Cognitive Neurodynamics最新文献

{"title":"Machine learning for missing data imputation in Alzheimer's research: predicting medial temporal lobe dynamic flexibility.","authors":"Soodeh Moallemian, Abolfazl Saghafi, Rutvik Deshpande, Jose M Perez, Miray Budak, Bernadette A Fausto, Fanny M Elahi, Mark A Gluck","doi":"10.1007/s11571-026-10422-6","DOIUrl":"10.1007/s11571-026-10422-6","url":null,"abstract":"<p><p>Alzheimer's disease (AD) pathology begins years before symptoms appear, and dynamic flexibility of the medial temporal lobe (MTL) may serve as an early functional biomarker. Using data from 656 older adults in the Rutgers Aging and Brain Health Alliance study, we evaluated whether cognitive, genetic, biochemical, and demographic predictors could estimate MTL dynamic flexibility, despite substantial missingness (1,866 missing values; 25.86%). Only 42 participants (6.40%) had complete data; therefore, we compared case deletion with five imputation strategies (MICE, GAIN, MissForest, MIWAE, ReMasker) and eight regression models, assessing prediction accuracy using repeated 5-fold cross-validation. Complete-case analysis yielded limited performance (average [Formula: see text], [Formula: see text]). After imputation, all methods improved accuracy, with MissForest paired with Bagging Trees or Random Forest achieving the lowest prediction error ([Formula: see text]). The greatest improvement in concordance occurred when GAIN was combined with Bagging Trees/Random Forest ([Formula: see text]), representing a  57% gain over the best complete-case model. A Scheirer-Ray-Hare ANOVA confirmed significant differences across imputation strategies ([Formula: see text]). Runtime analyses showed GAIN and MissForest to be both accurate and computationally efficient, while deep generative imputers were slower. These findings demonstrate that robust imputation is essential for maximizing data utility and predictive reliability in high-missingness neuroimaging studies and highlight the potential of ensemble tree models combined with advanced imputation techniques for estimating MTL dynamic flexibility in aging populations.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"51"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12891276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146178219","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":"Emotional neuro-control of higher-order nonlinear leader-follower systems with limited communication and fully unknown dynamics.","authors":"F Baghbani, M-R Akbarzadeh-T, M-B Naghibi Sistani","doi":"10.1007/s11571-026-10532-1","DOIUrl":"10.1007/s11571-026-10532-1","url":null,"abstract":"<p><p>Emotional models have demonstrated fast response times, quick adaptation, and promising performance, particularly in uncertain and complex environments. However, these results have been for single agents, and the social aspect of emotion-based architectures in multi-agent control systems (MACS) has yet to be adequately explored. MACS comprises a class of problems with considerably higher operational and environmental uncertainties due to agent variations, larger state-space dimensions, and limited communication with neighbors, thereby making its design and theoretical analysis considerably more challenging. Here, we propose an emotion-based cooperative adaptive neuro-controller (ECANC) for such higher-order heterogeneous, nonlinear leader-follower systems with entirely unknown dynamics, local communication, and limited access to the leader's state. This innovative approach is consistent with the non-decreasing adaptation law of the amygdala in the brain, opening the possibility of further inspiration from nature and posing a considerably higher challenge for controller design and stability analysis. For this purpose, we employ the continuous radial basis emotional neural network (CRBENN) paradigm in the MAS domain to approximate the unknown dynamics. We further employ artificial potential functions (APFs) to model agents' interactions, enabling a more straightforward mathematical analysis than graph-based methods. A new control law addresses local interactions among agents and the stability of the leader-follower system and is rigorously derived from Lyapunov stability theory. Simulation results demonstrate that ECANC achieves lower tracking error with less control effort, particularly when handling sudden disturbances, compared with two competing approaches.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"174"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13586092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149008021","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":"Exploiting frontotemporal asymmetry in low-channel EEG emotion recognition via spectral fusion network.","authors":"Xindong Huang, Ling Li, Xingen Gao, Shiqi Chen, Chao Feng, Zhenzhe Zhong, Zhijie Zhang, Jianxiongwen Huang","doi":"10.1007/s11571-026-10535-y","DOIUrl":"10.1007/s11571-026-10535-y","url":null,"abstract":"<p><p>Portable EEG devices hold promise for pervasive affective computing, yet sparse channel counts limit the resolution for capturing global cortical dynamics of emotional processing. To address this, we propose the Spatiotemporal Spectral Asymmetric Fusion Network (STSANet), a neurophysiologically grounded framework designed to decode emotional states from low-density recordings. Unlike traditional methods limited by spatial resolution, we introduce a dual-branch architecture prioritizing Frequency and Spatial