Expert Systems with Applications最新文献

{"title":"JHN-Seg: Multi-scale vascular segmentation via Joint Hierarchical Morphology learning and noisy label refinement","authors":"Zhiyong Zhou ,&nbsp;Zheyuan Zhang ,&nbsp;Jisu Hu ,&nbsp;Yudong Zhang ,&nbsp;Xianhai Mao ,&nbsp;Chen Geng ,&nbsp;Xusheng Qian ,&nbsp;Bo Peng ,&nbsp;Bin Dai ,&nbsp;Yakang Dai","doi":"10.1016/j.eswa.2025.128096","DOIUrl":"10.1016/j.eswa.2025.128096","url":null,"abstract":"<div><div>Vascular segmentation is essential for various medical applications, such as computer-aided diagnosis, treatment planning, and surgical interventions. However, current deep learning-based vascular segmentation methods face two significant challenges: the complex morphological diversity of vascular structures, which results in discontinuous segmentation in small-scale vessels and incomplete preservation of topological integrity, and the adverse effects of noisy labels during network optimization. To address these challenges, we propose a Joint Hierarchical Vascular Morphology Learning and Noise Label Refinement method (JHN-Seg). JHN-Seg introduces a Hierarchical Vascular Morphology-Aware Network (HVMA-Net) that integrates a Multi-Scale Local Morphology-Aware (MLMA) module, employing a multi-pattern convolutional strategy to adaptively capture intricate vascular features across scales. A Global Morphology Preserving (GMP) loss function is incorporated into HVMA-Net to enforce the continuity of small-scale vessels and maintain the integrality of global vascular structures. Furthermore, JHN-Seg introduces an Uncertainty-Aware Distillation (UD) strategy, which incorporates an Uncertainty Label Refinement (ULR) Module for uncertainty-guided noisy label correction by leveraging pixel-wise KL divergence and consistency generated by teacher-student framework. Comprehensive experiments on liver vessel datasets demonstrate that JHN-Seg outperforms other state-of-the-art segmentation methods. The framework’s adaptability and performance advancements position it as a transformative solution for vascular segmentation and broad applicability to medical image analysis tasks requiring precise morphological representation and noise-robust learning.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"285 ","pages":"Article 128096"},"PeriodicalIF":7.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941841","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":"Aerial video classification with Window Semantic Enhanced Video Transformers","authors":"Feng Yang ,&nbsp;Xi Liu ,&nbsp;Botong Zhou ,&nbsp;Xuehua Guan ,&nbsp;Anyong Qin ,&nbsp;Tiecheng Song ,&nbsp;Yue Zhao ,&nbsp;Xiaohua Wang ,&nbsp;Chenqiang Gao","doi":"10.1016/j.eswa.2025.127883","DOIUrl":"10.1016/j.eswa.2025.127883","url":null,"abstract":"<div><div>With their exceptional flexibility and cost-effectiveness, unmanned aerial vehicles can capture vast amounts of high-quality aerial videos. Consequently, the research on unmanned aerial vehicle video classification, aiming to analyze the spatio-temporal patterns embedded in these videos automatically, is currently flourishing. Compared to conventional ground videos, aerial videos offer a broader perspective, introducing complex visual patterns of both global scenes and local motions. Although current Transformer-based methods have achieved impressive results in video classification, they struggle to capture small key subject movements from the large backgrounds of aerial videos due to a fixed global receptive field. To address these issues, we propose <em>Window Semantic Enhanced Aerial Video Transformers</em> that explicitly enhance local semantics and learn spatio-temporal features through self-attention design. We introduce a <em>Window Semantic Enhanced Transformer Block</em>, comprising a <em>Window Localization</em> module to identify crucial local regions in aerial videos and then enhance local semantics through <em>Window-based Time Attention</em>. Furthermore, we devise a <em>Video Class Attention Transformer Block</em> that directly learns video-level features by late class embedding of video semantic tokens, preventing intermediate frame-level representation that may lead to information loss. To validate the effectiveness of our approach, we conduct extensive experiments on two aerial video classification datasets, ERA and MOD20, demonstrating superior performance with accuracies of 73.9% and 97.0%, respectively.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"285 ","pages":"Article 127883"},"PeriodicalIF":7.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942617","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":"Image-text semantic learning for unsupervised cross-resolution person re-identification","authors":"Fuqi Liu ,&nbsp;Zhiqi Pang ,&nbsp;Chunyu Wang","doi":"10.1016/j.eswa.2025.128092","DOIUrl":"10.1016/j.eswa.2025.128092","url":null,"abstract":"<div><div>Cross-resolution person re-identification (CR-ReID) focuses on matching person images of the same identity across different resolutions. Most existing CR-ReID methods rely on manually annotated identity labels for training. Although some researchers have proposed unsupervised CR-ReID (UCR-ReID) methods, the feature fusion techniques they rely on still require a large number of parameters and significant computational resources, limiting the widespread application of UCR-ReID technology. To address the aforementioned issues, we propose an image-text semantic learning (ITSL) method, which incorporates text semantics to enhance recognition performance. During the testing phase, ITSL requires only a single encoder to obtain resolution-invariant features. Specifically, ITSL