Cognitive Neurodynamics最新文献

{"title":"Eeg emotion recognition based on channel spatio-temporal multi-dimensional feature extraction network.","authors":"Jingjie Yan, Siya Zhao, Jing Li, Jinsheng Wei, Xiaoli Wang","doi":"10.1007/s11571-026-10509-0","DOIUrl":"10.1007/s11571-026-10509-0","url":null,"abstract":"<p><p>Electroencephalogram (EEG) signals capture the temporal and spatial characteristics of neuronal activities recorded by multiple electrodes and their data complexity is high. In order to effectively extract deeper emotional features from EEG signals, this paper proposes a Channel Spatio-temporal Multi-dimensional Feature Extraction Network (CSMFEN) for EEG emotion recognition. CSMFEN conducts feature learning on EEG signals from three dimensions which include channel domain, spatial domain and temporal domain. In the channel domain, CSMFEN models the relative importance of different EEG electrodes by assigning adaptive weights to each channel, thereby enhancing discriminative channel representations for emotion classification. In the spatial domain, an enhanced dynamic convolution mechanism is proposed to capture inter-channel spatial relationships, where the input signal space size information and convolution kernel weight information are jointly considered. Different attention mechanisms are employed to dynamically generate convolution kernels, enabling flexible spatial feature extraction. In the temporal domain, a Long Short-Term Memory (LSTM) network is utilized to learn the temporal dependencies and sequential dynamics of EEG signals. Experimental validation of CSMFEN is conducted on the SEED and DEAP datasets. The results demonstrate that CSMFEN can effectively learn discriminative representations and achieve competitive emotion recognition performance on both datasets.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"146"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13402277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148599636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph enhanced ContextFusion-EmoNet (CFEN): integrating facial, postural, and environmental cues for emotion recognition in dynamic video scenes.","authors":"Navneet Gupta, R Vishnu Priya, Chandan Kumar Verma","doi":"10.1007/s11571-026-10514-3","DOIUrl":"10.1007/s11571-026-10514-3","url":null,"abstract":"<p><p>Emotion recognition in videos is still difficult due to the fact that affective cues are spread throughout facial expressions, body movements and scene context. Face only approaches tend to perform poorly in most cases because of being affected by such factors as varying illumination, occlusion, camera motion, dynamic backgrounds, and multiple interacting subjects. To overcome these drawbacks, a novel and flexible model called ContextFusion-EmoNet (CFEN) is proposed in this paper to utilize appearance, motion and relational information for robust emotion recognition from video. CFEN uses an Average Contextual Loss (ACL) guided key-frame selection method, incorporating the differences of the VGG16 features and Farneback optical flow weight to select 4 key frames that are rich in motion and extremely informative per video. The selected images are then fed into ResNet18 feature extractor, a temporal context modeler, which is a Transformer encoder, and a deterministic Graph Convolutional Network (GCN) module for modeling the relation between frame tokens. Good intra-dataset performance is shown through extensive experimentation on the four datasets, CAER, CK + , DFEW, and FERV39k, with accuracy rates of 97.71%, 98.00%, 91.43% and 89.71% respectively.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"154"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of the effects of balance exercises on visuospatial skills using EEG brain oscillations.","authors":"Yasin Yıldırım, İrem Yemeniciler, Devrim Tarakcı, Bahar Güntekin","doi":"10.1007/s11571-026-10494-4","DOIUrl":"10.1007/s11571-026-10494-4","url":null,"abstract":"<p><p>Visual-spatial attention (VSA) selects relevant sensory information and supports the preparation of responses to this information. Mental rotation (MR) is the ability to rotate an object seen from a certain perspective to a new orientation in space. Exercise stands out as a promising non-pharmacological treatment for cognitive functions. Balance control is known to be related to the visual system. Therefore, the aim was to investigate the effectiveness of video-based balance games and structured balance exercises on VSA and MR with EEG brain oscillations. 30 healthy participants were included in the study. Participants were divided into two groups (structured balance exercises group (SBEG) and video-based balance exercises group (VBBEG)) by randomization. Both groups received exercise sessions 2 days a week for a total of 6 weeks. The mentioned cognitive functions were evaluated by selecting tests previously used in the literature. For the VSA task, after 6 weeks of exercise, occipital theta (4-7 Hz) power decreased in the VBBEG group, while SBEG increased. In the MR task results, high alpha (11-13 Hz) power decreased in VBBEG and increased in SBEG when centroparietal areas were examined. In conclusion, it is thought that the two different exercise methods may affect visual-spatial attention and mental rotation skills in different ways.