Artificial Intelligence Review最新文献

{"title":"Evaluating AI-powered predictive solutions for MRI in lumbar spinal stenosis: a systematic review","authors":"Mugahed A. Al-antari,&nbsp;Saied Salem,&nbsp;Mukhlis Raza,&nbsp;Ahmed S. Elbadawy,&nbsp;Ertan Bütün,&nbsp;Ahmet Arif Aydin,&nbsp;Murat Aydoğan,&nbsp;Bilal Ertuğrul,&nbsp;Muhammed Talo,&nbsp;Yeong Hyeon Gu","doi":"10.1007/s10462-025-11185-y","DOIUrl":"10.1007/s10462-025-11185-y","url":null,"abstract":"<div><p>Lumbar spinal stenosis (LSS) involves the narrowing of the spinal canal, leading to compression of the spinal cord and nerves in the lower back. Common causes include injuries, degenerative age-related changes, congenital conditions, and tumors, all of which contribute to back pain. Early diagnosis is critical for symptom management, preventing progression, and preserving quality of life. This study systematically reviews AI-based approaches for predicting LSS using MRI axial and sagittal imaging. The review focuses on various AI tasks: detection, segmentation, classification, hybrid approaches, spinal index measurements (SIM), and explainable AI frameworks. The aim is to highlight current knowledge, identify limitations in existing models, and propose future research directions. Following PRISMA guidelines and the PICO method (Population, Intervention, Comparison, Outcome), the review collects data from databases like PubMed, Web of Science, ScienceDirect, and IEEE Xplore (2005–2024). The Rayyan AI tool is used for duplicate removal and screening. The screening process includes an initial review of titles and abstracts, followed by full-text appraisal. The Meta Quality Appraisal Tool (MetaQAT) assesses the quality of selected articles. Of 1323 records, 97 duplicates were removed. After screening, 895 records were excluded, leaving 331 for full-text review. Among these, 184 articles were excluded for lacking AI relevance. Ultimately, 95 key articles (91 technical papers and 4 reviews) were identified for their contributions to AI-based LSS prediction. This review provides a comprehensive analysis of AI techniques in LSS prediction, guiding future research and advancing understanding in areas like explainable AI and large language models (LLMs).</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 8","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11185-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for pancreas segmentation on computed tomography: a systematic review","authors":"Andrea Moglia,&nbsp;Matteo Cavicchioli,&nbsp;Luca Mainardi,&nbsp;Pietro Cerveri","doi":"10.1007/s10462-024-11050-4","DOIUrl":"10.1007/s10462-024-11050-4","url":null,"abstract":"<div><p>Pancreas segmentation has been traditionally challenging due to its small size in computed tomography abdominal volumes, high variability of shape and positions among patients, and blurred boundaries due to low contrast between the pancreas and surrounding organs. Many deep learning models for pancreas segmentation have been proposed in the past few years. We present a thorough systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. The literature search was conducted on PubMed, Web of Science, Scopus, and IEEE Xplore on original studies published in peer-reviewed journals from 2013 to 2023. Overall, 130 studies were retrieved. We initially provide an overview of the technical background of the most common network architectures and publicly available datasets. Then, the analysis of the studies combining visual presentation in tabular form and text description is reported. The tables group the studies specifying the application, dataset size, design (model architecture, learning strategy, loss function, and training protocol), results, and main contributions. We first analyze the studies focusing on parenchyma segmentation using datasets with only pancreas annotations, followed by those using datasets with multi-organ annotations. Then, we analyze the studies on the segmentation of tumors, cysts, and inflammation. The studies are clustered according to the different deep learning architectures. Finally, we discuss the main findings from the published literature, the challenges, and the directions for future research on the clinical need, deep learning and foundation models, datasets, and clinical translation.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 8","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11050-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-enhanced hierarchical encoding with multi-decoder for diversified MCQ distractor generation","authors":"Xiaohui Dong,&nbsp;Zhengluo Li,&nbsp;Haoming Su,&nbsp;Jixiang Xue,&nbsp;Xiaochao Dang","doi":"10.1007/s10462-025-11237-3","DOIUrl":"10.1007/s10462-025-11237-3","url":null,"abstract":"<div><p>The validity of multiple-choice questions (MCQs) in reading comprehension assessments relies heavily on the quality of the distractors. However, the manual design of these distractors is both time-consuming and costly, prompting researchers to turn to computer technology for the automatic generation of distractors. This task involves the process of taking a reading comprehension article, a question and its corresponding correct answer as input, with the goal of generating distractors that are related to the answer, semantically