IEEE transactions on visualization and computer graphics最新文献

SERES: Semantic-Aware Neural Reconstruction from Sparse Views. SERES：稀疏视图的语义感知神经重构。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-08 DOI: 10.1109/TVCG.2025.3619144

Bo Xu, Yuhu Guo, Yuchao Wang, Wenting Wang, Yeung Yam, Charlie C L Wang, Xinyi Le

引用次数: 0

Casual-VRAuth: A Design Framework Bridging Focused and Casual Interactions for Behavioral Authentication in Virtual Reality. 休闲vrauth：一种设计框架，连接虚拟现实中行为认证的重点和休闲交互。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-08 DOI: 10.1109/TVCG.2025.3616834

GuanYu Ye, BoYu Gao, Huawei Tu

{"title":"Casual-VRAuth: A Design Framework Bridging Focused and Casual Interactions for Behavioral Authentication in Virtual Reality.","authors":"GuanYu Ye, BoYu Gao, Huawei Tu","doi":"10.1109/TVCG.2025.3616834","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3616834","url":null,"abstract":"Current behavioral authentication systems in Virtual Reality (VR) require sustained focused interaction during task execution - an assumption frequently incompatible with real-world constraints across two factors: (1) physical limitations (e.g., restricted hand/eye mobility), and (2) psychological barriers (e.g., task-switching fatigue or break-in-presence). To address this attentional gap, we propose a design framework bridging focused and casual interactions in behavior-based VR authentication (Casual-VRAuth). Based on this framework, we designed an authentication prototype using a modified ball-and-tunnel task (propelling a ball along a circular path), supporting three interaction modes: baseline Touch, and two eyes-free options (Hover/Tapping). Experimental results demonstrate that our framework effectively guides the design of authentication systems with varying interaction engagement levels (Touch >Hover >Tapping) to accommodate scenarios requiring casual interaction (e.g., multitasking or eyes-free operation). Furthermore, we revealed that reducing interaction engagement enhances resistance to mimicry attacks while decreasing cognitive workload and error rates in multitasking or eyes-free environments. However, this approach compromises the average classification accuracy of Interaction behavior under different algorithms (including InceptionTime, FCN, ResNet, CNN, MLP, and MCDCNN). Notably, moderate reduction of interaction engagement enhances authentication speed, while excessive reduction may conversely slow it down. Overall, our work establishes a novel design paradigm for VR authentication that supports casual interactions and offers valuable insights into balancing usability and security.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EgoTrigger: Toward Audio-Driven Image Capture for Human Memory Enhancement in All-Day Energy-Efficient Smart Glasses. EgoTrigger：在全天节能智能眼镜中实现音频驱动图像捕获以增强人类记忆。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-07 DOI: 10.1109/TVCG.2025.3616866

Akshay Paruchuri, Sinan Hersek, Lavisha Aggarwal, Qiao Yang, Xin Liu, Achin Kulshrestha, Andrea Colaco, Henry Fuchs, Ishan Chatterjee

{"title":"EgoTrigger: Toward Audio-Driven Image Capture for Human Memory Enhancement in All-Day Energy-Efficient Smart Glasses.","authors":"Akshay Paruchuri, Sinan Hersek, Lavisha Aggarwal, Qiao Yang, Xin Liu, Achin Kulshrestha, Andrea Colaco, Henry Fuchs, Ishan Chatterjee","doi":"10.1109/TVCG.2025.3616866","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3616866","url":null,"abstract":"All-day smart glasses are likely to emerge as platforms capable of continuous contextual sensing, uniquely positioning them for unprecedented assistance in our daily lives. Integrating the multi-modal AI agents required for human memory enhancement while performing continuous sensing, however, presents a major energy efficiency challenge for all-day usage. Achieving this balance requires intelligent, context-aware sensor management. Our approach, EgoTrigger, leverages audio cues from the microphone to selectively activate power-intensive cameras, enabling efficient sensing while preserving substantial utility for human memory enhancement. EgoTrigger uses a lightweight audio model (YAMNet) and a custom classification head to trigger image capture from hand-object interaction (HOI) audio cues, such as the sound of a drawer opening or a medication bottle being opened. In addition to evaluating on the QA-Ego4D dataset, we introduce and evaluate on the Human Memory Enhancement Question-Answer (HME-QA) dataset. Our dataset contains 340 human-annotated first-person QA pairs from full-length Ego4D videos that were curated to ensure that they contained audio, focusing on HOI moments critical for contextual understanding and memory. Our results show EgoTrigger can use 54% fewer frames on average, significantly saving energy in both power-hungry sensing components (e.g., cameras) and downstream operations (e.g., wireless transmission), while achieving comparable performance on datasets for an episodic memory task. We believe this context-aware triggering strategy represents a promising direction for enabling energy-efficient, functional smart glasses capable of all-day use - supporting applications like helping users recall where they placed their keys or information about their routine activities (e.g., taking medications).","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nanouniverse: Virtual Instancing of Structural Detail and Adaptive Shell Mapping. 纳米宇宙：结构细节和自适应外壳映射的虚拟实例。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-07 DOI: 10.1109/TVCG.2025.3618914

