Sensors最新文献

{"title":"Machine Learning-Based Prediction of Pulmonary Function Test Outcomes Using Oscillometry in Patients with COPD.","authors":"Seyedfakhreddin Nabavi, Lennart K A Lundblad, Ronald J Dandurand, Bryan A Ross","doi":"10.3390/s26206629","DOIUrl":"https://doi.org/10.3390/s26206629","url":null,"abstract":"<p><p>Oscillometry has been recognized as a non-invasive method for assessing respiratory conditions and is increasingly being implemented in routine clinical practice. Currently, a full pulmonary function test (<i>PFT</i>), which requires extensive infrastructure and trained personnel, remains the standard for comprehensive lung function assessment. Furthermore, while a full <i>PFT</i> can be performed by most patients with mild chronic obstructive pulmonary disease (COPD), this can be exceedingly difficult and cumbersome to complete in patients with more advanced disease. The present study sought to investigate the relationship between lung function parameters obtained by oscillometry, a non-invasive technique requiring only quiet breathing, with the parameters obtained from the more technically demanding components of a standard <i>PFT,</i> plethysmography and diffusing capacity, in patients with mild and very severe COPD. Through Spearman analyses, the strongest associations were observed between resistance at 5 Hz (<i>R</i>₅) and the difference in resistance between 5 Hz and 19 Hz (<i>R</i>₅-<i>R</i>₁₉), by oscillometry, with residual volume (<i>RV</i>) to total lung capacity (<i>TLC</i>) ratio <i>(RV/TLC</i>-gas trapping) and diffusing capacity of the lung for carbon monoxide (<i>DLco</i>-diffusing capacity), by <i>PFT</i> (0.69 and -0.74, respectively). To extend the clinical applicability of oscillometry, three machine learning models suitable for small datasets, Extreme Gradient Boosting (<i>XGBoost</i>), Random Forest (<i>RF</i>), and Support Vector Regression (<i>SVR</i>), were implemented to predict these key <i>PFT</i> outcomes from oscillometry metrics and baseline demographic data alone. <i>XGBoost</i> achieved the best performance, with mean absolute errors of 9.7 for <i>RV/TLC</i> and 8.1 for <i>DLco</i>. In contrast, none of the models could accurately predict <i>TLC</i>. Feature importance analysis revealed that age enhanced <i>RV/TLC</i> prediction, while <i>DLco</i> prediction was consistently influenced by the addition of body mass index (BMI). In conclusion, oscillometry has the potential to serve as complementary to <i>PFT</i> for assessing otherwise tedious, resource-intensive, and often unattainable information on gas trapping and gas exchange, especially for older or frail adults with advanced disease, while still offering high accuracy.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149273071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"MSDGFM-YOLO11n-OBB: Selective High-Resolution Difference-Guided RGB-Infrared Fusion for Aerial Oriented Vehicle Detection.","authors":"Chao Huang, Zhihong Xu","doi":"10.3390/s26206634","DOIUrl":"https://doi.org/10.3390/s26206634","url":null,"abstract":"<p><p>Aerial vehicle detection using red-green-blue (RGB) and infrared (IR) imagery supports unmanned aerial vehicle (UAV) perception, but oriented localization remains difficult because vehicles are small, dense, arbitrarily oriented, and affected by illumination. Fusion must decide both how cross-modal differences are used and where adaptive fusion is placed. We propose MSDGFM-YOLO11n-OBB, a dual-branch oriented bounding box (OBB) detector with a Modality-Selective Difference-Guided Fusion Module (MSDGFM). Independent backbones preserve modality-specific features. MSDGFM learns sample-dependent RGB/IR weights and injects absolute cross-modal differences through a spatial gate and learnable channel-wise residual; zero initialization makes equal-weight Mean Fusion the exact starting solution. Adaptive fusion is applied only at high-resolution P3, while P4/P5 retain parameter-free Mean Fusion. On DroneVehicle Shared-label data, the three-seed results reach 0.7853 ± 0.0027 mean average precision (mAP)@0.50 and 0.6472 ± 0.0013 mAP@0.50:0.95, exceeding the strongest evaluated single-modal nano-scale OBB baseline by 3.88 and 6.82 percentage points. Relative to matched Mean Fusion, the gain averages 0.83 points in mAP@0.50:0.95 and is positive on all three seeds. In seed-0 capacity control, it exceeds YOLO11s-OBB (IR) by 4.32 points with 58.1% fewer parameters and 54.0% fewer FLOPs. These results support compact, selective difference-guided fusion for aerial RGB-IR OBB detection.