Displays最新文献_第7页

MHG-Adapter: A parameter-efficient multimodal hypergraph adapter for text-driven medical image segmentation MHG-Adapter：一个参数高效的多模态超图适配器，用于文本驱动的医学图像分割

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-12-26 DOI: 10.1016/j.displa.2025.103330

Shaocong Mo , Ming Cai , Lanfen Lin , Ruofeng Tong , Fang Wang , Qingqing Chen , Yinhao Li , Hongjie Hu , Yen-Wei Chen

{"title":"MHG-Adapter: A parameter-efficient multimodal hypergraph adapter for text-driven medical image segmentation","authors":"Shaocong Mo , Ming Cai , Lanfen Lin , Ruofeng Tong , Fang Wang , Qingqing Chen , Yinhao Li , Hongjie Hu , Yen-Wei Chen","doi":"10.1016/j.displa.2025.103330","DOIUrl":"10.1016/j.displa.2025.103330","url":null,"abstract":"<div><div>Leveraging language semantics to strengthen vision information has become an advancing research in medical image segmentation. However, most existing studies restrict vision-language interactions in pair-wise correlations and only employ full-parameter fine-tuning under a single fusion scheme, leading to semantic inconsistency and potentiality ignorance of pre-trained medical vision-language models. In this paper, we introduce a parameter-efficient multimodal hypergraph adapter based model under a hybrid fusion paradigm of bidirectional early-fusion and late-fusion. Specifically, the multimodal hypergraph adapter is proposed to enhance interactions between several stages (blocks) of the vision and language encoders in medical CLIP models, which exploits high-order correlations by treating visual features as nodes and textual features as hyperedges, and propagates complementary information to refine features concurrently through hypergraph message passing. Additionally, we explore the hybrid utilization of word- and sentence-level textual features for fine-grained alignments in the task-specific decoder. Furthermore, two training strategies are employed for encoders from medical CLIP models: keeping the language encoder frozen only, or keeping both encoders frozen as parameter-efficient fine-tuning (PEFT) to fully exploit prior knowledge in the pre-trained model via adapters. Our experimental evaluations on the QaTa-COV19, CheXlocalize and MosMedData+ datasets demonstrate that our method significantly surpasses existing approaches, highlighting its superiority and effectiveness for text-driven medical image segmentation.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103330"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated prompt-guided multi-modality cell segmentation with shape-aware classification and boundary-aware SAM adaptation 具有形状感知分类和边界感知SAM适应的自动提示引导多模态细胞分割

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2026-01-07 DOI: 10.1016/j.displa.2025.103337

Deboch Eyob Abera , Jiaye He , Jia Liu , Nazar Zaki , Wenjian Qin

{"title":"Automated prompt-guided multi-modality cell segmentation with shape-aware classification and boundary-aware SAM adaptation","authors":"Deboch Eyob Abera , Jiaye He , Jia Liu , Nazar Zaki , Wenjian Qin","doi":"10.1016/j.displa.2025.103337","DOIUrl":"10.1016/j.displa.2025.103337","url":null,"abstract":"<div><div>Robust and accurate cell segmentation across diverse imaging modalities remains a critical challenge in microscopy image analysis. While foundation models like the Segment Anything Model (SAM) have demonstrated exceptional performance in natural image segmentation, their adaptation to multi-modal cellular analysis is hindered by domain-specific knowledge gaps and morphological complexity. To bridge this gap, we present a novel SAM-driven framework featuring three systematic innovations: First, we propose Shape-Aware Classification to enhance segmentation of cells with diverse morphologies. Second, Auto Point Prompt Generation (APPGen) module guides the segmentation model with automatically generated point cues to improve segmentation accuracy. Third, we implement Boundary-Aware SAM Adaptation to effectively resolve overlapping cells in microscopy images. Our experiments show that the proposed framework reduces manual effort through automated prompts, adapts well to different imaging modalities, and enhances segmentation accuracy by incorporating boundary-aware techniques. The source code is available at <span><span>https://github.com/MIXAILAB/Multi_Modality_CellSeg</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103337"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RAP-SORT: Advanced Multi-Object Tracking for complex scenarios RAP-SORT：用于复杂场景的高级多目标跟踪

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2026-01-21 DOI: 10.1016/j.displa.2026.103361