Ruwayda Alharbi, Ondrej Strnad, Markus Hadwiger, Ivan Viola

{"title":"Nanouniverse: Virtual Instancing of Structural Detail and Adaptive Shell Mapping.","authors":"Ruwayda Alharbi, Ondrej Strnad, Markus Hadwiger, Ivan Viola","doi":"10.1109/TVCG.2025.3618914","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3618914","url":null,"abstract":"Rendering huge biological scenes with atomistic detail presents a significant challenge in molecular visualization due to the memory limitations inherent in traditional rendering approaches. In this paper, we propose a novel method for the interactive rendering of massive molecular scenes based on hardware-accelerated ray tracing. Our approach circumvents GPU memory constraints by introducing virtual instantiation of full-detail scene elements. Using instancing significantly reduces memory consumption while preserving the full atomistic detail of scenes comprising trillions of atoms, with interactive rendering performance and completely free user exploration. We utilize coarse meshes as proxy geometries to approximate the overall shape of biological compartments, and access all atomistic detail dynamically during ray tracing. We do this via a novel adaptive technique utilizing a volumetric shell layer of prisms extruded around proxy geometry triangles, and a virtual volume grid for the interior of each compartment. Our algorithm scales to enormous molecular scenes with minimal memory consumption and the potential to accommodate even larger scenes. Our method also supports advanced effects such as clipping planes and animations. We demonstrate the efficiency and scalability of our approach by rendering tens of instances of Red Blood Cell and SARS-CoV-2 models theoretically containing more than 20 trillion atoms.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How Do We Read Texts in VR?: The Effects of Text Segment Quantity and Social Distraction on Text Readability in Virtual Museum Contexts. 我们如何在VR中阅读文本？虚拟博物馆语境中文本片段数量和社交干扰对文本可读性的影响。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-07 DOI: 10.1109/TVCG.2025.3616803

Jungmin Lee, Hyuckjin Jang, Jeongmi Lee

{"title":"How Do We Read Texts in VR?: The Effects of Text Segment Quantity and Social Distraction on Text Readability in Virtual Museum Contexts.","authors":"Jungmin Lee, Hyuckjin Jang, Jeongmi Lee","doi":"10.1109/TVCG.2025.3616803","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3616803","url":null,"abstract":"Virtual museums are increasingly used to present cultural and educational content, often relying on textual descriptions to convey essential information. However, when users interact with text objects in virtual environments, the optimal text segment quantity for readability and comprehension remains unclear, especially when social distractions such as other visitors are present. This study investigated the effects of text segment quantity and the presence of other visitors on text readability and comprehension in the context of virtual museums. Participants read exhibit descriptions under four text-segment length conditions (1, 2, 4, and 8 lines) with or without simulated visitor agents. The results indicated that readability and comprehension were maximized when text was presented in intermediate segmentation lengths (2 and 4 Lines), while both excessively short (1 line) and long (8 lines) text segments hindered reading performance. Additionally, a significant interaction between text length and the presence of visitors was observed. Specifically, the presence of visitors led to increased comprehension task completion times only in the intermediate segmentation conditions, suggesting that social presence imposes an additional cognitive demand as social distractions in optimal text segment conditions. These findings provide empirical guidelines for designing effective text-based information systems in virtual museums, optimizing both user engagement and learning outcomes in immersive cultural environments.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Handows: A Palm-Based Interactive Multi-Window Management System in Virtual Reality. handos：虚拟现实中基于掌机的交互式多窗口管理系统。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-06 DOI: 10.1109/TVCG.2025.3616843

