Information Fusion最新文献

{"title":"Expert knowledge-guided multi-granularity multi-scale fusion for weakly-supervised histological segmentation","authors":"Xifeng Hu ,&nbsp;Yankun Cao ,&nbsp;Weifeng Hu ,&nbsp;Shanshan Wang ,&nbsp;Subhas Chandra Mukhopadhyay ,&nbsp;Yu Liu ,&nbsp;Huafeng Li ,&nbsp;Yujun Li ,&nbsp;Qing Cai ,&nbsp;Zhi Liu","doi":"10.1016/j.inffus.2025.103432","DOIUrl":"10.1016/j.inffus.2025.103432","url":null,"abstract":"<div><div>Medical image fusion plays a crucial role in enhancing weakly-supervised segmentation performance while alleviating the over-reliance on dense annotations. However, existing methods tend to integrate unfiltered textual data with visual features, resulting in semantic redundancy and ambiguity that ultimately impair visual-textual alignment . Furthermore, they often rely on single-scale fusion schemes, which can lead to the loss of critical semantic information. To address these challenges, we propose an Expert Knowledge-Guided Multi-granularity Multi-scale fusion framework for weakly-supervised histological segmentation, which leverages fine-grained text representations and multi-scale label-pixel fusion and alignment to suppress redundancy and strengthen supervision. Specifically, to address the interference of redundant text on homogeneous pixels, we start by constructing an expert knowledge-guided fine-grained text representation paradigm, progressively refining and extracting key information to uncover the multi-level clues and semantic features of the images. To effectively represent and convey fine-grained guidance knowledge, a multi-scale label-pixel fusion and alignment module is proposed, which emphasizes the fusion and interaction between fine-grained text prompts and image features, enhancing category sensitivity. Additionally, a visual state-space adaptive layer is embedded into a multi-stage pre-trained transformer encoder to improve long-range dependency modeling with low computational cost. The experiments on public datasets demonstrate the effectiveness of the proposed method. Our approach outperforms current methods in both quantitative and qualitative evaluations.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103432"},"PeriodicalIF":14.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515818","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":"A fusion method for large-scale online review ranking","authors":"Mengchun Ma ,&nbsp;Bin Yu ,&nbsp;Weiping Ding","doi":"10.1016/j.inffus.2025.103429","DOIUrl":"10.1016/j.inffus.2025.103429","url":null,"abstract":"<div><div>Online reviews constitute a pivotal informational asset that significantly influences consumer purchasing decisions. While prior research has predominantly focused on product ranking based on these reviews, the challenge of handling large-scale online review datasets has been largely overlooked. Furthermore, the consensus among ranking outcomes, a critical factor in ensuring the reliability of rankings, has seldom been addressed. This study introduces a novel consensus-based ranking approach tailored for large-scale rating datasets, incorporating cluster analysis, multi-attribute decision-making (MADM), and a consensus-reaching mechanism. Initially, a rating matrix is constructed to consolidate the extensive rating data. Subsequently, cluster analysis segments the vast user base, and MADM is leveraged to produce group-specific rankings. Ranking aggregation technique is then applied to synthesize the rankings from disparate user groups into a unified collective ranking. Ultimately, a consensus-reaching process, which accounts for both intra-cluster and inter-cluster agreement, refines the ranking to ensure a harmonized consensus among all user groups. The efficacy and applicability of this methodology are substantiated through an empirical case study examining hotel rankings in New York City.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103429"},"PeriodicalIF":14.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514400","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":"Group decision-making in heterogeneous multi-scale information fusion: Integrating overconfident and non-cooperative behaviors","authors":"Yibin Xiao,&nbsp;Xueling Ma,&nbsp;Jianming Zhan","doi":"10.1016/j.inffus.2025.103401","DOIUrl":"10.1016/j.inffus.2025.103401","url":null,"abstract":"<div><div>In the field of group decision-making (GDM), the complex heterogeneous data continuously challenges the traditional single decision-making model, highlighting the limitations of traditional methods in handling multi-dimensional data and dynamic scenarios. Although information fusion is of great significance for GDM, there are still significant deficiencies in existing research within the framework of multi-scale information systems (MSIS). In particular, there is an urgent need to address the challenges of dealing with multi-structural data and managing the complex behaviors of decision-makers (DMs). Firstly, a novel concept, the heterogeneous multi-scale information system (HMSIS), is put forward. This system innovatively integrates utility value quantification analysis, fuzzy preference relation modeling, preference ranking algorithms, and equivalence class partitioning techniques, thereby constructing a highly realistic simulation framework for real-world data. Through this cross-paradigm data integration approach, the HMSIS provides a more adaptable and scalable theoretical foundation for GDM, effectively resolving the limitations of traditional models in handling complex data structures. Building on this foundation, this paper further develops the consensus-trust multi-network opinion