IEEE Transactions on Automation Science and Engineering最新文献

{"title":"Design and Experimental Evaluation of a Novel Robot-Assisted Surgical System for Pelvic Fracture Closed Reduction","authors":"Xiao Cheng;Wei Kou;Xinyu Chen;Dongya Li;Haixia Wang;Shaolong Kuang","doi":"10.1109/TASE.2025.3615799","DOIUrl":"10.1109/TASE.2025.3615799","url":null,"abstract":"This paper designs a novel clinical-oriented robot-assisted surgical system (RASS) for pelvic fracture closed reduction, including three core parts: preoperative planning system, intraoperative navigation system and reduction robot. In preoperative planning system, a virtual reduction method based on the Statistical Shape Model (SSM) and pelvic partial surface data is adopted, providing personalized and detailed preoperative plans with high-efficiency and enhanced adaptability. Then, a serial-parallel hybrid reduction robot with robot autonomous and main-secondary reduction mode switching control strategy is designed, combined with the intuitive and real-time guidance of the intraoperative navigation system, achieving flexible and precise reduction operations. Additionally, clinical solution research based on the designed RASS is developed and evaluated by ten pelvic models and five animal experiments. In the pelvic model experiments, the root mean square error (RMSE) of the distance between the reduced and intact pelvic point cloud is 1.0mm, and the three points error of 10 cases are all within 2.0mm, achieving an excellent reduction effect. In the animal experiments, the four point-pairs distance decreases from <inline-formula> <tex-math>$26.7pm 1.04$ </tex-math></inline-formula>mm to <inline-formula> <tex-math>$5.86pm 0.82$ </tex-math></inline-formula>mm, and the mean distance between the two point clouds of the “Pig-081” before modeling and after reduction is 2.81mm, which further confirmed the effectiveness of the system, as well as the feasibility and safety of the clinical solution. The designed novel RASS provides a promising solution in the treatment of pelvic fractures, and the experimental results lay the foundation for further clinical application. <italic>Note to Practitioners</i>—RASS is crucial for pelvic fracture closed reduction. However, the clinical application of RASS for pelvic fracture closed reduction still face some challenges, such as the poor adaptability of preoperative virtual reduction, the limited mechanical structure and control strategy of reduction robot, the lack of integration all technical modules of RASS into a unified clinical workflow. To address these challenges, this paper designs a novel clinical-oriented RASS, including a more adaptive preoperative planning system, an intuitive intraoperative navigation system, as well as a flexible and accurate serial-parallel hybrid reduction robot. Based on the designed RASS, a comprehensive solution for RASS-integrated clinical workflow is proposed and evaluated through pelvic model and animal experiments. Experimental results show that our designed RASS can effectively address these challenges and demonstrates excellent reduction effects. The system’s integration design and adaptive workflow also offer insights for advancing robot-assisted solutions in broader orthopedic and trauma surgery applications.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21936-21947"},"PeriodicalIF":6.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145188368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Platform-Centric Framework for Intelligent Parking Traffic Prediction and Resource Optimization in Shared AVPC Systems","authors":"Gordon Owusu Boateng;Huang Xia;Haonan Si;Xiansheng Guo;Cheng Chen;Nirwan Ansari","doi":"10.1109/TASE.2025.3614684","DOIUrl":"10.1109/TASE.2025.3614684","url":null,"abstract":"To address the challenges posed by uncertainties in the parking behaviors of private owners and temporary users, as well as the complexities involved in integrating shared parking with Electric Vehicle (EV)-charging, this paper proposes a novel platform-centric intelligent framework for shared Automated Valet Parking and Charging (AVPC) systems. The framework leverages Long Short-Term Memory (LSTM) and Deep Reinforcement Learning (DRL) to optimize both parking traffic prediction and resource allocation, respectively. Specifically, to mitigate uncertainties in vehicle parking and EV-charging demand and supply, we utilize an LSTM prediction model to forecast the average day-ahead arrival times, departure times, and service pricing for parking space owners (O-users) and temporary users (R-users). Then, we design an improved Proximal Policy Optimization (PPO)-based algorithm with large warm-up training steps that integrates the LSTM prediction results with real-time supply and demand information from O-users and R-users to determine optimal shared AVPC resource allocation. Extensive simulations using real-world parking datasets demonstrate that the LSTM model achieves an average Mean Absolute Percentage Error (MAPE) of 1.71% and 0.08% for O-users and R-users’ parking traffic predictions, respectively. Additionally, the proposed LSTM-PPO-based approach improves platform profit and parking resource utilization by at least 9% and 15%, respectively, compared with state-of-the-art. Note to Practitioners—Intelligent Transportation Systems (ITS) are increasingly facing critical challenges in urban planning, especially in