Expert Systems最新文献

{"title":"A Large-Scale Dataset and Robust Multifeature Representation With Maximum Correlation-Based Feature Fusion and Matching for Apparel Image Retrieval","authors":"Marryam Murtaza,&nbsp;Muhammad Fayyaz,&nbsp;Mussarat Yasmin,&nbsp;Muhammad Anwar,&nbsp;Kashif Naseer Qureshi,&nbsp;Usman Ahmed Raza","doi":"10.1111/exsy.70097","DOIUrl":"https://doi.org/10.1111/exsy.70097","url":null,"abstract":"<p>Finding the correct match to a probe image from a vast amount of data is critical for the online retrieval of apparel images. These images are captured under an uncontrolled environment (e.g., viewpoint and illumination changes); therefore, such type of data is extremely challenging in Content-Based Image Retrieval (CBIR) research. Even in Google searches, most of the time the query results are provided with inaccurate results or duplicate results due to the minor variations between apparel. Another major challenge is that the extracted feature vector dimensions are too high and difficult to handle. In this paper, a method named Multifeature Representation with Maximum Correlation-based Feature Fusion, and Matching (MFR-MCF²M) is proposed for apparel retrieval. This method consists of three modules: (1) Multifeature Representation Module (MFR-M), (2) Maximum Correlation-based Feature Fusion Module (MCF²-M) and (3) Multifeature Matching Module (MFM-M). In the MFR module, the shape, texture and deep features of apparel images are extracted using a Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and a pretrained deep CNN model, respectively. Also, the dimensionality of extracted features is reduced using the proposed Feature Subselection (FSS) method. The MCF module is implemented to measure the maximum correlation between reduced feature vectors. Finally, MCF² is performed using Euclidean distance and a generated Feature Correlation Vector (FCV) to improve the retrieval accuracy and as the benchmark to assess the efficacy of the proposed method. In addition, a new large-scale dataset named Apparel Images Gallery (AIG), which consists of 130,000 images, has been provided to the community. The performance of the proposed MFR-MCF²M method is evaluated on three datasets, including two publicly available datasets and the proposed AIG dataset. The retrieval results are obtained after passing through the threshold function of both the Euclidean distance and the computed FCV. The proposed method achieved an accuracy of 78.3% on the clothing dataset, 94.8% on the CR dataset and 89.1% on the proposed AIG dataset. Consequently, the MFR-MCF²M outperformed state-of-the-art (SOTA) apparel retrieval methods.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144673198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Review of Unimodal and Multimodal Emotion Detection: Datasets, Approaches, and Limitations","authors":"Priyanka Thakur,&nbsp;Nirmal Kaur,&nbsp;Naveen Aggarwal,&nbsp;Sarbjeet Singh","doi":"10.1111/exsy.70103","DOIUrl":"https://doi.org/10.1111/exsy.70103","url":null,"abstract":"<div>\u0000 \u0000 <p>Emotion detection from face and speech is inherent for human–computer interaction, mental health assessment, social robotics, and emotional intelligence. Traditional machine learning methods typically depend on handcrafted features and are primarily centred on unimodal systems. However, the unique characteristics of facial expressions and the variability in speech features present challenges in capturing complex emotional states. Accordingly, deep learning models have been substantial in automatically extracting intrinsic emotional features with greater accuracy across multiple modalities. The proposed article presents a comprehensive review of recent progress in emotion detection, spanning from unimodal to multimodal systems, with a focus on facial and speech modalities. It examines state-of-the-art machine learning, deep learning, and the latest transformer-based approaches for emotion detection. The review aims to provide an in-depth analysis of both unimodal and multimodal emotion detection techniques, highlighting their limitations, popular datasets, challenges, and the best-performing models. Such analysis aids researchers in judicious selection of the most appropriate dataset and audio-visual emotion detection models. Key findings suggest that integrating multimodal data significantly improves emotion recognition, particularly when utilising deep learning methods trained on synchronised audio and video datasets. By assessing recent advancements and current challenges, this article serves as a fundamental resource for researchers and practitioners in the field of emotional AI, thereby aiding in the creation of more intuitive and empathetic technologies.