International journal of neural systems最新文献_第4页

Frequency Band Personalization for Seizure Network Analysis in Multifocal Patients. 多病灶患者癫痫发作网络分析的频带个性化。

IF 6.4

International journal of neural systems Pub Date : 2026-11-01 Epub Date: 2026-04-28 DOI: 10.1142/S0129065726500371

Genchang Peng, Mehrdad Nourani, Omar Nofal, Jay Harvey

{"title":"Frequency Band Personalization for Seizure Network Analysis in Multifocal Patients.","authors":"Genchang Peng, Mehrdad Nourani, Omar Nofal, Jay Harvey","doi":"10.1142/S0129065726500371","DOIUrl":"10.1142/S0129065726500371","url":null,"abstract":"Stereo-electroencephalography (SEEG) is commonly used for pre-surgical evaluation in patients with multifocal epilepsy undergoing responsive neurostimulation (RNS). Seizure network modeling, a data-driven approach to assist RNS implantation and adjustment, benefits from individualized ictal signatures learned from SEEG data, including repetitive sharp waves, rhythmic discharges, and prominent frequency bands in seizure activities. This is all in pursuit of the earliest possible detection so that the RNS therapy can be delivered as soon as a potential seizure is on the horizon. To improve individualized treatment, this paper proposes a data-driven methodology for personalizing frequency-band selection using a SEEG-based seizure network in patients with multifocal epilepsy. A directed seizure network is first proposed using SEEG data, where network nodes are selected SEEG points, and spectral edges are characterized by a directed transfer function. To further enhance the robustness of network modeling, surrogate data analysis is applied to ensure the significance of network estimation. Then, the density differences between the seizure onset zone and remaining regions are extracted using subgraph density as a heuristic to select the discriminative frequency range with the highest difference as the optimal band. The selection result is further validated using a machine learning classifier for seizure detection. Across ten multifocal patients, our personalized selection achieved an Area-Under score of 0.94 out of 1 in the seizure classification task, outperforming standard bands in distinguishing ictal from pre-ictal states, and exhibiting type-specific spectral patterns with consistency through different frequency-domain network metrics.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2650037"},"PeriodicalIF":6.4,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793060","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

Enhancing Cross-Patient Seizure Detection with Test-Time Adaptation. 基于测试时间适应的跨患者癫痫检测。

IF 6.4

International journal of neural systems Pub Date : 2026-11-01 Epub Date: 2026-05-19 DOI: 10.1142/S0129065726500425

Hailing Feng, Yanna Zhao, Chenxi Nie, Han Wang, Yijia Li, Jilong Lian

引用次数: 0

EEG-Based Cognitive Overload-Driven Adaptive Training for Enhancing Performance Under Simulated High-Stress Space Operation Tasks. 基于脑电图的认知过载驱动自适应训练提高模拟高应激空间作战任务的表现。

IF 6.4

International journal of neural systems Pub Date : 2026-10-30 DOI: 10.1142/S0129065727500328

Ying Zeng, Zhongrui Li, Mengran Wan, Yi Xiao, Xiaoping Chen, Yun Qin, Bin Yan

{"title":"EEG-Based Cognitive Overload-Driven Adaptive Training for Enhancing Performance Under Simulated High-Stress Space Operation Tasks.","authors":"Ying Zeng, Zhongrui Li, Mengran Wan, Yi Xiao, Xiaoping Chen, Yun Qin, Bin Yan","doi":"10.1142/S0129065727500328","DOIUrl":"https://doi.org/10.1142/S0129065727500328","url":null,"abstract":"Given the potential for highly complex and high-risk space operation tasks to induce cognitive overload in operators, effective training is crucial for maintaining mission performance and safety. Conventional adaptive training often struggles to balance task challenge while preventing overload. To address this limitation, this study proposes an EEG-based cognitive overload-driven adaptive training method that integrates hybrid feature selection, Normalized Fast Dynamic Time Warping (NFDTW)-based feature alignment and a shared knowledge layer to improve online cognitive overload detection under nonstationary conditions. A dual-factor task regulation strategy combining task performance and cognitive overload states is further developed to adjust task difficulty based on these two factors. The results showed that the proposed detection method achieved online detection accuracies of 92.07% and 84.67% on the two datasets, respectively, with maximum relative improvements of 7.74% and 8.34% over the comparison methods. The adaptive training strategy was further evaluated in a VR-based robotic arm docking task involving three groups: fixed, control, and adaptive. Mixed-design ANOVA showed that the adaptive group exhibited significant improvements in task performance and SAS as well as more favorable changes in EEG features. Bonferroni-adjusted post hoc comparisons further revealed significant group differences ([Formula: see text]). These findings demonstrate the feasibility of EEG-based cognitive overload-driven adaptive training for complex space operation scenarios.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750032"},"PeriodicalIF":6.4,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149303270","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

