Expert Systems最新文献

{"title":"Hybridizing Machine Learning Algorithms With Numerical Models for Accurate Wind Power Forecasting","authors":"Álvaro Abad-Santjago,&nbsp;C. Peláez-Rodríguez,&nbsp;J. Pérez-Aracil,&nbsp;J. Sanz-Justo,&nbsp;C. Casanova-Mateo,&nbsp;S. Salcedo-Sanz","doi":"10.1111/exsy.13830","DOIUrl":"https://doi.org/10.1111/exsy.13830","url":null,"abstract":"<p>An accurate prediction of wind power generation is crucial for optimizing the integration of wind energy into the power grid, ensuring energy reliability. This research focuses on enhancing the accuracy of wind power generation forecasts by combining data from mesoscale and reanalysis models with Machine Learning (ML) approaches. We utilized <i>WRF</i> forecast data alongside <i>ERA5</i> reanalysis data to estimate wind power generation for a wind farm located at Valladolid, Spain. The study evaluated the performance of ML models based on <i>WRF</i> and <i>ERA5</i> data individually, as well as a combined model using inputs from both datasets. The hybrid model combining WRF and ERA5 data with ML resulted in a 15% improvement in root mean square error (RMSE) and a 10% increase in <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {R}^2 $$</annotation>\u0000 </semantics></math> compared with standalone models, providing a more reliable 1-h forecast of wind power generation. Additionally, the availability of data over time was addressed: <i>WRF</i> provides the advantage of projecting data into the future, whereas <i>ERA5</i> offers retrospective data.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113060","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":"Automatic Code Summarization Using Abbreviation Expansion and Subword Segmentation","authors":"Yu-Guo Liang,&nbsp;Gui-Sheng Fan,&nbsp;Hui-Qun Yu,&nbsp;Ming-Chen Li,&nbsp;Zi-Jie Huang","doi":"10.1111/exsy.13835","DOIUrl":"https://doi.org/10.1111/exsy.13835","url":null,"abstract":"<div>\u0000 \u0000 <p>Automatic code summarization refers to generating concise natural language descriptions for code snippets. It is vital for improving the efficiency of program understanding among software developers and maintainers. Despite the impressive strides made by deep learning-based methods, limitations still exist in their ability to understand and model semantic information due to the unique nature of programming languages. We propose two methods to boost code summarization models: context-based abbreviation expansion and unigram language model-based subword segmentation. We use heuristics to expand abbreviations within identifiers, reducing semantic ambiguity and improving the language alignment of code summarization models. Furthermore, we leverage subword segmentation to tokenize code into finer subword sequences, providing more semantic information during training and inference, thereby enhancing program understanding. These methods are model-agnostic and can be readily integrated into existing automatic code summarization approaches. Experiments conducted on two widely used Java code summarization datasets demonstrated the effectiveness of our approach. Specifically, by fusing original and modified code representations into the Transformer model, our Semantic Enhanced Transformer for Code Summarizsation (SETCS) serves as a robust semantic-level baseline. By simply modifying the datasets, our methods achieved performance improvements of up to 7.3%, 10.0%, 6.7%, and 3.2% for representative code summarization models in terms of <i>BLEU-4</i>, <i>METEOR</i>, <i>ROUGE-L</i> and <i>SIDE</i>, respectively.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113092","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":"Bi-LORA: A Vision-Language Approach for Synthetic Image Detection","authors":"Mamadou Keita,&nbsp;Wassim Hamidouche,&nbsp;Hessen Bougueffa Eutamene,&nbsp;Abdelmalik Taleb-Ahmed,&nbsp;David Camacho,&nbsp;Abdenour Hadid","doi":"10.1111/exsy.13829","DOIUrl":"https://doi.org/10.1111/exsy.13829","url":null,"abstract":"<div>\u0000 \u0000 <p>Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images. While this technological progress has captured significant interest, it has also raised concerns about the high challenge in distinguishing real images from their synthetic counterparts. This paper takes inspiration from the potent convergence capabilities between vision and language, coupled with the zero-shot nature of vision-language models (VLMs). We introduce an innovative method called Bi-LORA that leverages VLMs, combined with low-rank adaptation (LORA) tuning techniques, to enhance the precision of synthetic image detection for unseen model-generated images. The pivotal conceptual shift in our methodology revolves around reframing binary classification as an image captioning task, leveraging the distinctive capabilities of cutting-edge VLM, notably bootstrapping language image pre-training (BLIP)2. Rigorous and comprehensive experiments are conducted to validate the effectiveness of our proposed approach, particularly in detecting unseen diffusion-generated images from unknown diffusion-based