Asymmetry Modeling. Instead of merely extracting features, the model explicitly quantifies non-linear hemispheric lateralization-a core mechanism of valence regulation. By modeling differential activation between homologous frontotemporal electrodes (e.g., prefrontal asymmetry), STSANet captures critical lateralized neurodynamics without high-density coverage. A cross-modal attention mechanism then adaptively fuses these lateralized signatures with spectral oscillations. Validating ecological feasibility, we confirm consistent spectral energy distributions between portable Xmuse and professional-grade Enobio devices. Experiments on SEED and a self-collected dataset demonstrate STSANet's superior performance, achieving accuracies of 86.80% and 87.43%, respectively. Crucially, the model maintains high robustness even under rigorous trial-level cross-validation, confirming its generalization capability against temporal distribution shifts. Ablation studies reveal success hinges on explicit spatial asymmetry modeling. Thus, capturing lateralized neurodynamics serves as a resource-efficient strategy for accurate, neurophysiologically interpretable pervasive BCI applications.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"177"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13589030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149007981","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":"Second-order memristor-coupled heterogeneous neuron model: multi-attractor, multi-stability, and hardware implementation.","authors":"Miao Xie, Hairong Lin, Qiuzhen Wan, Fei Yu, Wei Yao","doi":"10.1007/s11571-026-10536-x","DOIUrl":"10.1007/s11571-026-10536-x","url":null,"abstract":"<p><p>Memristor-based neuron models offer a promising framework for reproducing biologically inspired firing activities and realizing neuromorphic hardware. In this work, we develop a heterogeneous neuron model by introducing a second-order memristor (SOM) synapse that couples a Tabu learning neuron (TLN) and a FitzHugh-Nagumo (FHN) neuron. A novel SOM model is first proposed and shown to possess dual-memory characteristics, providing richer state-dependent regulation than conventional first-order memristors. By exploiting this enhanced memory mechanism, the SOM functions as an adaptive nonlinear synapse that substantially enriches the dynamical repertoire of the coupled neuronal system. The proposed model exhibits a wide range of complex behaviors, including chaos, hyperchaos, coexisting attractors, multistability, and intricate multi-butterfly attractors. Comprehensive nonlinear analyses based on bifurcation diagrams, Lyapunov exponent spectra, entropy complexity measures, attraction basins, and the 0-1 chaos test reveal the underlying dynamical evolution. Remarkably, the SOM parameters can flexibly regulate the number and distribution of coexisting butterfly attractors, whereas minute perturbations of the memristor initial states lead trajectories toward distinct asymptotic states, demonstrating extreme sensitivity and memory-dependent state selection. These findings uncover a previously unexplored mechanism through which higher-order memristive memory shapes heterogeneous neuronal dynamics. To validate the theoretical results, an analog circuit implementation of the proposed model is developed. Both circuit simulations and FPGA-based hardware experiments successfully reproduce the predicted dynamical phenomena, confirming the physical realizability and robustness of the proposed neuron model. The results highlight the potential of second-order memristive synapses for constructing highly reconfigurable neuromorphic systems and advancing the understanding of memory-induced cognitive neurodynamics.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"162"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13500451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812089","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":"Synaptic summation shapes information transfer in GABA-glutamate co-transmission.","authors":"Belle Krubitski, Cesar Ceballos, Ty Roachford, Rodrigo F O Pena","doi":"10.1007/s11571-025-10383-2","DOIUrl":"https://doi.org/10.1007/s11571-025-10383-2","url":null,"abstract":"<p><p>Co-transmission, the release of multiple neurotransmitters from a single neuron, is an increasingly recognized phenomenon in the nervous system. A particularly interesting combination of neurotransmitters exhibiting co-transmission is glutamate and GABA, which, when co-released from neurons, demonstrate complex biphasic activity patterns that vary depending on the time or amplitude differences from the excitatory (AMPA) or inhibitory (GABA_A) signals. Naively, the outcome signal produced by these differences can be functionally interpreted as simple mechanisms that only add or remove spikes by excitation or inhibition. However, the complex interaction of multiple time-scales and amplitudes may deliver a more complex temporal coding, which is experimentally difficult to access and interpret. In this work, we employ an extensive computational approach to distinguish these postsynaptic co-transmission patterns and how they interact with dendritic filtering and ionic currents. We specifically focus on modeling the summation patterns and their flexible dynamics that arise from the many combinations of temporal and amplitude co-transmission differences. Our results indicate a number of summation patterns that excite, inhibit, and act transiently, which have been previously attributed to the interplay between the intrinsic active and passive electrical properties of the postsynaptic dendritic membrane. Our computational framework provides an insight into the complex interplay that arises between co-transmission and dendritic filtering, allowing for a mechanistic understanding underlying the integration and processing of co-transmitted signals in neural circuits.