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-026-10494-4.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"116"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13309604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148344117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural synchrony and cognitive map formulation in collaborative navigation: leader-follower couplings revealed by EEG hyperscanning.","authors":"Chun-Hsiang Chuang, Po-Hsun Peng, Yi-Chieh Chen","doi":"10.1007/s11571-026-10521-4","DOIUrl":"10.1007/s11571-026-10521-4","url":null,"abstract":"<p><p>This study aims to address a theoretical gap in how people formulate cognitive maps in a social context. The concept of a cognitive map, a mental representation of spatial relations, has been widely used to explain individual navigation. When navigating with others, the exchange of spatial knowledge and the formation of cognitive maps in leaders and followers are natural and ubiquitous, yet their neural underpinnings remain insufficiently understood. We investigated these mechanisms by recording hyperscanning electroencephalography (EEG) from 70 participants engaged in dyadic route-planning and navigation tasks within a virtual reality environment. Intrabrain and interbrain couplings were analyzed across frequency bands using connectivity measures. We observed robust neural synchronization patterns associated with collaborative navigation performance and role. Intrabrain connectivity analyses showed increased delta coupling in both leaders and followers, whereas theta connectivity was particularly enhanced in followers. Alpha-band connectivity displayed divergent patterns between roles, suggesting distinct neural strategies for spatial processing. Interbrain analyses revealed increased delta causality between partners but decreased theta and gamma couplings from followers to leaders. Faster-performing dyads exhibited overall reduced interbrain coupling, especially in theta bands, indicating more efficient neural coordination. Collaborative navigation relies on frequency-specific, role-dependent neural coordination both within and between brains, and more efficient dyads may require less sustained interbrain coupling. These findings can inform the design of navigation aids, training protocols, and collaborative interfaces that better support leader-follower coordination and improve joint spatial decision making in real-world settings.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-026-10521-4.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"149"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148629470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A task-aligned multimodal machine learning framework for studying working memory dysfunction in Parkinson's disease.","authors":"Mercedes Terry, Samuel A Birkholz, Jeffery S Johnson, Jau-Shin Lou, Asenath X Heuther, Jessica Keller, Enrique Alvarez Vazquez","doi":"10.1007/s11571-026-10526-z","DOIUrl":"https://doi.org/10.1007/s11571-026-10526-z","url":null,"abstract":"<p><p>Cognitive impairment is one of the most functionally debilitating non-motor symptoms in Parkinson's disease (PD). Yet, current diagnostic and clinical practices rely heavily on subjective assessments and burdensome behavioral testing, which lack the temporal resolution to detect subtle deficits. While EEG and pupillometry offer promising non-invasive insights into cognitive processing, more scalable and objective methods are needed to detect subtle and often elusive dysfunction in early PD. Machine learning (ML) leverages patterns in neurophysiological signals recorded during cognitive tasks to identify subtle impairments that may not be evident in standard evaluations. We recorded EEG and pupillometry data from 68 participants (35 PD, 33 healthy controls (HC)) during a visual Change Detection working memory task. Using our custom, standardized feature-extraction toolbox, we extracted 108 features and incorporated the resulting feature matrices into an ML pipeline. We applied SMOTE to balance the classes, then used principal component analysis (PCA) to reduce dimensionality. An automated elbow method identified optimal cutoffs for principal components (PCs) and original features, which guided subsequent recursive elimination (RE) for computational efficiency. We trained a support vector machine with a radial basis function kernel (SVM-RBF) to classify PD and evaluated the final model's performance on a hold-out dataset. The automated elbow method identified optimal cutoffs at 20 PCs and 22 original features. The RE revealed that a model using 14 PCs and the top 7 PCA-weighted features achieved the highest performance, with 71% accuracy and an F1 score of 0.701. The classifier's performance declined slightly when tested on held-out data, with an accuracy of 63% and an F1 score of 0.626. This study demonstrates the potential of a task-aligned ML pipeline for uncovering early cognitive markers of PD. The framework supports post-hoc feature contribution mapping, linking retained physiological features and task stages back to classification-relevant patterns, supporting mechanistic investigation of cognitive dysfunction in PD.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-026-10526-z.