generative models during training, showcasing robustness to noise, and demonstrating generalisation capabilities to GANs. The experiments show that Bi-LORA outperforms state of the art models in cross-generator tasks because it leverages multi-modal learning, open-world visual knowledge, and benefits from robust, high-level semantic understanding. By combining visual and textual knowledge, it can handle variations in the data distribution (such as those caused by different generators) and maintain strong performance across different domains. Its ability to transfer knowledge, robustly extract features and perform zero-shot learning also contributes to its generalisation capabilities, making it more adaptable to new generators. The experimental results showcase an impressive average accuracy of 93.41% in synthetic image detection on unseen generation models. The code and models associated with this research can be publicly accessed at https://github.com/Mamadou-Keita/VLM-DETECT.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113061","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":"A Novel Hyper-Tuned Multilayer Perceptron With Effective Stochastic Learning Strategies for Missing Values Imputation","authors":"Muhammad Hameed Siddiqi,&nbsp;Madallah Alruwaili,&nbsp;Yousef Alhwaiti,&nbsp;Saad Alanazi,&nbsp;Faheem Khan","doi":"10.1111/exsy.13828","DOIUrl":"https://doi.org/10.1111/exsy.13828","url":null,"abstract":"<div>\u0000 \u0000 <p>A vast amount of data in many different formats is produced and stored daily, offering machine learning a valuable resource to enhance its predictive capabilities. However, the pervasiveness of inaccuracies in real-world data presents a significant barrier that can seriously limit the effectiveness of learning algorithms. The ensemble models and hyper-tuned multi-layer perceptron (MLP) with need-based hidden neuron layers are effective frameworks for data imputation. Addressing the issue of missing data is a complex and demanding task, and much remains to be explored in developing effective and precise methods for predicting and imputing missing values across different datasets. The study offers important perspectives on using algorithms in machine learning to predict and impute gaps in data in recently updated datasets. The findings indicate that finely tuned MLP classifiers notably improve prediction accuracy and dependability compared to models with a static or reduced number of neurons. Furthermore, the study highlights the promising potential of ensemble models within the error-correcting output code (ECOC) framework as an effective approach for this task. It also suggests future research directions to refine further and strengthen machine learning-based imputation methods regarding precision and stability. ECOC framework includes all kinds of MLP classifiers and regressors such as binary classifiers, multi-class classifiers, or regression models. MLP models predict complex relationships in modern datasets. Hugging Face, COSMIC, SKlearn, and Kaggle have relevant and updated datasets. The weighted average recognition (96%) shows that the proposed MLP-based stochastic learning strategies achieved better results.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113090","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":"Leveraging Transfer Learning Domain Adaptation Model With Federated Learning to Revolutionise Healthcare","authors":"Priyanka Verma,&nbsp;Nitesh Bharot,&nbsp;John G. Breslin,&nbsp;Donna O'Shea,&nbsp;Anand Kumar Mishra,&nbsp;Ankit Vidyarthi,&nbsp;Deepak Gupta","doi":"10.1111/exsy.13827","DOIUrl":"https://doi.org/10.1111/exsy.13827","url":null,"abstract":"<div>\u0000 \u0000 <p>The application of artificial intelligence (AI) in healthcare has been witnessing an increasing interest. Particularly, federated learning (FL) has become favourable due to its potential for enhancing model quality whilst maintaining data privacy and security. However, the effectiveness of present FL methodologies could underperform under non-IID conditions, characterised by divergent data distributions across clients. The globally constructed FL model may suffer potent issues by allowing the least-performing models to equal participation. Thus, we propose a new accuracy-based FL approach (FedAcc) which only takes into account the clients' validation accuracy to consider their participation during global aggregation, also called Smart Healthcare Amplified (SHA). However, with limited supervised data it is challenging to increase the model performance thus concept of transfer learning (TL) is used. TL enables the global model to integrate knowledge from precomputed systems, resulting in an efficient model. However, the complexity of the global system is amplified by these TL models, leading to challenges related to vanishing gradients, particularly when dealing with a substantial number of layers. To mitigate this, we present a Transfer Learning Domain Adaptation Model (TLDAM). TLDAM employs a two-layered sequentially trained TL model, which contains approximately 50% fewer layers compared to traditional TL models. TLDAM is trained on multiple datasets such as MNIST and CIFAR10, to enhance its knowledge and make it domain-adaptive. Moreover, experimental results conducted on the UCI-HAR dataset reveal the supremacy of our proposed framework with an accuracy of 94.2990%, F-score of 94.2820%, precision of 94.3058%, and recall of 94.2993% over traditional FL techniques and state-of-the-art techniques.