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Securing Autonomous Vehicles: An In-Depth Review of Cyber Attacks and Anomaly Detection Challenges","authors":"Ratnapal Kumarswami Mane,&nbsp;Poonam Sharma","doi":"10.1111/exsy.70100","DOIUrl":"https://doi.org/10.1111/exsy.70100","url":null,"abstract":"<div>\u0000 \u0000 <p>Autonomous Vehicle (AV) initiatives have rapidly grown in recent years, significantly impacting daily life and enhancing transportation safety and efficiency. Autonomous driving technology promises a future with fully self-driving vehicles while presenting new challenges in safety assurance. In this review, the evolution, obstacles, and methodologies of the statistical approaches for defining cyber threats and discovering anomalies in AVs, specifically under negative conditions and different datasets, are discussed. More critically, this survey assesses the strengths and weaknesses of these methods for their current and future directions. Discussing anomaly detection in AVs under adverse conditions through Federated Learning (FL) and Deep Learning (DL) techniques enhances threat detection capabilities. Additionally, the review explores security vulnerabilities of intra-vehicle and inter-vehicle communication systems concerning various sensors and perception systems, and examines possible attacks on AV software and hardware, emphasising their effects. In addition, the research proposes defensive schemes, founded on statistical methods, DL, optimisations, FL, and blockchain, for strengthening AVs' security. The review aims to improve AVs' resilience against cyberattacks in reconstructing the weaknesses in sensor and perception systems, thereby resulting in the growth and safety of self-driving technology. Furthermore, the review covers anomaly detection in various scenarios, examining advancements in methodologies for better detection performance. Performance evaluations using publicly available datasets are thoroughly analysed, offering a comprehensive overview of current research trends and suggesting pathways for future improvements in AV detection technology.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Cloud Layer and Precipitation Forecasting via Multi-Scale Gated Temporal and Spatial Attention Network","authors":"Jiabing Liu,&nbsp;Jianhao Sun,&nbsp;Haiwen Wei,&nbsp;Junzhi Shi,&nbsp;Mingliang Gao","doi":"10.1111/exsy.70099","DOIUrl":"https://doi.org/10.1111/exsy.70099","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud layer and precipitation forecasting play a crucial role in daily life and decision-making. Most existing deep learning models extract features at a single scale and ignore the correlation between features at different scales in the cloud layer and precipitation data. This hinders the ability to extract multi-scale cloud layer features and precipitation features and further constrains the predictive accuracy of the model. To address these challenges, we propose the multi-scale gated temporal and spatial attention network (MGTSA-Net). This network is designed to capture multi-scale spatiotemporal features in the cloud layer and precipitation data more effectively. As a result, it can improve the accuracy of cloud layer and precipitation forecasting. The core component is the multi-scale temporal gated (MTG) module, which integrates multi-scale convolutions and gated recurrent unit (GRU). To further enhance the model's capability of spatial feature extraction, we integrate a multi-scale spatial attention (MSA) module into the encoder. Experimental evaluations on the WeatherBench dataset demonstrate that the MGTSA-Net outperforms state-of-the-art models in predictive accuracy and computational efficiency.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Transformer-Based Contrastive Learning With Dynamic Masking and Adaptive Pathways for Time Series Anomaly Detection","authors":"Qian Liang,&nbsp;Xiang Yin","doi":"10.1111/exsy.70102","DOIUrl":"https://doi.org/10.1111/exsy.70102","url":null,"abstract":"<div>\u0000 \u0000 <p>Time Series Anomaly Detection (TSAD) has demonstrated broad applicability across various industries, including manufacturing, healthcare, and finance. Its primary objective is to identify unusual deviations in the test set by capturing the typical behavioral patterns of timing data. Despite their strong detection capabilities when labeled data is not available, current reconstruction-based approaches still struggle with anomalous interference and inadequate semantic information extraction at higher time series levels. To tackle these problems, we provide a multi-scale dual-domain patch attention contrast learning model (DMAP-DDCL) that incorporates adaptive path selection and adaptive dynamic context-aware masking. Dynamic context-aware masks are specifically used by DMAP-DDCL to improve the model's generalization ability and mitigate bias resulting from the influence of anomalous data during training. Multi-scale patch segmentation and dual attention to the segmented patches are introduced to capture local details and global correlations as time dependencies. By enlarging the contrast between the two data perspectives, global and local, DMAP-DDCL improves the capacity to differentiate between normal and abnormal patterns. In addition, we enhance the adaptive path of the multi-scale bi-domain attention network, which adapts the multi-scale modeling process to the temporal dynamics of the inputs and enhances the model's accuracy. According to experimental results, DMAP-DDCL performs better on five real datasets from various domains than eight state-of-the-art baselines. Specifically, our model enhances F1 and R_AUC_ROC by an average of 7.5% and 16.67%.