Applied Soft Computing最新文献

{"title":"A lightweight ultrasound welding surface defect detection network based robust feature downsampling","authors":"Rui Liu ,&nbsp;Zhonghua Shen ,&nbsp;Lun Zhao ,&nbsp;Yu Ren ,&nbsp;Lan Zhang ,&nbsp;Liya Li ,&nbsp;Jiajin Zhang ,&nbsp;Amr Monier","doi":"10.1016/j.asoc.2026.114736","DOIUrl":"10.1016/j.asoc.2026.114736","url":null,"abstract":"<div><div>Ultrasonic welding is widely used in high-precision manufacturing because of its environmental benefits and high efficiency. Ultrasonic welding surface defects have significant large span and a background noise. Currently, the accuracy and computational complexity of deep learning-based industrial surface defect detection networks are challenging to meet the requirements. To address these problems, this research proposes a lightweight ultrasonic welding surface defect detection network (LUWDNet) for surface defects in ultrasonic welding wire harness terminals. Firstly, an efficient multi-scale residual block (EMR) is proposed, which uses residual structure and reparameterization technology to improve the feature extraction capability and enhance the network’s ability to extract multi-scale features. Subsequently, a deformable directional residual block (DDR) is proposed, which uses a bidirectional convolution kernel to expand the model’s receptive field. Reduces the impact of noise generated by background information in the model. Moreover, a hierarchical cascade feature fusion block (HCFF) is proposed, which uses dynamic channel adjustment to reduce network computation so that network computation and model size can be better adapted to edge deployment. Finally, the Robust Feature Downsampling Module (RFD) is applied in the network to mitigate the problem of small target loss and excessive noise generated by small targets during the downsampling process. To validate the effectiveness and generalizability of LUWDNet, it achieved 94.2% precision and 88.6% mean average precision (mAP50) with 2.6M parameters and a 6.5M model size in the surface defect dataset of ultrasonic welded wire harness terminals (UWSD). In addition, the satisfactory performance on the public dataset NEU-DET demonstrates the generalizability and stability of LUWDNet, showing its broad prospects in the field of industrial defect detection.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114736"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Achelous++: Power-oriented multi-task panoptic waterway perception framework based on vision-radar fusion","authors":"Runwei Guan ,&nbsp;Haocheng Zhao ,&nbsp;Shanliang Yao ,&nbsp;Limin Yu ,&nbsp;Xiaohui Zhu ,&nbsp;Ryan Wen Liu ,&nbsp;Eng Gee Lim ,&nbsp;Weiping Ding ,&nbsp;Yutao Yue ,&nbsp;Hui Xiong","doi":"10.1016/j.asoc.2026.114787","DOIUrl":"10.1016/j.asoc.2026.114787","url":null,"abstract":"<div><div>Multi-task panoptic perception leveraging multi-sensor fusion is crucial for comprehensively understanding waterway environments, which enhances the robust monitoring and autonomous navigation of unmanned surface vessels. However, the fragmented design inherent in multi-modal and multi-task neural networks inevitably leads to decreased inference speed and increased energy consumption. Therefore, we focus on developing a low-power, lightweight multi-task panoptic perception framework with high liberty for development. In this paper, we propose an end-to-end framework named <em>Achelous</em><span><math><mo>+</mo><mo>+</mo></math></span>, capable of executing five perception tasks concurrently with high speed and low power consumption, which include object detection, semantic segmentation, drivable-area segmentation, waterline segmentation, and radar point cloud semantic segmentation. Notably, we design an efficient vision-radar fusion module, termed Gating Adaptive Fusion (GAF), to enhance fusion-based perception tasks cost-effectively within a shared computational space. Moreover, we design a dynamic feature routing module called Edge-Context Weighting (ECW) for feature selection in multi-segmentation tasks. Building on this, we also design a series of metrics to evaluate the energy consumption of multi-task perception. Overall, our <em>Achelous</em><span><math><mo>+</mo><mo>+</mo></math></span> framework achieves state-of-the-art performance on WaterScenes benchmark. Specifically, the optimal model of Achelous<span><math><mo>+</mo><mo>+</mo></math></span> framework outperforms other models by approximately 5% mAP and 7% mIoU in object detection and multiple semantic segmentation tasks, while maintaining over 20 FPS and power consumption under 20W on Orin. To the best of our knowledge, <em>Achelous</em><span><math><mo>+</mo><mo>+</mo></math></span> is the pioneering fusion-based framework for panoptic perception that integrates five perception tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114787"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-feature collaborative computational neural architecture search method for classifying continuous time series signals","authors":"Rui Zhang ,&nbsp;Xin-Yu Li ,&nbsp;Yan-Jun Zhang","doi":"10.1016/j.asoc.2026.114731","DOIUrl":"10.1016/j.asoc.2026.114731","url":null,"abstract":"<div><div>This paper proposes a multi-feature collaborative computational neural architecture search (MFCC-NAS) method for classifying continuous time series signals. It is designed to enhance the value density of the signals and