methods, and ablation study validates the efficacy of each component.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 23","pages":"12576 - 12588"},"PeriodicalIF":3.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142412770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DAGCN: hybrid model for efficiently handling joint node and link prediction in cloud workflows DAGCN：高效处理云工作流中节点和链路联合预测的混合模型

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2024-09-18 DOI: 10.1007/s10489-024-05828-w

Ruimin Ma, Junqi Gao, Li Cheng, Yuyi Zhang, Ovanes Petrosian

{"title":"DAGCN: hybrid model for efficiently handling joint node and link prediction in cloud workflows","authors":"Ruimin Ma, Junqi Gao, Li Cheng, Yuyi Zhang, Ovanes Petrosian","doi":"10.1007/s10489-024-05828-w","DOIUrl":"10.1007/s10489-024-05828-w","url":null,"abstract":"<div><p>In the cloud computing domain, significant strides have been made in performance prediction for cloud workflows, yet link prediction for cloud workflows remains largely unexplored. This paper introduces a novel challenge: joint node and link prediction in cloud workflows, with the aim of increasing the efficiency and overall performance of cloud computing resources. GNN-based methods have gained traction in handling graph-related tasks. The unique format of the DAG presents an underexplored area for GNNs effectiveness. To enhance comprehension of intricate graph structures and interrelationships, this paper introduces two novel models under the DAGCN framework: DAG-ConvGCN and DAG-AttGCN. The former synergizes the local receptive fields of the CNN with the global interpretive power of the GCN, whereas the latter integrates an attention mechanism to dynamically weigh the significance of node adjacencies. Through rigorous experimentation on a meticulously crafted joint node and link prediction task utilizing the Cluster-trace-v2018 dataset, both DAG-ConvGCN and DAG-AttGCN demonstrate superior performance over a spectrum of established machine learning and deep learning benchmarks. Moreover, the application of similarity measures such as the propagation kernel and the innovative GRBF kernel-which merges the graphlet kernel with the radial basis function kernel to accentuate graph topology and node features-reinforces the superiority of DAGCN models over graph-level prediction accuracy conventional baselines. This paper offers a fresh vantage point for advancing predictive methodologies within graph theory.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"54 23","pages":"12505 - 12530"},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0