Frontiers in Neurorobotics最新文献

{"title":"Interpretable side-aware kinematic-sEMG gait-state representations relevant to adaptive neurorobotic assistance after stroke: a public-dataset study.","authors":"Rocco Salvatore Calabrò, Andrea Calderone, Alessio Baricich, Andrea Santamato, Francesca Antonia Arcadi, Alessandro Marco De Nunzio, Angelo Quartarone","doi":"10.3389/fnbot.2026.1863916","DOIUrl":"10.3389/fnbot.2026.1863916","url":null,"abstract":"<p><strong>Background: </strong>Adaptive lower-limb neurorobotics requires gaitd-state representations that preserve locomotor structure without reducing post-stroke walking to a single asymmetry score or opaque latent embedding. Because post-stroke gait is multimodal and side dependent, transparent side-aware representations may better support future adaptive-assistance design than modality-isolated summaries.</p><p><strong>Methods: </strong>This secondary analysis used a public multimodal gait dataset comprising 138 able-bodied adults and 50 adults with stroke. The analytic space was restricted to 11 waveform domains shared across public exports: four sagittal kinematic waveforms and seven repository-normalized surface electromyography waveforms, each represented by 1,001 time-normalized points. Stroke waveforms were organized into paretic, non-paretic, bilateral-mean, and side-difference views, with side difference defined as paretic minus non-paretic. Domain-view functional principal component analysis retained 90% cumulative variance, capped at three components per block; family-level reduction retained 90% variance, capped at eight components. Candidate Ward hierarchical and K-means solutions from two to five states were screened in kinematics-only, sEMG-only, fused, paretic-only, and erector-spinae-excluded spaces.</p><p><strong>Results: </strong>The retained fused side-aware solution organized the strict complete-case stroke cohort (<i>n =</i> 43) into three states: State 1 (<i>n =</i> 12), State 2 (<i>n =</i> 18), and State 3 (<i>n =</i> 13). The strongest fused two-state K-means comparator showed higher compactness and resampling stability than the retained three-state solution [silhouette 0.189; bootstrap adjusted Rand index (ARI) 0.876 versus silhouette 0.155; bootstrap ARI 0.633]. However, the three-state solution was retained as a representation-level choice because it avoided trivial micro-clusters, preserved explicit multimodal side-aware structure, and enabled clearer waveform-level interpretation. Sensitivity analyses showed identical assignments after erector-spinae exclusion (ARI = 1.000), partial concordance under robust scaling (ARI = 0.785), and material reassignment when the block cap was reduced to two components (ARI = 0.335). The strongest domain contributors were ankle angle (1.000), vastus lateralis sEMG (0.898), knee angle (0.866), gastrocnemius sEMG (0.851), and tibialis anterior sEMG (0.840).</p><p><strong>Conclusion: </strong>Public waveform exports supported an internally interpretable, side-aware multimodal representation of post-stroke gait relevant to neurorobotic state-representation design. This contribution remains exploratory and representational, not clinical, interventional, real-time, or controller-validating; for future studies, it should be interpreted as a hypothesis-generating framework.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1863916"},"PeriodicalIF":3.5,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13243435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148211103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-inspired sensorimotor controller for dynamic motion adaptation: a study in robotic arms.","authors":"Maxime Marchal, Raphaël Furnémont, Tom Verstraten, Ghilés Mostafaoui","doi":"10.3389/fnbot.2026.1821320","DOIUrl":"10.3389/fnbot.2026.1821320","url":null,"abstract":"<p><p>Robotic systems operating in dynamic environments often struggle to adapt their movements to external sensory signals without relying on explicit analytical models or conventional position-tracking control schemes. This limitation is especially problematic in systems where detailed dynamic models or positional feedback are limited or unreliable. Addressing this gap requires control architectures that, like human sensorimotor systems, can learn and generalize from experience without requiring an accurate model of the system's dynamics. In this paper, we present a human-inspired controller based on sensorimotor learning for articulated robotic arms. Departing from conventional control strategies relying on precise dynamic models, this approach emphasizes adaptability to dynamic tasks and external stimuli while accounting for gravity and system dynamics. The controller operates in two phases: an offline exploration phase that builds a discretized database of system responses for sampled initial states and actuator current commands, and an online exploitation phase that selects control actions by matching the current state to the closest explored state and applying a probabilistic command-selection rule. Rather than relying on an explicit analytical dynamics model, the approach uses empirical state-action-response relationships learned from the system's own behavior. During exploitation, the current joint positions and velocities are used to identify the closest explored state, while the command selection itself is based on velocity-response distributions learned during exploration. The controller's evaluation on various dynamic trajectories, including sinusoidal and trapezoidal profiles, demonstrates the feasibility of the framework on a 2-DOF RR robotic arm. Further analysis investigates the influence of control time parameters, the impact of actuator friction, and the controller's redeployability to modified mechanical configurations after re-exploration. These results underline the potential of biologically inspired learning mechanisms for data-driven robotic control while also highlighting limitations related to discretization, scalability, and future online adaptation.