Information Fusion最新文献

{"title":"Distributed multi-agent fusion state estimation method based on finite-time average consensus for large-scale power systems","authors":"Tengpeng Chen ,&nbsp;Chen Zhang ,&nbsp;Weize Jing ,&nbsp;Eddy Y.S. Foo ,&nbsp;Lu Sun ,&nbsp;Nianyin Zeng","doi":"10.1016/j.inffus.2025.103753","DOIUrl":"10.1016/j.inffus.2025.103753","url":null,"abstract":"<div><div>Considering that the increasing scale of power systems may lead to high measurement transmitted load and the large amount of measurements also includes many bad data and outliers, a novel distributed multi-agent fusion state estimation (DMFSE) method leveraging the finite-time average consensus algorithm and influence function is proposed for large-scale power systems in this paper. Large-scale power systems are partitioned into multiple subareas, where each subarea deploys a local estimator. Measurements from each subarea are sent directly to their respective local estimator rather than to the central estimator, which reduces the burden of extensive data transmission. The finite-time average consensus algorithm and the influence function are combined together so as to make each local estimator obtain the global state estimation results. The optimization function for the proposed DMFSE method is derived from the generalized correntropy loss function, aiming to mitigate issues arising from bad data and outliers. The simulation results obtained from the IEEE 30-bus, 118-bus and 300-bus systems demonstrate the superior performances of the proposed DMFSE method.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103753"},"PeriodicalIF":15.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ConVLM: Context-guided vision-language model for fine-grained histopathology image classification","authors":"Anabia Sohail ,&nbsp;Iyyakutti Iyappan Ganapathi ,&nbsp;Basit Alawode ,&nbsp;Sajid Javed ,&nbsp;Mohammed Bennamoun ,&nbsp;Arif Mahmood","doi":"10.1016/j.inffus.2025.103737","DOIUrl":"10.1016/j.inffus.2025.103737","url":null,"abstract":"<div><div>Vision-Language Models (VLMs) have recently demonstrated exceptional results across various Computational Pathology (CPath) tasks, such as Whole Slide Image (WSI) classification and survival prediction. These models utilize large-scale datasets to align images and text by incorporating language priors during pre-training. However, the separate training of text and vision encoders in current VLMs leads to only coarse-level alignment, failing to capture the fine-level dependencies between image-text pairs. This limitation restricts their generalization in many downstream CPath tasks. In this paper, we propose a novel approach that enhances the capture of finer-level context through language priors, which better represent the fine-grained tissue morphological structures in histology images. We propose a Context-guided Vision-Language Model (ConVLM) that generates contextually relevant visual embeddings from histology images. ConVLM achieves this by employing context-guided token learning and token enhancement modules to identify and eliminate contextually irrelevant visual tokens, refining the visual representation. These two modules are integrated into various layers of the ConVLM encoders to progressively learn context-guided visual embeddings, enhancing visual-language interactions. The model is trained end-to-end using a context-guided token learning-based loss function. We conducted extensive experiments on 20 histopathology datasets, evaluating both Region of Interest (ROI)-level and cancer subtype WSI-level classification tasks. The results indicate that ConVLM significantly outperforms existing State-of-the-Art (SOTA) vision-language and foundational models. Our source code and pre-trained model is publicly available on: <span><span>https://github.com/BasitAlawode/ConVLM</span><svg><path></path></svg></span></div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103737"},"PeriodicalIF":15.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensively evaluating the perception systems of autonomous vehicles against hazards","authors":"Xiaodong Zhang ,&nbsp;Jie Bao ,&nbsp;Jianlei Chi ,&nbsp;Jun Sun ,&nbsp;Zijiang Yang","doi":"10.1016/j.inffus.2025.103729","DOIUrl":"10.1016/j.inffus.2025.103729","url":null,"abstract":"<div><div>Perception systems are vital for the safety of autonomous driving. In complex autonomous driving scenarios, autonomous vehicles must overcome various natural hazards, such as heavy rain or raindrops on the camera lens. Therefore, it is essential to conduct comprehensive testing of the perception systems in autonomous vehicles against these hazards, as demanded by the regulatory agencies of many countries for human drivers. Since there are many hazard scenarios, each of which has multiple configurable parameters, the challenges are (1) how do we systematically and adequately test an autonomous vehicle against these hazard scenarios, with measurable outcome; and (2) how do we efficiently explore the huge search space to identify scenarios that would induce failure?