Frontiers in Robotics and AI最新文献

{"title":"Grasping like humans: a stacked-fabric grasp localization strategy based on convex structure perception and hierarchical fuzzy decision-making.","authors":"Jinde Chen, Hubo Chu, Zhenyang Chao, Tie Zhang","doi":"10.3389/frobt.2026.1917009","DOIUrl":"https://doi.org/10.3389/frobt.2026.1917009","url":null,"abstract":"<p><strong>Introduction: </strong>Due to the complex structures, variable morphologies, and multiple feasible grasping strategies for entangled fabrics during stacked sorting, achieving stable robotic grasping in such scenarios remains highly challenging. This study formulates the stacked-fabric grasping task as a problem of structural perception and multi-attribute decision-making.</p><p><strong>Methods: </strong>A stacked-fabric grasping method is proposed. Specifically, a convex-structure perception method based on point cloud geometric features is developed to extract operable regions from point cloud data, and a set of candidate grasp poses is generated based on these structures. Furthermore, to address the difficulty of effectively evaluating candidate poses, a hierarchical fuzzy neural network is developed by simulating the human fuzzy decision-making process, enabling interpretable evaluation and selection of the optimal grasp pose.</p><p><strong>Results: </strong>Comparative experiments are conducted on a constructed fabric grasping dataset. Compared with the best-performing baseline model, the proposed method improves the prediction accuracy of grasp feasibility, grasp separation effectiveness, and grasp reliability by 1.3%, 0.3%, and 0.7%, respectively. Robotic grasping experiments further show that the proposed method achieves a grasping success rate of 93.3% in randomly stacked scenarios.</p><p><strong>Discussion: </strong>The results demonstrate that integrating convex structure perception with hierarchical fuzzy decision-making provides an effective and interpretable strategy for grasp pose localization and selection in stacked-fabric grasping.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1917009"},"PeriodicalIF":3.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-cost robotics and AI technologies for rehabilitation and health monitoring.","authors":"João Silva Sequeira","doi":"10.3389/frobt.2026.1919797","DOIUrl":"https://doi.org/10.3389/frobt.2026.1919797","url":null,"abstract":"<p><p>This paper reviews robotics technologies applied to rehabilitation. Besides a literature review, selected to highlight relevant topics/aspects, the paper presents a few examples of low cost technologies (which are particularly important in ageing demographics and struggling economies) and discusses applications in the overlap between rehabilitation and regular health monitoring. The purpose of this study is to frame rehabilitation robotics in terms of the existing technologies and to showcase low cost demonstrators with relevance in rehabilitation scenarios. Besides the encouraging results, obtained in real-world tests (a distinctive characteristic of the paper), the role of AI techniques in the development of low cost devices is also highlighted. Moreover, the paper also discusses the influence of human factors in the acceptance and adoption of alternative/disruptive technologies.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1919797"},"PeriodicalIF":3.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneity-aware risk-adaptive trajectory planning for UAVs in air-ground multi-agent interaction.","authors":"Ying Wang, Jingwei Wei, Shuai Sun, Erjian Liu","doi":"10.3389/frobt.2026.1920106","DOIUrl":"https://doi.org/10.3389/frobt.2026.1920106","url":null,"abstract":"<p><p>Autonomous UAVs sharing low-altitude airspace with ground traffic must continuously interpret the heterogeneous and dynamically varying behaviors of ground agents, a challenge that conventional risk models, largely designed for static obstacles or aircraft-to-aircraft conflicts, are not equipped to address. This study formulates the air-ground interaction problem as a heterogeneous multi-agent risk perception task and proposes a three-stage computational intelligence framework for risk-adaptive UAV trajectory planning. First, a GMM-LightGBM model performs online identification of ground agent driving styles, which is coupled with a Full Velocity Difference (FVD) model to generate personalized agent trajectory predictions. Second, a fuzzy inference system fuses macroscopic traffic context, agent behavioral features, and air-ground geometric relationships into an interpretable, real-time risk score. Third, an MS-DGST-VNet model predicts UAV maneuver parameters, and a risk-adaptive cost function dynamically reweights safety, efficiency, and altitude constraints to generate adaptive trajectories. The proposed driving style classifier achieves 0.96 identification accuracy, and the resulting risk score correlates strongly with three-dimensional air-ground time-to-collision, supporting the framework's validity. Case studies across representative risk scenarios further demonstrate the framework's adaptive decision-making capability. By combining machine learning-based behavior identification, fuzzy reasoning, and learning-based trajectory generation, this work contributes a computational intelligence methodology for autonomous UAV decision-making in heterogeneous multi-agent environments.