mitigate the subjective nature of the design of the model architecture while reducing the computational cost of assessing its performance. We first design a representation of sample richness and a metric of their contributions to enhance the richness of the features in them, so that they provide a basis of dense and high-value data. Following this, we develop a MFCC-NAS method that can efficiently construct a model to classify continuous time series signals by using a search space designed for global–local feature separation based on cells, an architecture search strategy for the collaborative computation of global–local features, and a low-cost and robust cascading strategy to evaluate the performance of the architecture. Finally, we design a multi-domain collaborative fusion mechanism to fully integrate convolutional visual features from different spatial domains and obtain a comprehensive representation of the features of the samples. We tested the proposed method through comparative generalization experiments on a dataset of welding defects and the Yaseen dataset. After respective search times of 1.52 h and 1.21 h on these datasets, our model achieved an accuracy of classification of over 98 % on both. Furthermore, the resulting model maintains a compact parameter size and short inference time. These results collectively demonstrate the effectiveness and strong generalization capability of the proposed MFCC-NAS method.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114731"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel graph neural network-based approach for android malware detection","authors":"Donghai Tian ,&nbsp;Zhanyun Niu ,&nbsp;Tao Leng ,&nbsp;Jiaqing Jiang ,&nbsp;Pengxuan Chen ,&nbsp;Changzhen Hu ,&nbsp;Chong Yuan ,&nbsp;Ruilong Deng","doi":"10.1016/j.asoc.2026.114689","DOIUrl":"10.1016/j.asoc.2026.114689","url":null,"abstract":"<div><div>With the rapid development of the Internet of Things (IoT) and the Internet of Vehicles (IoV), smartphones have evolved into central hubs for connecting users with various smart devices, making their security increasingly vital. However, the growing prevalence of malicious mobile applications poses significant threats to user privacy and digital assets. Existing machine learning-based mobile malware detection methods often face limitations in terms of robustness and interpretability. To address these challenges, we propose GRED (GNN-based Robust and Explainable Malware Detection), an Android malware detection model that leverages graph neural networks to precisely identify malicious behavior while enhancing both robustness and interpretability. GRED refines Android function call graphs, extracts semantic and structural API features, and utilizes a Top-K-based GNN architecture for effective malware detection. Additionally, it offers multi-perspective interpretability analysis to support an in-depth understanding of detection results. Extensive experiments conducted on two large datasets demonstrate that GRED achieves superior performance compared to existing methods. The interpretability module effectively pinpoints malicious behaviors, thereby assisting security analysts in subsequent investigation and mitigation efforts.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114689"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relation-aware heterogeneous graph network multi-modal predictive modeling of stock movements","authors":"Abdullah Ali Salamai","doi":"10.1016/j.asoc.2026.114703","DOIUrl":"10.1016/j.asoc.2026.114703","url":null,"abstract":"<div><div>The predictive modeling of movements in stock price information has been common but a challenging task for achieving sustainable management of stock marketplaces. This can be attributed to the importance of this information in evading market risks and improving financing decisions. This task becomes more challenging because of the availability of different modalities of stock information (i.e., stock prices, tweets, events, charts, etc.) and the presence of lead-lag relationships. In this regard, graph neural networks (GNNs) have recently achieved great improvements in providing effective modeling and analysis of stock relationships. However, multi-modal stock data evolve with dynamism in the form of heterogeneous graph topologies that limit the representation power of the existing GNNs. To this end, this study presents a novel graph intelligence framework called relation-aware heterogeneous graph network (RHGN) for efficient prediction of stock movements from multi-modal information by learning different forms of relational knowledge from heterogeneous graphs constructed for every trading day. In particular, the relational graph convolutions are presented to extract distinctive node-wise relational knowledge from the subgraph of each type of relationship. Then, the multi-relation knowledge passing module is designed to empower the network to model the dynamicity of relational knowledge throughout various types of relationships. Simultaneously, the edge relations are encoded into trainable temporal representations, from which edgewise relational knowledge is learned by capturing the contextual interactions among stock entities. In RHGN, an adversarial graph learning mechanism is introduced to adaptively augment and perturb the node representations based on the gradient information to make the model robust against small oscillations in input data. Experimentations on four public multi-modal stock movement datasets have validated the efficiency of the RHGN over the state-of-the-art.