managing limited parking and EV-charging resources for autonomous vehicles and shared AVPC systems. This paper addresses these challenges by proposing an intelligent framework that combines LSTM-based prediction and DRL-based resource allocation for dynamic parking demand and EV-charging coordination in shared AVPC systems. Unlike traditional rule-based methods, our solution adapts to real-time parking space supply-demand fluctuations while optimizing for both operational efficiency and user convenience, from the perspective of the parking management platform. Validated using real-world datasets, the framework is readily deployable in smart cities, logistics hubs, and commercial complexes to alleviate congestion, maximize parking revenue, and enable the seamless integration of EV-charging infrastructure. Its automated decision-making capability minimizes operational overhead, offering a scalable solution for modern urban parking and mobility challenges. Practitioners managing smart parking systems, mobility platforms, and infrastructures can leverage this framework to automate parking and EV-charging resource allocation, optimize parking resource pricing, and improve parking resource utilization with minimal manual oversight.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21621-21634"},"PeriodicalIF":6.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Agent-Based Robotic Ultrasound Path Planning and Interaction Control in External-Vision-Independent Environments","authors":"Xinye Wang;Zhiyuan He;Peng Chen;Zhe Wang;Tao Sun","doi":"10.1109/TASE.2025.3614996","DOIUrl":"10.1109/TASE.2025.3614996","url":null,"abstract":"Ultrasound imaging has emerged as a crucial tool for the diagnosis and navigation of spinal diseases. However, high-quality image acquisition heavily relies on experienced sonographers, which restricts its further popularization. In this paper, a robotic system designed for automated spinal ultrasound scanning is proposed. Drawing inspiration from the spinal anatomy and the actions of seasoned sonographers, the system integrates both a deep learning agent and a reinforcement learning agent to collaboratively guide the adjustment of the ultrasound probe in external-vision-independent environments, relying on real-time ultrasound images and contact force. Then, a hybrid force-to-velocity control framework is proposed to ensure proper ultrasound coupling during the scanning process. Experimental results on a phantom and human participants demonstrated that this system can accurately track spinal features (mean error: less than 1 mm) and maintain normal probe orientation (out-of-plane angular error: <inline-formula> <tex-math>$1.61~pm ~1.1^{circ }$ </tex-math></inline-formula>, in-plane angular error: <inline-formula> <tex-math>$1.27~pm ~0.9^{circ }$ </tex-math></inline-formula>), resulting in high-quality and reproducible ultrasound images. Overall, our system shows great potential for clinical applications. Note to Practitioners—This paper is motivated by the increasing needs of human-robot interaction in medical applications, with a specific emphasis on robotic ultrasound imaging. Clinical sonographers suffer from repetitive workload during the diagnostic process, highlighting the significance of automated scanning solutions. In this work, we propose a modular control framework for ultrasound probe positioning that supports a multi-modal autonomous ultrasound scanning system. The system operates independently of external optics or prior geometric knowledge of the scanning object. Comprehensive experimental results demonstrate that the system can effectively cover the region of interest of the spine, facilitating high-quality ultrasound image acquisition and related disease assessment. This advancement is expected to enhance the efficiency of human-robot interaction in healthcare settings and holds promising clinical applications. Furthermore, our research offers valuable insights for the implementation of robotic ultrasound scanning systems applicable to other human tissues.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21674-21685"},"PeriodicalIF":6.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Battery Electric Vehicles Safety Optimization With Adaptive MOEA/D Based on Dynamic Grid and Multiple Dominance","authors":"Mingran Li;Li Huang;Hua Han;Chunyuan Wang","doi":"10.1109/TASE.2025.3614750","DOIUrl":"10.1109/TASE.2025.3614750","url":null,"abstract":"With the rapid development of electric vehicles (EVs), the safety of battery systems has become an increasing concern. However, the application of multi-objective optimization algorithms in the safety domain of EVs is rare, particularly in optimizing complex, non-linear multi-objective problems (MOPs) such as crashworthiness and thermal management. This work proposes an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D), which introduces a dynamic grid system for building stability matrices to determine whether the current population reaches a stable convergence state with less computational demands and designs a novel sparsity level evaluation to measure the weight vectors. Additionally, we propose a multiple dominance to address the Pareto dominance limitation. These aim to optimize the key issues in EV safety. To further demonstrate the performance of our proposed algorithm, we compare it with 10 state-of-the-art algorithms on 26 benchmark problems. The