consistent with the question, and traceable within the article. Initially, heuristic rule-based approaches were employed, to generate only word-level or phrase-level distractors. Recent studies have shifted towards using sequence-to-sequence neural networks for sentence-level distractor generation. Despite these advancements, these methods face two key challenges: difficulty in capturing long-distance semantic relationships within the context, leading to overly general or context-independent distractors, and the tendency for the generated distractors to be semantically similar. To address these limitations, this paper proposes a Transformer-Enhanced Hierarchical Encoding with Multi-Decoder (THE-MD) network, composed of a hierarchical encoder and multiple decoders. Specifically, the encoder employs the Transformer architecture to encode the context and capture long-range semantic information, thereby generating more contextually relevant distractors. The decoder utilizes multiple decoding strategies and a dissimilarity loss function to collaboratively generate diverse distractors. The experimental results show that the THE-MD model outperforms existing baselines on both automatic and manual evaluation metrics. On the RACE and RACE++ datasets, the model increased the BLEU-4 scores to 7.45 and 10.60, and the ROUGE-L scores to 22.96 and 34.88, while also demonstrating excellent performance in fluency and coherence metrics. These improvements highlight their potential to enhance the generation of MCQ distractors in educational assessments.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 8","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11237-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised clustering optimization-based efficient attention in YOLO for underwater object detection","authors":"Xin Shen,&nbsp;Guoliang Yuan,&nbsp;Huibing Wang,&nbsp;Xianping Fu","doi":"10.1007/s10462-025-11218-6","DOIUrl":"10.1007/s10462-025-11218-6","url":null,"abstract":"<div><p>Underwater object detection is a prerequisite for underwater robots to realize ocean exploration and autonomous grasping. However, underwater detection tasks face some inevitable interference factors, such as poor imaging quality, strong environment randomness, and high organism concealment. These phenomena will lead to strong underwater background interference and weak underwater object perception, which greatly aggravates the difficulty of underwater object detection. In order to deal with the above problems, we propose an unsupervised clustering optimization-based efficient attention (UCOEA). Different from the channel-wise strategy, cross-channel strategy and channel grouping strategy, we design a channel clustering strategy, which achieves autonomous dynamic screening of channel information by using the K-Means algorithm. Same types of channel information with high redundancy are learned uniformly to share the same operation. Different types of channel information with high specificity are learned independently to avoid channel noise information interference. Different from the single spatial strategy and multiple spatial strategy, we design a spatial clustering strategy, which achieves autonomous dynamic stripping of spatial information by using the EM algorithm. This strategy can extract multiple required spatial information at one time from different spatial locations. We further assign learnable weight parameters to distinguish dominant information and auxiliary information, which can alleviate spatial noise information interference. Our strategies can better balance additional cost overhead and information processing quality, which is crucial for the proposed attention to achieve fast and accurate underwater information calibration. In order to achieve high-precision and real-time underwater object detection, we propose a combined system of UCOEA underwater adapter and one-stage YOLO detector, which can efficiently detect small, medium and large targets at the same time. Extensive experiments demonstrate the effectiveness of our work. More importantly, we publish an underwater detection dataset DLMU2024 with low image continuity and high data diversity, which provides reliable support for the rapid development of underwater detection research. Our dataset is available at https://github.com/shenxin-dlmu/DLMU2024.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11218-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SFIAD: Deepfake detection through spatial-frequency feature integration and dynamic margin optimization","authors":"Yi Kou,&nbsp;Peng Li,&nbsp;Hongjiang Ma,&nbsp;Jiliu Zhou,&nbsp;Zhan ao Huang,&nbsp;Xiaojie Li","doi":"10.1007/s10462-025-11225-7","DOIUrl":"10.1007/s10462-025-11225-7","url":null,"abstract":"<div><p>The rapid advancement of generative models has profoundly transformed the field of digital content creation, bringing unprecedented opportunities for media generation. However, the widespread adoption of this technology has also led to the emergence of highly realistic fake facial images and videos, which pose significant threats to public trust and societal security. To address the challenges of deepfake detection, this paper proposes a novel method based on Spatial-Frequency Feature Integration (SFFI), which effectively identifies fake content by combining spatial and frequency features of images. Additionally, to tackle the issue of class imbalance in the datasets, we propose an Authenticity-Aware Margin Loss (AAML). This loss function dynamically adjusts the decision boundary to enhance the model’s ability to recognize minority class samples. The proposed method was trained and evaluated on four challenging datasets: FaceForensics++, Celeb-DF v1, Celeb-DF v2, and the DeepFake Detection Challenge Preview, and compared against ten state-of-the-art methods. Experimental results demonstrate that the proposed method consistently outperforms all existing approaches across all datasets.