Jin-Du Wang, Ke Zhou, Haoyu Ren, Per Ola Kristensson, Xiang Li

{"title":"Handows: A Palm-Based Interactive Multi-Window Management System in Virtual Reality.","authors":"Jin-Du Wang, Ke Zhou, Haoyu Ren, Per Ola Kristensson, Xiang Li","doi":"10.1109/TVCG.2025.3616843","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3616843","url":null,"abstract":"Window management in virtual reality (VR) remains a challenging task due to the spatial complexity and physical demands of current interaction methods. We introduce Handows, a palm-based interface that enables direct manipulation of spatial windows through familiar smartphone-inspired gestures on the user's non-dominant hand. Combining ergonomic layout design with body-centric input and passive haptics, Handows supports four core operations: window selection, closure, positioning, and scaling. We evaluate Handows in a user study (N = 15) against two common VR techniques (virtual hand and controller) across four core window operations. Results show that Handows significantly reduces physical effort and head movement while improving task efficiency and interaction precision. A follow-up case study (N = 8) demonstrates Handows' usability in realistic multitasking scenarios, highlighting user-adapted workflows and spontaneous layout strategies. Our findings also suggest the potential of embedding mobile-inspired metaphors into proprioceptive body-centric interfaces to support low-effort and spatially coherent interaction in VR.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AttentionPainter: An Efficient and Adaptive Stroke Predictor for Scene Painting. AttentionPainter：一个高效和自适应的笔画预测器。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-06 DOI: 10.1109/TVCG.2025.3618184

Yizhe Tang, Yue Wang, Teng Hu, Ran Yi, Xin Tan, Lizhuang Ma, Yu-Kun Lai, Paul L Rosin

{"title":"AttentionPainter: An Efficient and Adaptive Stroke Predictor for Scene Painting.","authors":"Yizhe Tang, Yue Wang, Teng Hu, Ran Yi, Xin Tan, Lizhuang Ma, Yu-Kun Lai, Paul L Rosin","doi":"10.1109/TVCG.2025.3618184","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3618184","url":null,"abstract":"Stroke-based Rendering (SBR) aims to decompose an input image into a sequence of parameterized strokes, which can be rendered into a painting that resembles the input image. Recently, Neural Painting methods that utilize deep learning and reinforcement learning models to predict the stroke sequences have been developed, but suffer from longer inference time or unstable training. To address these issues, we propose AttentionPainter, an efficient and adaptive model for single-step neural painting. First, we propose a novel scalable stroke predictor, which predicts a large number of stroke parameters within a single forward process, instead of the iterative prediction of previous Reinforcement Learning or auto-regressive methods, which makes AttentionPainter faster than previous neural painting methods. To further increase the training efficiency, we propose a Fast Stroke Stacking algorithm, which brings 13 times acceleration for training. Moreover, we propose Stroke-density Loss, which encourages the model to use small strokes for detailed information, to help improve the reconstruction quality. Finally, we design a Stroke Diffusion Model as an application of AttentionPainter, which conducts the denoising process in the stroke parameter space and facilitates stroke-based inpainting and editing applications helpful for human artists' design. Extensive experiments show that AttentionPainter outperforms the state-of-the-art neural painting methods.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

WonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human Reconstruction. 神奇的人：幻觉看不见的部分在动态3D人体重建。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-06 DOI: 10.1109/TVCG.2025.3618268

Zilong Wang, Zhiyang Dou, Yuan Liu, Cheng Lin, Xiao Dong, Yunhui Guo, Chenxu Zhang, Xin Li, Wenping Wang, Xiaohu Guo

