Computational Intelligence最新文献

{"title":"A Survey of Machine Learning and Deep Learning Techniques Over Advanced Robotics in Surgical Applications","authors":"S. S. P. M. Sharma B,&nbsp;Indrajeet Kumar","doi":"10.1111/coin.70292","DOIUrl":"https://doi.org/10.1111/coin.70292","url":null,"abstract":"<div>\u0000 \u0000 <p>The innovative robotic surgical approach brings a modern solution to complex minimal procedures through precise execution of small instruments through limited opening sites. Through this technique, multiple advantages emerge, which reduce bleeding as well as shorten patients' medical stays and accelerate their healing time, mainly affecting bladder, prostate, heart, and digestive conditions. The da Vinci robotic system represents the first-ever single-site platform, which served as a foundation for multiple advanced robotic systems that followed. The article provides an exhaustive evaluation of how machine learning (ML), deep learning (DL), generative adversarial networks (GAN), and reinforcement learning (RL) influence robotic surgery. The research targets the analysis of learning technologies and how these technologies improve surgery precision, healthcare results, and clinical management decisions. Also, this study provides an application of robotics surgery in gynecology, oncology, cardiology, neurology, and urology. This review research analyzes the literature thoroughly to present ML and DL methodology implementations across the studied areas, paying attention to the problems of limited data availability, real-time system adaptivity, and system interoperability challenges. The study adopts new ideas regarding robotic surgery and AI unification through actionable recommendations that enhance performance against data insufficiency and system integration issues. The paper presents detailed information about new trends together with predictions about ML and DL while providing essential knowledge to robotic surgery specialists and artificial intelligence (AI) scientists to advance robotic surgical assimilation with AI technology.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 5","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849138","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":"Multimodal Deep Content-Based Music Recommendation System Using MIDI and Lyrics","authors":"Naina Yadav,&nbsp;Anil Kumar Singh","doi":"10.1111/coin.70295","DOIUrl":"https://doi.org/10.1111/coin.70295","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, recommendation systems are used in various web applications, which include online music streaming services. Suggesting relevant music in a web application is an open research problem with numerous promising results reported. The most common approach for recommendation systems is learning user preferences, which are based on various modalities. There has been more focus on user modalities like rating information and online reviews to understand the user's music preferences. The proposed model uses multimodal auxiliary song information (MIDI and lyrics) to develop the feature representations, improve user satisfaction with the generated recommendation, and overcome the recommendation system's cold-start problem. Our novel contribution to this work is our multimodal music recommendation system, which captures musical features using MIDI data and semantic features using lyrics in an attempt to provide music recommendations with different multimodal fusion techniques. We also present a comparative analysis of varying word embedding models for musical lyrics to analyze which model performed best for our multimodal music recommendation system. The proposed model is a hybrid two-stage music recommendation model that adequately leverages multimodal item embedding representation to improve recommendation performance substantially.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 5","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848603","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":"Dual-Path Higher-Order Information Interaction With Time-Frequency Attention and Multi-Scale Feature Extraction Based Nested U-Net for Speech Enhancement","authors":"Shaik AreefaBegam,&nbsp;Sunny Dayal Vanambathina","doi":"10.1111/coin.70289","DOIUrl":"https://doi.org/10.1111/coin.70289","url":null,"abstract":"<div>\u0000 \u0000 <p>Speech enhancement plays a crucial role in enhancing the perceptual quality and intelligibility of speech signals that are degraded by noise. Conventional U-Net-based architectures effectively capture local spectral patterns but exhibit limited long-range dependency modeling and may propagate residual noise through skip connections. Transformer-based approaches enhance global context modeling but often incur high computational cost and insufficient preservation of fine-grained spectral cues, limiting real-time applicability. To address these limitations, this paper proposes a novel encoder–decoder speech enhancement framework that integrates Multi-Scale Feature Extraction (MSFE), Dual-Path Higher-Order Information Interaction with Time-Frequency Attention Module (DPH-TFA), and Bottleneck-Guided Feature Calibration (FC) strategy, with its hierarchical extension, Hybrid Cross-Scale Feature Calibration (H-CS-FC). The MSFE blocks extract rich local patterns across multiple receptive fields, capturing both fine-grained and global time-frequency cues. While stacked DPH-TFA blocks at the bottleneck model structured long-range dependencies along time and frequency axes. The FC and H-CS-FC modules perform bottleneck- and cross-scale-guided feature recalibration to suppress noise leakage in skip pathways and enhance decoder reliability. Experimental results on Common Voice and LibriSpeech datasets demonstrate that the proposed DPH-TFA-MSFENet achieves superior perceptual evaluation of speech quality, short-time objective intelligibility, and signal-to-distortion ratio performance, particularly under low-SNR conditions, while maintaining computational efficiency.