IEEE Sensors Journal最新文献

{"title":"Fiber-Optic Ultrasonic Sensing for Partial-Discharge Detection in Submarine Cable Flexible Joints","authors":"Qichao Chen;Yanju Li;Zhongyuan Li;Cong Chen;Weichao Zhang","doi":"10.1109/JSEN.2026.3727417","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3727417","url":null,"abstract":"Traditional active partial-discharge (PD) detection techniques are difficult to deploy for effective monitoring of PD signals in submarine cable flexible joints located far offshore. In addition, owing to the high-frequency damping effect of polymer insulating media, acoustic waves undergo severe attenuation during propagation, and existing fiber-optic ultrasonic detection techniques suffer from insufficient sensitivity. To address these problems, this study integrates a mandrel-type acousto-optic transducer with a fiber-optic interferometric sensing system and establishes a sensitivity–frequency-response model for high-sensitivity narrowband resonant detection. After structural optimization, the mandrel contact surface was curved to match the cylindrical cable surface, enabling conformal installation and stable acoustic coupling between the sensor and the flexible joint. The finite detection range of the sensor is further characterized, and a quasi-distributed array of sensing units is implemented along the sensing arm of a Michelson interferometer to realize effective detection of ultrasonic signals generated by partial discharges in submarine cable flexible joints. Experimental results show that the developed fiber-optic ultrasonic sensor has a resonance frequency of 33 kHz, which is in close agreement with the theoretical value of 33.22 kHz. When the sensor is positioned directly above an artificial PD defect, the minimum detectable apparent charge is 9.4 pC. Taking the sensor sensitivity under this condition as the normalized reference, the sensor is shown to exhibit a circumferential overlap sensing region of 60° between adjacent probes, within which a minimum detectable apparent charge of approximately 18 pC can be maintained. Along the cable axis, a single probe provides an axial physical sensing boundary of ±25 mm, within which a minimum detectable apparent charge of approximately 25 pC can be maintained. These results provide a reliable sensing solution for early fault warning and high-sensitivity detection of partial discharges in submarine cable flexible joints.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30418-30428"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computationally Efficient Static North-Finding: Decoupled AHRS, RLS Bias Estimation, and Attitude Error Analysis","authors":"Mohammad Hassan Najjarnasab;Hassan Salarieh","doi":"10.1109/JSEN.2026.3731528","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3731528","url":null,"abstract":"Accurate autonomous north-finding in GNSS-denied environments is critical; however, conventional gyrocompassing often suffers from heavy computational complexity and high sensitivity to sensor inaccuracies. To address these challenges, this article proposes a novel, computationally efficient attitude and heading reference system (AHRS) framework. The primary contributions are twofold: first, a minimal three-state error-state extended Kalman Filter (EKF) employing a horizontal specific force constraint (HSFC); second, a decoupled, parallel recursive least squares (RLS) algorithm that estimates gyroscope biases post-EKF convergence, thereby eliminating the computational overhead of augmented-state filters. Furthermore, rigorous stationary observability analysis and closed-form analytical models are derived to quantify error propagation, demonstrating how leveling and heading errors are strictly decoupled and influenced by geographic latitude. To predict bias estimation limits analytically, a novel design chart is introduced. Comprehensive Monte Carlo simulations and experimental validations utilizing multiple grades of fiber optic gyroscopes (FOGs) corroborate the theoretical findings. Ultimately, the outcomes confirm that the proposed framework significantly reduces computational burden while maintaining robust estimation fidelity, providing a highly scalable solution for static initial alignment.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31494-31502"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149175993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Interaction Learning for Ultrawideband Radar-Based Non-Line-of-Sight Human Activity Recognition","authors":"Yijia Chen;Jiawen Liao;Xiaoling Zhong;Shaoqin Yuan;Yong Jia","doi":"10.1109/JSEN.2026.3718053","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3718053","url":null,"abstract":"In non-line-of-sight corner scenarios, ultrawideband (UWB) radar is able to obtain multiview echoes of moving human targets through multiple propagation paths formed by wall reflections and corner diffraction. To exploit the activity-related information embedded in these multipath phenomena, the spatiotemporal fusion neural network (STFNet) architecture is proposed. The STFNet consists of three core modules, namely, the deep feature extraction module, the spatial feature interaction module, and