interaction mechanism. This mechanism shatters the constraints of one-way information transmission in traditional decision-making processes. By devising an adaptive opinion exchange protocol, it enables multi-round and multi-dimensional information interactions among decision-makers. Additionally, innovative behavior monitoring and intervention rules are introduced, which can detect irrational behaviors of DMs, such as overconfidence and non-cooperation, in real time. Through dynamic weight adjustment, intelligent guidance strategies, and other means, targeted management is implemented to ensure the stability and effectiveness of the group decision-making process. Moreover, this paper constructs an optimized consensus reaching process (CRP). By embedding an optimization model and under the intelligent regulation of a virtual decision-making coordinator, it optimizes both the efficiency of decision-making information transmission and the accuracy of opinion convergence simultaneously. With the core objectives of minimizing the decision adjustment distance and shortening the consensus-reaching time, and combined with a dynamic weight allocation algorithm, this model achieves efficient and fair consensus building in complex decision-making environments. Finally, empirical studies conducted on a real-world dataset demonstrate the remarkable superiority of the proposed method. The experimental results further validate the method’s robust performance in handling heterogeneous data and complex decision-making scenarios.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103401"},"PeriodicalIF":14.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515705","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":"Identifying spatial domains from spatial multi-omics data using consistent and specific deep subspace learning","authors":"Guangchang Cai ,&nbsp;Fuqun Chen ,&nbsp;Kepei Wen ,&nbsp;Ying Li ,&nbsp;Le Ou-Yang","doi":"10.1016/j.inffus.2025.103428","DOIUrl":"10.1016/j.inffus.2025.103428","url":null,"abstract":"<div><div>The rapid advancement of spatial omics technologies has revolutionized the ability to simultaneously capture multi-omics data along with spatial information, offering unprecedented insights into tissue architecture and cellular heterogeneity. Spatial domain identification is a fundamental step in spatial omics analysis, as it facilitates the delineation of functional tissue regions. However, most existing methods are tailored for single omic data and face substantial limitations when extended to spatial multi-omics contexts. Integrating consistent and complementary signals across multiple omics within spatially structured data remains a key challenge. In this study, we propose SpaMICS, a deep subspace learning framework designed for spatial domain identification from spatial multi-omics data. SpaMICS captures spatial dependencies and latent inter-spot relationships to learn high-level representations for each omic. To enhance information integration, we introduce a subspace learning module that explicitly disentangles consistent and complementary information across omics. Furthermore, we incorporate dual constraints to enhance information extraction: a low-rank constraint to emphasize consistent information across omics and a discriminative constraint that facilitates the extraction of complementary information. Extensive experiments on five real-world spatial multi-omics datasets, including spatial transcriptomics–proteomics and spatial transcriptomics–epigenomics data, demonstrate that SpaMICS consistently outperforms existing approaches, effectively integrating multi-omics data for accurate spatial domain identification.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103428"},"PeriodicalIF":14.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515819","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":"BMCST: Balanced multi-view clustering for spatially resolved transcriptomics with Mamba-driven dynamic feature refinement","authors":"Yanran Zhu ,&nbsp;Xiao Zheng ,&nbsp;Xiao He ,&nbsp;Xin Zou ,&nbsp;Peihong Wang ,&nbsp;Chang Tang ,&nbsp;Xinwang Liu ,&nbsp;Kunlun He","doi":"10.1016/j.inffus.2025.103425","DOIUrl":"10.1016/j.inffus.2025.103425","url":null,"abstract":"<div><div>Spatially resolved transcriptomics (SRT) provides histological images, spatial location, and gene expression profiles for spatial clustering analysis, offering profound insights into cellular interactions and disease progression mechanisms. Despite the progress made in spatial clustering research, several primary challenges persist due to the inherent noise and view heterogeneity in SRT data: (1) Intra-view: existing methods are prone to overlooking the interference from low-quality or redundant features when modeling global dependencies among spots, which leads to excessive information propagation and redundant computations; (2) Inter-view: the widely adopted joint training paradigm tends to result in an imbalance and suboptimal optimization of view-specific features. To this end, we propose a novel balanced multi-view clustering method for SRT data, referred to as BMCST. Specifically, we introduce a state-adaptive processing architecture, the Mamba-driven Dynamic Feature Refinement (MDFR) module, which adapts to the state of the input to dynamically select and prioritize the most informative features within the intra-view context, disregarding the noise and irrelevant information. This strategy ensures comprehensive global modeling while precisely capturing local spatial dependencies. Additionally, an unsupervised dominant view mining mechanism is introduced to dynamically identify the most discriminative perspectives prior to the feature fusion process, coupled with optimized alignment among views and consistent similarity distributions between nodes, aiming to mitigate inter-view information imbalance. Extensive experiments show that the proposed BMCST outperforms other state-of-the-art methods in spatial domain identification.