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114703"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization method of process parameters based on the constraints of surface quality and geometric feature of elements during copying turning","authors":"Zhiheng Chen ,&nbsp;Ning Li ,&nbsp;Shiying Tu ,&nbsp;Kaimao Zeng ,&nbsp;Juan Lu ,&nbsp;Dan Chen","doi":"10.1016/j.asoc.2026.114711","DOIUrl":"10.1016/j.asoc.2026.114711","url":null,"abstract":"<div><div>Machining quality and performance are key indicators of CNC machine tool. To enhance the machining of non-circular curves and rotating parts on mid- to low-end CNC machines, this study proposes an optimization method for contour turning parameters based on surface quality and geometric feature constraints. The method integrates trajectory and parameter optimization. First, the Double-Heads Snake (DHS) algorithm generates a smooth G-code tool path comprising straight lines, convex arcs, and concave arcs with varying curvatures. Multi-objective optimization models are then developed for these elements to enhance machining quality and performance stability. Using Back Propagation Neural Networks (BPNN), the models predict surface roughness (<em>Ra</em>) and cutting force (<em>F</em>) based on process parameters and curvature radius, analyzing their influence mechanisms. An improved African Vulture multi-objective optimization algorithm (MOAVOA_improve) is proposed and validated against other algorithms using CEC2009 benchmarks, demonstrating superior performance. Surface quality and geometric constraints guide tool path segmentation, merging adjacent path segments with minimal curvature impact on <em>Ra</em> into single regions. Optimized process parameters are then determined for each region based on curvature and multi-objective solutions. The method is applied to non-circular rotating workpieces and compared with Mastercam and experience-based parameters. Results show significant improvements in machining quality, validating the method's effectiveness and practicality.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114711"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-modal brain image fusion technique using nakagami imaging and intuitionistic fuzzy sets","authors":"K.G. Lavanya,&nbsp;P. Dhanalakshmi,&nbsp;M. Nandhini","doi":"10.1016/j.asoc.2026.114706","DOIUrl":"10.1016/j.asoc.2026.114706","url":null,"abstract":"<div><div>Medical image fusion is essential for consolidating images from diverse modalities into a single image, offering comprehensive information for diagnosis and analysis. With the rapid evolution of brain imaging technologies, researchers increasingly focus on refining fuzzy fusion techniques to utilize the full potential of these modalities. However, existing fuzzy fusion approaches often face three key challenges: difficulty in enhancing low-contrast images, uncertainty in selecting appropriate fuzzy membership functions, and redundancy in preserving important features. To address these shortcomings, a Nakagami distribution and intuitionistic fuzzy set-based multi-modal brain image fusion (NIFMBF) framework is proposed. This method integrates three core innovations: First, Nakagami imaging (NI) is employed to enhance low-contrast areas and reveal imperceptible lesions; second, a novel intuitionistic fuzzy generator (IFG) is designed to transform NI outputs into intuitionistic fuzzy images that effectively handle vagueness and enhance fine structural details; third, gray-level co-occurrence matrix (GLCM)-based contrast feature extraction guides the fusion process, enabling the retention of crucial information while reducing redundant data. Extensive experiments conducted on three benchmark datasets and compared against five state-of-the-art fusion techniques demonstrate the superior performance of the proposed NIFMBF method. Quantitatively, the method achieves an average improvement of 12–20% for every considered fusion metric. Additionally, the proposed framework reduces computational time by nearly 30% highlighting its efficiency. These findings, validated by both qualitative and quantitative evaluations, underscore the efficacy and potential of NIFMBF for medical image fusion.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"191 ","pages":"Article 114706"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A structure-guided framework for multimodal optimization leveraging chaotic search and persistence-based clustering","authors":"Xiang Meng ,&nbsp;Yan Pei","doi":"10.1016/j.asoc.2026.114687","DOIUrl":"10.1016/j.asoc.2026.114687","url":null,"abstract":"<div><div>Multimodal optimization remains a fundamental challenge due to the difficulty of preserving population diversity while efficiently locating multiple optima. To address this challenge, we propose the structure-guided multimodal optimization (SRS-GMO) framework, which explicitly models the evolutionary process as a structured search over topological regions in the decision space, characterized by a novel structural relevance score (SRS). This score integrates persistence, spatial distinctiveness, and recent evolutionary activity to adaptively allocate computational resources to promising niches while maintaining overall structural diversity. SRS-GMO employs chaotic evolution based on logistic maps to enhance global exploration and facilitate escaping from local optima. We instantiate the framework into two variants: SRS-GMO-SO for single objective multimodal optimization problems and SRS-GMO-MO for multi-objective multimodal optimization problems, respectively. Extensive experiments on the CEC2013 and MMF benchmark suites demonstrate that SRS-GMO achieves superior performance in peak coverage, convergence stability, and structural diversity preservation. We also provide a per-module computational complexity analysis and discuss practical accelerations for scalability. These results validate the effectiveness of the proposed framework in addressing the challenges of both single- and multi-objective multimodal optimization.