</div><div>In this work, we propose a Hazards Generation and Testing framework (HazGT) to generate a customizable and comprehensive repository of hazard scenarios for evaluating the perception system of autonomous vehicles. HazGT not only allows us to measure how comprehensively an autonomous vehicle (AV) has been tested against different hazards but also supports the identification of important hazards through optimization. HazGT supports a total of 70 kinds of hazards relevant to the visual perception of AVs, which are based on industrial regulations. HazGT automatically optimizes the parameter values to efficiently achieve different testing objectives. We have implemented HazGT based on two popular 3D engines, i.e., Unity and Unreal Engine. For the two mainstream perception models (i.e., YOLO and Faster RCNN), we have evaluated their performance against each hazard through extensive experiments, and the results show that both systems have much room to improve. In addition, our experiments also found that ChatGPT4 performs slightly worse than YOLO. Our optimization-based testing system is effective in finding perceptual errors in the perception models. The hazard images generated by HazGT are instrumental for improving perception models.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103729"},"PeriodicalIF":15.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RQCMFuse: a reduced biquaternion-driven collaborative modeling network of infrared saliency and visible color-detail for infrared and visible image fusion","authors":"Shan Gai,&nbsp;Qiyao Liang,&nbsp;Yihao Ni","doi":"10.1016/j.inffus.2025.103754","DOIUrl":"10.1016/j.inffus.2025.103754","url":null,"abstract":"<div><div>Existing infrared and visible image fusion methods typically use a single-channel fusion strategy, limiting their ability to capture the interdependencies between multi-channel data. This leads to the inability to preserve both infrared saliency and visible color-detail simultaneously. Furthermore, most methods focus on spatial feature analysis, neglecting valuable frequency information and failing to fully explore frequency characteristics. To address these issues, we propose a novel fusion framework driven by reduced biquaternion (RQ), named RQCMFuse. This framework not only utilizes RQ to model infrared and visible information in a unified manner but also explores frequency characteristics for superior fusion performance. Specifically, our model is designed based on RQ, maintaining low parameter complexity while improving the coordination between infrared and visible features, thereby naturally preserving infrared saliency and visible color-detail. We also introduce an RQ-frequency collaborative block (RQFCB) to efficiently explore frequency characteristics and facilitate the fusion of RQ and frequency domain features. Additionally, we design the invertible downsampling block (IDB) and adaptive integration block (AIB). The IDB enables efficient multi-scale feature extraction without losing high-frequency information, while the AIB adaptively integrates different layers of RQ features, preserving both structural semantics and texture details. Extensive experiments on multiple datasets demonstrate the efficiency and generalization ability of our proposed method. The results show that RQCMFuse significantly enhances infrared saliency and visible color-detail, providing visually superior fusion outcomes that align with human visual perception. Code is available at <span><span>https://github.com/PPBBJL/RQCMFuse</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103754"},"PeriodicalIF":15.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards scalable indoor positioning systems (IPS): User-centric challenges, methods, and recommendations for user-friendly crowd-powered framework","authors":"Ahmed Mansour ,&nbsp;Wu Chen ,&nbsp;Eslam Ali ,&nbsp;Jingxian Wang ,&nbsp;Duojie Weng","doi":"10.1016/j.inffus.2025.103718","DOIUrl":"10.1016/j.inffus.2025.103718","url":null,"abstract":"<div><div>Crowd-powered Indoor Positioning Systems (IPS) offer a cost-efficient and scalable alternative to traditional site-survey-based methods for generating the offline prerequisites of ubiquitous, measurement-driven IPS. However, the widespread adoption of such paradigms depends on resolving critical user-centric challenges that span all layers of the crowd-powered architecture. This survey provides a systematic investigation of these challenges, including user participation schemes, incentive mechanisms, privacy and security threats, and the impact of data collection and localization on user devices. To the best of our knowledge, this is the first in-depth review that examines these issues and their implications for data quality, reliability, and scalability, with a specific emphasis on user-friendliness. It maps these challenges across the architectural layers of crowd-powered IPS, reviews prior studies to analyze the user’s role and assigned tasks in active, opportunistic, and passive participation schemes, emphasizing the objectives of these tasks and the trade-offs associated with each scheme. Next, it distinguishes incentive mechanisms in crowd-powered IPS from those in other domains, highlighting how intrinsic and extrinsic motivations can be aligned with IPS-specific objectives. It then surveys the mathematical models employed in current incentive mechanisms, along with their goals and limitations. Subsequently, it reviews the privacy and security risks, the preservation techniques proposed in existing literature, and their shortcomings. In addition, the survey discusses the adverse impacts of data collection and localization on user devices, identifying potential user burdens and associated mitigation strategies. Finally, it outlines a roadmap of recommendations for developing user-friendly, sustainable, and scalable IPS.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103718"},"PeriodicalIF":15.