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1920106"},"PeriodicalIF":3.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-centric digital twins in industry 5.0: technologies, applications, and future directions.","authors":"Sagheer Khan, Saifullah Tumrani, Nguyen Van Nam, Moataz Ahmed, Ashfaq Khan, Mushtaq Khan","doi":"10.3389/frobt.2026.1983733","DOIUrl":"10.3389/frobt.2026.1983733","url":null,"abstract":"<p><p>Industry 5.0 (I5.0) emphasizes industrial technologies that are human-centric, sustainable, and resilient. Human-Centric Digital Twins (HCDTs) support this transition by integrating worker data with robots, machines, tasks, and industrial environments. However, the relevant research remains fragmented across human modelling, Human-Robot Collaboration, Artificial Intelligence (AI), simulation, and Digital Twin (DT) research. Moreover, systems that use human data or adaptive automation are often described as human-centric without demonstrating measurable benefits to workers. This review addresses these limitations by distinguishing HCDTs from conventional human models, Human DTs, and collaborative robotic systems. It critically examines human-focused sensing, multi-modal data integration, biomechanical modelling, intention and motion prediction, robotic simulation, optimization, reinforcement learning, immersive interfaces, industrial connectivity, and data governance. Based on this analysis, a literature-derived conceptual framework is proposed that connects sensing, integration, human modelling, analytics, simulation, decision-making, adaptation, human interaction, and governance. The framework organizes the functional requirements of HCDTs and is intended to inform future system design and comparison; its practical applicability and effectiveness require further validation. The review argues that HCDTs must extend beyond worker monitoring and productivity maximization: human data should inform industrial decisions that produce measurable improvements in safety, ergonomics, wellbeing, autonomy, accessibility, or skill development. Such systems should also uphold privacy, transparency, uncertainty awareness, explainability, and appropriate human authority. By linking technological capabilities to explicit worker outcomes, this synthesis enables researchers and engineers to assess the human-centeredness of HCDT implementations and supports the development of trustworthy systems for I5.0.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1983733"},"PeriodicalIF":3.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to express social presence: a comparison of perceptions of social robots' cognitive abilities.","authors":"Thomas Sievers, Nele Russwinkel","doi":"10.3389/frobt.2026.1938840","DOIUrl":"10.3389/frobt.2026.1938840","url":null,"abstract":"<p><p>In light of the problems posed by an aging society and the associated challenges-particularly regarding the care and support of older citizens-questions arise concerning technological solutions. Social robots equipped with appropriate cognitive abilities can provide support in this area by reducing loneliness and social isolation through communication and personalized interaction. To this end, the robot's perceived social presence is of crucial importance. Social presence (or co-presence) refers to the sense of being with another person and depends on how easily one feels able to access that person's intelligence, intentions, and sensory impressions. This study examined the potential of robots' cognitive abilities for social dialogic interactions with humans, such as (i) unrestricted conversational abilities, (ii) the incorporation of visual cues into interactions, and (iii) memories of individual encounters, and investigates the perceived social presence in each case. In addition, we evaluated the degree of anthropomorphism using the Human-Robot Interaction Evaluation Scale. Our results show a trend toward a higher degree of social presence in terms of visual perception, but only one statistically significant difference out of a total of seven questions on this topic. Memories, as well, had only one statistically significant influence, namely, on the anthropomorphic dimension of disturbance. Gender-specific differences were observed with regard to social presence and the anthropomorphic dimensions. However, these differences are most likely attributable to professional background or certain predispositions rather than to gender itself.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1938840"},"PeriodicalIF":3.