Circuit Topology Shapes the Mapping Between Excitatory-Inhibitory Input Composition and Aperiodic Spectral Slope. 电路拓扑形状之间的映射兴奋-抑制输入组成和非周期频谱斜率。

IF 6.4

International journal of neural systems Pub Date : 2026-10-30 DOI: 10.1142/S012906572750033X

Ziliang Cai, Yuan Chang, Xin Yu, Jiarong Liu, Jian Zhang, Xinmeng Guo, Shuang Liu, Dong Ming

{"title":"Circuit Topology Shapes the Mapping Between Excitatory-Inhibitory Input Composition and Aperiodic Spectral Slope.","authors":"Ziliang Cai, Yuan Chang, Xin Yu, Jiarong Liu, Jian Zhang, Xinmeng Guo, Shuang Liu, Dong Ming","doi":"10.1142/S012906572750033X","DOIUrl":"https://doi.org/10.1142/S012906572750033X","url":null,"abstract":"Aperiodic ([Formula: see text]) spectral components are widely interpreted as markers of excitation-inhibition (E/I) balance, but this interpretation spans levels from synaptic currents and microcircuit spiking to population field potentials. Whether macroscopic spectral slope can index microcircuit-level E/I input independently of circuit architecture remains unresolved. This study introduces a connectome- and morphology-constrained framework that embeds electron-microscopy-reconstructed cortical microcircuits in near-critical propagation dynamics and generates field potentials under fixed synaptic, membrane, and forward-modeling parameters. By linking controlled synaptic E/I input, microcircuit spiking propagation, and macroscopic spectra, the framework directly tests the explanatory level of the aperiodic exponent. Across matched E/I regimes, the strength of the E/I input-[Formula: see text] relationship differed between biological and topology-randomized networks, yielding a significant E/I input × topology interaction. To test whether this architecture-dependent mapping extends to biological systems, paired cortical ECoG and thalamic EThG recordings from juvenile pigs were analyzed. Under the same systemic propofol perturbation, exponent changes were stronger in thalamus than cortex and remained so after accounting for global state shifts, baseline spectra, and distance from criticality. These findings identify circuit architecture as a determinant of how microcircuit E/I input is transformed into macroscopic aperiodic spectra, defining the level-specific interpretability of spectral slope.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750033"},"PeriodicalIF":6.4,"publicationDate":"2026-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149303216","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

Mapping Lifespan Functional Connectivity with Vision Transformer and Attention-Guided Trajectory Analysis. 基于视觉转换和注意引导轨迹分析的寿命功能连接映射。

IF 6.4

International journal of neural systems Pub Date : 2026-10-24 DOI: 10.1142/S0129065727500304