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1821320"},"PeriodicalIF":3.5,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13236880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148198756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active physical human-exoskeleton interaction based on motion intention adaptive recognition and synchronous tracking.","authors":"Weiguo Shi, Weiqun Wang, Jiaxing Wang, Shengda Liu, Xu Liang, Tianyu Lin, Kexin Xiang, Zeng-Guang Hou","doi":"10.3389/fnbot.2026.1779125","DOIUrl":"10.3389/fnbot.2026.1779125","url":null,"abstract":"<p><p>Active physical human-exoskeleton interaction has been widely studied. However, the challenges of human motion intention recognition and synchronous tracking have not been well-addressed. In this article, a motion intention recognition method based on biophysical information fusion and adaptive learning was proposed to overcome the limitations of existing approaches. First, a lower-limb joint angle prediction model was developed by integrating surface electromyography (sEMG), historical joint angles and centers of gravity. The convolutional neural network, Mamba network, and multilayer perceptron network were used respectively for feature extraction, information fusion, and joint angle prediction. Second, an online adaptive method for the angle prediction model was designed based on a style transfer mapping technique to address the issue of recognition accuracy decline. In this method, the new sEMG features were mapped into the initial feature space, by which the prediction model can maintain the predictive performance during long-term implementation. Furthermore, a real-time control method for the exoskeleton synchronous tracking was given based on the predicted angles. Finally, the feasibility of the proposed methods was validated through the offline and online experiments.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1779125"},"PeriodicalIF":3.5,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13230213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148162790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cognitive synergetic hierarchical framework for UAV swarm combat via speculative inference and role-decoupled reinforcement learning.","authors":"Lixa Wang, Yunqing Liu, Linyang Guo, Ran Zhang, Yu Cui, Yihan Cui, Chunlin Xu","doi":"10.3389/fnbot.2026.1799054","DOIUrl":"10.3389/fnbot.2026.1799054","url":null,"abstract":"<p><p>In the high-stakes arena of aerial combat-a domain defined by extreme dynamics and unforgiving physical constraints-UAV swarms are currently squeezed between two extremes: the \"tactical short-sightedness\" of Multi-Agent Reinforcement Learning (MARL) and the \"inference lag\" of Large Language Models (LLMs). While MARL struggles to internalize the complex maneuverability priors required for expert flight, LLMs are simply too heavy to meet millisecond-level control demands. We bridge this gap by introducing a cognitive synergetic hierarchical framework that decouples strategic reasoning from tactical execution. Our architecture splits the workload between a \"Strategic Brain\" and a \"Tactical Torso.\" For the Brain, we utilize a synergy between DeepSeek-R1 (70B) and its 7B distilled counterpart to create a collaborative inference engine. By capitalizing on the inherent sparsity of tactical logic in air combat, we implemented a speculative decoding mechanism that achieves an effective boost in decision throughput while maintaining the deep logic of the full 70B model. For the Torso, we developed an enhanced MAPPO algorithm that processes relative pose graphs via graph attention. By integrating a KL-divergence constraint into the loss function, we essentially force agents with different payloads-like scouts and attackers-to evolve specialized tactical personalities within a shared latent space. Experimental results using the JSBSim high-fidelity 6-DOF engine demonstrate that the swarm does more than just improve its exchange ratio. Further t-SNE manifold analysis and Chain-of-Thought visualizations confirm that our architecture successfully aligns symbolic intent with raw physical control. Most notably, through our \"decision-reflection-evolution\" loop, the system proved it could diagnose its own failures, and iteratively refine its own tactical instructions.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1799054"},"PeriodicalIF":3.5,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13226550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148155700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A context-aware transformer with weighted residual for efficient remote sensing target detection.","authors":"Zhongyu Li, Yimin Shen, Xiaoping Jing, Rong Wang, Di Zhou, Yangyong Guo, Wei Yu, Fei Du","doi":"10.3389/fnbot.2026.1774633","DOIUrl":"10.3389/fnbot.2026.1774633","url":null,"abstract":"<p><p>Remote sensing image target detection has important applications in disaster prevention and mitigation. However, current detection models still have shortcomings in multi-scale feature fusion and in representing contextual information, and are prone to class imbalance during training. To address these issues, this paper proposes a target detection model that combines a context transformer and a weighted