International journal of neural systems最新文献_第9页

A Novel Morlet Convolutional Neural Network. 一种新的Morlet卷积神经网络。

IF 6.4

International journal of neural systems Pub Date : 2026-03-01 Epub Date: 2025-11-19 DOI: 10.1142/S0129065725500777

Peilin Zhu, Zirong Li, Chao Cao, Zhida Shang, Guoyang Liu, Weidong Zhou

{"title":"A Novel Morlet Convolutional Neural Network.","authors":"Peilin Zhu, Zirong Li, Chao Cao, Zhida Shang, Guoyang Liu, Weidong Zhou","doi":"10.1142/S0129065725500777","DOIUrl":"10.1142/S0129065725500777","url":null,"abstract":"Automatic seizure detection holds significant importance for epilepsy diagnosis and treatment. Convolutional neural networks (CNNs) have shown immense potential in seizure detection. Though traditional CNN-based seizure detection models have achieved significant advancements, they often suffer from excessive parameters and limited interpretability, thus hindering their reliability and practical deployment on edge computing devices. Therefore, this study introduces an innovative Morlet convolutional neural network (Morlet-CNN) framework with its effectiveness demonstrated in seizure detection tasks. Unlike traditional CNNs, the convolutional kernels in the Morlet-CNN contain only two learnable parameters, allowing for a lightweight architecture. Additionally, we propose a frequency-domain-response-based kernel pruning algorithm for Morlet-CNN and implement an INT8 quantization algorithm by incorporating Kullback-Leibler (KL) divergence calibration with a Morlet lookup table (LUT). With the pruning and quantization algorithms, the model's parameter scale achieves over 90% reduction while maintaining minimal accuracy loss. Furthermore, the model exhibits enhanced interpretability from a signal processing perspective, distinguishing it from many previous CNN models. Extensive experimental validation on the Bonn and CHB-MIT datasets confirms the Morlet-CNN model's efficacy with a compact Kilobyte (KB)-level model size, making it highly suitable for real-world applications.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550077"},"PeriodicalIF":6.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145552444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge Graph Embedding Model Based on Spiking Neural-like Graph Attention Network for Relation Prediction. 基于类神经注意网络的关系预测知识图嵌入模型。

IF 6.4

International journal of neural systems Pub Date : 2026-03-01 Epub Date: 2025-11-12 DOI: 10.1142/S0129065725500789

Yu Cao, Bing Li, Hong Peng, Nijing Yang

{"title":"Knowledge Graph Embedding Model Based on Spiking Neural-like Graph Attention Network for Relation Prediction.","authors":"Yu Cao, Bing Li, Hong Peng, Nijing Yang","doi":"10.1142/S0129065725500789","DOIUrl":"10.1142/S0129065725500789","url":null,"abstract":"Knowledge graphs (KGs) which represent entities and their relations in a structured manner, have become a fundamental resource for various natural language processing tasks. However, the incompleteness of KGs significantly hinders their effectiveness, thereby reducing their practical utility. The challenge of predicting missing relations between entities and performing these predictions efficiently has become a focal point of research. To address the challenge of incomplete KGs, we propose GEGS, a novel KG embedding framework that enhances scalability and expressiveness for relation prediction. GEGS introduces GAT-SNP, a graph attention network that, for the first time, integrates nonlinear spiking neural P (SNP) mechanisms into graph attention models and applies them to the KG domain, effectively capturing complex relational structures. The GAT-SNP network assigns distinct attention weights to each node, enabling the model to focus on the most relevant nodes in the graph. To mitigate information loss in long-range and sequential path features, we incorporate a BiLSTM-SNP component, which alleviates long-term dependency issues while preserving global path information. By leveraging GAT-SNP and BiLSTM-SNP, GEGS achieves superior performance in link prediction tasks, paving the way for applications in large-scale knowledge base completion. Kinship, FB15k-237, and WN18RR are used to evaluate the proposed GEGS model. The experimental results indicate that the proposed GEGS model has achieved state-of-the-art results in multiple evaluation metrics(e.g. Hits@10 and MRR).","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550078"},"PeriodicalIF":6.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145497971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Class Intra-Trial Trajectory Analysis Technique to Visualize and Quantify Variability of Mental Imagery EEG Signals. 一种多类别试验内轨迹分析技术可视化和量化心理图像脑电图信号的可变性。