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixture of experts (MoE): A big data perspective","authors":"Wensheng Gan ,&nbsp;Zhenyao Ning ,&nbsp;Zhenlian Qi ,&nbsp;Philip S. Yu","doi":"10.1016/j.inffus.2025.103664","DOIUrl":"10.1016/j.inffus.2025.103664","url":null,"abstract":"<div><div>As the era of big data arrives, traditional artificial intelligence algorithms have difficulty processing the demands of massive and diverse data. Mixture of experts (MoE) has shown excellent performance and broad application prospects. This paper provides an in-depth review and analysis of the latest progress in this field from multiple perspectives, including the basic principles, algorithmic models, key technical challenges, and application practices of MoE. First, we introduce the basic concept of MoE and its core idea and elaborate on its advantages over traditional single models. Then, we discuss the basic architecture of MoE and its main components, including the gating network, expert networks, and learning algorithms. Next, we review the applications of MoE in addressing key technical issues in big data, including high-dimensional sparse data modeling, heterogeneous multisource data fusion, real-time online learning, and the interpretability of the model. For each challenge, we provide specific MoE solutions and their innovations. Furthermore, we summarize the typical use cases of MoE in various application domains, including natural language processing, computer vision, and recommendation systems, and analyze their outstanding achievements. This fully demonstrates the powerful capability of MoE in big data processing. We also analyze the advantages of MoE in big data environments, including high scalability, efficient resource utilization, and better generalization ability, as well as the challenges it faces, such as load imbalance and expert utilization, gating network stability, and training difficulty. Finally, we explore the future development trends of MoE, including the improvement of model generalization capabilities, the enhancement of algorithmic interpretability, and the increase in system automation levels. We believe that MoE will become an important paradigm of artificial intelligence in the era of big data. In summary, this paper systematically elaborates on the principles, techniques, and applications of MoE in big data processing, providing theoretical and practical references to further promote the application of MoE in real scenarios.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103664"},"PeriodicalIF":15.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum-inspired recommendation approach based on user holographic perception under deep collaboration of large model","authors":"Shanshan Wan ,&nbsp;Shuyue Yang","doi":"10.1016/j.inffus.2025.103725","DOIUrl":"10.1016/j.inffus.2025.103725","url":null,"abstract":"<div><div>Recommender systems powered by highly large model-collaboration can rapidly align with users’ preconceived expectations. However, conventional recommendations fail to fully consider the coupling of user behavior factors and user’s real motives hidden under high dependency of large models. In turn, the superficiality of user portraits causes dimensional collapses of recommendation tasks, giving rise to phenomena such as consumption trajectory constriction and preference rigidity. To address the above issues, this paper proposes a Quantum-Inspired Recommendation Approach Based on User Holographic Perception under Deep Collaboration of Large Model (QIHP), enabling recommendation extrapolation driven by users’ inherent motivations. First, a quantum spatial representation model in large model micro-environments is established. By proposing a progressive dissociation strategy of psychology/character capsules, user “nucleus” sustainable basic portraits are constructed. Then a quantum subnet collaborative method is proposed, emphasizing the extraction of implicit entanglement patterns in users’ behaviors. User shopping internal drives are stripped away to facilitate the construction of user “sand” refined decision-making portraits. Finally, a quantum state shunt attention is introduced to model user latent behavior patterns of trend-burst-mimicry. By harnessing the quantum tunneling mechanism, excessively entangled quantum correlations are disentangled, enabling the reconstruction of emergent hypergraphs that to establish user “cloud” self-organized sensory portraits. Building upon the “nucleus-sand-cloud” holographic polymorphic portraits of users, we develop a multi-task inference extrapolation theory. This leverages quantum fuzzy logic interventions to exploit multi-task inference extrapolation theory that satisfy users’ non-preconceived expectations. Experimental results show that QIHP has substantial enhancements, providing a new solution for recommendations in large model contexts.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103725"},"PeriodicalIF":15.