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114687"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The attention mechanism for intelligent text generation of traditional cultural symbols in artistic design","authors":"Xi Xu ,&nbsp;Da Guo","doi":"10.1016/j.asoc.2026.114733","DOIUrl":"10.1016/j.asoc.2026.114733","url":null,"abstract":"<div><div>Amid the digital wave, how to leverage artificial intelligence technologies to accurately interpret and reconstruct traditional cultural symbols has become a key issue in the field of artistic design. Existing image caption generation methods can identify the visual features of objects. But they often fail to capture the inherent cultural spirit and symbolic meanings when dealing with cultural symbols with profound historical connotations and abstract metaphors. This results in generated texts that remain at the level of superficial visual descriptions, lacking cultural depth. In response to this semantic gap, this study proposes an intelligent text generation model integrated with a cross-modal attention mechanism. The model constructs a dual-stream encoding architecture based on the Visual Geometry Group 19 (VGG19) and Bidirectional Encoder Representations from Transformers (BERT). By introducing multi-head self-attention and cross-modal attention modules, it achieves dynamic alignment between visual regions and cultural semantics. Different from traditional methods, the model can actively retrieve key visual clues from images according to the generated textual context. It accurately describes the physical forms of symbols and effectively interprets the cultural implications behind them. Experimental results show that this model achieves scores of 0.42 and 1.31 on the Bilingual Evaluation Understudy-4 (BLEU-4) and Consensus-based Image Description Evaluation (CIDEr) metrics, respectively, significantly outperforming benchmark models. It provides a new paradigm with both accuracy and cultural expressiveness for the digital inheritance and innovative design of traditional cultural symbols.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114733"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical attentive feature refinement network with cross-resolution detail preservation for medical image segmentation","authors":"Xin Shu ,&nbsp;Anqi Shi ,&nbsp;Xiaofang Guo ,&nbsp;Yan Fan ,&nbsp;Xin Zhang","doi":"10.1016/j.asoc.2026.114760","DOIUrl":"10.1016/j.asoc.2026.114760","url":null,"abstract":"<div><div>Deep learning technology, particularly hierarchical encoder-decoder architectures, is developing rapidly in medical image segmentation, which aids in precise identification, localization, and quantification of anatomical structures and diseased areas in clinical diagnosis. However, most of the existing models face two critical challenges: (1) hierarchical semantic gaps caused by inconsistent receptive adaptation across different network depths; (2) gradual loss of fine details in traditional multi-scale fusion schemes, which disproportionately affects small-scale pathological features. Therefore, this paper proposes a Hierarchical Attentive Feature Refinement Network with Cross-Resolution Detail Preservation (HAFRN-CDP) to tackle these problems. Firstly, a Dual-Branch Pooling Fusion Encoder (DPFE) structure is designed to capture features of different scales and types to effectively alleviate information loss in the encoder. Secondly, a Fine Detail Retention (FDR) module is proposed to emphasize the high-resolution features in the encoder head, perceiving features at different scales and semantic levels. Thirdly, a novel Convolution Block (CB), which is composed of a Multi-Scale Residual Aggregation (MSRA) module and a Bi-Branch Global Attention (BGA) module, is presented to reduce the interference of redundant information caused by multi-scale strategy. Finally, in the decoder part, this paper proposes a Cross-Level Feature Fusion (CLFF) module to recover the details and structure by fusing features at different stages and scales. Extensive comparative experiments are conducted on three biomedical image datasets, and the results demonstrate that our HAFRN-CDP exhibits competitive performance compared to the state-of-the-art, with mDice of 92.87 % on JSUAH-Cerebellum and 91.77 % on 2018DSB, while also maintaining high mPrecision of 85.42 % and mRecall of 95.77 % on BUSI (malignant), confirming both its accuracy and robustness across diverse biomedical segmentation tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"192 ","pages":"Article 114760"},"PeriodicalIF":6.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}