IF 6.4

International journal of neural systems Pub Date : 2026-02-01 Epub Date: 2025-11-26 DOI: 10.1142/S0129065725500753

Nicolas Ivanov, Madeline Wong, Tom Chau

{"title":"A Multi-Class Intra-Trial Trajectory Analysis Technique to Visualize and Quantify Variability of Mental Imagery EEG Signals.","authors":"Nicolas Ivanov, Madeline Wong, Tom Chau","doi":"10.1142/S0129065725500753","DOIUrl":"10.1142/S0129065725500753","url":null,"abstract":"High inter- and intra-individual variation is a prominent characteristic of electroencephalography (EEG) signals and a significant inhibitor to the practical implementation of brain-computer interfaces (BCIs) outside of research laboratories. However, a few methods exist to assess EEG signal variability. Here, a novel multi-class intra-trial trajectory (MITT) analysis to study EEG variability for mental imagery BCIs is presented. The methods yield insight into different aspects of signal variation, specifically (i) inter-individual, (ii) inter-task, (iii) inter-trial, and (iv) intra-trial. A novel representation of the time evolution of EEG signals was developed. Task trials were segmented into short temporal windows and represented in a feature space derived from unsupervised clustering of trial covariance matrices. Using this representation, temporal trajectories through the feature space were constructed. Two metrics were defined to assess user performance based on these trajectories: (1) InterTaskDiff, based on time-varying distances between the mean trajectories of different tasks, and (2) InterTrialVar, which measured the inter-trial variation of the temporal trajectories along the feature dimensions. Analysis of three-class BCI data from 14 adolescents revealed both metrics correlated significantly with classification results. Further analysis of intra-trial trajectories suggested the existence of characteristic task- and user-specific temporal dynamics. The participant-specific insights provided by MITT analysis could be used to overcome EEG-variability challenges impeding practical implementation of BCIs by elucidating avenues to improve user training feedback or selection of user-optimal classifiers and hyperparameters.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550075"},"PeriodicalIF":6.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145608099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multivariate Cloud Workload Prediction Method Integrating Convolutional Nonlinear Spiking Neural Model with Bidirectional Long Short-Term Memory. 基于卷积非线性尖峰神经模型和双向长短期记忆的多元云工作负荷预测方法。

IF 6.4

International journal of neural systems Pub Date : 2026-02-01 Epub Date: 2025-09-27 DOI: 10.1142/S0129065725500716

Minglong He, Nan Zhou, Hong Peng, Zhicai Liu

{"title":"A Multivariate Cloud Workload Prediction Method Integrating Convolutional Nonlinear Spiking Neural Model with Bidirectional Long Short-Term Memory.","authors":"Minglong He, Nan Zhou, Hong Peng, Zhicai Liu","doi":"10.1142/S0129065725500716","DOIUrl":"10.1142/S0129065725500716","url":null,"abstract":"Multivariate workload prediction in cloud computing environments is a critical research problem. Effectively capturing inter-variable correlations and temporal patterns in multivariate time series is key to addressing this challenge. To address this issue, this paper proposes a convolutional model based on a Nonlinear Spiking Neural P System (ConvNSNP), which enhances the ability to process nonlinear data compared to conventional convolutional models. Building upon this, a hybrid forecasting model is developed by integrating ConvNSNP with a Bidirectional Long Short-Term Memory (BiLSTM) network. ConvNSNP is first employed to extract temporal and cross-variable dependencies from the multivariate time series, followed by BiLSTM to further strengthen long-term temporal modeling. Comprehensive experiments are conducted on three public cloud workload traces from Alibaba and Google. The proposed model is compared with a range of established deep learning approaches, including CNN, RNN, LSTM, TCN and hybrid models such as LSTNet, CNN-GRU and CNN-LSTM. Experimental results on three public datasets demonstrate that our proposed model achieves up to 9.9% improvement in RMSE and 11.6% improvement in MAE compared with the most effective baseline methods. The model also achieves favorable performance in terms of MAPE, further validating its effectiveness in multivariate workload prediction.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550071"},"PeriodicalIF":6.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visually-Inspired Multimodal Iterative Attentional Network for High-Precision EEG-Eye-Movement Emotion Recognition. 高精度脑电图-眼动情感识别的视觉启发多模态迭代注意网络。