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149273076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Baseline Wearable Turning-Derived Signature and Dopamine Transporter-Derived Measures in Relation to Subsequent Motor Progression in Parkinson's Disease: An Exploratory Longitudinal Study.","authors":"Hwayoung Park, Changhong Youm, Bohyun Kim, Juseon Hwang, Minsoo Kim, Sang-Myung Cheon, Young Jin Jeong","doi":"10.3390/s26206610","DOIUrl":"https://doi.org/10.3390/s26206610","url":null,"abstract":"<p><p>Parkinson's disease (PD) shows heterogeneity in motor progression, complicating prognostic assessment. Dopamine transporter (DAT) imaging provides an in vivo measure of nigrostriatal dopaminergic integrity, whereas wearable gait assessment quantifies motor performance. However, the prognostic value of baseline (BL) DAT-derived measures and wearable gait characteristics remains incompletely understood. We investigated whether BL DAT-derived measures and a limb-focused wearable turning movement-dynamics signature (turning-derived signature) were associated with subsequent motor progression in PD. Twenty-three individuals with PD underwent BL PET imaging, wearable gait assessment, and clinical follow-up. Motor progression was defined using a cohort-specific exploratory threshold of ΔUnified Parkinson's Disease Rating Scale (UPDRS) Part III ≥ 1.5 points, derived within this cohort using Hoehn and Yahr (H&Y)-stage worsening as the clinical anchor. Twelve participants were classified as motor progressors and 11 as non-progressors. The turning signature yielded an AUC of 0.750, while DAT-model AUCs ranged from 0.462 to 0.500. The clinical model yielded an AUC of 0.962; adding the turning signature yielded an AUC of 0.871. These findings suggest that the BL turning-derived signature contains an exploratory prognostic signal but does not establish incremental value beyond clinical severity. The progression outcome was derived within this small cohort, and external validation is required.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149272789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"DRAF-Net: Quality-Aware RGB-Skeleton Temporal Fusion for Driver Risk-Behavior Recognition.","authors":"Wei Shen, Xingang Mou, Qun Yuan, Yichao Wang, Peixuan Zhu","doi":"10.3390/s26206620","DOIUrl":"https://doi.org/10.3390/s26206620","url":null,"abstract":"<p><p>Occlusion in vehicle cabins and pose-estimation errors can reduce the reliability of skeleton information. Conventional fusion methods may propagate unreliable evidence when the RGB, joint, and bone streams produce conflicting predictions. We therefore propose DRAF-Net, a Dual-stage Risk-Aware Fusion Network. The Pose-Quality-guided Risk Bottleneck Temporal Adapter (PQR-BTA) regulates information exchange among the three streams using 12 skeleton-quality measures, prediction confidence, and cross-stream disagreement. Risk Correction Protection (RCP) then uses the baseline prediction as a reference and bounds changes to the high-risk probability and four-level classification scores. Experiments used three mutually exclusive subject splits from Drive&Act, with three random seeds per split and a shared protocol for input features, training, and validation-based model selection. Across the three subject splits, DRAF-Net achieved a high-risk AUPRC of 96.16±2.44%, exceeding the strongest comparison method by 3.13 percentage points. At the validation threshold targeting a FAR of approximately 5%, Recall reached 89.18±5.48%, an improvement of 5.82 points over the strongest comparison method. The deviations are sample standard deviations across the three split-level seed means. These results show that DRAF-Net organizes complementary RGB and skeleton evidence effectively while supporting both the ranking and detection of high-risk samples.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149273070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Wavelet Interactive Mamba Fusion Network for UAV Small Object Detection with Visible-Thermal Data.","authors":"Qi Gao, Tianwen Guo","doi":"10.3390/s26206625","DOIUrl":"https://doi.org/10.3390/s26206625","url":null,"abstract":"<p><p>Accurate small object detection in low-altitude unmanned aerial vehicle (UAV) visible-thermal (RGB-T) scenarios remains challenging under complex illumination and background clutter due to thermal noise, weak cross-modal interaction, and attenuation of ultra-small target features. This paper proposes Wavelet Interactive Mamba Fusion Network (WIM-Net), a lightweight RGB-T detector with 10.4 million parameters. The network adopts a dual-branch backbone and introduces two modules for frequency-domain denoising and adaptive feature fusion. The Wavelet Interactive Mamba (WIM) module decomposes visible and thermal feature maps into multi-frequency sub-bands through a discrete wavelet transform and uses bidirectional Mamba interaction to model cross-modal dependencies with linear complexity. Instead of independent single-modal denoising, WIM uses low-frequency semantic cues to constrain high-frequency filtering, suppressing thermal noise while preserving target structures. The Cross-Modal Synergistic Fusion (CMSF) module builds bimodal mutual attention to adaptively combine visible texture and thermal radiation cues, reducing feature attenuation for tiny objects. Ablation studies show that WIM and CMSF contribute to noise suppression and small-object feature retention, respectively. Experiments on the RGBT-Tiny dataset demonstrate that WIM-Net achieves the best mean average precision at an intersection-over-union threshold of 0.5 (mAP50) of 52.0% and Precision of 79.9% among the evaluated single-modal and RGB-T detectors with only 10.4 million parameters, whereas its Recall and F1-score remain below those of QFDet and CALNet, reflecting a precision-oriented operating point. The compact architecture also preserves a favorable balance between detection accuracy and inference efficiency, with measured offline throughput on an NVIDIA Jetson Orin NX, while live airborne performance remains to be tested.