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120398","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":"Point Class-Adaptive Transformer (PCaT): A Novel Approach for Efficient Point Cloud Classification and Segmentation","authors":"Husnain Mushtaq,&nbsp;Xiaoheng Deng,&nbsp;Ping Jinag,&nbsp;Shaohua Wan,&nbsp;Rawal Javed,&nbsp;Irshad Ullah","doi":"10.1111/exsy.13831","DOIUrl":"https://doi.org/10.1111/exsy.13831","url":null,"abstract":"<div>\u0000 \u0000 <p>Recent 3D point cloud classification has predominantly focused on local spatial attention, neglecting distant contextual relationships due to the inherent sparsity of LiDAR-generated data over longer distances. Existing 3D object detection methods prioritize local features, hindering the extraction of semantic information. Despite attempts with transformers, methods often reduce computations through local spatial attention, neglecting content class and scarcely establishing connections among distant global points. Our proposed point class-adaptive transformer (PCaT) addresses these limitations by establishing long-range feature dependencies while significantly reducing computations. PCaT includes three key modules: the class-adaptive transformer (CaT), which utilizes local self-attention and global self-attention based on class similarity to facilitate an efficient trade-off between capturing extended-global dependencies and managing computational challenges; nested binary clustering (NbC), which dynamically partitions queries into multiple clusters based on content features in each Transformer block; and the AfA, which aggregates high-dimensional features using max-pooling alongside a residual MLP component and low-dimensional features using average pooling and a CaT block. Additionally, PCaT incorporates point cloud segmentation via local–global feature aggregation (PcSeg) to facilitate effective point cloud segmentation. Extensive experimentation on the ModelNet40, ScanObjectNN, and S3DIS datasets demonstrates the superior performance and reasonable stability of PCaT compared with existing methods. PCaT achieves 94.2% overall accuracy (OA) and mIoU scores of 89.2% and 86.2% for the ScanObjectNN and S3DIS datasets, respectively.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118822","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":"Advancing Disability Healthcare Solutions Through Privacy-Preserving Federated Learning With Theme Framework","authors":"Madallah Alruwaili,&nbsp;Muhammad Hameed Siddiqi,&nbsp;Muhammad Idris,&nbsp;Salman Alruwaili,&nbsp;Abdullah Saleh Alanazi,&nbsp;Faheem Khan","doi":"10.1111/exsy.13807","DOIUrl":"https://doi.org/10.1111/exsy.13807","url":null,"abstract":"<div>\u0000 \u0000 <p>The application of machine learning, particularly federated learning, in collaborative model training, has demonstrated significant potential for enhancing diversity and efficiency in outcomes. In the healthcare domain, particularly healthcare with disabilities, the sensitive nature of data presents a significant challenge as sharing even the computation on these data can risk exposing personal health information. This research addresses the problem of enabling shared model training for healthcare data—particularly with disabilities decreasing the risk of leaking or compromising sensitive information. Technologies such as federated learning provide solution for decentralised model training but fall short in addressing concerns related to trust building, accountability and control over participation and data. We propose a framework that integrates federated learning with advanced identity management as well as privacy and trust management technologies. Our framework called <i>Theme</i> (Trusted Healthcare Machine Learning Environment) leverages digital identities (e.g., W3C decentralised identifiers and verified credentials) and policy enforcements to regulate participation. This is to ensure that only authorised and trusted entities can contribute to the model training. Additionally, we introduce the mechanisms to track contributions per participant and offer the flexibility for participants to opt out of model training at any point. Participants can choose to be either contributors (providers) or consumers (model users) or both, and they can also choose to participate in subset of activities. This is particularly important in healthcare settings, where individuals and healthcare institutions have the flexibility to control how their data are used without compromising the benefits. In summary, this research work contributes to privacy preserving shared model training leveraging federated learning without exposing sensitive data; trust and accountability mechanisms; contribution tracking per participant for accountability and back-tracking; and fine-grained control and autonomy per participant. By addressing the specific needs of healthcare data for people with disabilities or such institutions, the Theme framework offers a robust solution to balance the benefits of shared machine learning with critical need to protecting sensitive data.