the temporal feature interaction module. The deep feature extraction module uses six multiscale convolutional blocks to extract local and global high-dimensional semantic features from range–time maps. The spatial feature interaction module adopts three parallel dilated convolutions with different dilation rates for efficient multipath feature fusion and spatial self-attention interaction. The temporal feature interaction module integrates a path-aware extractor to form latent path groups and constructs joint temporal-path token tensors. The modified lightweight Transformer encoder is employed for temporal self-attention modeling to capture dynamic characteristics of human activities. Finally, the network fuses spatial and temporal self-attention weights to guide the classifier toward discriminative spatiotemporal path features. Experimental results show that the proposed STFNet achieves a classification accuracy of 89.05% on the self-collected eight-class NLOS human activity dataset, outperforming the baseline MPFNet (85.88%) by 3.17%.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30692-30702"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multistimuli Selectivity CuO/V2O5 Memristive Devices for Broadband Sensing Applications","authors":"Sharmila B;Priyanka Dwivedi","doi":"10.1109/JSEN.2026.3725743","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3725743","url":null,"abstract":"This article presents a wafer-scalable synthesis process for the efficient CuO/V₂O₅-based memristive devices for broadband sensing applications. The memristive devices are tested using three different sensing stimuli to analyze the trigger selectivity and widen the real-time sensing applications. The electrical, broadband optical, and thermal stimuli are used to stimulate the CuO/V₂O₅ heterostructure-based memristive devices. The tested devices have demonstrated bipolar resistive sweeps with enhancement in the performance metrics like current switching ratio, resistance switching ratio, and set/reset time for the thermal stimuli as compared with the other tested stimuli. The demonstrated memristor has shown the current switching ratio of ~<inline-formula> <tex-math>$0.8 times 10^{{3}}$ </tex-math></inline-formula> for the thermal stimuli (<inline-formula> <tex-math>$90~^{circ }$ </tex-math></inline-formula>C optimum temperature), which is ~<inline-formula> <tex-math>$~38$ </tex-math></inline-formula> and 28 times higher than the electrical and optical stimulation, respectively. The testing results highlight the potential of the proposed devices for scalable batch processing for mass production of multifunctional broadband sensing devices.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30376-30381"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic-Feature-Based Temperature Compensation for a Cr2O3–SnO2 Resistive Hydrogen Sensor","authors":"Wanting Rong;Diqun Yan;Shanshan Wang;Yuekang Guo;Xiangyu Li","doi":"10.1109/JSEN.2026.3729165","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3729165","url":null,"abstract":"This article proposes a dynamic-feature-driven temperature compensation method for a Cr₂O₃–SnO₂ resistive hydrogen sensor based on Bayesian optimization and an extremely randomized trees regressor (BO-ETR). For a Cr₂O₃–SnO₂ hydrogen sensor, eight dynamic response features were extracted from step-response curves and combined with the ambient temperature to predict H₂ concentration. Experiments were performed over 25–<inline-formula> <tex-math>$85~^{circ }$ </tex-math></inline-formula>C and 100–1500 ppm, using temperature-interpolation validation to evaluate cross-temperature generalization. Experimental results show that the BO-ETR achieved a mean absolute error (MAE) of 11.69 ppm and an R-squared of 0.9967, outperforming BO-Backpropagation (BP), BO-support vector regression (SVR), BO-least-squares support vector machine (LSSVM), and the extremely randomized trees regressor (ETR). An ablation study further demonstrates that incorporating the complete dynamic feature set reduces the MAE from 31.42 to 11.69 ppm compared with using only the ambient temperature and the steady-state response. These results demonstrate the effectiveness of the proposed dynamic-feature-based compensation framework for cross-temperature H₂ concentration estimation using the investigated Cr₂O₃–SnO₂ resistive sensor.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31677-31685"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Room-Temperature-Operable and Cost-Effective CO2 Sensor for Monitoring Respiratory Health","authors":"Xinzhe Zhang;Zeyi Lu;Rui Yao;Vaithinathan Karthikeyan;Yahui Lv;Wenqiong Li;Yun He;Xingyuan San;Xiaoguang Liang;Vellaisamy A. L. Roy","doi":"10.1109/JSEN.2026.3728751","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3728751","url":null,"abstract":"Driven by the rising prevalence of respiratory diseases and the increasing demand for portable medical devices, the development of innovative CO₂ sensing materials that operate at room temperature—while ensuring cost-effectiveness and biosafety—is crucial for the widespread adoption of early screening for respiratory disorders and daily health monitoring. Herein, Cu–Al dual-atom anchored on nitrogen-doped carbon nanofibers (CuAl–NCFs) were fabricated via electrospinning and employed to construct a chemiresistive CO₂ gas sensor operable at room-temperature. Among all samples, the 8.3 wt.