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11225-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges","authors":"Jongseon Kim,&nbsp;Hyungjoon Kim,&nbsp;HyunGi Kim,&nbsp;Dongjun Lee,&nbsp;Sungroh Yoon","doi":"10.1007/s10462-025-11223-9","DOIUrl":"10.1007/s10462-025-11223-9","url":null,"abstract":"<div><p>Time series forecasting is a critical task that provides key information for decision-making across various fields, such as economic planning, supply chain management, and medical diagnosis. After the use of traditional statistical methodologies and machine learning in the past, various fundamental deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have been developed and applied to solve time series forecasting problems. However, the structural limitations caused by the inductive biases of each deep learning architecture constrained their performance. Transformer models, which excel at handling long-term dependencies, have become significant architectural components for time series forecasting. However, recent research has shown that alternatives such as simple linear layers can outperform Transformers. These findings have opened up new possibilities for using diverse architectures, ranging from fundamental deep learning models to emerging architectures and hybrid approaches. In this context of exploration into various models, the architectural modeling of time series forecasting has now entered a renaissance. This survey not only provides a historical context for time series forecasting but also offers comprehensive and timely analysis of the movement toward architectural diversification. By comparing and re-examining various deep learning models, we uncover new perspectives and present the latest trends in time series forecasting, including the emergence of hybrid models, diffusion models, Mamba models, and foundation models. By focusing on the inherent characteristics of time series data, we also address open challenges that have gained attention in time series forecasting, such as channel dependency, distribution shift, causality, and feature extraction. This survey explores vital elements that can enhance forecasting performance through diverse approaches. These contributions help lower entry barriers for newcomers by providing a systematic understanding of the diverse research areas in time series forecasting (TSF), while offering seasoned researchers broader perspectives and new opportunities through in-depth exploration of TSF challenges.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11223-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive survey of specularity detection: state-of-the-art techniques and breakthroughs","authors":"Fengze Li,&nbsp;Jieming Ma,&nbsp;Hai-Ning Liang,&nbsp;Zhongbei Tian,&nbsp;Zhijing Wu,&nbsp;Tianxi Wen,&nbsp;Dawei Liu","doi":"10.1007/s10462-025-11233-7","DOIUrl":"10.1007/s10462-025-11233-7","url":null,"abstract":"<div><p>Specularity poses significant challenges in computer vision (CV), often leading to performance degradation in various tasks. Despite its importance, the CV field lacks a comprehensive review of specularity detection techniques. This survey addresses this gap by synthesizing diverse definitions of specularity and providing a unified framework to enhance consistency. It also presents a systematic review of traditional and deep learning-based methods for detecting specularity. Comparative experiments on a standardized dataset enable in-depth evaluation of each method, highlighting their strengths and limitations. The survey further provides structured insights and guidance for selecting appropriate methods across diverse scenarios. Through this, it identifies key areas for future research, aiming to support the development of more advanced detection models. By integrating diverse methodologies and quantitative analyzes, this survey contributes to a deeper understanding of current advancements and potential innovations in specularity detection.