IF 6.4

International journal of neural systems Pub Date : 2026-02-01 Epub Date: 2025-10-09 DOI: 10.1142/S0129065725500728

Wei Meng, Fazheng Hou, Kun Chen, Li Ma, Quan Liu

{"title":"Visually-Inspired Multimodal Iterative Attentional Network for High-Precision EEG-Eye-Movement Emotion Recognition.","authors":"Wei Meng, Fazheng Hou, Kun Chen, Li Ma, Quan Liu","doi":"10.1142/S0129065725500728","DOIUrl":"10.1142/S0129065725500728","url":null,"abstract":"Advancements in artificial intelligence have propelled affective computing toward unprecedented accuracy and real-world impact. By leveraging the unique strengths of brain signals and ocular dynamics, we introduce a novel multimodal framework that integrates EEG and eye-movement (EM) features synergistically to achieve more reliable emotion recognition. First, our EEG Feature Encoder (EFE) uses a convolutional architecture inspired by the human visual cortex's eccentricity-receptive-field mapping, enabling the extraction of highly discriminative neural patterns. Second, our EM Feature Encoder (EMFE) employs a Kolmogorov-Arnold Network (KAN) to overcome the sparse sampling and dimensional mismatch inherent in EM data; through a tailored multilayer design and interpolation alignment, it generates rich, modality-compatible representations. Finally, the core Multimodal Iterative Attentional Feature Fusion (MIAFF) module unites these streams: alternating global and local attention via a Hierarchical Channel Attention Module (HCAM) to iteratively refine and integrate features. Comprehensive evaluations on SEED (3-class) and SEED-IV (4-class) benchmarks show that our method reaches leading-edge accuracy. However, our experiments are limited by small homogeneous datasets, untested cross-cultural robustness, and potential degradation in noisy or edge-deployment settings. Nevertheless, this work not only underscores the power of biomimetic encoding and iterative attention but also paves the way for next-generation brain-computer interface applications in affective health, adaptive gaming, and beyond.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550072"},"PeriodicalIF":6.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Prompt-Guided Generative Language Model for Unifying Visual Neural Decoding Across Multiple Subjects and Tasks. 一种跨主题和任务统一视觉神经解码的提示引导生成语言模型。

IF 6.4

International journal of neural systems Pub Date : 2026-02-01 Epub Date: 2025-09-26 DOI: 10.1142/S0129065725500686