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13668740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Quechua speech in humanoid social robots through fine-tuned neural TTS on the UBTech Walker Tienkung.","authors":"Percy Castro-Mejia, William Atalaya-Urrutia, Alejandro Cruzata-Martinez, Erick Salazar-Montoya, Alberto Jimenez-Garcia, Juan Arcila-Diaz","doi":"10.3389/frobt.2026.1909076","DOIUrl":"https://doi.org/10.3389/frobt.2026.1909076","url":null,"abstract":"<p><p>Humanoid social robots deployed in multilingual societies-particularly in indigenous communities-face a critical linguistic gap: their speech interfaces are predominantly designed for high-resource languages, excluding hundreds of millions of indigenous language speakers from equitable human-robot interaction. This study presents the first neural text-to-speech (TTS) system for Southern Quechua-the most extensive indigenous language family of the Americas, with more than ten million active speakers-deployed on a humanoid social robot. XTTS-v2, a multilingual neural TTS model, was fine-tuned on 22,727 validated utterances (30.5 h, 77 speakers) of the Cusco-Puno dialect, located in southern Peru, through a tokenizer adaptation that leverages the phonological overlap between Quechua and Spanish. Fine-tuning over 15 epochs on an NVIDIA A100 40 GB GPU achieved a 35.0% reduction in training cross-entropy loss (4.084 → 2.652), confirming effective adaptation to Quechua phonology and morphology with stable training dynamics across 35,000 gradient update steps. Inference latency was evaluated on two hardware environments: the training GPU (mean latency: 2,037 ms; Real-Time Factor (RTF) = 0.472) and the embedded SoC of the UBTech Walker Tienkung humanoid robot (Jetson AGX Orin 64 GB; mean latency: 5,913 ms; RTF = 0.961). Both configurations achieved a mean RTF below 1.0, demonstrating real-time Quechua speech synthesis directly on the robot's hardware without external computing infrastructure. Intelligibility evaluation using Whisper large-v3 with automatic language detection yielded a Word Error Rate of 147.4% and a Character Error Rate of 69.1%; these values reflect the absence of a native Quechua automatic speech recognition (ASR) model rather than the quality of TTS synthesis. These results establish the first quantitative benchmark for TTS in American indigenous languages on a humanoid robotic platform and provide a reproducible framework-encompassing training configuration, tokenizer adaptation, and deployment pipeline-for extending neural speech synthesis to other low-resource languages of the Americas.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1909076"},"PeriodicalIF":3.7,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new hybrid optimization intelligence for cable-driven continuum robot motion: two-obstacle simulation and real-data assessment.","authors":"Selman Djeffal, Abdelhamid Ghoul, Mohamed Razi Morakchi, Badreddine Bendriss, Merabet Eddine Salah, Atef Chibani","doi":"10.3389/frobt.2026.1935721","DOIUrl":"10.3389/frobt.2026.1935721","url":null,"abstract":"<p><strong>Introduction: </strong>Cable-driven continuum robots enable dexterous motion in confined spaces, but inverse kinematics remains difficult because different internal shapes and cable states can produce the same end-effector position.</p><p><strong>Methods: </strong>This study presents a design-aware hybrid optimization framework, DA-IHHO-DELS, for inverse kinematics of a three-section continuum robot. A piecewise-constant-curvature forward model generates physically consistent section positions, cable-length estimates, and feasibility terms. The optimizer combines geometry-consistent seeding, opposition learning, improved Harris hawks search, differential-evolution exploitation, grey-wolf leader guidance, Cauchy/Levy perturbations, and bounded local refinement. Its multi-objective score includes the arc-length consistency residual, orientation error, cable-length dispersion, inter-section smoothness, actuation effort, and constraint violation, and it is compared with GA, ABC, ACO, DE, HHO, TLBO, BOA, GWO, WOA, and POA.</p><p><strong>Results: </strong>In the regenerated obstacle-constrained simulation, DA-IHHO-DELS achieved a median <math> <mrow> <msub><mrow><mi>F</mi></mrow> <mrow><mn>1</mn></mrow> </msub> </mrow> </math> residual of 0.00491 mm, a median reconstructed Cartesian error of 0.00255 mm, 100% success under the strict <math> <mrow> <msub><mrow><mi>F</mi></mrow> <mrow><mn>1</mn></mrow> </msub> <mo><</mo> <mn>0.01</mn></mrow> </math> criterion, the lowest median aggregate score, and a mean runtime of 0.56883 s. A secondary benchmark using measured CBHA/Robotino XT actuator lengths further assessed transfer of the geometry-guided optimization consistency to real measured continuum-robot data.</p><p><strong>Discussion: </strong>The results show that design-aware hybrid optimization can combine analytical continuum-robot kinematics with robust search to obtain accurate, feasible, and smooth inverse-kinematic solutions. The real-data benchmark supports consistency transfer while remaining distinct from a direct hardware test of the same three-section cable-driven robot.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1935721"},"PeriodicalIF":3.7,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion models in robotics: a comprehensive review.","authors":"Linxin Bai, Chenkang Du, Jinfang Wang, Xinrong Chen","doi":"10.3389/frobt.2026.1905499","DOIUrl":"https://doi.org/10.3389/frobt.2026.1905499","url":null,"abstract":"<p><p>Diffusion models are a powerful class of generative models that emerge in recent years to transform Gaussian noise into samples of the target distribution through an iterative denoising process. Due to the high training stability and powerful generative capabilities, diffusion models have surpassed previous generative models and demonstrate potential applications in the field of robotics. In the past few years, this area has gained increasing attention, and the number of studies