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11233-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural language processing in the patent domain: a survey","authors":"Lekang Jiang,&nbsp;Stephan M. Goetz","doi":"10.1007/s10462-025-11168-z","DOIUrl":"10.1007/s10462-025-11168-z","url":null,"abstract":"<div><p>Patents, which encapsulate crucial technical and legal information in text form and referenced drawings, present a rich domain for natural language processing (NLP). As NLP technologies evolve, large language models (LLMs) have demonstrated outstanding capabilities in general text processing and generation tasks. However, the application of LLMs in the patent domain remains under-explored and under-developed due to the complexity of patents, particularly their language and legal framework. Understanding the unique characteristics of patent documents and related research in the patent domain becomes essential for researchers to apply these tools effectively. Therefore, this paper aims to equip NLP researchers with the essential knowledge to navigate this complex domain efficiently. We introduce the relevant fundamental aspects of patents to provide solid background information. In addition, we systematically break down the structural and linguistic characteristics unique to patents and map out how NLP can be leveraged for patent analysis and generation. Moreover, we demonstrate the spectrum of text-based and multimodal patent-related tasks, including nine patent analysis and four patent generation tasks.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11168-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel fuzzy neural network approach with triangular fuzzy information for the selection of logistics service providers","authors":"Lifang Wang,&nbsp;Saleem Abdullah,&nbsp;Ariana Abdul Rahimzai,&nbsp;Ihsan Ullah","doi":"10.1007/s10462-025-11209-7","DOIUrl":"10.1007/s10462-025-11209-7","url":null,"abstract":"<div><p>In this article, we presents a novel fuzzy neural network approach designed to address multi criteria decision making (MCDM) problems, specifically for selecting logistics service providers. The proposed decision making model integrates triangular fuzzy numbers (TFNs) with a triangular fuzzy Einstein weighted averaging (TFEWA) aggregation operator to enhance the decision making process under uncertainty. Initially, we discussed the concept of triangular fuzzy numbers, which allows for the representation of uncertain and imprecise data typically presented in real-world decision making environments. The operational laws, score function, and Hamming distance measures for TFNs are presented to ensure accurate handling of the fuzzy input data. The TFEWA aggregation operator, which is based on Einstein norms and plays a crucial role in aggregating expert opinions in the evaluation process. In the decision making process, we collect expert opinions regarding logistics service providers, expressed as TFNs, which are then processed through the fuzzy neural network model. After that, we apply the proposed decision making model to select the best logistics service providers. The TFEWA operator computes values at the hidden and output layers, and activation functions are applied to produce final output values. These outputs provide a ranked list of logistics service providers based on their overall performance across multiple criteria. The effectiveness of this novel approach is validated through a comparative analysis with existing MCDM methods. The results demonstrate that the triangular fuzzy neural network approach outperforms traditional methods in terms of flexibility, accuracy, and its ability to handle uncertain, fuzzy data. Our method provides a robust decision support system, capable of managing complex decision making tasks in logistics and other fields.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11209-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review on 3D Gaussian splatting for sparse view reconstruction","authors":"Haitian Liu,&nbsp;Binglin Liu,&nbsp;Qianchao Hu,&nbsp;Peilun Du,&nbsp;Jing Li,&nbsp;Yang Bao,&nbsp;Feng Wang","doi":"10.1007/s10462-025-11171-4","DOIUrl":"10.1007/s10462-025-11171-4","url":null,"abstract":"<div><p>Sparse view 3D reconstruction remains challenging due to inherent data scale limitations. Mainstream sparse view 3D reconstruction algorithms based on the NeRF framework struggle to balance generation quality and real-time performance. Recently, the advent of 3D Gaussian Splatting technology has demonstrated remarkable results, becoming increasingly prominent in 3D scene representation and reconstruction. Exploring the application of 3D Gaussian Splatting technology for sparse view 3D reconstruction represents a promising research avenue. Based on this, our paper provides a comprehensive review of current sparse view 3D reconstruction methods leveraging 3D Gaussian Splatting, with an emphasis on extracting effective reconstruction information from input images and utilizing these data to generate realistic scenes efficiently and reliably. We then provide a detailed discussion on how the algorithm addresses issues such as artifacts and scale ambiguous, which are common challenges in this field. In the subsequent sections, we present both quantitative and qualitative comparisons of various sparse-view 3D reconstruction methods, roughly demonstrating the advantages of sparse view 3D Gaussian splatting methods in terms of reconstruction quality and efficiency. Furthermore, we analyze the potential applications of sparse view 3D Gaussian splatting methods. Finally, we identify the challenges faced by sparse-view 3D Gaussian splatting reconstruction and suggest potential solutions. We hope that our analysis will provide valuable insights for future research efforts.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11171-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}