experimental results based on multiple performance metrics show that the proposed algorithm have outstanding performance. Note to Practitioners—Ensuring the safety of the battery system in EVs under collision conditions is important. This paper proposes an improved MOEA/D, aiming to optimize the maximum energy absorption of battery protection materials, enhance crash force efficiency, and improve the temperature difference of the battery system coolant. In proposed improved MOEA/D, the dynamic grid system and sparsity level evaluation ensure the acquisition of feasible solutions that align with users’ diverse preferences. Additionally, the multiple dominance strategy further enhances the quality of the solutions, leading to more superior optimization results. Experimental results demonstrate that the proposed algorithm shows outstanding optimization performance. We also further analyze the influence of each DAH structural parameter on performance, which enables users to make a trade-off according to their specific application requirements. Future research will explore the interdisciplinary integration of battery protection optimization to achieve more refined and precise optimization.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21635-21650"},"PeriodicalIF":6.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation and Detection of Intermittent Faults for Nonlinear Systems Disturbed by Noises With Uncertain Covariances","authors":"Li Sheng;Yifan Liu;Ming Gao;Donghua Zhou","doi":"10.1109/TASE.2025.3614648","DOIUrl":"10.1109/TASE.2025.3614648","url":null,"abstract":"In this paper, the problems of intermittent fault (IF) estimation and detection are investigated for stochastic nonlinear systems disturbed by noises with uncertain covariances. The research methodology begins with the transformation of conventional nonlinear systems into descriptor systems through state and fault augmentation. For IF estimation, an innovative moving horizon estimator is introduced by utilizing the variational Bayesian technique. This approach enables the simultaneous and iterative estimation of both IFs and noise covariance matrices through the strategic selection of appropriate conjugate prior distributions. Furthermore, a rigorous analysis is conducted on the statistical properties of the estimation outcomes, leading to the development of an IF detection framework based on Hotelling’s <inline-formula> <tex-math>$T^{2}$ </tex-math></inline-formula> statistic. To validate the effectiveness of the proposed methodology, extensive experimental evaluations are performed on a rotary steerable drilling tool system, demonstrating the superior performance of our algorithm in practical applications. Note to Practitioners—In recent decades, intermittent faults (IFs) have emerged as a critical safety challenge across multiple industrial sectors, with particular prominence in aerospace systems and electronic infrastructure. The precise detection of appearance and disappearance time of IFs constitutes a crucial requirement for ensuring industrial safety. Conventional approaches dependent on truncated residuals frequently exhibit significant limitations in practical implementations, primarily attributable to their insufficient capacity in dealing with nonlinear dynamic behaviors and statistically ambiguous noise profiles. To address these challenges, an adaptive moving horizon estimation framework is designed for nonlinear systems with uncertain noise covariance matrices. Leveraging the estimation results, a novel detection methodology is developed to accurately identify the appearance and disappearance of IFs. Experimental validation through comparative case studies demonstrates the operational viability and detection efficacy of the proposed methodology, providing industry practitioners with a reliable tool for enhancing operational reliability in IF-affected systems.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21843-21852"},"PeriodicalIF":6.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voronoi-Diagram-Based Nonconvex NMPC in Multi-Obstacle Environments for Robot Systems with Limited Detection Abilities","authors":"Gan Zhao, Guoguang Wen, Ahmed Rahmani, Bofan Wu, Sara Ifqir, Zhaoxia Peng","doi":"10.1109/tase.2025.3614858","DOIUrl":"https://doi.org/10.1109/tase.2025.3614858","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"18 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Opacity Verification of Networked Discrete Event Systems Modeled With Labeled Petri Nets","authors":"Tengbo Li, Huorong Ren, Ruotian Liu, Maria Pia Fanti, Zhiwu Li","doi":"10.1109/tase.2025.3614662","DOIUrl":"https://doi.org/10.1109/tase.2025.3614662","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"25 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision-Based Autonomous Robotic Arc Welding: State-of-the-Art Review and Perspectives","authors":"Yunkai Ma;Junfeng Fan;Jun Hou;Yichen Fu;Rui Tao;Shuo Wang;Min Tan;Fengshui Jing","doi":"10.1109/TASE.2025.3614992","DOIUrl":"10.1109/TASE.2025.3614992","url":null,"abstract":"Robotic welding is an essential technology in industrial production. Various sensors are integrated into robotic welding systems to enhance welding efficiency and quality. Due to their advantages of non-contact measurement, high information capacity, and high accuracy, vision sensors play an increasingly important role in robotic autonomous welding. Therefore, the