residual pyramid. Specifically, a weighted residual pyramid module is designed to fuse deep and shallow target-instance features effectively, and a learnable balancing factor is introduced to alleviate the imbalance in contributions across network layers. Simultaneously, a context transformer module is introduced into the feature extraction and fusion networks to enhance the model's multi-scale feature representation capability. Furthermore, a rotated bounding box is introduced to locate target instances, reducing the influence of redundant background information, and a CIoU-based multi-task loss function is designed to reduce the contribution of different instance targets to the regression task. Experimental results show that, while ensuring real-time performance, the proposed model achieves mAP@0.5 scores of 0.754 and 0.714 on the DOTAv1.0 and DOTAv1.5 datasets, respectively, demonstrating consistent improvements across both datasets. Meanwhile, visualization of detection results across various disaster scenarios shows that the model proposed in this paper has strong practical value.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1774633"},"PeriodicalIF":3.5,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148137715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Biomechanical and cognitive pattern assessment in human-machine collaborative tasks for industrial robotics.","authors":"Alessandro Mengarelli, Andrea Tigrini, Ali H Al-Timemy, Silvia Muceli, Federica Verdini","doi":"10.3389/fnbot.2026.1858458","DOIUrl":"https://doi.org/10.3389/fnbot.2026.1858458","url":null,"abstract":"","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1858458"},"PeriodicalIF":2.8,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13212267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148054080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight Chinese-English translation model integrating compressed BERT attention and phrase discard mechanism.","authors":"Xin Qi, Hang Bao","doi":"10.3389/fnbot.2026.1795490","DOIUrl":"10.3389/fnbot.2026.1795490","url":null,"abstract":"<p><p>Chinese-English machine translation based on neural network model strictly adopts the sequential modeling method of encoder-decoder. However, this traditional method cannot make effective use of syntactic information and linguistic hierarchy information. Therefore, to integrate syntactic structure information into Chinese-English machine translation to improve its translation performance, this paper proposes a new Chinese-English machine translation method based on graph convolutional network and BERT (Bidirectional Encoder Representation from Transformers) knowledge enhancement. In this work, we present an enhanced approach to neural machine translation that integrates multiple techniques to improve translation quality. The multi-BERT context is first compressed and aligned into the semantic space of the translation model using learnable compression vectors. This alignment ensures that the rich contextual information from BERT is effectively utilized within our translation framework. At the end of source language, we employ a dual encoder to encode both the source sentence and its syntactic dependency tree, thereby capturing both lexical and structural information. To further enrich the source-side semantic representation, the compression vector is concatenated with the input vector of the encoder. Additionally, we introduce a phrase discard mechanism that randomly discards target phrases during training. This mechanism enhances the model's robustness against mistranslated phrases, thereby reducing their impact on subsequent phrase translations. Experiments on NIST dataset demonstrate the effectiveness of our proposed lightweight Chinese-English translation method. Different from general-purpose large chatbot models (e.g., ChatGPT) with high computing costs, this model achieves 39.68 BLEU with low parameters, solving issues of low-resource scenarios and phrase mistranslation. It offers a novel lightweight paradigm for private-oriented translation chatbots, outperforming the baseline Transformer (35.75 BLEU) significantly.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1795490"},"PeriodicalIF":3.5,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13194120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148014904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HarmoAtt-IK: an adaptive multimodal feature fusion network for real-time neural inverse kinematics.","authors":"Xukun Liu, Fengjuan Xie, Yu Liu, Jiaqiu Song","doi":"10.3389/fnbot.2026.1769924","DOIUrl":"10.3389/fnbot.2026.1769924","url":null,"abstract":"<p><strong>Introduction: </strong>Recent advances in neural networks have introduced a new paradigm for robotic inverse kinematics. However, existing methods remain limited by insufficient feature extraction and suboptimal integration of multi-source information, preventing them from achieving high accuracy, broad generalization, and real-time performance on robots with diverse and complex kinematic structures.</p><p><strong>Methods: </strong>In this work, we propose HarmoAtt-IK, an adaptive multimodal neural inverse kinematics approach designed for real-time inference and zero-collection training. Built upon the CycleIK framework, the proposed method introduces a novel adaptive multimodal attention fusion mechanism (HarmoAtt) that dynamically integrates the complementary strengths of spatial, channel, and cross-dimensional attention. It employs a temperature-adaptive Softmax function coupled with a compact weight-generation network to perform multidimensional extraction and adaptive enhancement of input features. We further introduce a composite loss function integrating an improved Smooth-L1 loss, a sign-invariant quaternion loss, and a Shannon entropy regularizer to enhance training stability and overall accuracy. Leveraging forward differential kinematics, our method enables rapid, cross-platform deployment by generating training data solely from URDF models, eliminating the need for costly physical data collection and manual annotation.