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103425"},"PeriodicalIF":14.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501349","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":"Uncertain multi-conceptual information acquisition and fusion for hierarchical classification","authors":"Xiaoyuan Deng ,&nbsp;Jinhai Li ,&nbsp;Weiping Ding ,&nbsp;Xizhao Wang","doi":"10.1016/j.inffus.2025.103421","DOIUrl":"10.1016/j.inffus.2025.103421","url":null,"abstract":"<div><div>Recently, hierarchical classification has become a hot research problem due to the wide existence of hierarchically structured data in the real world. However, some existing studies on hierarchical classification proposed a series of feature selection methods without considering the design of a stopping mechanism, while others designed the stopping mechanism without taking the feature selection into account. Despite the simplicity of integrating the above two parts, they are usually not compatible with each other, which lead to reduced performance of hierarchical classification models. In this work, we obtain a novel hierarchical classifier by constructing a hierarchical fuzzy concept-cognitive learning model (HFCCLM), in which incremental hierarchical feature selection is realized by the update of inclusion degree of fuzzy concepts, and a stopping mechanism is designed by learning uncertainties of samples matching nodes in a hierarchical tree structure. That is, feature selection and design of a stopping mechanism can be unified in the fuzzy concept-cognitive learning framework. Furthermore, by integrating the uncertainties of set approximation of clue based fuzzy reasoning and clue based fuzzy concept-cognitive learning for stopping samples at appropriate nodes, it significantly enhances the performance of the proposed hierarchical classifier. Finally, experiments are carried out to show the effectiveness of our model in achieving hierarchical classification tasks.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103421"},"PeriodicalIF":14.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514399","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":"Street Space Quality Improvement: Fusion of Subjective Perception in Street View Image Generation","authors":"Chenbo Zhao ,&nbsp;Yoshiki Ogawa ,&nbsp;Shenglong Chen ,&nbsp;Takuya Oki ,&nbsp;Yoshihide Sekimoto","doi":"10.1016/j.inffus.2025.103467","DOIUrl":"10.1016/j.inffus.2025.103467","url":null,"abstract":"<div><div>The development of sustainable cities and communities aligns with the Sustainable Development Goals (SDGs) and smart city initiatives, emphasizing the integration of residents' subjective perceptions into urban street space planning. While previous research has quantitatively assessed streetscape quality, existing methods remain largely conceptual and lack actionable strategies for improvement. Recent advances in generative AI have enabled the generation of realistic and visually compelling images across various domains. However, most existing image generation frameworks lack a mechanism to directly incorporate residents' subjective perceptions when modifying street view imagery. This gap results in generated images that, while aesthetically impressive, may not fully align with the preferences and lived experiences of local communities. To address this issue, we propose a novel, data-driven approach that conditionally fuses subjective perception data into the transformation of original street view images. Our method integrates multidimensional perception cues, including beautiful, safety, lively, etc., fused the 8.8 million perception survey data to generate street views that are more reflective of public sentiment. Experimental evaluations demonstrate an 86.36% success rate in enhancing 22 distinct subjective perception metrics based on initial street view inputs. This fusion-based methodology advances both image generation and smart city development by aligning generated landscapes with resident preferences. It also provides urban planners and community stakeholders with a robust framework for visualizing targeted street space improvements and designing more livable, human-centric urban environments.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103467"},"PeriodicalIF":14.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515706","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":"DIFNet: Dual-Information Fusion Network for depth completion","authors":"Kunyang Wu ,&nbsp;Jun Lin ,&nbsp;Jiawei Miao ,&nbsp;Zhengpeng Li ,&nbsp;Xiucai Zhang ,&nbsp;Genyuan Xing ,&nbsp;Yiyao Fan ,&nbsp;Jinxin Luo ,&nbsp;Huanyu Zhao ,&nbsp;Yang Liu ,&nbsp;Guanyu Zhang","doi":"10.1016/j.inffus.2025.103424","DOIUrl":"10.1016/j.inffus.2025.103424","url":null,"abstract":"<div><div>Depth completion, the task of reconstructing dense depth maps from sparse measurements, is crucial for scene understanding and autonomous systems. Leveraging aligned, high-resolution RGB images as guidance is a common and powerful approach, yet the inherent frequency heterogeneity between RGB and sparse depth data presents a significant challenge for effective cross-modal fusion. Conventional methods often employ simplistic fusion strategies that overlook these distinct frequency characteristics, limiting their ability to fully exploit