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149273130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Open-Set Recognition of Fire Smoke from Four-Channel Optical Smoke Detector.","authors":"Liangbo Li, Ming Zhu, Mengxue Lin, Jiahao Gao, Linsen Zhao, Maosen Wang","doi":"10.3390/s26206606","DOIUrl":"https://doi.org/10.3390/s26206606","url":null,"abstract":"<p><p>In fire smoke detection, interference aerosols frequently cause false alarms for photoelectric smoke detectors. Conventional closed-set classification models trained on limited samples cannot reliably handle unknown aerosol types, which further leads to false and missed alarms. To address this issue, we propose FireOSR-Net, an open-set recognition network for fire smoke identification using the signals of a four-channel optical scattering detector, which integrates three modules. First, Conv1d-LSTM is used to extract features from dual-wavelength, dual-angle scattered signals. Next, the out-of-distribution detection module constructs an open boundary between known and unknown aerosols and generates pseudo-unknown features to enhance the detection capability for out-of-domain samples. Finally, the distance of all scattered intensity signals to both the fire and the non-fire domain is calculated, and fire and non-fire proximity scores are output to assess their risk propensity. Experiments are performed on eight-class aerosol data acquired with our self-developed four-channel detector, including six known and two unknown test categories. It achieves a peak F1-score of 0.95 on known samples, while unknown smoldering-wire smoke and oil fumes reach 96.29% fire proximity and 89.68% non-fire proximity correspondingly. Such results demonstrate FireOSR-Net's capacity for aerosol classification, out-of-domain detection and risk evaluation towards interpretable low-false-alarm fire sensing.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149273205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Cooperative Multi-AP Sensing and Dynamic-Echo Suppression for Static Environment Reconstruction in Cell-Free ISAC.","authors":"Xiongwei Liao, Ruoyun Liu, Xihan Zhao, Yuchen Si, Jingting Ye, Junjie Yan, Jiamin Li","doi":"10.3390/s26206622","DOIUrl":"https://doi.org/10.3390/s26206622","url":null,"abstract":"<p><p>Distributed access points (APs) provide complementary bistatic views for cell-free integrated sensing and communication (ISAC), but moving-object echoes can obscure static surfaces in the same channel state information (CSI). We combine covariance-informed state fitting with temporal echo estimation from the observations. Their residual CSI means feed a shared multi-AP network with surface supervision and forward measurement consistency. On 24 held-out simulated geometries, the complete pipeline increased mean surface F-score from 0.446 to 0.480 and reduced squared Chamfer distance from 1.409 to 1.136 m². This pipeline combines expanded training data, surface-oriented fine-tuning and phase averaging. With decoder and inference settings fixed, a controlled +10 dB moving-path test on independently ray-traced channels increased F-score from 0.256 to 0.391 through combined suppression (paired gain 0.1353; 95% interval [0.0960, 0.1729]). In the CSI-domain sensing-error test, combined input retained F-score of 0.4793 under an added velocity-error standard deviation of 8 m/s. A separate spatial-prior study increased F-score from 0.468 to 0.657 through surface refinement. On 24 independent ray-tracing test geometries, receiver adaptation yielded F-score of 0.412 versus 0.369 for a no-CSI occupancy prior (paired gain 0.0427; 95% interval [0.0108, 0.0772]). These results establish reconstruction gains from the complete pipeline and surface refinement, with a distinct suppression benefit under strong dynamic interference.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149272892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"FasterGeo-YOLO: Multi-Scale Geometric Adaptive Feature Enhancement for Lightweight Road Damage Detection.","authors":"Longshuai Zhang, Ru Yang, Zhentao Qin, Yuanjie Lu, Boyi Zhang, Shuqi He, Yuwei Jin","doi":"10.3390/s26206619","DOIUrl":"https://doi.org/10.3390/s26206619","url":null,"abstract":"<p><p>Automatic road damage detection is essential for intelligent transportation infrastructure inspection and road health monitoring. However, road damage targets often exhibit small scales, weak boundaries, irregular geometries, low texture contrast, and large inter-class appearance variations, which make accurate and efficient detection challenging for lightweight detectors. To address these issues, this paper proposes FasterGeo-YOLO, a lightweight multi-scale geometric adaptive enhancement framework based on the YOLO26n baseline. FasterNet is introduced as an efficiency-oriented lightweight backbone, while C2f_DCN deformable convolution modules are embedded at the P3 and P4 feature levels to provide