{"title":"WonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human Reconstruction.","authors":"Zilong Wang, Zhiyang Dou, Yuan Liu, Cheng Lin, Xiao Dong, Yunhui Guo, Chenxu Zhang, Xin Li, Wenping Wang, Xiaohu Guo","doi":"10.1109/TVCG.2025.3618268","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3618268","url":null,"abstract":"In this paper, we present WonderHuman to reconstruct dynamic human avatars from a monocular video for high-fidelity novel view synthesis. Previous dynamic human avatar reconstruction methods typically require the input video to have full coverage of the observed human body. However, in daily practice, one typically has access to limited viewpoints, such as monocular front-view videos, making it a cumbersome task for previous methods to reconstruct the unseen parts of the human avatar. To tackle the issue, we present WonderHuman, which leverages 2D generative diffusion model priors to achieve high-quality, photorealistic reconstructions of dynamic human avatars from monocular videos, including accurate rendering of unseen body parts. Our approach introduces a Dual-Space Optimization technique, applying Score Distillation Sampling (SDS) in both canonical and observation spaces to ensure visual consistency and enhance realism in dynamic human reconstruction. Additionally, we present a View Selection strategy and Pose Feature Injection to enforce the consistency between SDS predictions and observed data, ensuring pose-dependent effects and higher fidelity in the reconstructed avatar. In the experiments, our method achieves SOTA performance in producing photorealistic renderings from the given monocular video, particularly for those challenging unseen parts. The project page and source code can be found at https://wyiguanw.github.io/WonderHuman/.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NAT: Neural Acoustic Transfer for Interactive Scenes in Real Time. 实时交互场景中的神经声学传递。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-06 DOI: 10.1109/TVCG.2025.3617802

Xutong Jin, Bo Pang, Chenxi Xu, Xinyun Hou, Guoping Wang, Sheng Li

{"title":"NAT: Neural Acoustic Transfer for Interactive Scenes in Real Time.","authors":"Xutong Jin, Bo Pang, Chenxi Xu, Xinyun Hou, Guoping Wang, Sheng Li","doi":"10.1109/TVCG.2025.3617802","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3617802","url":null,"abstract":"Previous acoustic transfer methods rely on extensive precomputation and storage of data to enable real-time interaction and auditory feedback. However, these methods struggle with complex scenes, especially when dynamic changes in object position, material, and size significantly alter sound effects. These continuous variations lead to fluctuating acoustic transfer distributions, making it challenging to represent with basic data structures and render efficiently in real time. To address this challenge, we present Neural Acoustic Transfer, a novel approach that leverages implicit neural representations to encode acoustic transfer functions and their variations. This enables real-time prediction of dynamically evolving sound fields and their interactions with the environment under varying conditions. To efficiently generate high-quality training data for the neural acoustic field while avoiding reliance on mesh quality of a model, we develop a fast and efficient Monte-Carlo-based boundary element method (BEM) approximation, suitable for general scenarios with smooth Neumann boundary conditions. In addition, we devise strategies to mitigate potential singularities during the synthesis of training data, thereby enhancing its reliability. Together, these methods provide robust and accurate data that empower the neural network to effectively model complex sound radiation space. We demonstrate our method's numerical accuracy and runtime efficiency (within several milliseconds for 30s audio) through comprehensive validation and comparisons in diverse acoustic transfer scenarios. Our approach allows for efficient and accurate modeling of sound behavior in dynamically changing environments, which can benefit a wide range of interactive applications such as virtual reality, augmented reality, and advanced audio production.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When LLMs Recognize Your Space: Research on Experiences with Spatially Aware LLM Agents. 当LLM识别你的空间：空间感知LLM代理的经验研究。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2025-10-06 DOI: 10.1109/TVCG.2025.3616809

Seungwoo Oh, Nakyoung An, Youngwug Cho, Myeongul Jung, Kwanguk Kenny Kim

{"title":"When LLMs Recognize Your Space: Research on Experiences with Spatially Aware LLM Agents.","authors":"Seungwoo Oh, Nakyoung An, Youngwug Cho, Myeongul Jung, Kwanguk Kenny Kim","doi":"10.1109/TVCG.2025.3616809","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3616809","url":null,"abstract":"Large language models (LLMs) have evolved into LLM agents that can use the user conversation context and respond according to the roles of the LLM agents. Recent studies have suggested that LLM-based agents can be used as human-like partners in social interactions. However, the role of the environmental context, particularly spatial information of user space, in the interaction between humans and LLM agents has not been explored. In this study, participants engaged in counselling conversations under three different conditions based on their spatial awareness levels. The dependent measures included copresence, trust, therapist alliances, and self-disclosure. The results suggested that participants in the condition where the LLM actively reflected spatial information generally reported higher levels of user experience. Interestingly, when the LLM actively reflected the spatial context of the user, the participants tended to describe themselves and express their emotions more. These findings suggest that spatially aware LLM agents can contribute to better social interactions between humans and LLM agents. Our findings can be used to design future augmented reality applications in the counselling, education, and healthcare industries.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0