Shuming Zhang , Yuhang Zhu , Yanhui Sun , Weiyong Liu , Zhangjin Huang

{"title":"RAP-SORT: Advanced Multi-Object Tracking for complex scenarios","authors":"Shuming Zhang , Yuhang Zhu , Yanhui Sun , Weiyong Liu , Zhangjin Huang","doi":"10.1016/j.displa.2026.103361","DOIUrl":"10.1016/j.displa.2026.103361","url":null,"abstract":"<div><div>Multi-Object Tracking (MOT) aims to detect and associate objects across frames while maintaining consistent IDs. While some approaches leverage both strong and weak cues alongside camera compensation to improve association, they struggle in scenarios involving high object density or nonlinear motion. To address these challenges, we propose RAP-SORT, a novel MOT framework that introduces four key innovations. First, the Robust Tracklet Confidence Modeling (RTCM) module models trajectory confidence by smoothing updates and applying second-order difference adjustments for low-confidence cases. Second, the Advanced Observation-Centric Recovery (AOCR) module facilitates trajectory recovery via linear interpolation and backtracking. Third, the Pseudo-Depth IoU (PDIoU) metric integrates height and depth cues into IoU calculations for enhanced spatial awareness. Finally, the Window Denoising (WD) module is tailored for the DanceTrack dataset, effectively mitigating the creation of new tracks caused by misdetections. RAP-SORT sets a new state-of-the-art on the DanceTrack and MOT20 benchmarks, achieving HOTA scores of 66.7 and 64.2, surpassing the previous best by 1.0 and 0.3, respectively, while also delivering competitive performance on MOT17. Code and models will be available soon at <span><span>https://github.com/levi5611/RAP-SORT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103361"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum-enhanced gold rush Optimizer for multi-threshold segmentation of lupus nephritis pathological images 狼疮性肾炎病理图像多阈值分割的量子增强淘金优化算法

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-12-28 DOI: 10.1016/j.displa.2025.103334

Mingyang Yu , Haotian Lu , Donglin Wang , Ji Du , Desheng Kong , Xiaoxuan Xu , Jing Xu

{"title":"Quantum-enhanced gold rush Optimizer for multi-threshold segmentation of lupus nephritis pathological images","authors":"Mingyang Yu , Haotian Lu , Donglin Wang , Ji Du , Desheng Kong , Xiaoxuan Xu , Jing Xu","doi":"10.1016/j.displa.2025.103334","DOIUrl":"10.1016/j.displa.2025.103334","url":null,"abstract":"<div><div>Lupus Nephritis (LN), a severe complication of Systemic Lupus Erythematosus (SLE), critically affects renal function. To improve diagnostic accuracy, multi-threshold image segmentation (MTIS) techniques based on metaheuristic (MH) algorithms are widely adopted. However, traditional MH algorithms often suffer from premature convergence, limiting their global search capabilities. This study proposes a Quantum-Enhanced Hybrid Gold Rush Optimizer (QHGRO) that integrates quantum computing to enhance optimization performance. QHGRO is applied to an MTIS framework that utilizes a non-local means two-dimensional histogram to encode image information and employs Rényi entropy as the fitness function. The optimizer incorporates a Quantum Computing-Driven Adaptive Variation strategy, where quantum superposition enables parallel exploration of multiple states, and quantum mutation introduces controlled randomness to enhance global search and avoid local optima. To further improve performance, QHGRO includes a Stochastic Lévy Flight strategy during the Collaboration between Prospectors phase to enhance exploration and population diversity, and a Dynamic Fitness Distance Balance strategy during the Gold Mining phase to improve convergence accuracy. Experimental results on CEC2017 and CEC2022 benchmark functions demonstrate that QHGRO achieves competitive performance, often approaching global optima. In two engineering design problems—Speed Reducer Design and Three-Bar Truss Design—QHGRO outperforms classical algorithms (PSO, GWO, DE), newer algorithms (NRBO, CPO, RUN, BKA, SBOA, GRO), and advanced variants (MPSO, IAGWO). In LN pathological image segmentation tasks, the proposed method generates clear, high-quality segmented images, offering valuable support for clinical diagnosis.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103334"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perceptually robust super-resolution through global feature awareness 通过全局特征感知实现感知鲁棒超分辨率

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-12-24 DOI: 10.1016/j.displa.2025.103332

Long Qian , Yilin Chen , Yuxuan Hong , Lizhuang Ma , Xiao Lin

引用次数: 0

TAFCNet: Task-aware feature coupling network for joint image motion deblurring and super-resolution 联合图像运动去模糊和超分辨率的任务感知特征耦合网络

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-11-07 DOI: 10.1016/j.displa.2025.103281