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10383-2.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"6"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12618799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145539075","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":"Leveraging Swin Transformer for advanced sentiment analysis: a new paradigm.","authors":"Gaurav Kumar Rajput, Saurabh Kumar Srivastava, Namit Gupta","doi":"10.1007/s11571-025-10378-z","DOIUrl":"https://doi.org/10.1007/s11571-025-10378-z","url":null,"abstract":"<p><p>As healthcare text data becomes increasingly complex, it is vital for sentiment analysis to capture local patterns and global contextual dependencies. In this paper, we propose a hybrid Swin Transformer-BiLSTM-Spatial MLP (Swin-MLP) model that leverages hierarchical attention, shifted-window mechanisms, and spatial MLP layers to extract features from domain-specific healthcare text better. The framework is tested on domain-specific datasets for Drug Review and Medical Text, and performance is assessed against baseline models (BERT, LSTM, and GRU). Our findings show that the Swin-MLP model performs significantly better overall, achieving superior metrics (accuracy, precision, recall, F1-score, and AUC) and improving mean accuracy by 1-2% over BERT. Statistical tests to assess significance (McNemar's test and paired t-test) indicate that improvements are statistically significant (p < 0.05), suggesting the efficacy of the architectural innovations. The results' implications indicate that the model is robust, efficiently converges to classification, and is potentially helpful for a wide range of domain-specific sentiment analyses in healthcare. We will examine future research directions into exploring lightweight attention mechanisms, cross-domain multimodal sentiment analysis, federated learning to protect privacy, and hardware implications for rapid training and inference.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"13"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12660549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145647175","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":"Rosmarinic acid mitigates age-related loss of EEG complexity in temporal cortex: a recurrence quantification analysis.","authors":"Serkan Uslu, Deniz Kantar","doi":"10.1007/s11571-026-10457-9","DOIUrl":"10.1007/s11571-026-10457-9","url":null,"abstract":"<p><p>Physiological aging is associated with progressive changes in brain function, including neural oscillations. Recurrence Quantification Analysis (RQA) may provide a nuanced perspective on neural dynamics in aging. Rosmarinic acid (RA) has shown promise in mitigating age-related neurodegeneration. Its effects on EEG complexity in aging models remain unexplored. This study aimed to investigate age-related changes in RQA metrics and to assess whether RA modulates these alterations in a rodent aging model. Aging was induced in female rats via D-galactose administration. RA was administered to aged model animals. Urethane-induced EEG were obtained and analyzed across delta and theta bands. RQA parameters-determinism (DET), entropy (ENTR), and laminarity (LAM)-were computed for frontal and temporal regions. Aging was associated with a significant increase in DET and LAM, particularly in the temporal cortex, indicating enhanced regularity and persistence of EEG patterns. Concurrently, ENTR values declined, suggesting reduced signal complexity. RA partially reversed these trends, notably decreasing DET and LAM while increasing ENTR values in the temporal cortex. No significant changes were observed in the frontal cortex. This study underscores the utility of RQA in capturing nonlinear EEG alterations associated with aging and highlights RA as a promising compound for preserving cortical dynamics in senescence.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-026-10457-9.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"87"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13153318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147867221","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":"Effect of changes in the arm physical parameters on the minimum torque-change trajectories of human reaching movements.","authors":"Kotaro Muramatsu, Takahiro Kagawa, Naomichi Ogihara","doi":"10.1007/s11571-026-10428-0","DOIUrl":"10.1007/s11571-026-10428-0","url":null,"abstract":"<p><p>The minimum torque-change model is a computational model describing the trajectory formation of the point-to-point reaching movement in humans. This model roughly predicts a straight hand trajectory with a bell-shaped velocity profile, as observed in human reaching movements. However, the minimum torque-change criterion is a dynamic quantity, and the calculated trajectories could be, at least to some extent, affected by changes in the arm's physical parameters such as mass, moment of inertia, and viscosity of each link. This study systematically investigates how changes in the arm's physical parameters affect the optimal arm trajectories calculated based on the minimum torque-change criterion. The calculated optimal trajectories were largely curved, particularly when the physical parameters of the forearm were doubled or halved from the original physical parameters. Furthermore, when the original parameters were modified to be biomechanically more appropriate, the trajectories were also largely curved, unlike those in actual human reaching movements. The results suggest that the hand trajectory in human reaching movements may be determined by a dynamic optimization criterion that is less sensitive to variations in the biomechanical properties.