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 5","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784528","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":"Sentiment Analysis Using Emotion-Guided Polarized Capsule Dropout With Bidirectional Long Short-Term Memory on User Review Data","authors":"Vallem Sushma Latha,&nbsp;Shanker Chandre,&nbsp;Erukala Sudarshan","doi":"10.1111/coin.70291","DOIUrl":"https://doi.org/10.1111/coin.70291","url":null,"abstract":"<div>\u0000 \u0000 <p>Sentiment Analysis (SA) is an essential task in Natural Language Processing (NLP) to discriminate emotions and opinions expressed in text. Though existing algorithms on sentiment analysis struggle to capture hierarchical semantic structure and contextual dependencies in text data. To address these limitations, this research paper developed Emotion-Guided Polarized Capsule Dropout with Bidirectional Long Short-Term Memory (EG-PCD-BiLSTM) for effective sentiment analysis. Emotion-guided routing with a Capsule network is incorporated in BiLSTM to preserve semantic relationships and improve the feature representation of text data. The emotion-guided routing improves capsule networks by including emotion scores from lexicons. It directed the emotionally rich tokens towards suitable sentiment capsules, enhancing subtle classification. The Polarized Capsule Dropout (PCD) selectively deactivates the low-confidence capsules based on vector magnitude. This process preserves meaningful feature representation and enhances model generalization by filtering out the noisy features. Moreover, the Bi-LSTM captures both past and future contextual features in sequential data. The EG-PCD-BiLSTM model obtains the highest accuracy of 97.94% on the Amazon review dataset and 99.03% on the IMDB dataset when compared to existing algorithms. The experimental outcomes show that EG-PCD-BiLSTM offers superior generalization ability and computational efficiency, making it robust for real-world sentiment analysis.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 5","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784340","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":"Multimodal Sensor Fusion Deep Learning Model for Early Prediction of Freezing of Gait in Parkinson's Disease","authors":"Rohit Gupta,&nbsp;Amit Bhongade,&nbsp;Tapan Kumar Gandhi","doi":"10.1111/coin.70286","DOIUrl":"https://doi.org/10.1111/coin.70286","url":null,"abstract":"<div>\u0000 \u0000 <p>Freezing of gait (FoG) is a common and debilitating symptom in individuals with advanced Parkinson's disease (PD), significantly increasing the risk of falls. Wearable devices have facilitated the detection of FoG and falls, but early prediction remains underexplored. This study investigates the application of multimodal sensor fusion and deep learning for the early detection of FoG events in patients with PD. Can a multimodal sensor fusion deep learning model accurately predict FoG events well before time in Parkinson's disease patients, and how robust is the model to noise and inter-subject variability? The proposed study utilized Inertial Measurement Unit (IMU), Electromyography (EMG), and Electroencephalography (EEG) signals from PD patients to develop and evaluate deep learning models. The CNN + LSTM architecture was employed and compared with other classifiers. Stratified 10-fold cross-validation was used to assess model accuracy. The robustness of IMU + EMG and IMU + EMG + EEG configurations to noise was tested, and the inter-subject performance evaluation was conducted. Pre-FoG detection capabilities were also analyzed to emphasize the importance of temporal dynamics in the multimodal approach. The CNN + LSTM model achieved an accuracy of 94.45% in detecting FoG events. The IMU + EMG and IMU + EMG + EEG configurations demonstrated robust performance across inter-subject evaluations. The models showed resilience to noise, with the CNN + LSTM and IMU + EMG + EEG configurations maintaining high accuracy. Pre-FoG detection achieved 94.20% accuracy, highlighting the model's effectiveness in capturing temporal dynamics. The CNN + LSTM model, particularly in the IMU + EMG + EEG configuration, proves to be a robust and accurate predictor of FoG events in patients with PD. The study findings highlight the potential impact of multimodal sensor fusion and deep learning in reducing false positives and negatives, thereby enhancing precision, sensitivity, and specificity. These insights are crucial for deploying reliable FoG prediction systems in real-world settings and advancing the management of PD. Future research should explore additional sensor modalities, transferability to different PD cohorts, longitudinal data, and real-time deployment in clinical environments.