Wei Huang, Hengjiang Li, Fan Qin, Diwei Wu, Kaiwen Cheng, Huafu Chen

{"title":"A Prompt-Guided Generative Language Model for Unifying Visual Neural Decoding Across Multiple Subjects and Tasks.","authors":"Wei Huang, Hengjiang Li, Fan Qin, Diwei Wu, Kaiwen Cheng, Huafu Chen","doi":"10.1142/S0129065725500686","DOIUrl":"10.1142/S0129065725500686","url":null,"abstract":"Visual neural decoding not only aids in elucidating the neural mechanisms underlying the processing of visual information but also facilitates the advancement of brain-computer interface technologies. However, most current decoding studies focus on developing separate decoding models for individual subjects and specific tasks, an approach that escalates training costs and consumes a substantial amount of computational resources. This paper introduces a Prompt-Guided Generative Visual Language Decoding Model (PG-GVLDM), which uses prompt text that includes information about subjects and tasks to decode both primary categories and detailed textual descriptions from the visual response activities of multiple individuals. In addition to visual response activities, this study also incorporates a multi-head cross-attention module and feeds the model with whole-brain response activities to capture global semantic information in the brain. Experiments on the Natural Scenes Dataset (NSD) demonstrate that PG-GVLDM attains an average category decoding accuracy of 66.6% across four subjects, reflecting strong cross-subject generalization, and achieves text decoding scores of 0.342 (METEOR), 0.450 (Sentence-Transformer), 0.283 (ROUGE-1), and 0.262 (ROUGE-L), establishing state-of-the-art performance in text decoding. Furthermore, incorporating whole-brain response activities significantly enhances decoding performance by enabling the integration of distributed neural signals into coherent global semantic representations, underscoring its methodological importance for unified neural decoding. This research not only represents a breakthrough in visual neural decoding methodologies but also provides theoretical and technical support for the development of generalized brain-computer interfaces.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550068"},"PeriodicalIF":6.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Closed-Loop Control of Epilepsy Based on Reinforcement Learning. 基于强化学习的癫痫闭环控制。

IF 6.4

International journal of neural systems Pub Date : 2026-02-01 Epub Date: 2025-10-18 DOI: 10.1142/S0129065725500741

Ruimin Dan, Honghui Zhang, Jianchao Bai

引用次数: 0

Evolutionary Channel Pruning for Style-Based Generative Adversarial Networks. 基于风格的生成对抗网络的进化通道修剪。

IF 6.4

International journal of neural systems Pub Date : 2026-02-01 Epub Date: 2025-09-27 DOI: 10.1142/S0129065725500704

Yixia Zhang, Ferrante Neri, Xilu Wang, Pengcheng Jiang, Yu Xue

{"title":"Evolutionary Channel Pruning for Style-Based Generative Adversarial Networks.","authors":"Yixia Zhang, Ferrante Neri, Xilu Wang, Pengcheng Jiang, Yu Xue","doi":"10.1142/S0129065725500704","DOIUrl":"10.1142/S0129065725500704","url":null,"abstract":"Generative Adversarial Networks (GANs) have demonstrated remarkable success in high-quality image synthesis, with StyleGAN and its successor, StyleGAN2, achieving state-of-the-art performance in terms of realism and control over generated features. However, the large number of parameters and high floating-point operations per second (FLOPs) hinder real-time applications and scalability, posing challenges for deploying these models in resource-constrained environments such as edge devices and mobile platforms. To address this issue, we propose Evolutionary Channel Pruning for StyleGANs (ECP-StyleGANs), a novel algorithm that leverages evolutionary algorithms to compress StyleGAN and StyleGAN2 while maintaining competitive image quality. Our approach encodes pruning configurations as binary masks on the model's convolutional channels and iteratively refines them through selection, crossover, and mutation. By integrating carefully designed fitness functions that balance model complexity and generation quality, ECP-StyleGANs identifies optimally pruned architectures that reduce computational demands without compromising visual fidelity, achieving approximately a 4 × reduction in FLOPs and parameters, while maintaining visual fidelity with only a slight increase in FID (Fréchet Inception Distance) compared to the original un-pruned model. This study should be interpreted as a preliminary step towards the formulation and management of the generative AI pruning problem as a multi-objective optimisation task, aimed at enhancing the trade-off between model efficiency and image quality, thereby making large deep models more accessible for real-world applications such as edge devices and resource-constrained environments. Source codes will be available.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550070"},"PeriodicalIF":6.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain Connectivity Variability Influences Anxiety Through the Behavioral Inhibition System. 大脑连接变异性通过行为抑制系统影响焦虑。