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851281","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":"Predictive Analysis of Global Terrorist Attacks Using Lexical Patterns Across Multiple Datasets","authors":"Mohammed Salem Atoum,&nbsp;Ala Abdulsalam Alarood,&nbsp;Eesa Abdullah Alsolmi,&nbsp;Areej Obeidat,&nbsp;Moutaz Alazab","doi":"10.1111/exsy.13808","DOIUrl":"https://doi.org/10.1111/exsy.13808","url":null,"abstract":"<div>\u0000 \u0000 <p>Worldwide terrorist activities continue to pose a significant threat to global security and stability. The unpredictable nature of these acts necessitates advanced analytical approaches to enhance prevention and response strategies. This study examines undetectable word extensions across multiple datasets, using terrorism-related datasets as a case study. This research aims to overcome constraints in current predictive models associated with terrorist attack prediction. While many studies have used the GTD for predicting global terrorist attacks, this study expands beyond GTD by evaluating a corpus of terrorism incidents to enhance predictive analysis through lexical usage. The study employs several machine learning algorithms including Decision Tree (DT), Bootstrap Aggregating (BA), Random Forest (RF), Extra Trees (ET) and XGBoost (XG) algorithms for evaluation. Our approach integrates multiple datasets to reduce dependence on GTD alone. Findings indicate that RF performs best on the GTD database, with 90.20% accuracy in predicting worldwide terrorist attacks. DT achieves 90.40% accuracy when applied to the TF–IDF dataset. XG demonstrates superior performance across various aggregation settings and feature sets, achieving 95.77% accuracy in forecasting worldwide terrorist acts. XG's consistent and effective performance across various contexts highlights its versatility. Its high adaptability and robust performance position it as the preferred algorithm for conducting predictive research on global terrorist acts using the available datasets. Our research findings underscore the importance of incorporating diverse datasets to enhance understanding of terrorist activities and improve predictive capabilities.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860900","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":"RETRACTION: DAE-GAN: An Autoencoder Based Adversarial Network for Gaussian Denoising","authors":"","doi":"10.1111/exsy.13809","DOIUrl":"https://doi.org/10.1111/exsy.13809","url":null,"abstract":"<p>\u0000 \u0000 <b>RETRACTION</b>: <span>A. Samanta</span>, <span>A. Saha</span>, <span>S. C. Satapathy</span>, <span>H. Lin</span>, “ <span>DAE-GAN: An Autoencoder Based Adversarial Network for Gaussian Denoising</span>,” <i>Expert Systems</i> (Early View): e12709, https://doi.org/10.1111/exsy.12709.\u0000 </p><p>The above article, published online on 06 May 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley &amp; Sons Ltd. The article was submitted as part of a guest-edited special issue. The retraction has been agreed on as the article was not reviewed in line with the journal's peer review standards. Furthermore, the methodology and model in this manuscript are insufficiently described. Accordingly, the results are not considered reliable. The authors disagree with the retraction.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113716","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":"RETRACTION: Diagnosis of Depression Level Using Multimodal Approaches Using Deep Learning Techniques with Multiple Selective Features","authors":"","doi":"10.1111/exsy.13812","DOIUrl":"https://doi.org/10.1111/exsy.13812","url":null,"abstract":"<p>\u0000 \u0000 <b>RETRACTION</b>: <span>P. Meshram</span>, <span>R. K. Rambola</span>, “ <span>Diagnosis of Depression Level Using Multimodal Approaches Using Deep Learning Techniques with Multiple Selective Features</span>,” <i>Expert Systems</i> <span>40</span>, no. <span>4</span> (<span>2023</span>): e12933, https://doi.org/10.1111/exsy.12933.\u0000 </p><p>The above article, published online on 13 January 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley &amp; Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to our attention that the article was not reviewed in line with the journal's peer review standards. Moreover, multiple inconsistencies and flaws were identified in this article that affect the validity of the conclusions. The underlying dataset and its processing are described insufficiently and explanation of information in several figures and tables is not appropriately provided.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113813","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}