Ying Shen, Hongen Wu, Liqiong Chen, Zhaobing Qiu, Feng Huang

{"title":"TAFCNet: Task-aware feature coupling network for joint image motion deblurring and super-resolution","authors":"Ying Shen, Hongen Wu, Liqiong Chen, Zhaobing Qiu, Feng Huang","doi":"10.1016/j.displa.2025.103281","DOIUrl":"10.1016/j.displa.2025.103281","url":null,"abstract":"<div><div>Joint image motion deblurring and super-resolution aims to restore blurry low-resolution images with respect to image details and spatial content. Existing joint methods usually directly fuse the features but neglect the inherently different feature requirements and reconstruction objectives of the two tasks, resulting in failures of blurry texture reconstruction and inaccurate recovery of authentic details. To address this issue, we propose a joint degradation model grounded in the visible-light imaging process, which unifies motion blur and low sampling frequency degradation to characterize the linear relationship between features of deblurring and super-resolution. Guided by this model, we propose a novel Task-Aware Feature Coupling Network (TAFCNet) to enable restoration of local non-uniformly blurred regions and accurate reconstruction of structurally consistent content. Specifically, our method employs Stacked Nonlinear Feature Separator (SNFS) branch to enhance the representation of local information, and a High-Low Frequency Feature Mixer (HLFFM) branch operating in the frequency domain to obtain texture features from low-resolution images. To adaptively couple dual-task features, we further propose a Task-Aware Gating (TAG) module to mitigate feature conflicts. Extensive experiments demonstrate that our TAFCNet outperforms state-of-the-art methods in terms of objective metrics and subjective quality, achieving superior reconstruction performance and stronger cross-dataset generalization capabilities.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103281"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Direct LiDAR-supervised surface-aligned 3D Gaussian Splatting for high-fidelity digital twin 直接激光雷达监督表面对准三维高斯溅射高保真数字孪生

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2026-01-10 DOI: 10.1016/j.displa.2026.103349

Xingdong Sheng , Qi Zhou , Xu Liu , Zhenyang Qu , Haoyu Xu , Shijie Mao , Xiaokang Yang

{"title":"Direct LiDAR-supervised surface-aligned 3D Gaussian Splatting for high-fidelity digital twin","authors":"Xingdong Sheng , Qi Zhou , Xu Liu , Zhenyang Qu , Haoyu Xu , Shijie Mao , Xiaokang Yang","doi":"10.1016/j.displa.2026.103349","DOIUrl":"10.1016/j.displa.2026.103349","url":null,"abstract":"<div><div>3D Gaussian Splatting (3DGS) has recently demonstrated remarkable rendering speed and photorealistic quality for 3D reconstruction. Yet precise surface reconstruction and view-consistent photometric fidelity remain challenging, because the standard pipeline lacks explicit geometry supervision. Several recent approaches incorporate dense LiDAR point clouds as guidance, typically by aligning Gaussian centers or projecting LiDAR points into pseudo-depth maps. However, such methods constrain positions only and overlook the anisotropic shapes of the Gaussians, often resulting in rough surfaces and residual artifacts. To overcome these limitations, we propose a direct LiDAR-supervised surface-aligned regularization loss that simultaneously constrains Gaussian positions and shapes without converting LiDAR scans into depth maps. We further introduce adaptive densification and a multi-view depth-guided pruning strategy to enhance fidelity and suppress floaters. Extensive experiments on diverse indoor and outdoor datasets that represent the demands of industrial digital-twin applications show that our method consistently improves photorealistic rendering, even under significant viewpoint deviations, demonstrating advantages over existing typical LiDAR-assisted 3DGS methods.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103349"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measuring points for video subjective assessment – Impact of memory and stimulus variability 视频主观评价用测量点。记忆和刺激变异性的影响

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-11-12 DOI: 10.1016/j.displa.2025.103283

Tomasz Konaszyński , Avrajyoti Dutta , Burak Gizlice , Dawid Juszka , Mikołaj Leszczuk