the complementary nature of RGB and depth information. In this paper, we introduce DIFNet: a Dual-Information Fusion Network, based on a novel frequency-aware fusion paradigm focused on image-guided completion. The core of DIFNet is the Dual Stream Modeling (DSM) block, which explicitly decouples and processes high-frequency edge details and low-frequency smooth regions with tailored architectures, leveraging a spatially-aware Mamba architecture for high-frequency streams and densely connected convolutions for low-frequency streams. Furthermore, DIFNet incorporates an innovative Initial Feature Fusion (IFF) layer to facilitate synergistic multi-scale RGB and depth feature integration from the input stage. Extensive evaluations on KITTI and NYUv2 datasets demonstrate that DIFNet achieves state-of-the-art performance in depth completion with competitive computational efficiency, highlighting the efficacy of our frequency-aware dual information fusion strategy. The code for this work is publicly available at <span><span>https://github.com/wuky2000/DIFNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103424"},"PeriodicalIF":14.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515707","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":"MCNet: A multi-level consistency network for 3D point cloud self-supervised learning","authors":"Hongshuo Liu ,&nbsp;Jing Bai ,&nbsp;Gan Lin","doi":"10.1016/j.inffus.2025.103410","DOIUrl":"10.1016/j.inffus.2025.103410","url":null,"abstract":"<div><div>With the rapid advancement of computer vision and artificial intelligence, point clouds have become a pivotal representation of 3D data. However, practical applications of point clouds are hindered by challenges such as noise, structural sparsity, and information occlusion, which complicate computations and degrade the performance of high-precision analyses. While self-supervised learning has proven effective in reducing reliance on annotated data, existing methods predominantly focus on local features, often neglecting global structures and the balance between geometric and semantic information. This paper introduces the Multi-level Consistency Network (MCNet), a novel framework designed to comprehensively explore multi-level feature information in point clouds. MCNet integrates geometric, structural, and high-order semantic supervisory signals, fostering alignment and complementarity of features through self-supervised learning. We propose the Global–Local Synergistic Noise Module (GLSNM), which combines Principal Component Analysis-based Non-Local Noise Addition (PCA-NLNA) with Mask-based Local Noise Injection (Mask-LNI) to balance the preservation of global structures and local details. Additionally, we develop the Multi-level Information Reconstruction Module (MIRM), which employs an attention fusion mechanism to dynamically balance geometric and high-order semantic information, thereby enhancing the model’s feature extraction capabilities in complex environments. Extensive experiments demonstrate that MCNet consistently outperforms existing methods across multiple tasks, including meta-classification, few-shot classification, real-world scene classification, fine-grained classification, and segmentation. These results validate the effectiveness of MCNet and its significant contribution to the field of point cloud processing.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103410"},"PeriodicalIF":14.7,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481726","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":"Multi-View Learning and Model Fusion Framework for Threat Detection in Multi-Protocol IoMT Networks","authors":"Sekione Reward Jeremiah, Abir El Azzaoui, Stefanos Gritzalis, Jong Hyuk Park","doi":"10.1016/j.inffus.2025.103435","DOIUrl":"https://doi.org/10.1016/j.inffus.2025.103435","url":null,"abstract":"The Internet of Medical Things (IoMT) holds significant transformative potential for modern healthcare systems. It enables real-time patient monitoring and data insights for making informed clinical decisions. Existing research on IoMT security has primarily focused on data security concerns, overlooking the complexity and vulnerabilities arising from the heterogeneity of devices and communication protocols. Due to the complexity of IoMT network traffic and the high volume of data, advanced methods are necessary to enhance the security and reliability of these networks. Machine Learning (ML)-based methods provide effective techniques for detecting, preventing, and mitigating cyber threats. However, conventional centralized ML approaches are susceptible to privacy risks and vulnerabilities to single points of failure (SPoFs). This study proposes a cyberthreat detection method that employs a multi-view-based model fusion approach within a Federated Learning (FL) framework to enhance detection capabilities across multi-protocol IoMT networks. Federated learning is adopted to preserve data privacy by avoiding data transfer to central servers and mitigating SPoFs. The proposed method is evaluated using the CICIoMT2024 dataset featuring 17 Wi-Fi devices and 14 simulated MQTT devices with 18 attack scenarios across five categories (DoS, DDoS, spoofing, Recon, and MQTT). Overall, the method achieves superior threat detection using TabNet as the base learner and MLP as the meta-learner, with accuracies of 99.7% and 99.4% in binary and multi-class classification, respectively.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"27 1","pages":"103435"},"PeriodicalIF":18.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144515708","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}