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"158"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13469056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148757747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual-input fuzzy parameter zeroing neural network for robust synchronization of chaotic systems.","authors":"Lv Zhao, Chen Zhu, Jie Jin","doi":"10.1007/s11571-026-10534-z","DOIUrl":"10.1007/s11571-026-10534-z","url":null,"abstract":"<p><p>As a research hotspot in control and engineering, chaotic systems synchronization has attracted considerable attention. However, traditional control methods are difficult to ensure that chaotic systems realize synchronization within a predefined time, and they also lack robustness against external disturbances. Therefore, chaotic systems synchronizations based on zeroing neural network (ZNN) have achieved great development in recent years. A dual-input fuzzy parameter zeroing neural network (DIFPZNN) is proposed to realize fast synchronization and adaptive disturbance suppression for chaotic systems without measuring unknown bounded external disturbances. Rigorous theoretical analysis and comparative simulations verify its convergence and disturbance attenuation performance for disturbances imposed only on the master chaotic system, rather than general universal robustness against all types of perturbations. In addition, comparative simulation results of the proposed DIFPZNN model with the other three recently reported models for the synchronization of the Qi chaotic systems further validate the superior performance of the proposed DIFPZNN model. Finally, the hardware implementation is completed based on the field programmable gate array (FPGA), and the synchronization process of the Qi chaotic system controlled by the DIFPZNN model is demonstrated on an oscilloscope. Meanwhile, the DIFPZNN-based Qi chaotic synchronization method is applied to signal encryption, which verifies the practical application value of the proposed model. The proposed controller achieves synchronization without measuring external disturbances.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"161"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13500450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EFS-NET: EEG-fNIRS multi-scale fusion network based on spatial calibration.","authors":"Wenhao Gu, Ian Daly, Xinjie He, Shurui Li, Andrew Ty Lau, Xingyu Wang, Andrzej Cichocki, Yong Chen, Jing Jin","doi":"10.1007/s11571-026-10529-w","DOIUrl":"https://doi.org/10.1007/s11571-026-10529-w","url":null,"abstract":"<p><p>Hybrid brain-computer interfaces (hBCIs) integrate multiple neuroimaging modalities and utilize their complementary information to address the inherent limitations of single-modality neural signal decoding. For electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) hybrid BCIs, advanced fusion algorithms are crucial to fully exploit the superior spatial localization capability of fNIRS and the millisecond-level temporal resolution of EEG. This work proposes an end-to-end spatial calibration-based multi-scale EEG-fNIRS fusion network named EFS-Net, which organically integrates EEG and fNIRS signals through a multi-scale spatio-temporal fusion architecture. The network consists of three complementary functional branches: a multi-scale temporal convolution branch for capturing rapidly changing cortical electrophysiological features of EEG, an EEG spatial branch for constructing latency-compensated cortical topographies to adapt to the delayed hemodynamic response of fNIRS, and a spatially calibrated fNIRS spatial branch for dynamically fusing spatial feature maps with EEG counterparts to generate temporally aligned and spatially enhanced neural representations. This three-branch fusion structure constructs abundant spatio-temporal feature embeddings and improves the discriminability of neural features. Evaluated on two public datasets including Word Generation (WG) and Mental Arithmetic (MA) with a rigorous subject-specific leave-one-session-out cross-validation protocol, EFS-Net achieves classification accuracies of 77.71 ± 8.23% and 81.69 ± 9.49% on the WG and MA datasets respectively, which surpasses state-of-the-art unimodal algorithms and traditional fusion models. Visualization results demonstrate that the designed alignment strategy can restore realistic cortical spatial distribution characteristics, providing a feasible solution for personalized neural signal decoding in hybrid BCIs.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"160"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13490330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148789223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gated triadic coupling in a Hopfield neural network: cooperative-competitive dynamics and synchronization.","authors":"Jiangying Shao, Zhijun Li","doi":"10.1007/s11571-026-10544-x","DOIUrl":"10.1007/s11571-026-10544-x","url":null,"abstract":"<p><p>Inspired by the tripartite synapse, where a third element dynamically modulates the effective coupling between two neurons, this study abstracts that idea into a minimal three-neuron Hopfield neural network with multiplicative gated coupling. The third neuron acts as a dynamic modulator, adjusting the synaptic strength between the other two units without invoking auxiliary astrocytic dynamics. Through nonlinear analyses including bifurcation diagrams, Lyapunov exponent spectra, and two-parameter dynamical maps, we show that the three gated coupling strengths engage in both cooperative and competitive interactions, collectively governing transitions among chaotic, periodic, and globally coordinated firing patterns. Phase synchronization analysis shows that the three coupling strengths differentially regulate the transition from chaotic to phase-coordinated states. Circuit design and simulations validate the theoretical predictions and confirm the physical realizability of the model. This work elucidates the regulatory functions of gated triadic coupling in a minimal three-neuron motif and provides a circuit-realizable framework for developing tunable neuromorphic systems.