Kangrim Cho, Junggu Choi, Mina Park, Na-Young Shin, Sanghoon Han

{"title":"Mapping Lifespan Functional Connectivity with Vision Transformer and Attention-Guided Trajectory Analysis.","authors":"Kangrim Cho, Junggu Choi, Mina Park, Na-Young Shin, Sanghoon Han","doi":"10.1142/S0129065727500304","DOIUrl":"https://doi.org/10.1142/S0129065727500304","url":null,"abstract":"Brain aging involves widespread reorganization of functional networks, yet characterizing these changes across the lifespan remains challenging. This study presents an innovative application of a Vision Transformer (ViT) to resting-state fMRI (rs-fMRI) functional connectivity for age group classification using the Human Connectome Project (HCP) Lifespan datasets. A ViT model was fine-tuned on [Formula: see text] ROI-to-ROI connectivity matrices to perform three binary age group classifications (Young versus Old, Adult versus Aging, Young versus Aging). The model achieved accuracies of 91%, 71%, and 93%, respectively, under five-fold cross-validation. To interpret model decisions, attention weight maps from the final transformer encoder layer were extracted using attention rollout and thresholded with a scree plot analysis, identifying 48 high-salience connections across 28 brain regions. These features were analyzed across the lifespan using generalized additive models (GAM), and k-means clustering of the trajectories revealed three dominant lifespan connectivity patterns: Age-Declining, Mid-Life Peaking, and Age-Increasing. This study demonstrates the viability of the ViT for functional connectome analysis and shows that attention-based interpretability combined with GAM trajectory analysis can enrich insights into age-related changes in brain network connectivity, offering a framework for interpretable brain-age modeling.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750030"},"PeriodicalIF":6.4,"publicationDate":"2026-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149279096","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

Channel-Adaptive Temporal Modeling with Grouped Convolutional Network and Informer for Seizure Detection and Prediction. 基于分组卷积网络和信息源的通道自适应时域建模用于癫痫检测和预测。

IF 6.4

International journal of neural systems Pub Date : 2026-10-23 DOI: 10.1142/S0129065727500316

Wenyan Zhao, Ruiping Liu, Yanqi Shao, Yihang Zhang, Xingchen Dong, Jie Zhou

{"title":"Channel-Adaptive Temporal Modeling with Grouped Convolutional Network and Informer for Seizure Detection and Prediction.","authors":"Wenyan Zhao, Ruiping Liu, Yanqi Shao, Yihang Zhang, Xingchen Dong, Jie Zhou","doi":"10.1142/S0129065727500316","DOIUrl":"https://doi.org/10.1142/S0129065727500316","url":null,"abstract":"Reliable identification and early prediction of epileptic seizures play a critical role in improving patient outcomes and supporting timely therapeutic decision-making. To address the challenges of spatiotemporal feature coupling and long-range dependency modeling in multi-channel EEG signals, this paper proposes a channel-adaptive temporal modeling framework named GCNN-Informer. The framework follows a three-stage pipeline: a grouped convolutional network (GCNN) extracts local features and selects the five most discriminative channels; a convolutional block attention module (CBAM) performs dual spatiotemporal enhancement; and a lightweight Informer encoder captures global temporal dependencies via the ProbSparse self-attention mechanism. Experiments on the CHB-MIT dataset, conducted under a patient-specific evaluation scheme, demonstrated that the proposed method achieved accuracies of 97.33% for seizure detection and 97.28% for seizure prediction, with a low false detection rate (FDR) of 0.30/h and a false prediction rate (FPR) of 0.03/h. Grad-CAM visualization further shows that as EEG signals evolve from normal/interictal to preictal and then to ictal stages, the model's attention heatmap progressively transitions from locally sparse highlighting to continuous block-like highlighting, and finally to dense deep-red responses - a pattern that closely aligns with clinical electrophysiological dynamics. This study provides an accurate and generalizable deep-learning solution for real-time epileptic monitoring and early-warning systems.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750031"},"PeriodicalIF":6.4,"publicationDate":"2026-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149248141","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

Modeling the Selective and Lossy Nature of Visual Processing Bridges the Discrepancy in Noninvasive Neural Decoding. 视觉处理的选择性和有损特性建模弥补了非侵入性神经解码的差异。

IF 6.4

International journal of neural systems Pub Date : 2026-10-22 DOI: 10.1142/S0129065727500298