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Transforming Healthcare With Artificial Intelligence and Blockchain: A Secure, Transparent and Energy-Efficient Approach","authors":"Sagnik Datta,&nbsp;Suyel Namasudra,&nbsp;Nageswara Rao Moparthi,&nbsp;Suchi Kumari,&nbsp;Ruben Gonzalez Crespo","doi":"10.1111/exsy.70101","DOIUrl":"https://doi.org/10.1111/exsy.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>The healthcare industry is undergoing a transformative shift with the integration of blockchain technology and Artificial Intelligence (AI). Traditional healthcare systems struggle with data security, lack of transparency, and inefficiencies in resource allocation, leading to increased risks and operational challenges. AI-driven models provide intelligent solutions by enabling predictive diagnostics, fraud detection, and personalised treatment plans, while blockchain ensures data integrity, security, and decentralised access control. The synergy between AI and blockchain enhances decision-making, optimises resource utilisation and fosters trust in healthcare systems by automating processes with greater transparency and security. AI-powered analytics can extract meaningful insights from vast healthcare datasets, improving patient outcomes and streamlining supply chains. Meanwhile, blockchain's immutable ledger safeguards medical data, preventing breaches and ensuring regulatory compliance. This paper presents a comprehensive review of blockchain solutions in healthcare, exploring their impact on enhancing security, promoting transparency and improving energy efficiency. Additionally, this paper also presents insights on the integration of AI and blockchain in healthcare. It categorises existing blockchain-based frameworks and highlights emerging trends, challenges, and future research directions. This review aims to serve as a foundational reference for researchers and practitioners developing secure, transparent, and intelligent healthcare systems.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Comprehensive Review of Graph Neural Networks: Challenges, Classification, Architectures, Applications, and Potential Utility in Bioinformatics","authors":"Adil Mudasir Malla,&nbsp;Asif Ali Banka","doi":"10.1111/exsy.70091","DOIUrl":"https://doi.org/10.1111/exsy.70091","url":null,"abstract":"<div>\u0000 \u0000 <p>Graphs are data structures that represent complex interactions in artificial and natural systems. While deep learning has revolutionised tasks like image processing, audio/video analysis, and natural language processing, these tasks can be viewed as special cases of graph representation learning. Real-world data is often graph-structured, representing complex dependencies in physical systems, molecular signatures, and disease prediction. Graph neural networks (GNNs) excel at processing such non-Euclidean data by capturing dependencies through message passing between graph nodes. This review provides an organised in-depth overview of existing GNN models, emphasising their applications in bioinformatics apart from most structured and unstructured GNN data utility. We provide formal mathematical foundations, compare key model variants, and evaluate their performance across real-world tasks. To enable systematic analysis, we propose a unified taxonomy based on three core axes: learning settings, expressive capacity, and aggregation mechanisms. The taxonomy defines four main GNN types: structure-agnostic, structure-aware, sparsity-optimized, and advanced learning-based models. Regarding applications, we studied them under a proposed taxonomy in detail. Additionally, we provide resources for evaluating and implementing GNN models, including open-source code, bioinformatics databases, and general GNN benchmark datasets. Finally, we propose eight GNN challenges along with corresponding research directions to advance the field. Our survey aims to establish a common reference point for researchers, empowering them to harness the full potential of GNNs in tackling the complexities of both natural and artificial systems.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Federated Learning: Concepts, Challenges and Implementation","authors":"Naeem Khan,&nbsp;Shibli Nisar,&nbsp;Muhammad Asghar Khan,&nbsp;Muhammad Attique Khan,&nbsp;David Camacho,&nbsp;Yasar Abbas Ur Rehman,&nbsp;Amir Hussain","doi":"10.1111/exsy.70096","DOIUrl":"https://doi.org/10.1111/exsy.70096","url":null,"abstract":"<div>\u0000 \u0000 <p>Federated Learning (FL) has emerged as an innovative approach for distributed neural networks, allowing multiple clients to collaboratively train a model without centralising their data, thus preserving decentralisation and data privacy. This review provides a comprehensive discussion of FL's core concepts, including its components, key challenges, and distinctions from traditional machine learning. The paper outlines the various types of FL, highlighting applications in privacy-sensitive fields like healthcare and finance. It also addresses recent advancements in self-supervised learning, personalisation, and multi-modal applications within FL, as well as the integration of