first learns text features based on a visual-language model, and then utilizes the dual semantic matching module to match inter-resolution positive clusters in both the image and text modalities. During the optimization process, ITSL not only incorporates image semantic contrastive loss to facilitate cross-resolution alignment but also integrates text semantic contrastive loss to leverage text semantics for promoting resolution-invariance learning. Additionally, we design random region downsampling in ITSL, which further enhances the model’s robustness to resolution gaps through data augmentation. Experimental results on multiple cross-resolution datasets show that ITSL not only outperforms existing unsupervised methods while maintaining efficiency, but also approaches the performance of earlier supervised methods on certain datasets.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"286 ","pages":"Article 128092"},"PeriodicalIF":7.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068677","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":"Enhancing one-Class classifiers performance in multivariate time series through dynamic clustering: A case study on hydraulic system fault detection","authors":"André Paulo Ferreira Machado,&nbsp;Celso Jose Munaro,&nbsp;Patrick Marques Ciarelli","doi":"10.1016/j.eswa.2025.128088","DOIUrl":"10.1016/j.eswa.2025.128088","url":null,"abstract":"<div><div>Datasets composed of multivariate time series arising from real applications are usually affected by many factors such as noise and disturbances. Any modeling procedure benefits from having its training data carefully selected. This paper presents a methodology designed to enhance the performance of one-class classifiers in time series by incorporating dynamic time series clustering. The clustering process leverages DTW Barycenter Averaging (DBA) and k-means to group multivariate time series based on similarity. The Apriori algorithm is used to generate subsets of instances, which are then used to train multiple one-class classifiers for the same class. Three distinct strategies are applied to combine the outputs of these classifiers for each class. The proposed method is evaluated on a hydraulic system dataset to investigate typical faults that occur simultaneously and with varying intensities. The results show that improving the similarity of the training subsets and combining the outputs of the classifiers led to a performance improvement of more than 89 %. In addition, the methodology successfully reduced a hydraulic system dataset from 17 variables to as few as 3 or even 1, while still achieving better classification performance compared with recent findings in the literature.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"286 ","pages":"Article 128088"},"PeriodicalIF":7.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072177","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":"PromptHC: Multi-attention prompt guided haze-weather crowd counting","authors":"Jienan Shen ,&nbsp;Liangang Tong ,&nbsp;Shaohua Li ,&nbsp;Weihang Kong","doi":"10.1016/j.eswa.2025.128023","DOIUrl":"10.1016/j.eswa.2025.128023","url":null,"abstract":"<div><div>Existing crowd counting methods encounter the challenge of degraded performance in hazy weather due to the blurring of pedestrian outlines. However, current hazy-weather crowd counting methods primarily focus on extracting crowd features, often neglecting the varying degrees of distortion in pedestrian outlines caused by inhomogeneous haze distribution. To this end, this paper develops a multi-attention prompt guided method for hazy-weather crowd counting, termed PromptHC. Specially, to explore the relationship between varying haze concentrations and the pedestrian outlines, a multi-attention dynamically adjustable prompt module is designed to provide crucial prompts about crowd features in hazy weather. Meanwhile, to further enhance the anti-interference capability of the model in hazy weather, a progressive guidance module is incorporated, which effectively reduces interference from different haze concentrations by guiding the learning of crowd attention. Furthermore, a global context-enhanced crowd feature extraction module is designed to capture precise global information. A series of ablation studies verify the actual effectiveness of each core component of the PromptHC. In addition, we conduct a performance comparison with the current mainstream methods on two hazy-weather datasets. Experimental results show the feasibility and superiority of the PromptHC for the hazy-weather crowd counting task.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"286 ","pages":"Article 128023"},"PeriodicalIF":7.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068655","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":"Enhanced multi-grade diabetic retinopathy detection and classification via ensembled deep learning model from retinal fundus images","authors":"Peddapullaiahgari Hariobulesu ,&nbsp;Fahimuddin Shaik","doi":"10.1016/j.eswa.2025.128116","DOIUrl":"10.1016/j.eswa.2025.128116","url":null,"abstract":"<div><div>Diabetic retinopathy (DR) represents a primary cause of vision impairment, highlighting the importance of early and precise detection to reduce its advancement. This study presents DiaRetULS-Net, an innovative Ensembled model developed for the automated detection and classification of diabetic retinopathy severity utilizing retinal fundus images. The proposed methodology utilizes advanced preprocessing techniques, such as Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement, alongside robust feature extraction methods including Discrete Wavelet Transform (DWT) and Local Binary Patterns (LBP) to effectively capture essential frequency and texture-based features. The DiaRetULS-Net architecture combines U-Net for accurate segmentation of retinal abnormalities, the Liquid Time Constant Neural Network (LTCN) for the extraction of dynamic spatial and temporal features, and a Multi-Class Support Vector Machine (SVM) for precise classification of diabetic retinopathy severity levels. The model was assessed using the Messidor-2 dataset and a 5-fold cross-validation approach, resulting in notable performance metrics: 98.83% accuracy, 98.87% specificity, and 99.21% sensitivity. Comprehensive analyses, such as the Receiver Operating Characteristic (ROC) curve, confusion matrix, and error histogram, substantiate the model’s reliability and efficiency.