{"title":"Measuring points for video subjective assessment – Impact of memory and stimulus variability","authors":"Tomasz Konaszyński , Avrajyoti Dutta , Burak Gizlice , Dawid Juszka , Mikołaj Leszczuk","doi":"10.1016/j.displa.2025.103283","DOIUrl":"10.1016/j.displa.2025.103283","url":null,"abstract":"<div><div>The work describes a QoE experiment concerning the assessment of the impact of memory and stimulus variability on subjective assessments of 2D videos, as well as an attempt to identify dominant points − moments in time or events influencing the overall assessment of the changing quality of the assessed films. Based on the results of the conducted QoE experiment, the impact of varying video quality on subjective assessment of 2D videos was clearly demonstrated, both in terms of results eligibility and subjective ratings.</div><div>The concept of “measurement points” was introduced, i.e., points in time or events that were associated with the highest impact on the values of subjective ratings when variable quality videos are assessed or videos are displayed in variable controlled environment.</div><div>The relationship between the memory of particular aspects of the video presentation, including the memory of subsequent appearances of the given video, and the values obtained from the assessment results were also demonstrated. There were observed regularities, including very strong negative effect of the variability of the technical quality of the rated videos on results eligibility, effect of boredom/annoyance from watching a longer video of variable quality, “last impression effect”, i.e. videos whose changing quality increases over time achieve higher MOS values than videos whose quality decreases over time and better assessments of “fresh” observations in comparison to the following ones.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103283"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards LiDAR point cloud geometry compression using rate-distortion optimization and adaptive quantization for human-machine vision 基于率失真优化和自适应量化的人机视觉激光雷达点云几何压缩

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2026-01-11 DOI: 10.1016/j.displa.2026.103344

Yihan Wang , Yongfang Wang , Shuo Zhu , Zhijun Fang

{"title":"Towards LiDAR point cloud geometry compression using rate-distortion optimization and adaptive quantization for human-machine vision","authors":"Yihan Wang , Yongfang Wang , Shuo Zhu , Zhijun Fang","doi":"10.1016/j.displa.2026.103344","DOIUrl":"10.1016/j.displa.2026.103344","url":null,"abstract":"<div><div>Due to rapid advances in 3-Dimensional (3D) sensing and rendering technologies, point clouds have become increasingly widespread, bring significant challenges for transmission and storage. Existing LiDAR Point Cloud Compression (PCC) methods primarily focus on enhancing compression efficiency and maintaining high signal fidelity, with insufficient considering human and machine joint perception. This paper proposes Rate Distortion Optimization (RDO) and Adaptive Quantization (AQ) for LiDAR Point Cloud Geometry Compression (PCGC) to balance human–machine vision performance. Specifically, we first propose Hybrid Distortion RDO (HDRDO) using hybrid distortion and Lagrange multiplier, where the optimal weights are determined by Differential Evolution (DE) algorithm. Furthermore, by comprehensively analyzing the impacts of point clouds on a Gaussian-based classification method on overall quality, we propose a HDRDO-based AQ method to adaptively quantify important and non-important points by optimal Quantization Parameter (QP) selection. We implement on Geometry-based Point Cloud Compression (G-PCC) Test Model Category 1 and 3 (TMC13), called the anchor method. Compared with the anchor method, the proposed algorithm achieves consistent PSNR for human vision tasks and improves by 2.66% and 21.18% on accuracy at low bitrates for detection and segmentation, respectively. Notably, the proposed overall method performs better than the existing method.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103344"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pixel alignment method with double-slit interference for high-dynamic-range laser display system 高动态范围激光显示系统的双缝干涉像素对准方法

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-12-22 DOI: 10.1016/j.displa.2025.103329

Dabo Guo , Yunchuan Chen , Bin Guo , Jia Yu , Guang Yuan

{"title":"Pixel alignment method with double-slit interference for high-dynamic-range laser display system","authors":"Dabo Guo , Yunchuan Chen , Bin Guo , Jia Yu , Guang Yuan","doi":"10.1016/j.displa.2025.103329","DOIUrl":"10.1016/j.displa.2025.103329","url":null,"abstract":"<div><div>In the field of laser display technology, the spatial light modulator (SLM) functions as the core device for achieving display. Nevertheless, due to its inherent physical characteristics, one single spatial light modulator system has an upper limit on contrast ratio, making it arduous to meet the requirements of high dynamic range (HDR) display. Therefore, two spatial light modulators are frequently utilized to extend the dynamic range, and the level of pixel alignment between the two devices emerges as the decisive factor for system performance. In this research, a method based on double-slit interference was proposed to achieve pixel alignment between phase-only and amplitude-only spatial light modulators, and a high dynamic range laser display system based on the phase light modulator (PLM) and digital micromirror (DMD) was constructed. By calculating the relative position of the interference fringes to reveal the relative alignment state of the PLM and the DMD in the laser light path, including the translation and the inclination angles in three dimensions. Experiments results demonstrate that the method is simple to operate and can meet the pixel alignment accuracy of PLM and DMD within 21.6 μm.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103329"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0