Yuanpeng Li, Xilin Tan, Minjie Tan, Shaobing Gao

{"title":"Modeling the Selective and Lossy Nature of Visual Processing Bridges the Discrepancy in Noninvasive Neural Decoding.","authors":"Yuanpeng Li, Xilin Tan, Minjie Tan, Shaobing Gao","doi":"10.1142/S0129065727500298","DOIUrl":"https://doi.org/10.1142/S0129065727500298","url":null,"abstract":"Noninvasive neural decoding seeks to infer visual stimuli from brain recordings, such as EEG and MEG, thereby providing a computational approach for probing human visual information processing. Current contrastive learning approaches align brain signals with image embeddings from pretrained neural networks. However, these methods often struggle with the intrinsic visual-brain discrepancy arising from systematic representational gaps and noise because brain signals are treated as direct, lossless replicas of visual inputs. To alleviate this mismatch, the proposed framework accounts for the selective and information-compressive characteristics of biological vision. On the visual side, the framework incorporates an adaptive, context-aware visual calibration mechanism, where multilevel blurred representations are dynamically fused under the guidance of structural salience to refine visual features toward the brain's abstraction level. On the brain-signal side, a robust and lightweight neural encoder is used to mitigate inter-channel and temporal variations. Experiments on standard EEG and MEG benchmarks show that the proposed method achieves strong brain-to-image retrieval performance. Our modeling hypothesis, supported by retrieval behavior, suggests that computational representations aligning with salient and structurally meaningful content better match macroscopic neural responses than fine-grained image details. These findings suggest that incorporating the lossy and selective characteristics of human visual processing into computational models is important for advancing noninvasive neural decoding and understanding brain-machine alignment. The source codes are available at SLVP.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750029"},"PeriodicalIF":6.4,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149248085","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

Local-Global Spherical Sliced Wasserstein Graph Kernel for Schizophrenia Identification. 局部-全局球面切片Wasserstein图核用于精神分裂症识别。

IF 6.4

International journal of neural systems Pub Date : 2026-10-17 DOI: 10.1142/S0129065727500286

Xiaoyan Jia, Xuebin Chang, Jinlu Chang, Yingying Jian, Debo Dong, Canhua Xu

{"title":"Local-Global Spherical Sliced Wasserstein Graph Kernel for Schizophrenia Identification.","authors":"Xiaoyan Jia, Xuebin Chang, Jinlu Chang, Yingying Jian, Debo Dong, Canhua Xu","doi":"10.1142/S0129065727500286","DOIUrl":"https://doi.org/10.1142/S0129065727500286","url":null,"abstract":"Accurate identification of schizophrenia (SZ) using functional connectivity (FC) is fundamentally challenged by the non-Euclidean geometry of neuroimaging data and information loss from arbitrary graph thresholding. To overcome these bottlenecks, this study proposes the local-global spherical sliced Wasserstein graph (LG-SSWG) kernel. By embedding FC profiles onto a hyperspherical manifold, the proposed framework utilizes geodesic optimal transport to construct a mathematically rigorous, positive-definite graph kernel. This threshold-free approach synergistically integrates macroscopic whole-brain topology and region-specific functional architectures. Evaluated on the public COBRE dataset, LG-SSWG achieves a superior 84% accuracy, significantly outperforming state-of-the-art baselines. Beyond predictive accuracy, the method exhibits strong clinical interpretability by identifying critical neuroanatomical biomarkers, particularly the left thalamus and right precuneus. Network analysis reveals pervasive functional hyperconnectivity within subcortical relays and cognitive networks in SZ patients. Notably, this hyperconnectivity correlates positively with PANSS positive symptom scores. Consequently, these altered thalamocortical and precuneus-anchored circuits indicate that SZ clinical symptoms may be closely associated with disrupted sensory gating and functional segregation. Ultimately, the LG-SSWG kernel provides a principled geometric learning tool that advances both the objective diagnosis and mechanistic understanding of schizophrenia.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750028"},"PeriodicalIF":6.4,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208416","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 Dual-Loop Task-Orthogonal Framework for Sequential Multitask Learning in Echo State Networks. 回声状态网络中顺序多任务学习的双环任务正交框架。

IF 6.4

International journal of neural systems Pub Date : 2026-10-10 DOI: 10.1142/S0129065727500274