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"171"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148982303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic quaternion-valued fuzzy cellular neural networks with a mixture of time delays: mean-square stability of measure pseudo almost automorphic solutions with application to image denoising.","authors":"Fritz Mbounja Béssémè, David Libouga Li Gwet","doi":"10.1007/s11571-026-10546-9","DOIUrl":"10.1007/s11571-026-10546-9","url":null,"abstract":"<p><p>This paper addresses a family of stochastic quaternion-valued fuzzy cellular neural networks with a mixture of time delays in a measure-theoretic framework. By combining the Banach fixed-point theorem with the Lebesgue-Radon-Nikodym theorem, sufficient conditions are established to guarantee existence and uniqueness of measure pseudo almost automorphic solutions. Furthermore, new criteria ensuring the mean-square stability of the considered system are derived. The proposed analysis provides a rigorous framework for studying the dynamical behavior of delayed stochastic quaternion-valued neural networks in measurable spaces. Numerical simulations are performed to corroborate the theoretical results and illustrate the validity of the theoretical stability results. Moreover, the proposed stochastic quaternion-valued fuzzy cellular neural network model is applied to image denoising, where its performance is validated through both qualitative visual assessment and quantitative evaluation metrics, showing its ability to effectively remove noise while preserving key image details. The main novelty of this study lies in integrating quaternion-valued dynamics, fuzzy interactions, stochastic perturbations, mixed time delays, and measure pseudo-almost automorphic behavior into a unified cellular neural network framework. The theoretical results extend existing stability analyses and demonstrate the applicability of the proposed model to multidimensional signal processing.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"189"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149073776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reduced task-switching flexibility in parietal-cingulate and frontal circuits associated with brooding.","authors":"Selena Singh, Saurabh Bhaskar Shaw, Suzanna Becker","doi":"10.1007/s11571-026-10425-3","DOIUrl":"10.1007/s11571-026-10425-3","url":null,"abstract":"<p><p>Ruminative brooding is marked by its perseverative nature. Existing mechanistic theories attribute this to cognitive control deficits linked to elevated functional connectivity within the default mode network and abnormal prefrontal activity. Here, we conceptualize ruminative brooding as an emergent property of a neural attractor state within the default mode network. Stable attractors are mathematically defined by two key properties: (1) convergence over time (assessing attractivity), and (2) resistance to perturbation (assessing stability). We tested whether brain states associated with brooding exhibited these properties in healthy volunteers using EEG during a task-switching protocol that interleaved cued rumination, working memory, and autobiographical memory tasks. Since cued rumination and working memory are thought to engage anticorrelated networks (default mode vs. central executive), switching from cued rumination to working memory effectively \"perturbs\" this system. Cued rumination was associated with beta power in the posterior cingulate cortex, with rumination disengagement marked by a reduction in beta power in posterior parietal and cingulate cortices. Moreover, high trait rumination was associated with impaired disengagement of these rumination-related dynamics and reduced recruitment of the dlPFC when transitioning from cued rumination to the working memory task, consistent with the \"resistance to perturbation\" criterion of a stable attractor. Furthermore, trait brooding was positively associated with a reduction in variance in posterior parietal and cingulate cortices time series over the course of cued rumination trials, consistent with the \"convergence\" criterion. These results provide support for framing brooding-related neural dynamics as pathological attractor states, providing a mechanistic account of rumination's perseverative quality.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-026-10425-3.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"55"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12920956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147269982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}