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"285 ","pages":"Article 128116"},"PeriodicalIF":7.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931726","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":"Diagnosis system for retinopathy of prematurity with Fourier parameterized rotation equivariant convolutions network and prompt mechanism","authors":"Sisi Chen ,&nbsp;Feng Chen ,&nbsp;Zewu Huang ,&nbsp;Yubo Gu ,&nbsp;Guiying Zhang","doi":"10.1016/j.eswa.2025.128069","DOIUrl":"10.1016/j.eswa.2025.128069","url":null,"abstract":"<div><div>Retinopathy of Prematurity (ROP) represents a significant ophthalmic disorder in preterm infants, posing substantial risks to visual development. While deep learning-based approaches have been increasingly applied to ROP diagnosis, current research predominantly focuses on plus disease detection and basic screening/staging of ROP, with insufficient attention to the critical aspect of disease zoning. Moreover, the integration of automated staging and zoning, which is essential for comprehensive disease severity assessment, remains unexplored in existing literature. Against this background, we propose a novel dual-task neural network framework, SegClass-Net, which incorporates Fourier series expansion-based equivariant convolution (F-Conv) for simultaneous segmentation and classification tasks. This framework is specifically designed to perform precise segmentation of the optic disc (OD) and retinal lesions while concurrently generating diagnostic outputs encompassing both staging and zoning information. The methodological innovation lies in the implementation of F-Conv, which significantly enhances segmentation precision through its advanced feature extraction capabilities. Furthermore, we introduce a novel prompting mechanism that utilizes lesion segmentation results as prior information to refine staging accuracy. This integrated approach not only establishes a foundation for accurate ROP zoning but also enhances overall diagnostic performance through synergistic information utilization. Extensive experimental evaluations demonstrate the effectiveness of our approach, with segmentation precision reaching 96.00 % for OD and 90.81 % for lesions, respectively. Notably, the overall ROP diagnostic accuracy achieves 91.78 %, representing a 6.85 % improvement over conventional methods that treat staging and zoning as separate tasks. These results suggest that SegClass-Net offers a promising solution for comprehensive ROP assessment, potentially facilitating earlier intervention and improved clinical outcomes in neonatal ophthalmology.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"286 ","pages":"Article 128069"},"PeriodicalIF":7.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072105","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":"Leading the green charge: A novel type-2 fuzzy VIKOR method applied to eco-conscious freight transport","authors":"Sepehr Hendiani ,&nbsp;Ali Ebadi Torkayesh ,&nbsp;Sandra Venghaus ,&nbsp;Grit Walther","doi":"10.1016/j.eswa.2025.128082","DOIUrl":"10.1016/j.eswa.2025.128082","url":null,"abstract":"<div><div>Imprecise and ambiguous information is critical in multi-criteria group decision-making (MCGDM) problems. Quantification of such information is essential in determining the best alternative. In this study, an interval type-2 fuzzy set (IT2FS) possibility-based Vlsekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) approach is developed to address MCGDM problems. The possibilities of IT2FSs are employed to establish a new decision matrix containing crisp information, which decreases the computational complexities involved with processing IT2FSs. With the use of the new decision matrix, decision-makers (DMs) may now assess alternatives in pairs to determine which alternatives have benefits over the others. Due to the adoption of possibilities of IT2FSs, the proposed approach works efficiently even in cases where the differences between alternatives are minor. Since road freight transport emissions represent a very significant contributor to transportation-related greenhouse gas emissions, there is an urgent need to seek sustainable solutions in this area. Thus, the model is applied to the road freight transport to rank different fuel alternatives. The proposed possibility-based VIKOR method provides a robust framework for evaluating renewable fuel alternatives by considering sustainability benefits and market barriers, while overcoming several key limitations of traditional MCDM methods. It is recommended that this approach be utilized in real-world decision-making for sustainable freight transport planning. Future research could explore its integration with dynamic data sources for more adaptive and real-time decision support.