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-026-10428-0.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"59"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12961019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147376227","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":"Applications of lump, breather, rogue, and various nonlinear waves in the modeling of blood vessels: analysis of the nonlinear Murray equation with heterogeneous wall properties.","authors":"Sajawal Abbas Baloch, Aysha Zafar","doi":"10.1007/s11571-026-10513-4","DOIUrl":"10.1007/s11571-026-10513-4","url":null,"abstract":"<p><p>In order to gain a deeper understanding of nonlinear wave dynamics, soliton solutions play an important role. In this research, using suitable transformations, we rigorously examine a wide range of wave solutions for the nonlinear Murray equation, exhibiting new wave patterns, including lump, lump with one kink, lump with two kink, rogue waves, periodic, breathers and multi-waves solutions. In individuals with cardiovascular illness, these solutions can be utilized to increase blood flow since they reflect a consistent fluctuation in blood vessel shape and diameter. Under the pathological conditions, localized pressure pulse occur in arteries, for which lump type solutions can be used as mathematical analogues. Rogue waves can describe sudden extreme spikes in blood pressure. Moreover, multi-wave solutions play in important role in hemodynamics, particularly in modeling the nonlinear arterial systems. These solutions have recently emerged in the literature, providing researchers with a crucial tool for understanding intricate biological systems. To illustrate the characteristics and development of the solutions, comprehensive graphical representations are provided, including 3D surface plots, 2D profiles, and contour plots. By examining these plots, we analyze how parameters affect the amplitude and propagation behavior of nonlinear waves which are related to change in the geometry of blood vessels. The derived solutions can deepen our comprehension of complex biological systems and may help in the development of new medical models and treatments. To the best of our knowledge, this is the first study to report such a diverse class of exact wave solutions for the nonlinear Murray equation with heterogeneous wall properties.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"147"},"PeriodicalIF":4.5,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13415428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148618581","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":"Locally active memristor-based Chialvo neuron model: bifurcation, multistability, and noise effects.","authors":"Shivakumar Rajagopal, Fatemeh Parastesh, Viet-Thanh Pham, Karthikeyan Rajagopal, Sajad Jafari","doi":"10.1007/s11571-026-10469-5","DOIUrl":"https://doi.org/10.1007/s11571-026-10469-5","url":null,"abstract":"<p><p>In this paper, a new memristive Chialvo neuron map is proposed by incorporating a locally active discrete memristor into the original Chialvo neuron model. The introduced memristor exhibits local activity, nonvolatility, and bistability, which fundamentally enrich the neuron dynamics. Equilibrium points and their stability are analyzed as functions of the memristive coupling strength, revealing that the number of equilibrium points increases with increasing memristor strength, indicating enhanced multistability. Comprehensive dynamical analyses based on bifurcation diagrams and largest Lyapunov exponents demonstrate that the proposed model exhibits a wide range of firing behaviors, including periodic, quasiperiodic, and chaotic dynamics. It is shown that increasing the memristive coupling strength generally suppresses chaotic regions, a result further confirmed by spectral entropy analysis. Time-series and phase-space investigations reveal that the memristor can induce qualitative transitions between different firing patterns and modify oscillation periods. In contrast to the original Chialvo model, which mainly exhibits bistability between resting and oscillatory states, the proposed memristive model is capable of exhibiting bistability and multistability between distinct oscillatory attractors, strongly dependent on the initial state of the memristor. Furthermore, the effect of stochastic perturbations is examined, showing that additive noise can induce spiking and bursting dynamics in parameter regions where the noise-free system remains quiescent. As the memristive coupling strength increases, a lower noise intensity is required to induce oscillatory activity.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"20 1","pages":"99"},"PeriodicalIF":3.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13212855/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148053153","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}