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault-tolerant consensus and fault estimation for uncertain multi-agent systems","authors":"Xufeng Chen ,&nbsp;Liang Yan ,&nbsp;Xiaoshan Gao","doi":"10.1016/j.inffus.2025.103747","DOIUrl":"10.1016/j.inffus.2025.103747","url":null,"abstract":"<div><div>This paper investigates fault-tolerant consensus and fault estimation problems for Multi-Agent Systems (MASs) subject to actuator faults, external disturbances, and model uncertainties. Considering information exchange among agents, we propose a novel distributed state observer with adjustable parameters (AP-DSO), which can simultaneously estimate system states and actuator faults in MASs. The observer structure improves estimation performance in terms of the <span><math><msub><mi>H</mi><mi>∞</mi></msub></math></span> performance index and provides additional design flexibility. Based on the fault estimates provided by the AP-DSO, we then develop a novel distributed fault-tolerant cooperative controller (DFTCC) using output feedback. The DFTCC is designed to ensure that the system can achieve the consensus tracking task even in the presence of actuator faults, external disturbances, and uncertainties. The DFTCC comprises a reference controller, a cooperative controller, and a fault-tolerant controller. Finally, simulation results are presented to demonstrate the effectiveness and feasibility of the proposed AP-DSO and DFTCC schemes.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103747"},"PeriodicalIF":15.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging imaging and genomics: Domain knowledge guided spatial transcriptomics analysis","authors":"Wei Zhang ,&nbsp;Xinci Liu ,&nbsp;Tong Chen ,&nbsp;Wenxin Xu ,&nbsp;Collin Sakal ,&nbsp;Ximing Nie ,&nbsp;Long Wang ,&nbsp;Xinyue Li","doi":"10.1016/j.inffus.2025.103746","DOIUrl":"10.1016/j.inffus.2025.103746","url":null,"abstract":"<div><div>Spatial Transcriptomics (ST) provides spatially resolved gene expression distributions mapped onto high-resolution Whole Slide Images (WSIs), revealing the association between cellular morphology and gene expression profiles. However, the high costs and equipment constraints associated with ST data collection have led to a scarcity of ST datasets. Moreover, existing ST datasets often exhibit sparse gene expression distributions, which limit the accuracy and generalizability of gene expression prediction models derived from WSIs. To address these challenges, we propose DomainST (Domain knowledge-guided Spatial Transcriptomics analysis), a novel framework that leverages domain knowledge through Large Language Models (LLMs) to extract effective gene representations and utilizes foundation models to obtain robust image features for enhanced spatial gene expression prediction. Specifically, we utilize public gene reference databases to retrieve comprehensive gene summaries and employ LLMs to refine gene descriptions and generate informative gene embeddings. Concurrently, we apply medical visual-language foundation models to distill robust image representations at multiple scales, capturing the spatial context of WSIs. We further design a multimodal mixture of experts fusion module to effectively integrate multimodal data, leveraging complementary information across modalities. Extensive experiments conducted on three public ST datasets indicate that our method consistently outperforms state-of-the-art (SOTA) methods, with increases ranging from 6.7 % to 13.7 % in PCC@50 across all datasets compared to the SOTA, demonstrating the effectiveness of combining foundation models and LLM-derived domain knowledge for gene expression prediction. Our code and gene features are available at <span><span>https://github.com/coffeeNtv/DomainST</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103746"},"PeriodicalIF":15.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FedVCPL-Diff: A federated convolutional prototype learning framework with a diffusion model for speech emotion recognition","authors":"Ruobing Li ,&nbsp;Yifan Feng ,&nbsp;Lin Shen ,&nbsp;Liuxian Ma ,&nbsp;Haojie Zhang ,&nbsp;Kun Qian ,&nbsp;Bin Hu ,&nbsp;Yoshiharu Yamamoto ,&nbsp;Björn W. Schuller","doi":"10.1016/j.inffus.2025.103745","DOIUrl":"10.1016/j.inffus.2025.103745","url":null,"abstract":"<div><div>Speech Emotion Recognition (SER), a key emotion analysis technology, has shown significant value in various research areas. Previous SER models have achieved good emotion recognition accuracy, but typical centrally-based training requires centralised processing of speech data, which has a serious risk of privacy leakage. Federated learning (FL) can avoid centralised data processing through distributed learning, providing a solution for privacy protection in SER. However, FL faces several challenges in practical applications, including imbalanced data distribution and inconsistent labelling. Furthermore, typical FL frameworks focus on client-side enhancement and ignore server-side aggregation strategy optimisation, which can increase the computational load on the client side. To address the aforementioned problems, we propose a novel approach, FedVCPL-Diff. Firstly, regarding information fusion, we introduce a diffusion model on the server side to generate Valence-Arousal-Dominance emotion space features, which replaces the typical aggregation framework and effectively promotes global information fusion. In addition, in terms of information exchange, we propose a lightweight and personalised FL transmission framework based on the exchange of VAD features. FedVCPL-Diff optimises the local model by updating the data distribution anchors, which not only avoids the privacy risk but also reduces the communication cost. Experimental results show that the framework significantly improves emotion recognition performance compared to four commonly used FL frameworks. The overall performance of our framework also shows a significant advantage compared to locally independent models.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"127 ","pages":"Article 103745"},"PeriodicalIF":15.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}