applying diffusion models to the field of robotics has grown exponentially. This review aims to provide an overview of this emerging field, helping researchers understand the current state of development, with the hope of inspiring new research directions. First, we overview the foundation of diffusion models along with their development in recent years. On this basis, we provide an overview of the application of diffusion modeling in robotics from four macro dimensions: environmental data preparation, decision-making control paradigms including reinforcement and imitation learning, high-level cognitive reasoning for task planning, and specialized mechanical or sensory applications. We discuss and summarize the innovations, contributions, and limitations of these works. We then discuss the limitations and challenges faced by the field in terms of safety issues, real-time inference and model size, simulation to the real world gap, datasets and unified benchmarks, and embodied foundation models. Finally, we summarize the review and provide an insight into future research directions.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1905499"},"PeriodicalIF":3.7,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric priors for reinforcement learning-based multi-robot ecological monitoring.","authors":"Tamil Selvan Gurunathan, Aryya Gangopadhyay","doi":"10.3389/frobt.2026.1934273","DOIUrl":"https://doi.org/10.3389/frobt.2026.1934273","url":null,"abstract":"<p><p>Autonomous ecological monitoring requires robotic systems capable of efficiently exploring large natural environments while minimizing redundant traversal under limited communication and partial environmental knowledge. Although reinforcement learning provides adaptive decision making for multi-robot exploration, sparse rewards often result in inefficient exploration and slow policy convergence. This paper investigates whether locality-preserving geometric priors can improve decentralized reinforcement learning for ecological monitoring. We incorporate Hilbert space-filling curve information through state augmentation, exploration biasing, and potential-based reward shaping while preserving the underlying reinforcement learning algorithms. The framework is implemented using Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) and evaluated in simulated multi-robot ecological survey environments using teams of 4-16 robots. Performance is evaluated using coverage ratio, redundancy, cumulative reward, convergence speed, and ablation studies. Policies are further validated through outdoor experiments using two Boston Dynamics Spot robots operating in an environment containing natural vegetation and terrain obstacles. Results demonstrate consistent improvements in exploration efficiency, survey coverage, redundancy reduction, and learning performance relative to conventional DQN and PPO, demonstrating the effectiveness of geometric priors for reinforcement learning-based autonomous ecological monitoring.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1934273"},"PeriodicalIF":3.7,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing simulation and physical kilobots performance in two ant-inspired foraging behaviours.","authors":"Niamh Ellis, Jonathan Kelly, Alexander Caravaggio, Ian Young, Fabio Manfredini, Maria Elena Giannaccini","doi":"10.3389/frobt.2026.1828495","DOIUrl":"10.3389/frobt.2026.1828495","url":null,"abstract":"<p><p>Robotics looks to nature for inspiration to perform effectively in unstructured environments and can be used to test biological hypotheses. Social animals often share information about food source locations like tandem running in ants, where a leader guides a naive recruit to a known profitable food site. This is extremely advantageous as it allows the sharing of important information among colony members, but it also has costs, such as waiting time inside the nest when no leaders are around, and a reduced walking speed for the tandem couple compared to individuals. Whether and when these costs outweigh the benefits is not well understood as it is challenging to study complex social behaviours in nature. We developed two kilobot-based approaches to compare tandem running and lone scout foraging (using an isotropic random walk) where an ant searches for food without any previous knowledge of its location: one approach based on real-life experiments and the other one on computer simulations. We tested whether the simulation-based comparison of the two bio-inspired strategies reliably predict the performance in the real-life scenario and we investigated the role that the size of the search arena played in the effectiveness of foraging. Tandem pairs discovered the food source faster in real-life experiments; however, this result was reversed in the simulations. These results highlight the inconsistencies between simulation and real-life experiments, previously reported for other systems and known as reality gap, and show how the use of simulations can lead to qualitatively different conclusions about the relative effectiveness of bio-inspired foraging strategies.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"13 ","pages":"1828495"},"PeriodicalIF":3.7,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149172038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}