</p><p><strong>Results: </strong>Experimental evaluations on five humanoid platforms exhibiting substantial kinematic diversity demonstrate that HarmoAtt-IK attains maximum reductions of 76.4% in terminal positional error and 55.1% in rotational error relative to the baseline, while consistently improving the model's inference success rate across all tested platforms by up to 5.76 percentage points.</p><p><strong>Discussion: </strong>These results indicate that the proposed HarmoAtt-IK significantly outperforms baseline methods in both accuracy and reliability across diverse kinematic structures, highlighting the effectiveness of the adaptive multimodal attention mechanism and composite loss design. This further supports its potential for scalable, real-time deployment on a wide range of robotic platforms.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1769924"},"PeriodicalIF":3.5,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13152839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147868224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable vision mamba for SAR images classification.","authors":"Kaiming Zou, Ningbo He, Leijun Yao, Jialin Li, Yongfeng Wang","doi":"10.3389/fnbot.2026.1753927","DOIUrl":"10.3389/fnbot.2026.1753927","url":null,"abstract":"<p><p>Deep learning techniques have been successfully applied to object classification in Synthetic Aperture Radar (SAR) images, achieving remarkable performance. However, the current Transformer architecture still faces two limitations in SAR object classification: (1) Transformers occupies substantial GPU memory during training, which hinders large-scale model deployment and hyperparameter optimization. (2) the vectorization of image patches destroys spatial information among pixels, thus the represented features are semantic-agnostic. In this paper, we propose a lightweight and interpretable vision mamba (IVim) whose feature maps from deep Mamba blocks are visually understandable. IVim consists of two modules: Token Semantic Re-allocation (TSR) and Token Attention Selection (TSA). TSR re-allocates the semantics to feature maps by optimizing a novel loss function for an extra convolutional layer. TAS selects the most discriminative attention map by evaluating the quantity of semantics in attention matrices of different Mamba layer. Finally, we provide a coupling strategy to form a saliency heatmap to visually show the interpretability of IVim by merging feature map and attention map. Experimental results demonstrate that IVim can re-allocate semantics to deep features and achieve better classification performance compared to its counterparts by reducing 67.8% parameters.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1753927"},"PeriodicalIF":3.5,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13149356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147868172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design an anthropomorphic dexterous hand for expressive piano performance.","authors":"Yanhong Liang, Xianwei Liu, Yanyan Yuan, Chengkai Su, Yucheng Tao, Yongbin Jin, Hongtao Wang","doi":"10.3389/fnbot.2026.1775834","DOIUrl":"10.3389/fnbot.2026.1775834","url":null,"abstract":"<p><strong>Background: </strong>Expressive piano performance poses extreme challenges for robotic manipulation, necessitating high-speed repetitive impacts, substantial force output, and coordinated multi-joint control under stringent dynamic constraints. However, existing robotic systems exhibit significant limitations in replicating human-level dexterity, as well as achievable motion speed and force output. This work presents a data-driven, bio-inspired dexterous robotic hand designed specifically for high-fidelity piano performance.</p><p><strong>Methods: </strong>We first extract kinematic primitives and stable inter-joint coupling patterns from large-scale motion capture data of professional pianists. These human motion priors are directly embedded into the mechanical architecture through morphological coupling and actuator allocation. Actuator selection is further guided by empirically measured human peak velocities and force profiles from biomechanics literature, ensuring sufficient bandwidth for high-speed repetitive motion and adequate force transmission.</p><p><strong>Results: </strong>Experimental results demonstrate that the proposed hand replicates human-like joint coordination, achieves peak joint velocities of 53.88 rad/s, and provides sufficient fingertip force for authentic piano interaction. As a demonstration of its capabilities, the hand successfully performs a Grade 7 piano piece, Croatian Rhapsody, illustrating its potential for expressive musical performance.</p><p><strong>Conclusion: </strong>This research establishes a principled pathway from human motion statistics to embodied robotic intelligence, providing a high-performance hardware foundation for autonomous musical performance.</p>","PeriodicalId":12628,"journal":{"name":"Frontiers in Neurorobotics","volume":"20 ","pages":"1775834"},"PeriodicalIF":3.5,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13125105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147813876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}