latest vision-based autonomous robotic arc welding technologies are comprehensively summarized, providing valuable reference and support for researchers. First, the research progress of visual sensing technology for welding robots is outlined, and its advantages and disadvantages are analyzed. Subsequently, the key technologies of robotic autonomous welding are introduced, covering weld type identification, initial point guidance, welding path generation, welding parameter planning, feature point extraction, seam tracking, welding posture adjustment, and welding quality control. Finally, the limitations existing in current research are summarized, and the future research directions of autonomous robotic welding are prospected. Note to Practitioners—Most current programming methods for welding robots rely on manual teaching and offline programming, limiting their adaptability to small batches and diverse categories in production. With the development of artificial intelligence and deep learning, vision-based technologies are propelling welding robots toward greater intelligence and autonomy. Autonomous welding robot technology is continuously advancing in areas such as weld type identification, initial point guidance, welding path generation, welding parameter planning, feature point extraction, seam tracking, welding posture adjustment, and welding quality control. This paper systematically reviews relevant research to date and outlines the future development directions of autonomous robotic arc welding.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21651-21673"},"PeriodicalIF":6.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing Grid-Connected Packed E-Cell Multilevel Inverter: A LSTM-AE Approach to Hybrid Attack Mitigation","authors":"Soroush Oshnoei;Meysam Gheisarnejad;Mohammad Sharifzadeh;Eric Laurendeau;Kamal Al-Haddad","doi":"10.1109/TASE.2025.3614757","DOIUrl":"10.1109/TASE.2025.3614757","url":null,"abstract":"The deployment of open communication infrastructure into power systems has drawn much attention due to its significant benefits, such as real-time monitoring, diagnostics, and regulatory purposes. But utilizing such technologies poses security challenges to the cyber-physical power systems (CPPS), which can highly degrade their operation. In this paper, a defense mechanism is adopted to tackle the hybrid attacks, including the Denial-of-Service (DoS) and false data injection (FDI) attacks in the grid-connected multilevel inverters from a systematic point of view. The proposed protection mechanism for the grid-connected multilevel inverter is realized in two stages. <inline-formula> <tex-math>$(i)$ </tex-math></inline-formula> A Long-Short Term Memory based on autoencoder (LSTM-AE) is developed to detect DoS attacks, and an event-trigger mechanism based on Lyapunov theory is implemented to eliminate the effect of false data. <inline-formula> <tex-math>$(ii)$ </tex-math></inline-formula> A sliding mode observer is adopted to recognize FDI threats, where the false data is eliminated by injecting the negative value of the identified false data. A prototype of a grid-connected nine-level Packed E-Cell (PEC9) topology as a targeted multilevel inverter is constructed to experimentally validate the feasibility of the proposed cyber resilience scheme for CPPS in microgrid applications. Note to Practitioners—The motivation of this work comes from the issue that gird-connected multilevel inverters, which can realize the large-scale application of sustainable generation units, are susceptible to cyber threats. The cyber threats will introduce security problems to the cyber-physical power systems (CPPS) with the photovoltaic (PV) modules. False data injection (FDI) and denial-of-service (DoS) attacks, as the most common cyber-attacks, can seriously compromise CPPSs’ performance. In this regard, the current work develops a two-stage defense mechanism to tackle cyber-attacks. The proposed cyber resilient framework is designed to identify and mitigate hybrid attacks, including DoS and FDI attacks. In particular, the Long-Short Term Memory based on autoencoder (LSTM-AE) is utilized to identify the DoS attack, while the FDI attack is detected by the sliding mode observer (SMO). An event-triggered mechanism is also developed in the proposed defense algorithm to block the signal falsified by the DoS attack and submit the signal predicted by LSTM-AE to the system’s controller. The SMO estimates the FDI disruption to the system and injects it into the system measurement signal to eliminate the FDI attack’s impact on the system dynamic performance. For this approach, the critical challenge is to develop the proposed protection mechanism in practical applications. To address this difficulty, the experimental examinations are carried out by building a prototype of the PEC9 inverter.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"22 ","pages":"21853-21863"},"PeriodicalIF":6.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Observer-Based Dual-Memory Controller for Lurie-Type CPSs Subject to Synergy Delays and DoS Attacks: Application to DC Motor System","authors":"Qing Zhu, Kaibo Shi, Bin Guo, Da Chen, Jinglei Tan, Xiao Cai","doi":"10.1109/tase.2025.3614384","DOIUrl":"https://doi.org/10.1109/tase.2025.3614384","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"94 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}