IF 6.4

International journal of neural systems Pub Date : 2026-01-01 Epub Date: 2025-09-19 DOI: 10.1142/S0129065725500558

Runyang He, Yan Zhu, Jiayu Ye, Dezhong Yao, Peng Xu, Fali Li, Lin Jiang, Yi Liang

{"title":"Brain Connectivity Variability Influences Anxiety Through the Behavioral Inhibition System.","authors":"Runyang He, Yan Zhu, Jiayu Ye, Dezhong Yao, Peng Xu, Fali Li, Lin Jiang, Yi Liang","doi":"10.1142/S0129065725500558","DOIUrl":"10.1142/S0129065725500558","url":null,"abstract":"The behavioral inhibition system (BIS), mediating responses to punishment cues and avoidance behaviors, is implicated in anxiety. However, the neural dynamics underpinning BIS, particularly regarding the temporal variability of brain network interactions, remain less explored. Using resting-state functional magnetic resonance imaging (rs-fMRI) of 181 healthy adults, this study investigated the association between BIS sensitivity and the temporal variability of functional connectivity within and between functional brain networks. This finding revealed a significant positive correlation between BIS scores and temporal variability, specifically in the connectivity involving subnetworks' sensory somatomotor hand network (SSHN)-ventral attention network (VAN), and sensory somatomotor mouth network (SSMN)-VAN. Notably, the high-BIS sensitivity group exhibited significantly greater temporal variability between VAN and SSMN/SSHN compared to the low-BIS sensitivity group. Furthermore, predicted BIS scores based on network variability showed a strong correlation with actual BIS scores (Pearson's [Formula: see text]). Moreover, significant mediation effects highlighted the bridging role of BIS scores between brain network variability and anxiety scale scores. This enhances the comprehension of the relationship between BIS, anxiety, and brain function, while also offering new insights into the pathogenesis of anxiety.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550055"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Enhanced Random Convolutional Kernel Transform for Diverse and Robust Feature Extraction from High-Density Surface Electromyograms for Cross-day Gesture Recognition. 基于增强随机卷积核变换的高密度表面肌电特征提取方法。

IF 6.4

International journal of neural systems Pub Date : 2026-01-01 Epub Date: 2025-10-09 DOI: 10.1142/S0129065725500625

Yonglin Wu, Xinyu Jiang, Jionghui Liu, Yao Guo, Chenyun Dai

{"title":"An Enhanced Random Convolutional Kernel Transform for Diverse and Robust Feature Extraction from High-Density Surface Electromyograms for Cross-day Gesture Recognition.","authors":"Yonglin Wu, Xinyu Jiang, Jionghui Liu, Yao Guo, Chenyun Dai","doi":"10.1142/S0129065725500625","DOIUrl":"10.1142/S0129065725500625","url":null,"abstract":"High-density surface electromyogram (HD-sEMG) has become a powerful signal source for hand gesture recognition. However, existing approaches suffer from limited feature diversity in hand-crafted methods and high data dependency in deep learning models, necessitating individual model calibration for each user due to neuromuscular differences. We propose EMG-ROCKET, an enhanced version of the RandOm Convolutional KErnel Transform (ROCKET), designed to extract diverse and robust HD-sEMG features without prior knowledge or extensive training. EMG-ROCKET integrates random channel fusion and enhanced aggregation functions to enhance robustness against cross-day signal variability in HD-sEMG applications. In cross-day evaluations of hand gesture recognition, a Ridge classifier using EMG-ROCKET features achieved 84.3% and 77.8% accuracy on two HD-sEMG datasets, outperforming all baseline methods. Furthermore, feature contribution analysis demonstrates the capability of EMG-ROCKET to capture spatial muscle activation patterns, offering insights into motion mechanisms. These results establish EMG-ROCKET as a promising, training-free solution for robust HD-sEMG feature extraction, facilitating practical human-machine interaction applications.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550062"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0