% Cu–Al dual-atom anchored on N-doped carbon fibers (CuAl–NCFs)-based sensor shows the best overall CO₂ sensing performance, with a relative response value of −4.04% (<inline-formula> <tex-math>$Delta $ </tex-math></inline-formula>R/R<inline-formula> <tex-math>${}_{{0}}$ </tex-math></inline-formula>) at 4% CO₂ and a recovery time of 63 s, together with good reversibility and repeatability. Moreover, this sensor enables real-time monitoring of exhaled CO₂ at different sensor-to-nose distances, demonstrating its potential for noncontact breath monitoring. The enhanced sensing performance is primarily attributed to the synergistic interaction between atomically dispersed Cu and Al sites, which improves the adsorption capacity for CO₂ molecules. This work offers a novel strategy for designing dual-atom gas-sensing materials suitable for wearable medical monitoring systems.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30087-30093"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Back-Mounted System Integrating Multiparameter Real-Time Monitoring With Automated Life Support","authors":"Ke Li;Zhiqiang Chen;Guangye Tian;Qiao Han;Yuanhui Li;Caiyun Feng;Fanchang Meng;Mingze Sun;Bozhi Ding","doi":"10.1109/JSEN.2026.3730169","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3730169","url":null,"abstract":"Cardiac arrest (CA) continues to be a leading cause of mortality during natural disasters. In postdisaster confined rescue operations, the effectiveness of conventional emergency devices is significantly constrained by their excessive size, operational complexity, and limited interoperability. To address these limitations, a back-mounted system integrating multiparameter real-time monitoring with automated life support is designed and developed for postdisaster emergency life support. The proposed device integrates four essential modules, namely, noncontact physiological monitoring, chest compression, respiratory support, and defibrillation, which are coordinated through an intelligent control system to ensure efficient intermodule collaboration and seamless data exchange. The device is designed in a modular backpack form, with dimensions of <inline-formula> <tex-math>$37times 28times 60$ </tex-math></inline-formula> cm and a total weight of 14.9 kg, thereby ensuring portability and rapid deployment. The noncontact monitoring module, utilizing red/green/blue (RGB) and infrared imaging, enables the real-time acquisition of multiple vital signs. The chest compression module provides adjustable parameters with high-precision control. Both pressure- and flow-regulated modes are incorporated into the ventilatory support module; a 30:2 cardiopulmonary resuscitation (CPR) ventilation ratio is supported, and the target ventilation pressure can be set using only three adjustments. The defibrillation module integrates rapid R-wave detection with adaptive impedance matching to mitigate the risk of myocardial injury. Furthermore, real-time data sharing and intermodule collaboration are facilitated through Wi-Fi connectivity. Experimental results showed that the noncontact monitoring module enabled simultaneous estimation of multiple physiological parameters under the tested conditions. The system achieved a chest compression rate of <inline-formula> <tex-math>$100.13~pm ~0.87$ </tex-math></inline-formula> cpm and a compression depth of <inline-formula> <tex-math>$49.95~pm ~0.32$ </tex-math></inline-formula> mm. During ventilation, the tidal volume (VT) and peak inspiratory pressure were <inline-formula> <tex-math>$382.40~pm ~2.85$ </tex-math></inline-formula> mL and <inline-formula> <tex-math>$10.01~pm ~0.03$ </tex-math></inline-formula> cmH₂O, respectively. During defibrillation, the energy-delivery error remained within ±5%, with a synchronization delay of 1 ms. By integrating these advances, the system establishes an intelligent, portable, and effective solution tailored for disaster-site and prehospital emergency care.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31435-31450"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EmgMotionLM: A Multimodal Large-Language Model for Cross-Subject sEMG-Based Upper Limb Motion Estimation","authors":"Ruida Liu;Dan Wang;Meng Xu;Jiaming Chen","doi":"10.1109/JSEN.2026.3729239","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3729239","url":null,"abstract":"Cross-subject upper limb motion estimation from surface electromyography (sEMG) faces significant challenges due to intersubject variability and distribution shifts. We propose EmgMotionLM, a unified multimodal framework that combines EMG tokenization, language-guided contextual fusion, and large-language model (LLM)-based multimodal representation learning for upper limb motion estimation. The method converts raw sEMG signals into tokens and jointly models them with textual context (task semantics and subject attributes) in a shared LLM latent space, using a two-stage training strategy. Experiments on the Ninapro DB2 