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784049","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":"Correction to “\u0000Denoising Multi-Level Preference Learning for Local Interest-Oriented Session-Based Recommendation”","authors":"","doi":"10.1111/coin.70285","DOIUrl":"https://doi.org/10.1111/coin.70285","url":null,"abstract":"<p>\u0000 <span>Y. Xiu</span>, <span>J. Guo</span>, <span>B. Qu</span>, and <span>M. Liu</span>, “ <span>Denoising Multi-Level Preference Learning for Local Interest-Oriented Session-Based Recommendation</span>,” <i>Computational Intelligence</i> <span>42</span>, no. <span>2</span> (<span>2026</span>): e70213, https://doi.org/10.1111/coin.70213.</p><p>In the published article, “Yang Xiu” is incorrectly listed as the corresponding author. The correct designation should be:</p><p><b>Correspondence:</b> Juncai Guo (<span>[email protected]</span>), Bing Qu (<span>[email protected]</span>), Miao Liu (<span>[email protected]</span>).</p><p>We apologize for this error.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753281","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":"An Explainable Detection and Classification of Breast Cancer Using a Novel Deep Learning Model by Using LIME on Histopathological Images","authors":"Raheel Zaman,&nbsp;Ibrar Ali Shah,&nbsp;Javed Ali Khan,&nbsp;Muhammad Shahid Anwar,&nbsp;Khursheed Aurangzeb","doi":"10.1111/coin.70284","DOIUrl":"https://doi.org/10.1111/coin.70284","url":null,"abstract":"<div>\u0000 \u0000 <p>Breast cancer (BC) is the most common disease among women globally and a major contributor to both illness and death. The timely and precise detection and classification (DAC) of this life-threatening disease are vital in reducing mortality rates and preventing further complications. Traditional BC detection methods are often time-consuming and costly. This research proposes a Deep Explainable Breast Cancer Detection and Classification Network (DeepEBCDCNet) to enable precise and early diagnosis of BC using histopathological images (HI). The DeepEBCDCNet model consists of 13 learnable layers, including nine convolutional layers (CN) followed by four fully connected (FC) layers. Additionally, the architecture incorporates one input layer, eight leaky ReLU (LR) layers, four ReLU layers, five max pooling layers (MPL), six batch normalization (BN) layers, one cross-channel normalization (CLN) layer, three dropout layers (DL), one softmax layer (SL), and one classification layer (CL). To enhance transparency and interpretability, the Local Interpretable Model-Agnostic Explanations (LIME) method is integrated to describe the model's predictions. The DeepEBCDCNet model is assessed using two BC image datasets: BC (for detection) and Breast Cancer Histology Images (BACH) (for classification). A 10-fold cross-validation method ensures the reliability of the results. The model's performance is compared with state-of-the-art hybrid approaches to assess its effectiveness in BC DAC. The model attained an accuracy of 98.53% in BC detection, while in BC classification (three-class: Benign [B], In situ [IS], and Invasive Carcinoma [IC]), it achieved 98.33% accuracy. The proposed DeepEBCDCNet significantly reduces incorrect diagnoses and enhances classification accuracy, offering a reliable second opinion for pathologists in BC DAC.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752709","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":"AlquistCoder: A Synthetic Data Approach to Training Compact Secure Coding Assistants and Building Security Benchmarks","authors":"Ondřej Kobza,&nbsp;Adam Černý,&nbsp;Ivan Dostál,&nbsp;Jan Šedivý,&nbsp;Maria Rigaki,&nbsp;Muris Sladić,&nbsp;Sebastian Garcia","doi":"10.1111/coin.70282","DOIUrl":"https://doi.org/10.1111/coin.70282","url":null,"abstract":"<p>Large language models are increasingly used as programming assistants, but their security behavior remains uneven: they may generate code with vulnerable patterns, and they may provide actionable help for malicious requests. This paper introduces <i>AlquistCoder</i>, a compact 3.8B-parameter coding assistant designed to address both risks through targeted synthetic-data alignment. Starting from Phi-4-mini, we train the model with supervised fine-tuning and direct preference optimization on data produced by our constitution-guided Design–Amplify–Refine framework, which generates secure-coding examples, refusal demonstrations, and preference pairs from structured specifications of vulnerability classes, coding domains, and malicious-intent patterns. We evaluate AlquistCoder on CyberSecEval, HumanEval, SecurityEval, and two benchmarks released with this work: <i>VulnBench</i>, for hard Python secure-coding prompts, and <i>MalBench</i>, for multi-turn adversarial manipulation. Across these benchmark-level evaluations, AlquistCoder reduces statically detected vulnerability patterns and judged malicious-assistance rates relative to its base model and to baselines of comparable or larger size, while retaining competitive coding performance for its size. We publicly release the trained model, datasets, benchmarks, and evaluation scripts to support reproducible research on security alignment for code-generation models.