complementary geometric adaptation at different spatial resolutions. The original YOLO26n baseline is DFL-free, whereas FasterGeo-YOLO intentionally reintroduces Distribution Focal Loss (DFL) in its modified detection formulation to strengthen distribution-based bounding-box regression. For fair baseline comparison, all detectors in the main comparison are evaluated at 640 × 640 input resolution; the subsequent 800 × 800 stage is treated separately as a high-resolution refinement strategy. At 640 × 640, FasterGeo-YOLO achieves an mAP@0.5 of 0.567 and an mAP@0.5:0.95 of 0.286. After high-resolution refinement, the final model reaches a Precision of 0.632, Recall of 0.538, mAP@0.5 of 0.580, and mAP@0.5:0.95 of 0.293. Three-seed controlled ablation further confirms the reproducibility of the sequential design. The results demonstrate that FasterGeo-YOLO improves road damage detection while maintaining a lightweight and practically efficient model scale.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149273058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Leveraging Bridge Scour Detection by Multi-Model Machine Learning and Drive-By Data.","authors":"Sinem Tola, Joaquim Tinoco, Eugene J OBrien, José C Matos","doi":"10.3390/s26206612","DOIUrl":"https://doi.org/10.3390/s26206612","url":null,"abstract":"<p><p>This study provides a detailed computational investigation of railway bridge scour detection using multiple machine learning algorithms and drive-by sensor data. A simulation-based framework generates synthetic train crossings representing track profiles acquired by Rail Infrastructure Alignment Acquisition, a far-from-axle sensing tool installed on in-service trains. Artificial Neural Networks (ANNs), Random Forests (RFs), and Support Vector Machines (SVMs) are trained, tested, and compared for binary bridge-level classification of imposed support-stiffness-reduction conditions using features derived from the Discrete Wavelet Transform and ARX-derived impulse-response Markov parameters, under leakage-free blocked nested cross-validation. RF achieved the highest overall accuracy, while ANN achieved the highest balanced accuracy in the rear-mounted 7-crossing case. Batch-size effects were non-monotonic, with short windows improving some configurations but longer windows reducing healthy-class recognition. Sensor orientation affected classifier performance and feature sensitivity, with neither configuration consistently superior across classifiers and batch sizes. The framework is further examined through class-balancing, feature-importance, and feature-family ablation analyses. The results demonstrate numerical feasibility in the adopted simulation setting and may support future bridge-condition screening after field validation.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149272982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Construction and Validation of an Editable Image Dataset for Black Hole Effect Scenes in YOLO Object Detection.","authors":"Jinyu Yang, Yun Chen, Shanhe Lou, Xinliang Geng, Zhongxiang Yang, Tengfei Wang, Yumei Liu, Xinyang Liu, Siying Li, Jinhe Lv, Qiushi Yan, Tie Li, Zhipeng Wang, Longchen Gong, Weirong Shu","doi":"10.3390/s26206615","DOIUrl":"https://doi.org/10.3390/s26206615","url":null,"abstract":"<p><p>The YOLO object detection algorithm is widely applied in intelligent assisted driving systems. However, existing datasets lack targeted coverage of the black hole effect scenarios at bridge and tunnel entrances. To address this issue, this paper constructs a black hole dataset, which consists of two subsets, namely BH-Normal and BH-Blackhole, involving four typical scenarios, namely suburban beltway tunnels, urban underground tunnels, railway overpasses, and interchanges, as well as multiple influencing factors such as different illumination angles and different distances from tunnel entrances. The dataset contains 27,868 labeled images in YOLO format, with 71,956 labeled instances. Based on statistical analysis of full-day time-series and distance-sequence data, this paper reveals the evolution pattern of black hole effect intensity at tunnel entrances with respect to observation distance and time of day. Meanwhile, this paper constructs an editable black hole dataset and develops a one-stop YOLO dataset creation and management toolkit. Baseline validation using mainstream full-scale YOLO models shows that the model fine-tuned solely on BH-Normal suffers an average mAP@0.5:0.95 drop of over 20% under black hole effect conditions; with the introduction of BH-Blackhole and secondary fine-tuning, the model's mAP@0.5:0.95 is relatively improved by more than 14.54%. This confirms that the black hole dataset can effectively enhance the target detection performance of YOLO models in black hole effect scenarios at bridge and tunnel entrances. Furthermore, the baseline results of the YOLO model obtained in the black hole dataset can provide benchmark support for optimizing intelligent assisted driving visual perception models.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 20","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149272965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}