dataset and a self-collected isokinetic sEMG dataset demonstrate that the proposed method achieves competitive performance under both cross-subject and within-subject protocols, attaining a normalized root mean square error (NRMSE) of 0.1813 and a correlation coefficient of 0.8721 on the Ninapro DB2 cross-subject evaluation. These results suggest that language-guided multimodal representation learning can improve robustness and generalization in sEMG-based motion estimation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30621-30633"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust DOA-Aided Sensor-Array Receiver for Mobile Underwater Acoustic Communications Under Directional Impulsive Noise","authors":"Zheming Zhang;Xiao Han;Zhichao Jiang;Xinrui Cai;Jingwei Yin","doi":"10.1109/JSEN.2026.3727619","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3727619","url":null,"abstract":"Underwater acoustic sensor-array receivers deployed in under-ice mobile communication scenarios are challenged by directional impulsive noise and path-dependent Doppler distortion. This article proposes a robust direction-of-arrival (DOA)-aided sensor-array receiving framework that integrates broadband beamforming and low-complexity multichannel decision feedback equalization (MDFE). At the array-processing front end, a normalized-kurtosis-ratio (NKR) frame weighting scheme is embedded into coherently focused wideband MUSIC to down-weight impulsively contaminated array snapshots and suppress nonpath DOA pseudo-peaks. The estimated multipath DOAs are then used to construct frequency-domain null-steering beamformers, which transform the element-domain array measurements into path-dominated branches and prevent noncommunication directions from entering the back-end receiver. At the back end, each branch is processed by symbol-level real-time resampling and phase compensation to correct path-specific Doppler, followed by MDFE-based multipath combining. A real-time-resampling-compatible dichotomous-coordinate-descent (DCD) recursive least squares (RLSs) update with block-wise recursive correlation updates is introduced to avoid explicit matrix inversion and reduce adaptive-filtering complexity. Simulations with measured under-ice impulsive noise evaluate the receiver under rapidly time-varying path-dependent Doppler, showing the lowest observed BER and a 41.7% runtime reduction compared with conventional real-time resampling MDFE. Songhua Lake trial data with superimposed measured impulsive noise further validate robust DOA estimation, path separation, residual Doppler tracking, decoding reliability, and runtime reduction under slow source motion and real-channel propagation, achieving the lowest average BER of 0.0020 and the shortest average runtime of 1.889 s among the compared methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30892-30906"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Guided Concentration Inference From Resistance Transients in a Mixed-Phase SnO–SnO2 Carbon Monoxide Sensor With p-n Switching","authors":"Sani Biswas;Amit Kumar Gangwar;Preetam Singh","doi":"10.1109/JSEN.2026.3723678","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3723678","url":null,"abstract":"This work presents a physics-guided machine-learning framework for carbon monoxide (CO) concentration inference from experimentally measured resistance transients of a mixed-phase SnO–SnO₂ material gas sensor exhibiting temperature-dependent p-n switching behavior. Cycle-level transient responses are represented through physically interpretable descriptors and complemented by compact fast Fourier transform (FFT) and discrete wavelet transform (DWT)-based summaries. Using leakage-aware grouped cross-validation, we study both multiclass concentration classification and continuous concentration regression for the p- and n-type sensing regimes separately. Across both regimes, fused features provide the strongest overall performance, while the physics-guided descriptor block remains highly competitive, indicating that the dominant concentration information is already encoded in physically meaningful transient dynamics. The p-type branch shows the best concentration-class discrimination, with the fused random forest (RF) classifier reaching approximately <inline-formula> <tex-math>${96}.{5}%$ </tex-math></inline-formula> accuracy, whereas the n-type branch yields the best quantitative concentration estimation, with the fused RF regressor achieving <inline-formula> <tex-math>$text {MAE}{,}approx {,}{1}.{48}{,}text {ppm}$ </tex-math></inline-formula> and <inline-formula> <tex-math>$textit {R}^{{2}}{,}approx {,}{0}.{992}$ </tex-math></inline-formula>. These results reveal a clear dual-regime behavior: p-type sensing is particularly favorable for classification, whereas n-type sensing is more favorable for high-fidelity regression. More broadly, the study demonstrates that leakage-aware, cycle-level, physics-guided machine learning can extend conventional gas-sensing analysis beyond single-response metrics while preserving physical interpretability.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30051-30060"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}