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"285 ","pages":"Article 128082"},"PeriodicalIF":7.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942547","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":"Food Full-Process and All-Information traceability based on Multi-Chain blockchain and trusted transmission protocols","authors":"Chenze Liu ,&nbsp;Jiping Xu ,&nbsp;Zhiyao Zhao ,&nbsp;Shichao Chen ,&nbsp;Yue Li ,&nbsp;Xin Zhang","doi":"10.1016/j.eswa.2025.128008","DOIUrl":"10.1016/j.eswa.2025.128008","url":null,"abstract":"<div><div>Food quality and safety are crucial for human health and social stability, and full-process and all-information traceability is a key factor in ensuring food quality and safety. The food supply chain is characterized by numerous participants, complex data structures, and difficulties in cross-domain information sharing, leading to challenges in ensuring the trustworthiness of data sources and secure data flow. To address these issues, blockchain technology has been gradually applied to food traceability. However, challenges such as low storage capacity and low consensus efficiency of single-chain blockchain systems remain, and issues related to the unreliability of data collection from various stakeholders have not been effectively resolved.This paper is based on the multi-chain theory of blockchain and integrates active data collection carrier technology based on trusted transmission protocols to study and verify a full-process and all-information traceability model for food throughout the supply chain based on a master–slave multi-chain architecture. Firstly, the study constructs a full-process and all-information traceability model for the food blockchain throughout the supply chain based on master–slave multi-chain architecture by integrating multi-modal storage, cryptography, and TBFT consensus mechanisms, following an analysis of full-process and all-information in the food supply chain. Secondly, using this model as a foundation, the research constructs a trusted collection model architecture based on blockchain and trusted transmission protocols, integrating active data collection carrier technology. An active data collection carrier based on RFID suitable for full-process and all-information traceability of food was developed, and protocol processes and trusted collection were verified. Finally, the traceability system was designed and implemented based on the ChainMaker open-source framework, and simulations were conducted to analyze the trustworthiness of data interactions, traceability accuracy, and traceability query efficiency, comparing the results with other classic solutions. The results demonstrate that the average data upload success rate is 98.86%, the average time for querying public data traceability is 1.489 s, and the average time for querying private data traceability is 1.812 s. The traceability model and system solution studied in this paper meet the requirements for efficient and secure full-process and all-information traceability of food, ensuring the trustworthiness of data sources and providing a feasible reference for food quality safety assurance and traceability, thereby significantly enhancing food safety.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"286 ","pages":"Article 128008"},"PeriodicalIF":7.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943384","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":"SIMSE: a contrastive learning method combining sample importance metric and semantic enhancement","authors":"Yangyang Gao ,&nbsp;Zhi Zheng ,&nbsp;Wenjun Huang ,&nbsp;Xiaomin Lin","doi":"10.1016/j.eswa.2025.128045","DOIUrl":"10.1016/j.eswa.2025.128045","url":null,"abstract":"<div><div>In complex application scenarios, the lack of accurate labels for training samples has positioned contrastive learning a key focus in self-supervised learning. This approach effectively extracts meaningful representations from unlabeled data. A significant challenge lies in identifying high-value samples and exploring their semantic features to improve performance. Most existing self-supervised learning methods do not focus much on mining or augmenting valuable samples, relying mainly on contrastive learning between original samples. As a result, performance suffers in cases with limited or sparse data. To address this, we propose a contrastive learning method that combines sample importance measurement and semantic enhancement, overcoming the limitations of traditional methods that only use original samples. First, we generate additional valuable samples using interpolation and apply a similarity-based strategy to better distinguish positive and negative samples, refining the sample partitioning process. Second, we design a semantic enhancement mechanism to better capture and strengthen shared high-level semantic features between samples. Third, we introduce a new metric to evaluate sample value by measuring oscillations in the learning model caused by gradients, determining the importance of each sample. We also use confidence learning to identify and correct mislabeled samples. Extensive evaluations conduct on multiple benchmark datasets demonstrate that our method improves linear classification accuracy by 2.23 %, 4.3 %, 1.6 %, 3.73 %, and 4.69 % on ImageNet-100, ImageNet-10, CIFAR-10, CIFAR-100, and STL-10, respectively. Additionally, accelerates convergence speed by 1.5x and effectively detects mislabeled samples.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"286 ","pages":"Article 128045"},"PeriodicalIF":7.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943387","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}