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148617043","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":"ASSFMamba2: Adaptive Spatial-Spectral Fusion via Mamba2 for Hyperspectral Image Classification","authors":"Xingqiao Li,&nbsp;Yapeng Li,&nbsp;Youfa Liu,&nbsp;Bo Du","doi":"10.1111/coin.70274","DOIUrl":"https://doi.org/10.1111/coin.70274","url":null,"abstract":"<div>\u0000 \u0000 <p>Hyperspectral image (HSI) classification is a fundamentally challenging task in remote sensing, requiring effective joint modeling of intricate spectral signatures and complex spatial dependencies. Although deep learning models such as convolutional neural networks (CNNs) and Transformers have achieved remarkable progress, they are inherently limited by local receptive fields or quadratic computational complexity. These limitations hinder their ability to efficiently capture long-range dependencies at the pixel level. To overcome these issues, we propose ASSFMamba2, a novel image-level HSI classification framework that leverages the selective state-space model (Mamba2) for efficient long-range modeling and an Adaptive Spatial-Spectral Fusion (ASSF) mechanism. Specifically, the framework comprises three core components. First, the Spectral Mamba2 Block (SpeMB) partitions spectral channels into groups and applies state-space modeling to capture long-range spectral dependencies. Second, the Spatial Mamba2 Block (SpaMB) flattens spatial sequences and aggregates global contextual information across pixels. Third, an Adaptive Fusion Module dynamically integrates spatial and spectral features through pixel-wise soft gating, cross-modality interaction, and channel-wise recalibration. These designs ensure that the most discriminative features are emphasized at every location. Extensive experiments on four benchmark HSI datasets demonstrate that ASSFMamba2 not only surpasses existing state-of-the-art methods in overall accuracy, average accuracy, and kappa coefficient, but also achieves faster convergence and enhanced robustness. These results highlight the potential of Mamba2 as a new backbone for hyperspectral image analysis and confirm the effectiveness of our adaptive spatial-spectral fusion strategy. Codes are available at \u0000https://github.com/lixingqiao/ASSFMamba2.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616158","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 Multiscale Level Set Algorithm With Adaptable and Attention-Based Graph Network Model for Fine-Grained Segmentation Over Medical Images","authors":"Sateesh Muddamsetti,&nbsp;A. Arulselvam","doi":"10.1111/coin.70277","DOIUrl":"https://doi.org/10.1111/coin.70277","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate segmentation of tumor regions is vital for detecting lesions in medical scans. It helps doctors diagnose conditions early, plan treatments effectively, and monitor disease progression. This process is especially important in radiology, oncology, and dermatology. Deep learning has made great strides in automating medical image investigation. However, many existing systems at rest resist the noisy data and complex anatomical structures. Low contrast and irregular shapes often lead to inaccurate segmentation outcomes. The aforementioned disadvantages can affect clinical decisions and delay proper treatment. To overcome these challenges, a more adaptive and reliable approach is needed. Such a method should work well across different imaging conditions and patient populations. Improving segmentation accuracy can directly enhance patient care and outcomes. Therefore, in this work, an effective image segmentation technique is developed using an advanced deep learning model for medical imaging, which enables the framework to achieve fine on small lesion segmentation tasks. At first, the necessary images are collected from reputable sources. Then, the gathered images are passed to the Multiscale Level Set Algorithm (MLSA) for the accurate segmentation of medical images. Here, the proposed model explores the use of Partial Differential Equations (PDEs) in boundary detection and contour evolution for organ and tissue segmentation. Particularly, the PDEs evolve a level set function, which implicitly defines a curve or surface within the medical images. Moreover, the Adaptive and Spatial Attention-based Graph UNet++ (ASA-GUNet++) is proposed for fine-grained segmentation. Here, the parameters of the ASA-GUNet++ model are tuned using the Improved Guidance Strategy of Superb Fairy-Wren Algorithm (IGSSA) for improving segmentation performance regarding accuracy. The proposed model is more useful for handling challenges like small lesion segmentation, irregular boundaries, and class imbalance because of the innovative deep learning model integrated with the optimization algorithm. Finally, the proposed model has undergone performance validation to evaluate its performance across various medical imaging modalities.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"42 4","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615993","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}