Syeda Shamaila Zareen, Junsong Wang, Hong Wang, Wenting Wang, Yi Wang, Xiaoxu Liu, Yan Kang

{"title":"A Dual-Loop Task-Orthogonal Framework for Sequential Multitask Learning in Echo State Networks.","authors":"Syeda Shamaila Zareen, Junsong Wang, Hong Wang, Wenting Wang, Yi Wang, Xiaoxu Liu, Yan Kang","doi":"10.1142/S0129065727500274","DOIUrl":"https://doi.org/10.1142/S0129065727500274","url":null,"abstract":"Sequential multitask learning is a major problem for Echo State Networks (ESNs) because of catastrophic forgetting and interference between the tasks. To solve this, a Dual-Loop Task-Orthogonal Framework of Echo State Networks (DLTOF-ESN) model is proposed. It has a well-scaling architecture based on orthogonal parameter updates and a new mechanism named Adaptive Stability Regulation (ASR). DLTOF-ESN is based on dual-loop learning. The inner loop uses orthogonal updates to minimize gradient overlap. The outer loop adapts regularization strength based on changes in the relationships of tasks. Also, in this loop the weight sensitivity and decay-weighted similarity are measured using an adaptive relevance mask. This dual-loop interactive mechanism enables the related tasks to be flexible whilst maintaining important information in the tasks that have a temporal distance. In contrast to the traditional Elastic Weight Consolidation (EWC), which uses a fixed regularization, this DLTOF-ESN dynamically varies the penalties according to the relevance of the task and time. This research integrated SHAP-driven layers of interpretability to the framework, which enables explainability and is useful in high-stakes areas. Model evaluations are performed on three benchmark continual learning datasets, Permuted MNIST, Split CIFAR 100 and CIFAR-10, which have shown that DLTOF-ESN shows improvement in tasks recall of up to 18%, accuracy of up to 17%, and interference of up to 44% compared to EWC + ESN and OL + ESN architectures. Ablation studies confirm the synergy between orthogonal updates and ASR methods, while scalability tests reveal reduced training time and memory overhead with increasing dataset sizes.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750027"},"PeriodicalIF":6.4,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148917","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

Prototype-Guided Test-Time Adaptation for Real-Time EMG-Based Gesture Recognition. 基于实时肌电图的手势识别的原型引导测试时间自适应。

IF 6.4

International journal of neural systems Pub Date : 2026-10-10 DOI: 10.1142/S0129065727500249

Yingtong Ma, Yalin Wang, Chenyun Dai, Yao Guo

{"title":"Prototype-Guided Test-Time Adaptation for Real-Time EMG-Based Gesture Recognition.","authors":"Yingtong Ma, Yalin Wang, Chenyun Dai, Yao Guo","doi":"10.1142/S0129065727500249","DOIUrl":"https://doi.org/10.1142/S0129065727500249","url":null,"abstract":"Surface electromyography (sEMG) signals facilitate intuitive human-machine interaction and are widely employed in applications such as prosthetics and rehabilitation. However, sEMG signal distributions are highly sensitive to variations in electrode placement, user physiology, and session conditions, which significantly hinder model generalization in real-world settings. To address this, we propose EMG-based structure-aware test-time adaptive refinement (EMG-STAR), a test-time adaptation (TTA) framework tailored for online deployment without access to target data during training. EMG-STAR improves model stability by fusing historical and batch-wise normalization statistics from both source and target domains, effectively alleviating domain shifts. Furthermore, it refines predictions using a prototype-assisted feature clustering method that enforces consistency among semantically similar target samples. To further enhance reliability, the framework jointly leverages prediction confidence and distributional alignment to filter out noisy pseudo-labels during adaptation. Comprehensive evaluations conducted on two public datasets, Hyser, and NinaPro-DB2, demonstrate that EMG-STAR consistently outperforms ten existing TTA methods. Interpretability analysis using Grad-CAM reveals that EMG-STAR predominantly attends to physiologically meaningful muscle activation regions, suggesting that the model captures neuromuscular structures rather than transient signal noise.","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2750024"},"PeriodicalIF":6.4,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148937","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