blockchain technology for enhanced privacy. Key advantages of FL are discussed, such as reduced communication overhead through the transmission of model parameters instead of raw data, which minimises network load and enhances privacy protection. Furthermore, the paper explores emerging questions for FL development, including scalability, fairness, and system standardisation. Real-world examples, such as Google Gboard and brain tumour segmentation, are presented to illustrate FL's practical impact. Finally, the paper discusses future directions, including potential integration with other AI techniques like reinforcement learning and transfer learning. This review provides valuable insights for researchers and professionals who are new to FL or seek a broader understanding of its ecosystem. While there are few studies that explore limited aspect of FL, this review adopts a holistic approach and covers all aspects of FL including foundational concepts, implementation, challenges faced by FL, and real-world implementation. The broader scope, which spans FL from concepts to practical implementation, makes it particularly distinctive and a valuable contribution.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144582352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Single Word Change Is All You Need: Using LLMs to Create Synthetic Training Examples for Text Classifiers","authors":"Lei Xu,&nbsp;Sarah Alnegheimish,&nbsp;Laure Berti-Equille,&nbsp;Alfredo Cuesta-Infante,&nbsp;Kalyan Veeramachaneni","doi":"10.1111/exsy.70079","DOIUrl":"https://doi.org/10.1111/exsy.70079","url":null,"abstract":"<p>In text classification, creating an adversarial example means subtly perturbing a few words in a sentence without changing its meaning, causing it to be misclassified by a classifier. A concerning observation is that a significant portion of adversarial examples generated by existing methods change only one word. This single-word perturbation vulnerability represents a significant weakness in classifiers, which malicious users can exploit to efficiently create a multitude of adversarial examples. This paper studies this problem and makes the following key contributions: (1) We introduce a novel metric <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ρ</mi>\u0000 </mrow>\u0000 <annotation>$$ rho $$</annotation>\u0000 </semantics></math> to quantitatively assess a classifier's <i>robustness against single-word perturbation</i>. (2) We present the <i>SP-Attack,</i> designed to exploit the single-word perturbation vulnerability, achieving a higher attack success rate, better preserving sentence meaning, while reducing computation costs compared to state-of-the-art adversarial methods. (3) We propose <i>SP-Defence,</i> which aims to improve <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ρ</mi>\u0000 </mrow>\u0000 <annotation>$$ rho $$</annotation>\u0000 </semantics></math> by applying data augmentation in learning. Experimental results on 4 datasets and 2 masked language models show that SP-Defence improves <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ρ</mi>\u0000 </mrow>\u0000 <annotation>$$ rho $$</annotation>\u0000 </semantics></math> by 14.6% and 13.9% and decreases the attack success rate of SP-Attack by 30.4% and 21.2% on two classifiers respectively, and decreases the attack success rate of existing attack methods that involve multiple-word perturbations.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Active Object Detection Using a Novel Network and Partial Prior Information","authors":"Jianyu Wang,&nbsp;Feng Zhu,&nbsp;Qun Wang,&nbsp;Pengfei Zhao","doi":"10.1111/exsy.70095","DOIUrl":"https://doi.org/10.1111/exsy.70095","url":null,"abstract":"<div>\u0000 \u0000 <p>Active object detection (AOD) enables a system to actively adjust camera parameters or plan the next viewpoint to improve detection accuracy when the current visual input is insufficient. However, most existing AOD methods assume that the target object is visible from the initial viewpoint, which is often unrealistic and reduces task efficiency. To address this limitation, we propose a novel AOD framework that leverages partial prior information to enhance detection performance and task efficiency. Specifically, we construct an extensible prior information library that describes large and easily identifiable adjacent objects (Adj-objects) that are spatially related to the target. This allows the system to initiate AOD based on the presence of an Adj-object, even when the target is initially out of view. Our approach incorporates a duelling deep Q-learning network (Duelling-DQN) with a newly designed reward function to effectively utilise prior information. Additionally, we introduce a viewpoint storage scheme to support fast retrieval and transition between viewpoints. We evaluate the proposed method on the Active Vision Dataset (AVD) and compare it with several state-of-the-art (SOTA) approaches. The experimental results show that our method achieves a superior average success rate of 81.3%, demonstrating its effectiveness in overcoming the initial state limitations of traditional AOD tasks.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}